Psychedelic Vision
A DISSERTATION
SUBMITTED TO THE FACULTY OF THE
UNIVERSITY OF MINNESOTA
by
Link Ray Swanson
IN PARTIAL FULFILLMENT OF THE REQUIREMENTS
FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY
faculty advisors
Dr. Daniel Kersten
Dr. Michael-Paul Schallmo
October 2022
Copyright © 2022 Link Ray Swanson
Acknowledgements
I am grateful for the guidance, support, and inspiration given to me by many individuals.
First and foremost is my wife, Kathryn RG Swanson, PhD, JD, who has helped me
more than I am capable of admitting. Our conversations continuously shape my
thinking and bring clarity to ideas. She inspired me to start graduate school. She
reads all of my work and provides pertinent and honest comments, edits, and criticisms.
I am a better writer and public speaker due to her instructive, constructive, and
critical feedback over the past decade. I am beyond lucky to have had the benefit
of Kathryn’s philosophical and pedagogical skills. She also kept our family running
smoothly during times when I was preoccupied with this work, and still found the
patience to support me in the process, which was no doubt a challenge.
Next I thank my academic advisors. Peter Hanks provided much-needed early
validation that my work was of sufficient caliber to make contributions in the arena
of professional academics, and modeled how to be clear about what I am saying.
Dan Kersten helped me see the connections between philosophy and neuroscience,
emphasized the value of historical perspectives, and encouraged me to continue on the
path of cognitive science. Michael-Paul Schallmo has been a valuable mentor and has
provided me with an enormous amount of conceptual and technical knowledge about
how to design experiments, refine methods, analyze data, and write scientific reports.
I also want to acknowledge my final exam committee members Kendrick Kay and
Thomas Metzinger. Kendrick’s scrutiny of and excitement for the empirical methods
and the theoretical narrative was a significant source of encouragement for me. I am
especially thankful for Thomas’ service on the committee as an external reviewer and
for the insight and expertise he provided in that role. I found further moral support
from other scientists in the UMN community, notably Alan Love, Andrew Harris,
Marco Pravetoni, Paul Schrater, Andrew Oxenham, Victoria Interrante, Sucharit
Katyal, and Bonnie Klimes-Dougan.
I was lucky to be able to conduct human research with a psychedelic drug. Jessica
Nielson kept the faith that psychedelic research could be a reality at the University of
Minnesota—and helped make it happen—working tirelessly to navigate regulatory
requirements, obtain approvals, secure funding, recruit participants, and collect data.
I am also thankful to Kathryn Cullen, Ranji Varghese, Sophia Jungers, and Nicole
Tosun for their efforts on the research study. Clinical-grade psilocybin was graciously
provided by Usona Institute of Madison, WI. Finally, I would like to thank the
research participants, who bravely took a large dose of psilocybin in a hospital room
and completed my visual tasks during the peak. If any of you are reading this: Thank
you!
Many other scholars, scientists, and thinkers have inspired and encouraged me. I am
particularly thankful for the words I received from people who read my work, including
the late Martin Fortier-Davy, Chris Letheby, Raphaël Millire, Luke McGowan, Andrew
Gallimore, and Rick Strassman. I thank Bill Linton and Karin Borgh for enabling
me to have casual conversations with many pioneer psychedelic researchers over the
past 15 years at an annual conference they host in Madison, WI, which included lively
dinners with Franz Vollenweider, Charlie Grob, Dave Nichols, Robin Carhart-Harris,
i
Christof Koch, Melanie Boly, and Erik Davis. I received a particularly impactful
mentoring from Jim Fadiman, who advised me on setting and getting my goals around
entering the field of psychedelic research.
My aspirations to do psychedelic research reach back to high school, during which,
in the year 2000, certain individuals found it important to encourage me to pursue
my interest in this controversial topic as a seventeen-year-old. Tom Anderson and
Dennis McKenna were true mentors in this regard, and have remained my friends for
the ensuing two decades. I was also encouraged by my high-school teachers Brian
Richards and Patricia Brain, and my undergraduate professors Joie Acheson, Bud
McClure, and George Avery. The early encouragement I received has continued to
motivate my efforts decades later.
While working on this dissertation, my close friends have provided helpful feedback,
valuable ideas, shared experiences, and have remained patient as my preoccupation
with this work interfered with my presence in friendship.
My children, Huxley and Juniper Swanson, have taught me more about life, the
mind, and what it means to be human than anyone else. They also had an impressive
amount of patience and understanding while I worked through graduate school and a
dissertation.
Roy Swanson, my brother, has sparked a shared sense of wonder and discovery,
and has been a model for how to cultivate discipline and devoted practice toward
personal goals. My parents, John and Janet Swanson, have always taken interest in
what I do, accepted my interests, and supported me in pursuing them. Jacquelyn
Swanson, my sister, has also encouraged me by letting me know she has a sense of
pride in whatever it is that I am working on.
Finally, a thank you to John MacFarlane for his wonderful open-source Pandoc
software, which enabled me to write this entire dissertation using Markdown files.
ii
For Huxley Swanson
iii
Abstract
The visual effects of psychedelic drugs are well-known but not well understood. In
this dissertation, I investigate psychedelic visual phenomenology. I provide evidence
and analysis to argue that psychedelics selectively impact contextual modulation at
multiple levels of mental function. I report preliminary results of two experiments
from a pilot study, in which we measured contrast surround suppression in humans
under the psychedelic drug psilocybin, using psychophysical and EEG event-related
potential (ERP) methods. We found that psilocybin enhanced perceptual surround
suppression, and strengthened surround suppression of neural responses, compared
with placebo. The results support the hypothesis that psychedelics enhance contextual
modulation in visual processes, which has theoretical as well as practical implications
for psychedelic therapy, cognitive neuroscience, and philosophy.
iv
Table of Contents
List of Figures viii
List of Tables x
Introduction 1
Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
Research Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
Research Aims . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
Significance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
Cognitive Science . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
1 Unifying theories of psychedelic drug effects 15
1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
1.2 Psychedelic Drug Effects . . . . . . . . . . . . . . . . . . . . . . . . . 19
1.2.1 Perceptual Effects . . . . . . . . . . . . . . . . . . . . . . . . . 20
1.2.2 Emotional Effects . . . . . . . . . . . . . . . . . . . . . . . . . 24
1.2.3 Cognitive Effects . . . . . . . . . . . . . . . . . . . . . . . . . 25
1.2.4 Ego Effects and Ego Dissolution Experiences . . . . . . . . . . 26
1.2.5 Clinical Efficacy and Long-Term Effects . . . . . . . . . . . . 28
1.2.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
1.3 19th and 20th Century Theories of Psychedelic Drug Effects . . . . . 29
1.3.1 Model Psychoses Theory . . . . . . . . . . . . . . . . . . . . . 30
1.3.2 Filtration Theory . . . . . . . . . . . . . . . . . . . . . . . . . 33
1.3.3 Psychoanalytic Theory . . . . . . . . . . . . . . . . . . . . . . 37
1.3.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
1.4 Neuropharmacology and Neurophysiological Correlates of Psychedelic
Drug Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
1.5 21st-Century Theories of Psychedelic Drug Effects . . . . . . . . . . . 44
1.5.1 Entropic Brain Theory . . . . . . . . . . . . . . . . . . . . . . 45
1.5.2 Integrated Information Theory . . . . . . . . . . . . . . . . . . 49
1.5.3 Predictive Processing . . . . . . . . . . . . . . . . . . . . . . . 51
1.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
v
2 Psychedelic visuals in context 62
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
2.2 What it’s like to see OEVs . . . . . . . . . . . . . . . . . . . . . . . . 67
2.2.1 Enhancements . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
2.2.2 Transformations . . . . . . . . . . . . . . . . . . . . . . . . . . 68
2.2.3 Overlays . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
2.3 Hypercontextual modulation in psychedelic vision . . . . . . . . . . . 71
2.3.1 Thought experiment . . . . . . . . . . . . . . . . . . . . . . . 73
2.3.2 OEV phenomenology as HCM . . . . . . . . . . . . . . . . . . 75
2.3.3 Psychedelic psychophysics . . . . . . . . . . . . . . . . . . . . 77
2.3.4 Divisive normalization, serotonin, and psychedelics . . . . . . 78
2.4 HCM, multisensory integration, and cognition . . . . . . . . . . . . . 82
2.4.1 Audio-visual context effects . . . . . . . . . . . . . . . . . . . 82
2.4.2 Semantic activation . . . . . . . . . . . . . . . . . . . . . . . . 83
2.4.3 Symbolic thinking . . . . . . . . . . . . . . . . . . . . . . . . . 85
2.4.4 Set, setting, and HCM . . . . . . . . . . . . . . . . . . . . . . 86
2.4.5 Bad HCM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
2.5 Practical implications . . . . . . . . . . . . . . . . . . . . . . . . . . . 88
2.5.1 Hallucination and HCM . . . . . . . . . . . . . . . . . . . . . 88
2.5.2 New context for old cues . . . . . . . . . . . . . . . . . . . . . 89
2.5.3 HCM and psychedelic therapy . . . . . . . . . . . . . . . . . . 90
2.5.4 Vision(s), symbols, and insight . . . . . . . . . . . . . . . . . 91
2.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
Image credits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94
3 Enhanced visual contrast suppression during peak psilocybin effects:
A psychophysical study 95
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96
3.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
3.2.1 Participants . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
3.2.2 Crossover design . . . . . . . . . . . . . . . . . . . . . . . . . 101
3.2.3 Apparatus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102
3.2.4 Stimuli . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102
3.2.5 Paradigm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
3.2.6 Subjective drug effects . . . . . . . . . . . . . . . . . . . . . . 105
3.2.7 First-person written narratives . . . . . . . . . . . . . . . . . . 106
3.2.8 Statistical analyses . . . . . . . . . . . . . . . . . . . . . . . . 106
3.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107
3.3.1 Effect of drug on PSE . . . . . . . . . . . . . . . . . . . . . . 107
3.3.2 Catch trials . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112
3.3.3 Subjective effects of drug . . . . . . . . . . . . . . . . . . . . . 113
3.3.4 Subjective drug effects and PSE . . . . . . . . . . . . . . . . . 114
3.3.5 Qualitative first-person narratives . . . . . . . . . . . . . . . . 120
3.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123
3.4.1 Psilocybin, surround suppression, and visual context processing 123
vi
3.4.2 Serotonin, divisive normalization, and surround suppression . 124
3.4.3 Surround suppression and psychedelic visual phenomenology . 125
3.4.4 Depression, surround suppression, and psilocybin . . . . . . . 127
3.4.5 Set and setting, suggestibility, flexibility, and surround suppression130
3.4.6 Schizophrenic psychosis vs psilocybin effects . . . . . . . . . . 131
3.4.7 Neural information processing and psychedelic effects . . . . . 132
3.5 Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133
3.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134
4 Stronger surround suppression of ERP responses under psilocybin:
A pharmaco-EEG study 136
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137
4.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141
4.2.1 Participants . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141
4.2.2 Drug administration and timing . . . . . . . . . . . . . . . . . 141
4.2.3 Crossover design . . . . . . . . . . . . . . . . . . . . . . . . . 142
4.2.4 Apparatus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142
4.2.5 Stimuli . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143
4.2.6 Paradigm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144
4.2.7 Subjective drug effects . . . . . . . . . . . . . . . . . . . . . . 146
4.2.8 Data processing . . . . . . . . . . . . . . . . . . . . . . . . . . 146
4.2.9 Statistical analyses . . . . . . . . . . . . . . . . . . . . . . . . 151
4.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152
4.3.1 P1 component (90.0 - 119.3 ms) . . . . . . . . . . . . . . . . . 152
4.3.2 N1 component (120.0 - 243.0 ms) . . . . . . . . . . . . . . . . 157
4.3.3 P2 component (244.3 - 283.3 ms) . . . . . . . . . . . . . . . . 160
4.4 Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 168
4.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169
4.5.1 Enhanced ERP surround suppression in the N1 component . . 170
4.5.2 The general effect of psilocybin on ERP responses . . . . . . . 171
4.5.3 Contextual modulation, top-down feedback, and 5-HT2A . . . 172
4.5.4 Future research . . . . . . . . . . . . . . . . . . . . . . . . . . 173
Conclusion 174
Main findings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174
Contributions and significance . . . . . . . . . . . . . . . . . . . . . . . . . 179
Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181
Future research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183
Final thoughts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185
Bibliography 186
vii
List of Figures
1 The classic psychedelic drugs and their natural sources. . . . . . . . . 3
2 The disciplines integrated by Cognitive Science . . . . . . . . . . . . 10
1.1 Extra-pharmacological model for predicting acute and long-term drug
effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
1.2 Example results from subjective rating scale (psilocybin) . . . . . . . 22
1.3 Aldous Huxley’s ‘cerebral reducing valve’ concept . . . . . . . . . . . 34
1.4 Diagram of psychedelic states, information, and entropy . . . . . . . . 50
2.1 Examples of (spatial) visual context effects. . . . . . . . . . . . . . . 72
2.2 Target stimuli without contextual cues. . . . . . . . . . . . . . . . . . 74
3.1 Psilocybin and psilocin molecules with fungi species illustrations. . . . 98
3.2 Stimuli examples from surround suppression psychophysics experiment 104
3.3 Plot of measured surround suppression . . . . . . . . . . . . . . . . . 109
3.4 Psilocybin effect on PSE. . . . . . . . . . . . . . . . . . . . . . . . . . 111
3.5 Plot of catch trial scores. . . . . . . . . . . . . . . . . . . . . . . . . . 112
3.6 Plot of 5D-ASC scores . . . . . . . . . . . . . . . . . . . . . . . . . . 113
3.7 Rmcorr plot for 5D-ASC . . . . . . . . . . . . . . . . . . . . . . . . . 115
3.8 Rmcorr plot for Visionary Restructuralization . . . . . . . . . . . . . 117
3.9 Rmcorr plot for Oceanic Boundlessness . . . . . . . . . . . . . . . . . 119
4.1 DSI-24 EEG headset sensor positions . . . . . . . . . . . . . . . . . . 143
4.2 Stimuli examples from surround suppression EEG experiment . . . . 145
4.3 EEG stimuli time course . . . . . . . . . . . . . . . . . . . . . . . . . 147
4.4 Grand average scalp maps . . . . . . . . . . . . . . . . . . . . . . . . 149
4.5 Grand average ERP plot . . . . . . . . . . . . . . . . . . . . . . . . . 150
4.6 ERP waveform plot . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153
4.7 P1 mean amplitudes . . . . . . . . . . . . . . . . . . . . . . . . . . . 154
4.8 Effect of drug on P1 . . . . . . . . . . . . . . . . . . . . . . . . . . . 155
4.9 P1 ERP amplitudes . . . . . . . . . . . . . . . . . . . . . . . . . . . . 156
4.10 N1 mean amplitudes . . . . . . . . . . . . . . . . . . . . . . . . . . . 157
4.11 Effect of drug on N1 . . . . . . . . . . . . . . . . . . . . . . . . . . . 158
4.12 N1 ERP amplitudes . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159
4.13 P2 mean amplitudes . . . . . . . . . . . . . . . . . . . . . . . . . . . 161
4.14 Effect of drug on P2 . . . . . . . . . . . . . . . . . . . . . . . . . . . 162
viii
4.15 P2 ERP amplitudes . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163
4.16 Rmcorr plot for 5D-ASC . . . . . . . . . . . . . . . . . . . . . . . . . 165
4.17 Rmcorr plot for Oceanic Boundlessness . . . . . . . . . . . . . . . . . 166
4.18 Rmcorr plot for Visionary Restructuralization . . . . . . . . . . . . . 167
ix
List of Tables
3.1 Psychophysics study design table . . . . . . . . . . . . . . . . . . . . 102
3.2 Psychophysical stimuli descriptions . . . . . . . . . . . . . . . . . . . 105
3.3 ANOVA table for psychophysical measurements . . . . . . . . . . . . 108
3.4 PSE means table . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109
3.5 PSE ASC correlations . . . . . . . . . . . . . . . . . . . . . . . . . . 115
3.6 5D-ASC items of interest from the Visionary Restructuralization di-
mension . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118
3.7 5D-ASC items of interest from the Oceanic Boundlessness dimension 119
4.1 EEG stimuli descriptions . . . . . . . . . . . . . . . . . . . . . . . . . 144
4.2 Trial exclusion statistics for the EEG analysis . . . . . . . . . . . . . 148
4.3 ERP component time windows . . . . . . . . . . . . . . . . . . . . . . 151
4.4 ERP amplitude means for the P1 component . . . . . . . . . . . . . . 154
4.5 ANOVA table for N1 ERP measurements . . . . . . . . . . . . . . . . 158
4.6 ERP amplitude means for the N1 component . . . . . . . . . . . . . . 160
4.7 ANOVA table for P2 ERP measurements . . . . . . . . . . . . . . . . 161
4.8 ERP amplitude means for the P2 component . . . . . . . . . . . . . . 164
4.9 P2-ASC correlations . . . . . . . . . . . . . . . . . . . . . . . . . . . 165
4.10 11D-ASC items from the Audio-Visual Synesthesia subscale correlated
with P2 amplitudes . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167
4.11 11D-ASC items from the Blissful State subscale correlated with P2
amplitudes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167
4.12 11D-ASC items from the Experience of Unity subscale correlated with
P2 amplitudes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 168
x
Introduction
“Vision is a fundamental component of the human conscious experience. Conse-
quently, an accurate characterization of where and how hallucinogens, such as
psilocybin, influence different stages of visual perception could be beneficial on
a number of counts.”
–Olivia Carter et al. (2004), “Psilocybin impairs high-level but not low-level
motion perception”
1
Background
Psilocybin, mescaline, lysergic acid diethylamide (LSD), and N,N-dimethyltryptamine
(DMT) (see Figure 1) fall into a class of drugs which is (still) officially named
hallucinogens, a name which emphasizes their perceptual effects.1 The visual effects
(termed ‘psychedelic visuals’) are by far the most “notorious”, “prominent,” (Aday et al.
2021) and “famous” (Kometer and Vollenweider 2016; Vollenweider and Preller 2020;
Letheby 2021) psychoactive properties of these drugs. Visual phenomena, which “are
the most frequent and robust features of the psychedelic experience” (Vollenweider and
Preller 2020), dominate first-person narrative accounts of psychedelic phenomenology,
and have a robust dose-response on psychometric instruments that measure the
subjective effects of LSD (Schmid et al. 2015; Liechti, Dolder, and Schmid 2017;
Holze et al. 2020, 2022), DMT (Strassman et al. 1994; Pallavicini et al. 2021), and
psilocybin (Hasler et al. 2004; Studerus et al. 2010; Carbonaro, Johnson, and Griffiths
2020; Hirschfeld and Schmidt 2021; Holze et al. 2022). Changes in visual perception
are often the first to be noticed as the drug takes effect and can be the last effects to
remain after all other psychedelic effects have subsided (Klüver 1926; Hofmann 1980).
In modern illicit markets, visuals continue to serve as a subjective benchmark for
the authenticity, quality, and potency of psychedelic drug material (see, e.g., Weasel
1994), and are among the top effects that motivate self-administration of these drugs
(Carbonaro, Johnson, and Griffiths 2020). Historically, visual (and visionary) effects
of psychedelics were and are highly regarded in many human societies (Schultes 1969;
Schultes, Hofmann, and Rätsch 2006; Carod-Artal 2015; Richards 2015; George et al.
2021)—“in such societies hallucinations are often cherished and regarded as potentially
valuable for the individual and the culture” Wallace (1959).
By the late nineteenth century, medical doctors and scientists began publishing
their ‘discoveries’ of the visual effects of psychedelic plants, mostly in the form of
first-person reports after observing the effects on their own minds (Lewin 1894, 1927;
Prentiss and Morgan 1895; Mitchell 1896; Ellis 1898). Systematic investigation of
1Abraham (1996, 287) explains: “The term hallucinogen while unduly emphasizing perceptual
effects connotes by convention their effects on emotion and cognition as well.”
2
Figure 1: The classic psychedelic drugs and their natural sources. Lophophora william-
sii (peyote; A) is one of dozens of species of cacti that produce the phenethylamine
mescaline (B). Psilocybe cubensis (C) is one of more than a hundred species of fungi
that produce the tryptamine psilocybin (D). Psychotria viridis (E) is one of hundreds
of species of plants and animals (including humans) that produce the tryptamine
N,N-dimethyltryptamine (DMT; F). LSD (H) is a synthetic molecule derived from
Claviceps purpurea (G). Illustrations by Elmer W. Smith (Schultes and Smith 1976).
3
psychedelics from the standpoint of visual neuroscience began with Knauer and
Maloney (1913) who inspired Klüver (1926, 1928, 1966) to investigate the visual effects
of mescaline using the methods of psychophysics. However, this early visual research
became less common as other research into the therapeutic and psychotomimetic
potential of these drugs, which began with Beringer (1927) and continued through
the 1960s (Stoll 1947; Sandison 1954; Osmond 1957; Cohen 1965; Leary, Litwin, and
Metzner 1963; Grof 1976) until all U.S. based psychedelic research ceased as a result
of the Controlled Substances Act of 1970, and did not resume until the early 1990s
(i.e., Strassman et al. 1994; Spitzer et al. 1996).
Recently, psychedelics are (once again) attracting significant scientific and public
interest. However, not all subjective effects of psychedelics have received equal levels
of attention: visual effects appear to be largely ignored and downplayed in the current
academic arena, where the focus has shifted to emotional effects, mystical experience,
and the ways in which these drugs affect the sense of self (Letheby 2021). This
is unsurprising, as visual ‘hallucinogenic’ effects have featured prominently in anti-
hallucinogen propaganda rhetoric and sensationalist media, not just since the War on
Drugs, but dating back to the Inquisition and Christian colonialism (Carod-Artal 2015;
George et al. 2021; Collins 2018). Moreover, early psychiatry labeled these drugs as
psychotomimetic—psychosis mimicking—in part because of their visual effects.
To date, systematic investigation of psychedelic visuals remains underdeveloped
(Abraham 1996; Aday et al. 2021). This situation has created knowledge gaps in our
understanding of how psychedelic drugs affect the mind. Most studies pre-1970 do not
meet current methodological standards—controls, blinding, standardization, statistical
techniques, transparency, reporting, etc.—making it problematic to draw conclusions
from this body of work. Furthermore, the technical tools available to neuroscientists
circa 1900–1970 were only in their infancy. For example, techniques for identifying
Event Related Potentials (ERP) in electroencephalography (EEG) recordings were
developed only in 1964 (Walter et al. 1964), so by the time psychedelic research halted
in 1970 virtually no studies had investigated the impact of psychedelics on visual ERPs
(Aday et al. 2021). Meanwhile, neuroscience progressed over the 70’s, 80’s and 90’s
4
while the neuroscience of psychedelic drugs remained stuck in the 1960s. Furthermore,
there have been several theoretical advances in the cognitive sciences during this time.
Theories provide prediction, explanation, understanding (Thagard 2009), allowing
researchers “to generate and vet ideas prior to full experimental testing” (Abbott
2008). Moreover, theorizing facilitates conceptual and computational models (Kay
2017) that guide researchers in making inferences, deductions, and conclusions from
available data. Consequently, our understanding of psychedelic drugs must now catch
up on decades of maturation in the cognitive sciences. The inverse is also true: the
cognitive sciences have missed out on the use of psychedelics as research tools—“The
study of psychedelics also provides an important—and neglected—source of data for
understanding the neural basis of consciousness” (Bayne and Carter 2018, 6)
Psychedelic effects are varied and complicated, emerging in all modalities of
perception, impacting multiple cognitive processes, sometimes fundamentally altering
the sense of self and producing mystical experiences. Because of this, scientific attempts
to understand psychedelics can be complicated and daunting. In this dissertation I
adopt an approach that I believe can provide scope and parsimony to investigating
psychedelic effects; namely, examine their effects on open-eyed visual perception.
Visual perception is a central feature of human subjective experience (Crick 1998) and
is perhaps the most well-characterized and extensively studied functions of the human
mind. We can leverage this platform of knowledge to understand how psychedelics
work. Psychedelics reliably cause subjects to report alterations in the subjective
appearance of low-level visual features (e.g., colors, textures, angles, motion) as well
as higher-level visual properties (objects, faces, scenes). Presently we know that
serotonin (5-HT) receptor agonism at 5-HT2A and 5-HT1A receptor sites is required
for psychedelic molecules to have visual effects (Glennon, Titeler, and McKenney
1984; Halberstadt 2015; Vollenweider and Smallridge 2022). Thus, by investigating
the visual effects, we can learn much about how psychedelics work more generally,
and perhaps also about the functional neuromodulatory role of serotonin in everyday
visual perception.
5
Research Problem
Existing literature on low-level visual effects of psychedelics includes contradictory
findings. Early-era researchers found significant changes in discrimination thresholds
for various low-level visual features (Aday et al. 2021). However, these early studies
“seem to contradict recent models of psychedelic effects which argue that low-level
processes are largely unaffected by the drugs” (Aday et al. 2021). This is one of several
examples where findings conflict regarding the effect of psychedelics on fundamental
visual processes. Psychedelics impact performance on some visual tasks but not others
(Carter et al. 2004; Gouzoulis-Mayfrank et al. 2002; Barrett et al. 2018; Kometer et
al. 2011). Furthermore, performance on ‘catch-trials’ is largely comparable to placebo,
suggesting that psychedelics do not cause whole-scale visual impairment or complete
perceptual chaos, but rather selectively impact only some aspects of visual function.
Thus, carefully designed psychophysical paradigms might elucidate the nature of this
selectivity. Yet very few experiments using such paradigms have been carried out.
Klüver (1928) reported that under mescaline “Very pronounced simultaneous
contrast is found,” yet no further investigations have measured contrast perception
under psychedelic drugs. A visual illusion known as apparent contrast surround
suppression (Ejima and Takahashi 1985; Snowden and Hammett 1998; Schallmo and
Murray 2016) is an important paradigm in visual neuroscience because it elucidates
contextual modulation—how perceptual and neural responses to a target stimulus
are altered by the presence of contextual cues (surrounding stimuli) (Heeger 1992;
Carandini, Heeger, and Senn 2002; Angelucci et al. 2002; Webb et al. 2005; Schwartz,
Hsu, and Dayan 2007; Coen-Cagli, Kohn, and Schwartz 2015; Schallmo et al. 2018;
Schallmo, Kale, and Murray 2019). An investigation of surround suppression under
peak effects of a psychedelic drug could thus shed light on how psychedelics impact
this important visual function.
The lack of research pairing psychedelics with contextual modulation paradigms
like surround suppression is thus a considerable knowledge gap. The neuromodulatory
mechanisms underpinning visual surround suppression remain unknown (Schwartz,
6
Hsu, and Dayan 2007; Schallmo et al. 2018). The hypothesis that surround suppression
is underpinned by the neuromodulator molecule gamma-aminobutyric acid (GABA)
was pursued but has not found significant empirical support (Read et al. 2015;
Schallmo et al. 2018; Schach, Surges, and Helmstaedter 2021). Surround suppression
has also been probed using drugs that activate acetylcholine receptors (Gratton et al.
2017; Kosovicheva et al. 2012; Nguyen et al. 2018); dopamine receptors (Gratton et al.
2017), and noradrenaline receptors (Gratton et al. 2017). However, drugs that bind
to serotonin receptors—including the classic psychedelics—have not been investigated
for their impact on surround suppression in humans.
This knowledge gap reaches into psychiatry. Weaker (attenuated) visual surround
suppression has been measured in patients with major depressive disorder (MDD)
(Golomb et al. 2009; Salmela et al. 2021), a difference that normalized somewhat as
MDD symptoms went into remission (Salmela et al. 2021). The neuromodulatory
correlates of weakened surround suppression in MDD are unknown. Furthermore, the
links between serotonin and MDD are widely assumed but not well understood (Cowen
and Browning 2015; Carhart-Harris and Nutt 2017). Psilocybin-assisted therapy has
rapid and enduring antidepressant effects in MDD patients (Davis et al. 2021; Ross et
al. 2016; Griffiths et al. 2016; Carhart-Harris, Bolstridge, et al. 2018; Gukasyan et al.
2022) but its therapeutic mechanisms are under debate (Letheby 2021; Yaden and
Griffiths 2020). Taken together, knowing the impact of a classic psychedelic molecule
on visual surround suppression could thus address multiple knowledge gaps in our
understanding of the links between contextual modulation, serotonin, visual surround
suppression, MDD, and the antidepressant mechanisms of psychedelic-assisted therapy.
Research Aims
In this dissertation I aim to address some of the gaps in our understanding of
how psychedelic drugs impact visual processes with both empirical and theoretical
contributions. First, I aim to address the disconnect between the theories that guided
early-era psychedelic research pre-1970 and the theories being developed in current
7
cognitive science. Second, I aim to address the absence of focused theoretical analysis
of psychedelic visual phenomena. Third and finally, I aim to address the lack of
empirical research into visual contrast perception and surround suppression under
psychedelic drugs.
My specific research objectives are:
1. To examine and compare theoretical explanations of psychedelic drug effects,
past and present.
2. To taxonomize and analyze the phenomenology of psychedelic visuals.
3. To measure the impact of a psychedelic drug on visual contrast perception and
surround suppression in humans.
Each research objective is driven by a corresponding research question.
1. What is the best explanation of psychedelic drug effects to date?
2. Do psychedelic drugs affect contextual modulation in visual processes?
3. Does psilocybin impact contrast perception and surround suppression in humans?
Significance
This work is significant because it elucidates the possible mechanisms by which
psychedelic drugs alter mental functions. Understanding these mechanisms has the
potential to improve our understanding of how the nervous system supports everyday
mental functions, which can aid our understanding of causes of—and treatments
for—mental health disorders.
There are currently several important clues in the puzzle of understanding mental
health. Psychedelics have therapeutic benefits but the mechanisms are not well
understood. Reduced contextual modulation on visual tasks has been found in
certain mental health disorders but the reasons for this are unclear. Psychedelics
impact visual processes via serotonin signaling but we do not know why. Mental
health disorders are linked to serotonin signaling but the mechanisms have not been
elucidated. Here, I use empirical and analytical methods to investigate the links
8
between visual processes, contextual modulation, serotonin, and psychedelic drugs.
The practical and pragmatic usefulness of this investigation is twofold. First, a better
understanding of how psychedelics shift the brain’s mode of processing might help
psychiatry understand why this shift has therapeutic benefits, and how to optimize
these benefits. Second, the focus of the work is to understand why psychedelic drugs
produce visual effects, which has direct application to visual neuroscience. Probing
visual function pharmacologically with serotonergic psychedelics has the potential
to facilitate understanding of how vision itself works, which might provide insights
into the causes of visual abnormalities, including those associated with certain mental
health diagnoses.
Taken together, the above points illustrate how my aim of understanding psychedelic
visuals has the potential to advance understanding of other psychedelic effects, the
possible mechanisms of psychedelic therapies, and the neuromodulatory functions
that underpin everyday vision. Such advances in knowledge could contribute to
improvements in our understanding of sensory impairments, mental health disorders,
and their treatments.
Cognitive Science
This is a dissertation for the degree of PhD in the field of Cognitive Science, a field
that has been interdisciplinary since its beginnings (Miller 2003; Brook 2007), founded
on the explicit goal of integrating psychology, linguistics, neuroscience, computer
science, anthropology and philosophy (see Figure 2). My contribution leverages three
of these disciplines—namely, psychology, neuroscience, and philosophy—to investigate
a phenomenon of interest (psychedelic visuals) that is appropriate for the domain of
cognitive science. Psychedelics alter neural activity (neuroscience), leading to changes
in perception and cognition (psychology, philosophy, artificial intelligence), as well
as patterns of language and semantic meaning (linguistics). Furthermore, cultures
around the globe have valued and used psychedelics for a wide variety of purposes
since prehistoric times (anthropology), and their implications for our understanding
9
of perception, mind, spirituality, and ethics are significant (philosophy). Finally, their
impact on human behavior and neural dynamics lends itself to computational modeling
(computer science, machine learning, artificial intelligence).
Thus, by investigating psychedelic visuals from the field of cognitive science, I am
addressing “the need and potential benefit of incorporating the expertise and interests
of the relatively independent fields of psychopharmacology, vision science, and mental
health in future research into perception and consciousness” (Carter, Pettigrew, et al.
2005).
Figure 2: “The six fields are connected in a hexagon. Each line in the figure represented
an area of interdisciplinary inquiry that was well defined in 1978 and that involved
the tools of the two disciplines it linked together” (Miller 2003).
Limitations
The experiments in Chapters 3 and 4 were from a pilot phase of a study and included a
very limited number of participants (n = 6). This small sample size limits the statistical
power and conclusions that can be drawn from them. Our inclusion criteria required
all participants to have at least one previous personal experience with psilocybin—we
excluded drug-naive candidates—which raises the possibility that our findings will
10
not generalize to first-time drug-naive individuals. Another limitation somewhat
unique to psychedelic research is the issue of placebo blinding—even if the study has a
‘double-blind placebo-controlled’ design, the drastic effects of the psychedelic drug (or
lack thereof) can become obvious to everyone involved, thus breaking the blind. We
cannot rule out the possibility that participants knew which drug they had received
by the time they completed the experimental tasks.
This work combines empirical and analytical methods together to investigate a
single phenomenon, creating a system of checks and balances. The psychophysics keeps
the neuroimaging connected to the phenomenology, while the neuroimaging keeps the
psychophysics connected to the brain. The empirical work keeps the theoretical and
philosophical work honest and on-track. The theoretical work guides the empirical
work and vice versa. The philosophical work gives the scientific work purpose and
keeps the science aware of its own inconsistencies and limitations.
Structure
In this section I briefly introduce each chapter, organized by research objectives
described above.
Objective 1
As stated above, my research objective 1 is: To examine and compare theoretical
explanations of psychedelic drug effects, past and present. The corresponding research
question is What is the best explanation of psychedelic drug effects to date? Chapter
1 addresses these with an in-depth literature review, historical perspectives, and
conceptual analyses of existing theories.
Chapter 1
In Chapter 1 I provide a literature review and historical survey of psychedelic research
since 1895. I emphasize the conceptual themes through which early 20th-century
researchers interpreted their findings, and I link concepts from these early theories to
11
current models from cognitive neuroscience. This chapter was originally published
March 2nd, 2018 (Swanson 2018), as a Review Article in the journal Frontiers in
Pharmacology, part of the special issue research topic “Psychedelic Drug Research in
the 21st Century.” The published manuscript is available open access under its DOI:
10.3389/fphar.2018.00172. It has been reprinted here exactly as published.
Research Objective 2
Research objective 2 of this dissertation is: To taxonomize and analyze the phenomenol-
ogy of psychedelic visuals. The corresponding research question is: Do psychedelic
drugs affect contextual modulation in visual processes? Chapter 2 addresses this
research objective with a phenomenological-functional analysis of psychedelic visuals.
Chapter 2
In Chapter 2 I narrow the scope and focus on the effects that psychedelics have on
visual perception with eyes open (known as ‘open-eye visuals’ or OEVs). I use examples
from the literature to describe the subjective phenomenology of OEVs and then classify
them into basic categories. I then analyze this phenomenology and ask what might
cause them considering what we know about everyday visual processing. I argue
that OEVs might stem from drug-induced hypersensitivity in contextual modulation,
the process that produces common visual illusions when the stimuli contain certain
contextual cues. I support this argument with interpretations of empirical evidence
from previous studies with psychedelics and perceptual tasks. I further argue that
psychedelics might produce hypersensitivity to contextual cues in non-visual areas
of cognitive processing, and that the general state of what I call ‘hypercontextual
modulation’ might have therapeutic benefits. This chapter was written as an invited
chapter (under review) to be included in a forthcoming edited volume Philosophical
Perspectives on the Psychedelic Renaissance from Oxford University Press (Letheby
and Gerrans, n.d.).
12
Research Objective 3
Research objective 3 of this dissertation is: To measure the impact of a psychedelic
drug on visual contrast perception and surround suppression in humans. The corre-
sponding research question is: Does psilocybin impact contrast perception and surround
suppression in humans? Chapters 3 and 4 address this objective with two experiments
that measured how psilocybin impacted the perceptual and neural responses to stimuli
designed to induce visual center-surround interactions in humans.
Chapter 3
Chapter 3 reports results from a pilot study with a small number of participants (n
= 6) who performed a contrast-matching task under peak effects of a 25mg (high)
dose of psilocybin in a double-blind, randomized, placebo-controlled psychophysical
experiment. The stimuli in this task were designed to induce and measure contrast
surround suppression, a form of contextual modulation in visual contrast perception
in which illusory (suppressed) contrast is perceived when a stimulus is surrounded by
higher-contrast (surround) stimuli. We found that psilocybin increased the strength
of the surround suppression illusion under psilocybin compared with placebo. Further-
more, the magnitude of this increase was ‘feature-selective’, as the difference between
psilocybin and placebo was greatest when the target stimulus appeared within a
surround that had parallel orientation. This finding is consistent with the arguments
I make in chapter 2 that psychedelic effects result from hypersensitivity to contextual
cues.
Chapter 4
Chapter 4 reports results from a second experiment completed by the same participants
in the same study as Chapter 3. We obtained electroencephalography (EEG) signals as
the participants viewed stimuli designed to induce and measure surround suppression
in neural responses to visual stimuli as measured by event-related potential (ERP)
techniques. We found that responses to the target stimulus showed greater surround
13
suppression under psilocybin compared with placebo. Moreover, like our psychophysical
findings, the effect of psilocybin on ERP surround suppression was greatest when the
surround stimulus had parallel orientation. This finding mirrors our psychophysical
findings, and further supports the arguments in Chapter 2 that the visual system
becomes hypersensitive to contextual cues under psychedelic drugs.
14
Chapter 1
Unifying theories of psychedelic
drug effects
“Hallucinogens sit at the crossroads of the mind-brain interaction.”
–Henry David Abraham (1996, 294) “The Pharmacology of Hallucinogens”
15
ABSTRACT
In this chapter I review theories of psychedelic drug effects and highlight key
concepts which have endured over the last 125 years of psychedelic science.
First, I describe the subjective phenomenology of acute psychedelic effects
using the best available data. Next, I review late 19th-century and early 20th-
century theories—model psychoses theory, filtration theory, and psychoanalytic
theory—and highlight their shared features. I then briefly review recent findings
on the neuropharmacology and neurophysiology of psychedelic drugs in humans.
Finally, I describe recent theories of psychedelic drug effects which leverage 21st-
century cognitive neuroscience frameworks—entropic brain theory, integrated
information theory, and predictive processing—and point out key shared features
that link back to earlier theories. I identify an abstract principle which cuts
across many theories past and present: psychedelic drugs perturb universal brain
processes that normally serve to constrain neural systems central to perception,
emotion, cognition, and sense of self. I conclude that making an explicit effort
to investigate the principles and mechanisms of psychedelic drug effects is a
uniquely powerful way to iteratively develop and test unifying theories of brain
function.1
1.1 Introduction
Lysergic acid diethylamide (LSD), N,N-dimethyltryptamine (DMT), psilocybin, and
mescaline—the ‘classic’ psychedelic drugs—can produce a broad range of effects in
perception, emotion, cognition, and sense of self. How do they do this? Western science
began its ‘first wave’ of systematic investigations into the unique effects of mescaline
125 years ago. By the 1950s, rising interest in mescaline research was expanded to
include drugs like DMT, LSD, and psilocybin in a ‘second wave’ of psychedelic science.
Because of their dramatic effect on the character and contents of subjective awareness,
psychedelic drugs magnified the gaps in our scientific understanding of how brain
chemistry relates to subjective experience (see Evarts 1957; Purpura 1968). Huxley
(1954, 12) commented that our understanding circa 1954 was “absurdly inadequate”
and amounted to a mere “clue” that he hoped would soon develop into a more
1Originally publishedMarch 2nd, 2018 (Swanson 2018), as a Review Article in the journal Frontiers
in Pharmacology, part of the special issue research topic “Psychedelic Drug Research in the 21st
Century.” The published manuscript is available open access under the DOI: 10.3389/fphar.2018.00172.
16
robust understanding. “Meanwhile the clue is being systematically followed, the
sleuths—biochemists, psychiatrists, psychologists—are on the trail” (Huxley 1954, 12).
A ‘third wave’ of psychedelic science has recently emerged with its own set of sleuths
on the trail, sleuths who now wield an arsenal of 21st-century scientific methodologies
and are uncovering new sets of clues.
Existing theoretical hurdles span five major gaps in understanding. The first gap
is that we do not have an account of how psychedelic drugs can produce such a broad
diversity of subjective effects. LSD, for example, can produce subtle intensifications
in perception—or it can completely dissolve all sense of space, time, and self. What
accounts for this atypical diversity?
The second gap is that we do not understand how pharmacological interactions at
neuronal receptors and resulting physiological changes in the neuron lead to large-scale
changes in the activity of neural populations, or changes in brain network connectivity,
or at the systems-level of global brain dynamics. What are the causal links in the
multi-level pharmaco-neurophysiological chain?
The third gap is that we do not know how psychedelic drug-induced changes in brain
activity—at any level of description—map onto the acute subjective phenomenological
changes in perception, emotion, cognition, and sense of self. This kind of question
is not unique to psychedelic drugs (i.e., Crick and Koch 1998; Tononi and Edelman
1998) but our current understanding of psychedelic drug effects clearly magnifies the
disconnect between brain science and subjective experience.
Fourth, there is a gap in our understanding of the relationships between psychedelic
effects and symptoms of psychoses, such as perceptual distortion, hallucination,
or altered self-reference. What is the relationship between psychedelic effects and
symptoms of chronic psychotic disorders?
Fifth and finally, there is a gap in our clinical understanding of the process by which
psychedelic-assisted therapies improve mental health (Carhart-Harris and Goodwin
2017). Which psychedelic drug effects (in the brain or in subjective experience) enable
clinical improvement? How?
Scientific efforts to understand diverse natural phenomena aim to produce a single
17
theory that can account for many phenomena using a minimal set of principles.
Such theories are sometimes called unifying theories. Not everyone agrees on the
meaning of ‘unification’ or ‘unifying theory’ in science.2 Morrison (2000) observed
that, although theory unification is a messy process which may not have discernible
universal characteristics, historically successful unifying scientific theories tend to
have two common features: (1) a formalized framework (quantitative mathematical
descriptions of the phenomena) and (2) unifying principles (abstract concepts that
unite diverse phenomena). On this conception, then, a unifying theory of psychedelic
drug effects would offer a single formalized (mathematical or computational) framework
capable of describing diverse psychedelic phenomena using a minimal set of unifying
principles. Unfortunately, the survey of literature in this review does not locate
an existing unifying theory of psychedelic drug effects. It does, however, highlight
enduring abstract principles that recur across more than a century of theoretical
efforts. Furthermore, it reviews recent formalized frameworks which, although currently
heterogeneous and divergent, hint at the possibility of a quantitative groundwork for
a future unifying theory.
The field of cognitive neuroscience offers formalized frameworks and general prin-
ciples designed to track and model the neural correlates of perception, emotion,
cognition, and consciousness. These broad frameworks span major levels of description
in the brain and attempt to map them onto behavioral and phenomenological data.
Corlett et al. (2009, 516) argue that until this is done “our understanding of how
the pharmacology links to the symptoms will remain incomplete.” Montague et al.
(2012, 1) argue that ‘computational psychiatry’ can remedy the “lack of appropriate
intermediate levels of description that bind ideas articulated at the molecular level to
those expressed at the level of descriptive clinical entities.” Seth (2009, 50) argues that
“computational and theoretical approaches can facilitate a transition from correlation
to explanation in consciousness science” and explains how a recent LSD, psilocybin,
and ketamine study (Schartner et al. 2017) was motivated by a need to elucidate
descriptions at intermediate levels somewhere between pharmacology and phenomenol-
2For example, see Kitcher (1981, 1989), Friedman (1983), and Morrison (2000).
18
ogy: “We know there’s a pharmacological link, we know there’s a change in experience
and we know there’s a clinical impact. But the middle bit if you like, what are these
drugs doing to the global activity of the brain, that’s the gap we’re trying to fill with
this study” (quoted in Osborne 2017). Taken together, the above quotations point to
an emerging sense that cognitive neuroscience frameworks can address gaps in our
understanding of psychedelic drug effects.
In this chapter I review theories of psychedelic drug effects. First, making an effort
to clearly define the target explananda, I review the acute subjective phenomeno-
logical properties of psychedelic effects as well as long-term clinical outcomes from
psychedelic-assisted therapies. Second, I review theories from first-wave and second-
wave psychedelic science—model psychoses theory, filtration theory, and psychoanalytic
theory—and identify core features of these theories. Third, I review findings from
recent neurophysiological research in humans under psychedelic drugs. Finally, I
review select 21st-century theories of psychedelic effects that have been developed
within cognitive neuroscience frameworks; namely, entropic brain theory, integrated
information theory, and predictive processing. My analysis of recent theoretical efforts
highlights certain features, first conceptualized in 19th- and 20th-century theories,
which remain relevant in their ability to capture both the phenomenological and neuro-
physiological dynamics of psychedelic effects. I describe how these enduring theoretical
features are now being operationalized into formalized frameworks and could serve as
potential unifying principles for describing diverse psychedelic phenomena.
1.2 Psychedelic Drug Effects
There are dozens of molecules known to cause psychedelic-like effects (Schultes and
Hofmann 1973; Shulgin and Shulgin 1997). This review focuses only on a limited set of
drugs dubbed ‘classical hallucinogens’ or ‘classic psychedelics’ which are: LSD, DMT,
psilocybin, and mescaline3 (Nichols 2016). Importantly, there are qualitative inter-drug
3Ayahuasca contains DMT but is importantly different from pure DMT (McKenna, Towers, and
Abbott 1984).
19
differences between the effects of the four classic psychedelic drugs (Strassman et al.
1994; Hasler et al. 2004; Studerus, Gamma, and Vollenweider 2010; Schmid et al.
2015; Liechti, Dolder, and Schmid 2017). Drug dosage is a primary factor in predicting
the types of effects that will occur Liechti, Dolder, and Schmid (2017). Effects unfold
temporally over a drug session; onset effects are distinct from peak effects and some
effects have a higher probability of occurring at specific timepoints over the total
duration of drug effects (Masters and Houston 1966; Preller and Vollenweider 2016).
Furthermore, effects are influenced by non-drug factors traditionally referred to as
set and setting, such as personality, pre-dose mood, drug session environment, and
external stimuli (Figure 1.1) (Leary, Litwin, and Metzner 1963; Studerus et al. 2012;
Hartogsohn 2016; Carhart-Harris and Nutt 2017).
The above variables, while crucial, do not completely prohibit meaningful character-
ization of general psychedelic effects, as numerous regularities, patterns, and structure
can still be identified (Masters and Houston 1966; Grinspoon and Bakalar 1979; Preller
and Vollenweider 2016). Indeed, common psychedelic effects can be reliably measured
using validated psychometric instruments consisting of self-report questionnaires and
rating scales (Strassman et al. 1994; Dittrich 1998; Dittrich, Lamparter, and Maurer
2010; Riba, Rodríguez-Fornells, et al. 2001; Studerus, Gamma, and Vollenweider 2010,
2010; Maclean et al. 2012; Turton, Nutt, and Carhart-Harris 2014; Barrett, Johnson,
and Griffiths 2015; Nour et al. 2016) though some of these rating scales may be in
need of further validation using modern statistical techniques (Bouso et al. 2016).
Items from these rating scales are wrapped in ‘scare quotes’ in the following discussion
in an effort to characterize the subjective phenomenology of psychedelic effects from a
first-person perspective. An example of rating scale results is given in (Figure 1.2).
1.2.1 Perceptual Effects
Perceptual effects occur along a dose-dependent range from subtle to drastic. The
range of different perceptual effects includes perceptual intensification, distortion,
illusion, mental imagery, elementary hallucination, and complex hallucination (Klüver
1928; Kometer and Vollenweider 2016; Preller and Vollenweider 2016). Intensifications
20
Figure 1.1: ‘Extra-pharmacological’ factors that can determine psychedelic drug
effects (Carhart-Harris and Nutt 2017). “Trait factors may be biological [e.g., receptor
polymorphisms (Ott 2007)] or psychological in nature [e.g., personality (MacLean,
Johnson, and Griffiths 2011) or suggestibility (Carhart-Harris et al. 2015)]. The
pre-state refers to such things as anticipatory anxiety, expectations and assumptions
(which account for so-called ‘placebo’ and ‘nocebo’ effects), and readiness to surrender
resistances and ‘let go’ to the drug effects (e.g., see Russ and Elliott 2017). In
the context of psychedelic research, the pre-state is traditionally referred to as the
‘set’ (Hartogsohn 2016). State refers to the acute subjective and biological quality
of the drug experience and may be measured via subjective rating scales or brain
imaging (see Roseman, Nutt, and Carhart-Harris 2017). Dose relates to the drug
dosage—which may be a critical determinant of state (Griffiths et al. 2011; Nour et
al. 2016)—as well as long-term outcomes (see Roseman, Nutt, and Carhart-Harris
2017). Environment relates to the various environmental influences. In the context of
psychedelic research this is traditionally referred to as ‘setting’ (Hartogsohn 2016).
We recognize that the environment can be influential at all stages of the process of
change associated with drug action. The long-term outcomes may include such things
as symptoms of a specific psychiatric condition such as depression—measured using a
standard rating scale (Carhart-Harris, Bolstridge, et al. 2016) as well as relatively
pathology-independent factors such as personality (MacLean, Johnson, and Griffiths
2011) and outlook” (Carhart-Harris and Nutt 2017, 1097).
21
Figure 1.2: Subjective rating scale items selected after psilocybin (blue) and placebo
(red) (n = 15) (Muthukumaraswamy et al. 2013). “Items were completed using a
visual analog scale format, with a bottom anchor of ‘no, not more than usually’ and a
top anchor of ‘yes, much more than usually’ for every item, with the exception of ‘I felt
entirely normal,’ which had bottom and top anchors of ‘No, I experienced a different
state altogether’ and ‘Yes, I felt just as I normally do,’ respectively. Shown are the mean
ratings for 15 participants plus the positive SEMs. All items marked with an asterisk
were scored significantly higher after psilocybin than placebo infusion at a Bonferroni-
corrected significance level of p < 0.0022 (0.5/23 items)” (Muthukumaraswamy et al.
2013, 15176).
22
of color saturation, texture definition, contours, light intensity, sound intensity, timbre
variation, and other perceptual characteristics are common (Kometer and Vollenweider
2016; Kaelen et al. 2018). The external world is experienced as if in higher resolution,
seemingly more crisp and detailed, often accompanied by a distinct sense of ‘clarity’ or
‘freshness’ in the environment (Hofmann 1980; Huxley 1954; Díaz 2010; Kometer and
Vollenweider 2016). Sense of meaning in percepts is altered, e.g., ‘Things around me
had a new strange meaning for me’ or ‘Objects around me engaged me emotionally
much more than usual’ (Studerus, Gamma, and Vollenweider 2010).
Perceptual distortions and illusions are extremely common, e.g., ‘Things looked
strange’ or ‘My sense of size and space was distorted’ or ‘Edges appeared warped’ or
‘I saw movement in things that weren’t actually moving’ (Dittrich 1998; Muthuku-
maraswamy et al. 2013). Textures undulate in rhythmic movements, object boundaries
warp and pulsate, and the apparent sizes and shapes of objects can shift rapidly (Kome-
ter and Vollenweider 2016). Controlled psychophysical studies have measured various
alterations in motion perception (Carter et al. 2004), object completion (Kometer et
al. 2011), and binocular rivalry (Frecska, White, and Luna 2004; Carter et al. 2007).
In what are known as elementary hallucinations—e.g., ‘I saw geometric pat-
terns’—the visual field can become permeated with intricate tapestries of brightly
colored, flowing latticework and other geometric visuospatial ‘form constants’ (Klüver
1928; Siegel and West 1975; Kometer and Vollenweider 2016). In complex hallucina-
tions visual scenes can present elaborate structural motifs, landscapes, cities, galaxies,
plants, animals, and human (and non-human) beings (Shanon 2002; Studerus, Gamma,
and Vollenweider 2010; Carhart-Harris et al. 2015; Kaelen et al. 2016; Preller and
Vollenweider 2016; Kraehenmann, Pokorny, Vollenweider, et al. 2017). Complex
hallucinations typically succeed elementary hallucinations and are more likely at
higher doses (Kometer and Vollenweider 2016; Liechti, Dolder, and Schmid 2017)
especially under DMT (Strassman et al. 1994; Shanon 2002). Both elementary and
complex hallucinations are more commonly reported behind closed eyelids (‘closed eye
visuals’; CEVs) but can dose-dependently occur in full light with eyes open (‘open eye
visuals’; OEVs) (Kometer and Vollenweider 2016). CEVs are often described as vivid
23
mental imagery. Under psychedelic drugs, mental imagery becomes augmented and
intensified—e.g., ‘My imagination was extremely vivid’—and is intimately linked with
emotional and cognitive effects (Carhart-Harris et al. 2015; Preller and Vollenweider
2016). “Sometimes sensible film-like scenes appear, but very often the visions consist of
scenes quite indescribable in ordinary language, and bearing a close resemblance to the
paintings and sculptures of the surrealistic school” (Stockings 1940, 31). Psychedelic
mental imagery can be modulated by both verbal (Carhart-Harris et al. 2015) and
musical (Kaelen et al. 2016) auditory stimuli. Synaesthesia (Ward 2013) has been
reported, especially visual phenomena driven by auditory stimuli—‘Sounds influenced
the things I saw’—but classification of these effects as ‘true’ synaesthesia is actively
debated (Sinke et al. 2012; Brogaard 2013; Luke and Terhune 2013; Terhune et al.
2016).
Somatosensory perception can be drastically altered—e.g., ‘I felt unusual bodily
sensations’—including body image, size, shape, and location (Savage 1955; Klee 1963;
Preller and Vollenweider 2016). Sense of time and causal sequence can lose their
usual linear cause-effect structure making it difficult to track the transitions between
moments (Heimann 1963; Wittmann et al. 2007; Wackermann et al. 2008; Studerus,
Gamma, and Vollenweider 2010; Schmid et al. 2015).
Overall the perceptual effects of psychedelics are extremely varied, multimodal,
and easily modulated by external stimuli. Perceptual effects are tightly linked with
emotional and cognitive effects.
1.2.2 Emotional Effects
Emotional psychedelic effects are characterized by a general intensification of feelings,
increased (conscious) access to emotions, and a broadening in the overall range of emo-
tions felt over the duration of the drug session. Psychedelics can induce unique states
of euphoria characterized by involuntary grinning, uncontrollable laughter, silliness,
giddiness, playfulness, and exuberance (Preller and Vollenweider 2016). Negatively
experience emotions—e.g., ‘I felt afraid’ or ‘I felt suspicious and paranoid’—are often
accompanied by a general sense of losing control, e.g., ‘I feared losing control of my
24
mind’ (Strassman 1984; Johnson, Richards, and Griffiths 2008; Barrett, Johnson, and
Griffiths 2017). However, the majority of emotional psychedelic effects in support-
ive contexts are experienced as positive (Studerus, Gamma, and Vollenweider 2010;
Schmid et al. 2015; Carhart-Harris, Kaelen, et al. 2016; Belser et al. 2017; Watts
et al. 2017). Both LSD and psilocybin can bias emotion toward positive responses
to social and environmental stimuli (Kometer et al. 2012; Carhart-Harris, Kaelen,
et al. 2016; Dolder et al. 2016; T. Pokorny et al. 2016). Spontaneous feelings of
awe, wonder, bliss, joy, fun, excitement (and yes, peace and love) are also consistent
themes across experimental and anecdotal reports (Huxley 1954; Kaelen et al. 2015;
Preller and Vollenweider 2016; Belser et al. 2017). In supportive environments,
classic psychedelic drugs can promote feelings of trust, empathy, bonding, closeness,
tenderness, forgiveness, acceptance, and connectedness (Dolder et al. 2016; Belser
et al. 2017; Carhart-Harris et al. 2017; T. Pokorny et al. 2017; Watts et al. 2017).
Emotional effects can be modulated by all types of external stimuli, especially music
(Bonny and Pahnke 1972; Shanon 2002; Kaelen et al. 2015, 2018).
1.2.3 Cognitive Effects
Precise characterization of cognitive psychedelic effects has proven enigmatic and
paradoxical (Shanon 2002; Carhart-Harris, Kaelen, et al. 2016). Acute changes in
the normal flow of linear thinking—e.g., ‘My thinking was muddled’ or ‘My thoughts
wandered freely’—are extremely common (Hasler et al. 2004; Studerus, Gamma, and
Vollenweider 2010). This is reflected in reduced performance on standardized measures
of working memory and directed attention (Carter, Burr, et al. 2005; Vollenweider et
al. 2007); however, reductions in performance have been shown to occur less often
in individuals with extensive past experience with the drug’s effects (Bouso et al.
2013). Crucially, cognitive impairments related to acute psychedelic effects are dose-
dependent (Wittmann et al. 2007). Extremely low doses, known as microdoses, have
been anecdotally associated with improvements in cognitive performance (Waldman
2017; Wong 2017) “a claim that urgently requires empirical verification through
controlled research” (Carhart-Harris and Nutt 2017, 1103). Theoretical attempts to
25
account for the reported effects of microdosing have yet to emerge in the literature
and therefore present an important opportunity to future theoretical endeavors.
Certain cognitive traits associated with creativity can increase under psychedelics
(Sessa 2008; Baggott 2015) such as divergent thinking (Kuypers et al. 2016), use of
unlikely language patterns or word associations (Natale et al. 1978), expansion of
semantic activation (Spitzer et al. 1996; Family et al. 2016), and attribution of meaning
to perceptual stimuli (Liechti, Dolder, and Schmid 2017; Preller et al. 2017) especially
musical stimuli (Kaelen et al. 2015, 2018; Atasoy, Roseman, et al. 2017; Barrett,
Preller, et al. 2017). Primary-process thinking (Rapaport 1950)—a widely validated
psychological construct (Arminjon 2011) associated with creativity (Suler 1980)—is
characterized phenomenologically by “image fusion; unlikely combinations or events;
sudden shifts or transformations of images; and contradictory or illogical actions,
feelings, or thoughts” (Kraehenmann, Pokorny, Aicher, et al. 2017, 2). Psilocybin
and LSD have been shown to increase primary-process thinking (Martindale and
Fischer 1977; Natale, Dahlberg, and Jaffe 1978; Family et al. 2016; Kraehenmann,
Pokorny, Aicher, et al. 2017) as well as the subjective bizarreness and dreamlike
nature of mental imagery associated with verbal stimuli (Carhart-Harris et al. 2015;
Kraehenmann, Pokorny, Vollenweider, et al. 2017). Cognitive flexibility (or ‘loosening’
of cognition) and optimism can remain for up to 2 weeks after the main acute drug
effects have dissipated (Carhart-Harris, Kaelen, et al. 2016). Furthermore, long-term
increases in creative problem-solving ability (Sweat, Bates, and Hendricks 2016) and
personality trait openness (MacLean, Johnson, and Griffiths 2011; Lebedev et al.
2016) have been measured after just one psychedelic experience.
1.2.4 Ego Effects and Ego Dissolution Experiences
Klüver (1926, 513) observed that under peyote “the line of demarcation drawn between
‘object’ and ‘subject’ in normal state seemed to be changed. The body, the ego, became
‘objective’ in a certain way, and the objects became ‘subjective’.” Similar observations
continued throughout first-wave and second-wave psychedelic science (Beringer 1927;
Klüver 1928; Savage 1955; Eisner and Cohen 1958; Klee 1963; Leary, Metzner, and
26
Alpert 1964; Grof 1976). Importantly, effects on sense of self and ego occur along a
dose-dependent range spanning from subtle to drastic (Letheby and Gerrans 2017;
Millière 2017). Subtle effects are described as a ‘softening’ of ego with increased insight
into one’s own habitual patterns of thought, behavior, personal problems, and past
experiences; effects which were utilized in ‘psycholytic’ psychotherapy (Grof 1980).
Drastic ego-effects, known as *ego dissolution4, are described as “the dissolution of the
sense of self and the loss of boundaries between self and world” (Millière 2017, 1)—e.g.,
‘I felt like I was merging with my surroundings’ or ‘All notion of self and identity
dissolved away’ or ‘I lost all sense of ego’ or ‘I experienced a loss of separation from my
environment’ or ‘I felt at one with the universe’ (Dittrich, Lamparter, and Maurer 2010;
Nour et al. 2016; Millière 2017). These descriptions resemble non-drug ‘mystical-type’
experiences (James 1902; Huxley 1945; Stace 1960; Forman 1998; Baumeister and
Exline 2002); however, the extent of overlap here remains an open question (Hood
2001; Maclean et al. 2012; Barrett and Griffiths 2017; Millière 2017; Winkelman
2017). Ego dissolution is more likely to occur at higher doses (Griffiths et al. 2011;
Studerus, Gamma, and Vollenweider 2010; Studerus et al. 2012; Liechti, Dolder, and
Schmid 2017). Furthermore, certain psychedelic drugs cause ego dissolution experience
more reliably than others; psilocybin, for example, was found to produce full ego
dissolution more reliably compared with LSD (Liechti, Dolder, and Schmid 2017).
Ego dissolution experiences can be driven and modulated by external stimuli, most
notably music (Carhart-Harris, Muthukumaraswamy, et al. 2016; Atasoy, Roseman,
et al. 2017; Kaelen et al. 2018). Interestingly, subjects who experienced ‘complete’
ego dissolution in psychedelic-assisted therapy were more likely to evidence positive
clinical outcomes (Griffiths et al. 2008, 2016; Majić, Schmidt, and Gallinat 2015; Ross
et al. 2016; Roseman, Nutt, and Carhart-Harris 2017) as well as long-term changes in
life outlook and the personality trait openness (MacLean, Johnson, and Griffiths 2011;
Carhart-Harris, Kaelen, et al. 2016; Lebedev et al. 2016).
4Variously termed ‘ego disintegration,’ ‘ego loss,’ and ‘ego death.’ For a comprehensive review,
see Millière (2017).
27
1.2.5 Clinical Efficacy and Long-Term Effects
Mescaline-assisted therapies showed promising results during first-wave psychedelic
science (Beringer 1927; Rouhier 1927) and this trend continued through second-wave
psychedelic research on LSD-assisted therapies (Sandison and Whitelaw 1957; Cohen
and Eisner 1959; Pahnke 1966; Grof 1976). Recent studies have produced significant
evidence for the therapeutic utility of psychedelic drugs in treating a wide range of
mental health issues (Tupper et al. 2015; Lieberman and Shalev 2016; Carhart-Harris
and Goodwin 2017), including anxiety and depression (Grob et al. 2011; Gasser et al.
2014; Carhart-Harris, Bolstridge, et al. 2016; Carhart-Harris, Bolstridge, et al. 2018;
Dos Santos et al. 2016; Griffiths et al. 2016; Ross et al. 2016), obsessive-compulsive
disorder (Moreno et al. 2006), and addiction (Michael P. Bogenschutz and Johnson
2016) to alcohol (Michael P. Bogenschutz et al. 2015) and tobacco (Johnson et al.
2014). In many clinical studies, ego-dissolution experience has correlated with positive
clinical outcomes (Griffiths et al. 2008, 2016; Majić, Schmidt, and Gallinat 2015; Ross
et al. 2016; Roseman, Nutt, and Carhart-Harris 2017).
Remarkably, as mentioned above, a single psychedelic experience can increase
optimism for at least 2 weeks after the session (Carhart-Harris, Kaelen, et al. 2016)
and can produce lasting changes in personality trait openness (MacLean, Johnson,
and Griffiths 2011; Lebedev et al. 2016). A study of regular (weekly) ayahuasca
users showed improved cognitive functioning and increased positive personality traits
compared with matched controls (Bouso et al. 2015). Interestingly, these outcomes
may expand beyond sanctioned clinical use, as illicit users of classic psychedelic
drugs within the general population self-report positive long-term benefits from their
psychedelic experiences (Carhart-Harris and Nutt 2010), are statistically less likely
to evidence psychological distress and suicidality (Hendricks et al. 2015; Argento et
al. 2017), and show an overall lower occurrence of mental health problems in general
(Krebs and Johansen 2013).
28
1.2.6 Summary
The above evidence demonstrates the broad diversity of acute subjective effects that
classic psychedelic drugs can produce in perceptual, emotional, and cognitive domains.
Unique changes in sense of self, ego, body image, and personal meaning are particularly
salient themes. How do these molecules produce such dramatic effects? What are
the relationships between acute perceptual, emotional, cognitive, and self-related
effects? What is the link between acute effects and long-term changes in mental health,
personality, and behavior? Theories addressing these questions emerged as soon as
Western science recognized the need for a scientific understanding of psychedelic drug
effects beginning in the late 19th century.
1.3 19th and 20th Century Theories of Psychedelic
Drug Effects
The effects described above are what captured the interest of first-wave and second-
wave psychedelic scientists, and the theories they developed in their investigations have
two central themes. The first theme is the observation that psychedelic effects share
descriptive elements with symptoms of psychoses, such as hallucination, altered self-
reference, and perceptual distortions. This theme forms the basis of model psychoses
theory and is what motivated the adoption of the term ‘psychotomimetic’ drugs. The
second theme is the observation that psychedelic drugs seem to expand the total
range of contents presented subjectively in our perceptual, emotional, cognitive, and
self-referential experience. This theme forms the basis of filtration theory and is what
motivated the adoption of the term ‘psychedelic’ drugs. A third theoretical account uses
psychoanalytic theory to address the expanded range of mental phenomena produced by
psychedelic drugs as well as the shared descriptive elements with symptoms of psychoses.
The next section reviews these themes along with their historically associated theories
before tracing their evolution into third-wave (21st-century) psychedelic science.
29
1.3.1 Model Psychoses Theory
When (Lewin 1894, 1927) ‘discovered’5 the peyote cactus, his reports caught the
attention of adventurous 19th-century scientists like (Prentiss and Morgan 1895;
Mitchell 1896; Ellis 1898), who promptly obtained samples and began consuming the
cactus and observing its effects on themselves. When Heffter (1898) isolated mescaline
from the peyote cactus and Späth (1919) paved the way for laboratory synthesis,
scientists began systematically dosing themselves (along with their colleagues and
students) with mescaline and publishing their findings in medical journals (Knauer and
Maloney 1913; Klüver 1926; Beringer 1927; Rouhier 1927; Guttmann 1936; Stockings
1940). Klüver (1926), intrigued by the approach of Knauer and Maloney (1913),
ingested peyote at the University of Minnesota Psychological Laboratory and, after
the effects had taken hold, completed standard psychophysical measures. Klüver (1926,
502) argued that systematic investigations into the neural mechanisms of mescaline
effects would help neurology “elucidate more general questions of the psychology
and pathology of perception.” However, it was the pathology aspect, not the general
psychology questions, which became the dominant focus of ensuing mescaline research
paradigms.
Model psychoses theory began long before any of the classic psychedelic drugs
became known to Western science. Moreau (1845) linked hashish effects with mental
illness and Kraepelin (1892) founded “pharmacopsychology” by dosing himself and
his students with various psychoactive drugs in the laboratory of Wilhelm Wundt
(Müller, Fletcher, and Steinberg 2006; Schmied, Steinberg, and Sykes 2006). These
scientists hoped to study psychotic symptoms using drugs to induce ‘model psychoses’
(1) in themselves, to gain first-person knowledge of the phenomenology of psychotic
symptoms by “administering to one another such substances as will produce in us
transitory psychoses” (Knauer and Maloney 1913, 426; see also Guttmann 1936),
and (2) in normal research subjects, allowing for laboratory behavioral observations
5An unnamed JAMA book reviewer critically notes that “it is interesting that attention had not
been paid by American scientists to this intoxicant used by the Mexican Indians until a European
called attention to it” (Beringer 1927).
30
on how the symptoms emerge and dissipate. Kraepelin and colleagues attempted
to model psychoses using many drugs—“tea, alcohol, morphine, trional, bromide,
and other drugs”—yet Kraepelin’s pupils (Knauer and Maloney 1913, 426) argued
that these drugs unfortunately “produce mental states which have little similarities
to actual insanities” and argued instead that mescaline was unique in its ability to
truly model psychoses. The dramatic subjective effects of mescaline invigorated the
model psychoses paradigm. Growing demand for the ideal chemical agent for model
psychoses eventually motivated Sandoz Pharmaceuticals to bring LSD to market in
the 1940s.6
Importantly, model psychoses theory was not initially a theory of drug effects; it
was an idealistic paradigm for researching psychoses that was already in use before
Western science ‘discovered’ classic psychedelic drugs. Nonetheless, it seeded the idea
that psychedelic effects themselves could be explained in terms of psychopathology and
motivated a search for common neural correlates. The founding figures of neurophar-
macology were driven by questions regarding the relationship between psychoactive
drugs and endogenous neurochemicals (see Abramson 1956). The putative psychoses-
mimicking effects of LSD and mescaline inspired the idea that psychotic symptoms
might be caused by a “hypothetical endotoxin” (Osmond 1957, 422) or some yet-
unknown endogenous neurochemical gone out of balance (Osmond and Smythies 1952;
Abramson 1956; Himwich 1959). The discovery that LSD can antagonize serotonin
led to the hypothesis that the effects of LSD are serotonergic and simultaneously
to the historic hypothesis7 that serotonin might play a role in regulating mental
function (Gaddum 1953; Gaddum and Hameed 1954; Woolley and Shaw 1954; Shaw
and Woolley 1956; Green 2008).
At the 1955 Second Conference on Neuropharmacology the whole class of drugs was
dubbed psychotomimetic (Abramson 1956). Interestingly, the word mimetic means to
6A marketing team at Sandoz Pharmaceuticals sent free samples of LSD to physicians around
the world and inside each package was a pamphlet which read: “By taking Delysid [LSD] himself,
the psychiatrist is able to gain an insight into the world of ideas and sensations of mental patients.
Delysid can also be used to induce model psychoses of short duration in normal subjects, thus
facilitating studies on the pathogenesis of mental disease” (Hofmann 1980, 47).
7In this sense LSD catalyzed the neuroscientific revolution of serotonin neurochemistry (Nichols
2016) and crystallized the emergence of the field of neuropharmacology.
31
“imitate” “mimic” or “exhibit mimicry” which is the act of appearing as something
else—for example, when one species mimics the appearance or behavior of another (e.g.,
the non-venomous bullsnake rattles its tail against dry leaves to mimic a venomous
rattlesnake). Psychotomimetic drug effects, on this literal reading of the term, would
merely mimic or imitate—appear as if they are—psychoses. However, to mimic is not to
model.8 A model intends to capture important structural or functional principles of the
entity or phenomena that it models. A mimic, by contrast, merely creates the illusion
that it possesses the properties it mimics. Thus, the term psychotomimetic implies
that the effects of these drugs merely resemble psychoses but do not share functional
or structural properties in their underlying biology or phenomenology. Nonetheless,
LSD and mescaline were used as models to investigate psychotic symptoms. Yet
the scientific utility of drug models hinges on our understanding of the mechanisms
underpinning the drugs’ effects; we still need a theory of how psychotomimetic drugs
work. A subtle explanation-explananda circularity can come into play here, in which
psychoses are explained using drug models yet the drug effects are explained using
theories of psychoses. Further complicating the matter is the clear difference between
acutely induced drug effects and the gradual development of a chronic mental illness
(Osmond and Smythies 1952). This cluster of conceptual challenges poured fuel on
the flaming debates about the merits of drug-induced model psychoses, which in 1957
had already “smoldered for nearly 50 years” (Osmond 1957, 421). An additional
conceptual challenge was the fact that mescaline had for years shown promise in treating
psychopathologies (Beringer 1927; Rouhier 1927) and LSD was gaining popularity for
pharmaceutically enhanced psychotherapy (Sandison and Whitelaw 1957; Eisner and
Cohen 1958; Cohen and Eisner 1959). Model psychoses theory needed to explain how
it was the case that drugs putatively capable of inducing psychotic symptoms could
simultaneously be capable of treating them—what Osmond (1957, 420) termed the
“hair of the dog” problem. In fact, to this day “the apparent paradox by which the
same compound can be both a model of, and yet a treatment for, psychopathology
8In fact, in the terminology of biological science, a model is “an organism whose appearance a
mimic imitates” (Merriam-Webster 2017).
32
has never been properly addressed” (Carhart-Harris, Kaelen, et al. 2016, 2) Taken
together, the above cluster of conceptual challenges drove Osmond (1957) to doubt
his own prior work on model psychoses (Hoffer, Osmond, and Smythies 1954; i.e.,
Osmond and Smythies 1952) and he declared ‘psychotomimetic’ an outmoded term,
arguing that the effects of these drugs could not be captured wholly in terms of
psychopathology. “If mimicking mental illness were the main characteristic of these
agents, ‘psychotomimetics’ would indeed be a suitable generic term. It is true that
they do so, but they do much more” (Osmond 1957, 429).
1.3.2 Filtration Theory
Osmond (1957) argued that the ‘psychotomimetic’ class of drugs needed a more
appropriate name. “My choice, because it is clear, euphonious, and uncontaminated
by other associations, is psychedelic, mind-manifesting” (Osmond 1957, 429). But how
exactly should we understand psychedelic effects as ‘mind-manifesting’? Osmond’s
nomenclature legacy was directly influenced by his friend Aldous Huxley, who described
the core idea to Osmond in the following personal letter dated April 10, 1953 (Huxley
1953, 29):
Dear Dr. Osmond,
. . .
It looks as though the most satisfactory working hypothesis about the human
mind must follow, to some extent, the Bergsonian model, in which the brain
with its associated normal self, acts as a utilitarian device for limiting, and
making selections from, the enormous possible world of consciousness, and for
canalizing experience into biologically profitable channels. Disease, mescaline,
emotional shock, aesthetic experience and mystical enlightenment have the
power, each in its different way and in varying degrees, to inhibit the function
of the normal self and its ordinary brain activity, thus permitting the ‘other
world’ to rise into consciousness.
Yours sincerely,
Aldous Huxley
Huxley’s letter can help unpack the intended ‘mind-manifesting’ etymology of
Osmond’s new term psychedelic. Huxley saw the biological function of the brain as a
“device” engaged in a continuous process of elimination and inhibition to sustain the
33
“normal self” of everyday waking experience to maximize adaptive fit. Huxley’s choice
metaphor for visualizing this was the cerebral reducing valve (Figure 1.3).
Figure 1.3: Aldous Huxley’s “cerebral reducing valve.” On the ‘inlet’ (right) side of
the cerebral reducing valve is a vast ocean of all possible perceptual, emotional, and
cognitive experiences. On the ‘outlet’ (left) side is our moment-to-moment stream of
experience in normal waking life. Mechanisms inside the valve ‘reduce’ the character
and contents of experience, ‘canalizing’ the ocean of possible experience into a more
limited stream of waking consciousness aimed at maximum biological utility.
“What I have called the cerebral reducing valve [is a] normal brain function that
limits our mental processes to an awareness, most of the time, of what is biologically
useful” (Huxley 1956, 121). Huxley (1961, 193) argued that this “normal brain
function” emerges developmentally during the course of psychological maturity, so
for a period during childhood, before the cerebral reducing valve has fully developed,
“there is this capacity to live in a kind of visionary world.” Once the valve is fully
developed, however, normal waking life becomes restricted to a “world fabricated by
our everyday, biologically useful and socially conditioned perceptions, thoughts and
feelings” (Huxley 1961, 214).
34
Huxley borrowed the core idea from 19th-century filtration theory accounts of
various mental phenomena (see Marshall 2005): “According to filtration theorists,
consciousness is ordinarily kept narrow by biological and psychological selection
processes that exclude a great deal of subconscious material” (Marshall 2005, 233).
Filtration theorists include founding figures of psychopharmacology (Kraepelin 1892),
psychology (James 1890), and parapsychology (Myers 1903), along with early 20th-
century philosophers Bergson (1911, 1931) and Broad (1923). Bergson (1931) applied
his own filtration framework to drug effects in his brief response to James’ (1882)
glowing descriptions of what it is like to inhale nitrous oxide. James’ peculiar state of
mind, explained Bergson, should be thought of as a latent potential of the brain/mind,
which nitrous oxide simply “brought about materially, by an inhibition of what
inhibited it, by the removing of an obstacle; and this effect was the wholly negative
one produced by the drug” (Bergson 1931). Huxley picked up Bergson’s line of
thinking and eventually convinced Osmond that it was important to reflect this
principle in scientific descriptions of the effects of LSD and mescaline. Smythies (1956,
96) also subscribed to this idea, stating that “mescaline may be supposed to inhibit
that function in the brain which specifically inhibits the mescaline phenomena from
developing in the sensory fields.”
Thus, Osmond’s (1957) proposed name-change—psychedelic—was intended to
capture the spirit of filtration theory. In this new descriptive model, psyche (mind)
delic (manifesting) drugs manifest the mind by inhibiting certain brain processes which
normally maintain their own inhibitory constraints on our perceptions, emotions,
thoughts, and sense of self. Osmond (1957) and Huxley (1954) both found this
principle highly applicable to their own direct first-person knowledge of what it is
like to experience the effects of mescaline and LSD—the expanded range of feelings,
intensification of perceptual stimuli, vivid vision-like mental imagery, unusual thoughts,
and expanding (or dissolving) sense of self and identity.
Osmond argued that his ‘mind-manifesting’ description had further theoretical
virtues that could address the conceptual challenges of model psychoses theory and
improve our understanding of (1) the diverse range of psychedelic effects, (2) their
35
relationship to psychotic symptoms, and (3) their role in psychedelic-assisted therapies.
First, the pharmacological disruption of hypothetical inhibitory brain mechanisms
that normally attenuate internal and external stimuli suggested that the kinds of
effects produced by the drug would depend on the kinds of stimuli in the system,
which is consistent with the diverse range of effects on multiple perceptual modalities,
emotional experience, and cognition.
Second, the brain’s selective filtration mechanisms, while evolutionarily adaptive
and biologically useful, could develop pathological characteristics in two fundamentally
distinct ways. First, a chronically overactive filter limits too much of the mind,
causing a rigid, dull, neurotic life in which mental contents become overly restricted to
“those enumerated in the Sears-Roebuck catalog which constitutes the conventionally
‘real’ world” (Huxley 1953, 30). Second, a chronically underactive or ‘leaky’ filter
places too few constraints on the mind and allows too much ‘Mind at Large’ to enter
conscious awareness, potentially resulting in perceptual instability, cognitive confusion,
or hallucination. This picture helped Huxley and Osmond understand the relationship
between psychedelic phenomena and psychotic phenomena: temporarily opening the
cerebral reducing valve with psychedelics could produce mental phenomena that
resembled symptoms of chronic natural psychoses precisely because both were the
result of (acute or chronic) reductions in brain filtration mechanisms.
Third and finally, filtration theory addressed the paradoxical “hair of the dog”
issue—why drugs that ‘mimic’ psychoses can aid psychotherapy—which, as described
in the previous section, was a conceptual challenge for model psychoses theory. The
solution to the paradox was in the filtration theory idea that psychedelic drugs tem-
porarily ‘disable’ brain filtration mechanisms, which could allow patients and therapists
to work outside of the patient’s everyday (pathological) inhibitory mechanisms. Thus,
filtration theory offered a way to understand psychedelic effects that was consistent
with both their psychotomimetic properties and their therapeutic utility.
Osmond and Huxley argued that filtration theory concepts were fully consistent
with the subjective phenomenology, psychotomimetic capability, and therapeutic
efficacy of psychedelic drugs. However, it remains unclear exactly what it is that
36
the brain is filtering and consequently what it is that emerges when the filter is
pharmacologically perturbed by a psychedelic drug. According to Huxley, LSD and
mescaline “inhibit the function of the normal self and its ordinary brain activity, thus
permitting the ‘other world’ to rise into consciousness” (Huxley 1953, 29; emphasis
mine). Huxley (and Bergson) spoke of the brain as a device that filters the world and
when the filter is removed we experience ‘more’ of reality. Osmond’s ‘mind-manifesting’
(psyche) (delic) name, by contrast, suggests that these drugs permit latent aspects of
mind to rise into conscious awareness. So which is it? Do psychedelic drugs manifest
latent aspects of mind or of world? How we answer this question will crucially
determine our ontological and epistemological conclusions regarding the nature of
psychedelic experience. Huxley and Osmond did not make this clear. Huxley seems to
favor the position that psychedelic experience reveals a wider ontological reality and
grants epistemic access to greater truth. Osmond’s view, on which these drugs reveal
normally hidden aspects of mind, seems less radical, more compatible with materialist
science, and less epistemically and ontologically committed. Still, if mind provides us
with access to world, then lifting restrictions on mind could in principle expand our
access to world. This important point resurfaces in section 1.5.3 below.
1.3.3 Psychoanalytic Theory
Freud (1895) developed an elaborate theoretical account of mental phenomena which,
like filtration theory, placed great emphasis on inhibition mechanisms in the nervous
system.9 Freud divided the psyche into two fundamentally distinct modes of activity:
the primary process and the secondary process (Freud 1895, 1940). In the primary
process, the exchange of “neuronal energy” is “freely mobile” and its psychological
dynamics are characterized by disorder, vagueness, conceptual paradox, symbolic
imagery, intense emotions, and animistic thinking (Freud 1940, 164). In the secondary
process, by contrast, the exchange of neuronal energy is “bound” and its psychological
dynamics are characterized by order, precision, conceptual consistency, controlled
9Huxley was overtly critical of Freud, yet Huxley’s cerebral reducing valve is strikingly similar to
Freud’s ego (see Benton 2016 for a comparison of Freud and Bergson).
37
emotions, and rational thinking (Freud 1895, 1940). Freud (1895) hypothesized that
the secondary process is maintained by an organizing neural “mass” called the ego
which “contains” and exerts control over the primary process by binding primary
process activity into its own pattern of activity.10 Freud hypothesized that secondary
process neural organization, sustained by the ego, is required for certain aspects of
perceptual processing, directed attention, reality-testing, sense of linear time, and
higher cognitive processes (Freud 1895, 1940). When Freud’s ego is suppressed,
such as during dream sleep, wider worlds of experience can emerge, but secondary
process functions are lost. The secondary process and its supporting neural organizing
pattern—the ego—emerges during ontogenetic development and solidifies with adult
maturity: “A unity comparable to the ego cannot exist from the start; the ego has to
be developed” (Freud 1915, 77). Furthermore, pathological characteristics can emerge
when Freud’s ego restricts either too much or too little of the primary process.
Freud himself was apparently uninterested in psychedelic drugs and instead em-
phasized dreams as “the royal road to a knowledge of the unconscious activities of the
mind” (Freud 1900, 769). Nonetheless, psychedelic drugs produce dreamlike visions
and modes of cognition that feature symbolic imagery, conceptual paradox, and other
hallmark characteristics of the primary process (Carhart-Harris and Friston 2010;
Kraehenmann, Pokorny, Aicher, et al. 2017; Sanz and Tagliazucchi 2018). How did
other psychoanalytic theorists describe psychedelic drug effects? The core idea is that
psychedelic drugs interfere with the structural integrity of the ego and thereby reduce
its ability to suppress the primary process and support the secondary process (Grof
1976). This ‘frees’ the primary process which then spills into conscious awareness,
resulting in perceptual instability, wildly vivid imagination, emotional intensity, con-
ceptual paradox, and loss of usual self-boundaries. Due in part to the close resemblance
10“The secondary process is characterized by a bound state in the neurone, which though there is
a high cathexis, permits only a small current. . . . Now the ego itself is a mass like this of neurones
which hold fast to their activity—are, that is in a bound state and this surely can only happen as a
result of the effect they have on one another. We can therefore imagine that a perceptual neurone
which is active with attention is as a result temporarily, as it were, taken up into the ego and is
now subject to the same binding of its energy as are all the other ego neurones . . . This bound
state, which combines high activity with small current, would thus characterize processes of thought
mechanically” (Freud 1895, 368).
38
between psychedelic effects and primary process phenomena, psychoanalytic theory
became the framework of choice during the mid 20th-century boom in psychedelic
therapy (Sandison 1954; Sandison and Whitelaw 1957; Cohen 1965; Grof 1976; Merkur
1998). Psychedelic ego effects, which range from a subtle loosening to a complete
dissolution of ego boundaries, were found to be great tools in psychotherapy because
of their capacity to perturb ego and allow primary process phenomena to emerge
(Sandison 1954, 509).
But how do psychedelic drugs disrupt the structure of the ego? Freud hypothesized
that the organizational structure of ego rests upon a basic perceptual schematic of the
body and its surrounding environment. Perceptual signals are continuously ‘bound’
and integrated into the somatic boundaries of the ego. Savage (1955) speculated that
the LSD’s perceptual effects and ego effects are tightly linked. “LSD acts by altering
perception. Continuous correct perception is necessary to maintain ego feeling and
ego boundaries. . . . Perception determines our ego boundaries. . . . disturbances in
perception caused by LSD make it impossible for the ego to integrate the evidence of
the senses and to coordinate its activities . . . ” (Savage 1955, 14). Klee (1963) expanded
Savage’s insights into a set of hypotheses aimed at elucidating the neurobiological
mechanisms of a Freudian ‘stimulus barrier’ and its dissolution under LSD:
Such barriers would presumably consist of processes limiting the spread of
excitation between different functional areas of the brain. The indications are
that LSD, in some manner, breaks down these stimulus barriers of which Freud
spoke. Nor is this merely a figure of speech. There is some reason to suspect
that integrative mechanisms within the central nervous system (CNS) which
handle inflowing stimuli are no longer able to limit the spread of excitation in
the usual ways. We might speculate that LSD allows greater energy exchanges
between certain systems than normally occurs, without necessarily raising the
general level of excitation of all cortical and subcortical structures (Klee 1963,
465; emphasis mine).
Freud hypothesized that ego is sustained by a delicate balance of ‘neuronal energy’
which critically depends on integrative mechanisms to process inflowing sensory stimuli
and to ‘bind’ neural excitation into functional structures within the brain. Psychedelic
drugs, according to Savage and Klee, perturb integrative mechanisms that normally
bind and shape endogenous and exogenous excitation into the structure of the ego. As
39
we will see below, Klee’s ideas strongly anticipate many neurophysiological findings
(Alonso et al. 2015; Tagliazucchi et al. 2016; Schartner et al. 2017) and theoretical
themes (Carhart-Harris and Friston 2010; Letheby and Gerrans 2017) from 21st-century
psychedelic science.
1.3.4 Summary
From the above analysis of first-wave and second-wave theories I have identified four
recurring theoretical features which could potentially serve as unifying principles. One
feature is the hypothesis that psychedelic drugs inhibit a core brain mechanism that
normally functions to ‘reduce’ or ‘filter’ or ‘constrain’ mental phenomena into an
evolutionarily adaptive container. A second feature is the hypothesis that this core
brain mechanism can behave pathologically, either in the direction of too much, or
too little, constraint imposed on perception, emotion, cognition, and sense of self. A
third feature is the hypothesis that psychedelic phenomena and symptoms of chronic
psychoses share descriptive elements because they both involve situations of relatively
unconstrained mental processes. A fourth feature is the hypothesis that psychedelic
drugs have therapeutic utility via their ability to temporarily inhibit these inhibitory
brain mechanisms. But how are these inhibitory mechanisms realized in the brain?
1.4 Neuropharmacology and Neurophysiological
Correlates of Psychedelic Drug Effects
Klee recognized that his above hypotheses, inspired by psychoanalytic theory and LSD
effects, required neurophysiological evidence. “As far as I am aware, however, adequate
neurophysiological evidence is lacking . . . The long awaited millennium in which
biochemical, physiological, and psychological processes can be freely correlated still
seems a great distance off” (Klee 1963, 466, 473). What clues have recent investigations
uncovered?
A psychedelic drug molecule impacts a neuron by binding to and altering the
40
conformation of receptors on the surface of the neuron (Nichols 2016). The receptor
interaction most implicated in producing classic psychedelic drug effects is agonist or
partial agonist activity at serotonin (5-HT) receptor type 2A (5-HT2A) (Nichols 2016).
A molecule’s propensity for 5-HT2A affinity and agonist activity predicts its potential
for (and potency of) subjective psychedelic effects (Glennon, Titeler, and McKenney
1984; McKenna et al. 1990; Halberstadt 2015; Nichols 2016; Rickli et al. 2016). When
a psychedelic drug’s 5-HT2A agonist activity is intentionally blocked using 5-HT2A
antagonist drugs (e.g., ketanserin), the subjective effects are blocked or attenuated
in humans under psilocybin (Vollenweider et al. 1998; Kometer et al. 2013), LSD
(Kraehenmann, Pokorny, Aicher, et al. 2017; Kraehenmann, Pokorny, Vollenweider,
et al. 2017; Preller et al. 2017), and ayahuasca (Valle et al. 2016). Importantly,
while the above evidence makes it clear that 5-HT2A activation is a necessary (if not
sufficient) mediator of the hallmark subjective effects of classic psychedelic drugs, this
does not entail that 5-HT2A activation is the sole neurochemical cause of all subjective
effects. For example, 5-HT2A activation might trigger neurochemical modulations
‘downstream’ (e.g., changes in glutamate transmission) which could also play causal
roles in producing psychedelic effects (Nichols 2016). Moreover, most psychedelic
drug molecules activate other receptors in addition to 5-HT2A (e.g., 5-HT1A, 5-HT2C,
dopamine, sigma, etc.) and these activations may importantly contribute to the overall
profile of subjective effects even if 5-HT2A activation is required for their effects to
occur (Ray 2010, 2016).
How does psychedelic drug-induced 5-HT2A receptor agonism change the behavior
of the host neuron? Generally, 5-HT2A activation has a depolarizing effect on the
neuron, making it more excitable (more likely to fire) (Andrade 2011; Nichols 2016).
Importantly, this does not necessarily entail that 5-HT2A activation will have an
overall excitatory effect throughout the brain, particularly if the excitation occurs in
inhibitory neurons (Andrade 2011). This important consideration (captured by the
adage ‘one neuron’s excitation is another neuron’s inhibition’) should be kept in mind
when tracing causal links in the pharmaco-neurophysiology of psychedelic drug effects.
In mammalian brains, neurons tend to ‘fire together’ in synchronized rhythms
41
known as temporal oscillations (brain waves). MEG and EEG equipment measure the
electromagnetic disturbances produced by the temporal oscillations of large neural
populations and these measurements can be quantified according to their amplitude
(power) and frequency (timing) (Buzsáki and Draguhn 2004). Specific combinations of
frequency and amplitude can be correlated with distinct brain states, including waking
‘resting’ state, various attentional tasks, anesthesia, REM sleep, and deep sleep (Tononi
and Koch 2008; Atasoy, Deco, et al. 2017). In what ways do temporal oscillations
change under psychedelic drugs? MEG and EEG studies consistently show reductions
in oscillatory power across a broad frequency range under ayahuasca (Riba et al. 2002,
2004; Schenberg et al. 2015; Valle et al. 2016), psilocybin (Muthukumaraswamy et
al. 2013; Kometer et al. 2015; Schartner et al. 2017), and LSD (Carhart-Harris,
Muthukumaraswamy, et al. 2016; Schartner et al. 2017). Reductions in the power of
alpha-band oscillations, localized mainly to parietal and occipital cortex, have been
correlated with intensity of subjective visual effects—e.g., ‘I saw geometric patterns’
or ‘My imagination was extremely vivid’—under psilocybin (Kometer et al. 2013;
Muthukumaraswamy et al. 2013; Schartner et al. 2017) and ayahuasca (Riba et
al. 2004; Valle et al. 2016). Under LSD, reductions in alpha power still correlated
with intensity of subjective visual effects but associated alpha reductions were more
widely distributed throughout the brain (Carhart-Harris, Muthukumaraswamy, et
al. 2016). Furthermore, ego-dissolution effects and mystical-type experiences (e.g.,
‘I experienced a disintegration of my “self” or “ego” ’ or ‘The experience had a
supernatural quality’) have been correlated with reductions in alpha power localized
to anterior and posterior cingulate cortices and the parahippocampal regions under
psilocybin (Muthukumaraswamy et al. 2013; Kometer et al. 2015) and throughout
the brain under LSD (Carhart-Harris, Muthukumaraswamy, et al. 2016).
The concept of functional connectivity rests upon fMRI brain imaging observations
that reveal temporal correlations of activity occurring in spatially remote regions of
the brain which form highly structured patterns (brain networks) (Buckner, Andrews-
Hanna, and Schacter 2008). Imaging of brains during perceptual or cognitive task
performance reveals patterns of functional connectivity known as functional networks;
42
e.g., control network, dorsal attention network, ventral attention network, visual
network, auditory network, and so on. Imaging brains in taskless resting conditions
reveals resting-state functional connectivity (RSFC) and structured patterns of RSFC
known as resting state networks (RSNs; Deco, Jirsa, and McIntosh 2011). One
particular RSN, the default mode network (DMN; Buckner, Andrews-Hanna, and
Schacter 2008), increases activity in the absence of tasks and decreases activity
during task performance (Fox and Raichle 2007). DMN activity is strong during
internally directed cognition and a variety of other ‘metacognitive’ functions (Buckner,
Andrews-Hanna, and Schacter 2008). DMN activation in normal waking states exhibits
‘inverse coupling’ or anticorrelation with the activation of task-positive functional
networks, meaning that DMN and functional networks are often mutually exclusive;
one deactivates as the other activates and vice versa (Fox and Raichle 2007).
In what ways does brain network connectivity change under psychedelic drugs?
First, functional connectivity between key ‘hub’ areas—mPFC and PCC—is reduced.
Second, the ‘strength’ or oscillatory power of the DMN is weakened and its intrinsic
functional connectivity becomes disintegrated as its component nodes become decou-
pled under psilocybin (Carhart-Harris, Erritzoe, et al. 2012; Carhart-Harris et al.
2013), ayahuasca (Palhano-Fontes et al. 2015), and LSD (Carhart-Harris, Muthuku-
maraswamy, et al. 2016; Speth et al. 2016). Third, brain networks that normally show
anticorrelation become active simultaneously under psychedelic drugs. This situation,
which can be described as increased between-network functional connectivity, occurs
under psilocybin (Carhart-Harris, Erritzoe, et al. 2012; Carhart-Harris et al. 2013;
Roseman et al. 2018; Tagliazucchi et al. 2014), ayahuasca (Palhano-Fontes et al. 2015),
and especially LSD (Carhart-Harris, Muthukumaraswamy, et al. 2016; Tagliazucchi et
al. 2016). Fourth and finally, the overall repertoire of explored functional connectivity
motifs is substantially expanded and its informational dynamics become more diverse
and entropic compared with normal waking states (Tagliazucchi et al. 2014, 2016;
Alonso et al. 2015; Lebedev et al. 2016; Viol et al. 2016; Atasoy, Roseman, et al.
2017; Schartner et al. 2017). Notably, the magnitude of occurrence of the above
four neurodynamical themes correlates with subjective intensity of psychedelic effects
43
during the drug session. Furthermore, visual cortex is activated during eyes-closed
psychedelic visual imagery (Araujo et al. 2012; Carhart-Harris, Muthukumaraswamy,
et al. 2016) and under LSD “the early visual system behaves ‘as if’ it were receiving
spatially localized visual information” as V1-V3 RSFC is activated in a retinotopic
fashion (Roseman et al. 2016, 3036).
Taken together, the recently discovered neurophysiological correlates of subjective
psychedelic effects present an important puzzle for 21st-century neuroscience. A key
clue is that 5-HT2A receptor agonism leads to desynchronization of oscillatory activity,
disintegration of intrinsic integrity in the DMN and related brain networks, and an
overall brain dynamic characterized by increased between-network global functional
connectivity, expanded signal diversity, and a larger repertoire of structured neuro-
physiological activation patterns. Crucially, these characteristic traits of psychedelic
brain activity have been correlated with the phenomenological dynamics and intensity
of subjective psychedelic effects.
1.5 21st-Century Theories of Psychedelic Drug Ef-
fects
How should we understand the growing body of clues emerging from investigations
into the neurodynamics of psychedelic effects? What are the principles that link
these thematic patterns of psychedelic brain activity (or inactivity) to their associated
phenomenological effects? Recent theoretical efforts to understand psychedelic drug
effects have taken advantage of existing frameworks from cognitive neuroscience
designed to track the key neurodynamic principles of human perception, emotion,
cognition, and consciousness. The overall picture that emerges from these efforts shares
core principles with filtration and psychoanalytic accounts of the late 19th and early
20th century. Briefly, normal waking perception and cognition are hypothesized to rest
upon brain mechanisms which serve to suppress entropy and uncertainty by placing
various constraints on perceptual and cognitive systems. In a ‘selecting’ and ‘limiting’
44
fashion, neurobiological constraint mechanisms support stability and predictability
in the contents of conscious awareness in the interest of adaptability, survival, and
evolutionary fitness. The core hypothesis of recent cognitive neuroscience theories of
psychedelic effects is that these drugs interfere with the integrity of neurobiological
information-processing constraint mechanisms. The net effect of this is that the range of
possibilities in perception, emotion, and cognition is dose-dependently expanded. From
this core hypothesis, cognitive neuroscience frameworks are utilized to describe and
operationalize the quantitative neurodynamics of key psychedelic phenomena; namely,
the diversity of effects across many mental processes, the elements in common with
symptoms of psychoses, and the way in which temporarily removing neurobiological
constraints is therapeutically beneficial.
This section is organized according to the broad theoretical frameworks informing
recent theoretical neuroscience of psychedelic effects: entropic brain theory, integrated
information theory, and predictive processing.
1.5.1 Entropic Brain Theory
Entropic Brain Theory (EBT; Carhart-Harris et al. 2014) links the phenomenology
and neurophysiology of psychedelic effects by characterizing both in terms of the
quantitative notions of entropy and uncertainty. Entropy is a quantitative index of
a system’s (physical) disorder or randomness which can simultaneously describe its
(informational) uncertainty. EBT “proposes that the quality of any conscious state
depends on the system’s entropy measured via key parameters of brain function”
(Carhart-Harris et al. 2014, 1). Their hypothesis states that hallmark psychedelic
effects (e.g., perceptual destabilization, cognitive flexibility, ego dissolution) can be
mapped directly onto elevated levels of entropy/uncertainty measured in brain activity,
e.g., widened repertoire of functional connectivity patterns, reduced anticorrelation
of brain networks, and desynchronization of RSN activity. More specifically, EBT
characterizes the difference between psychedelic states and normal waking states in
terms of how the underlying brain dynamics are positioned on a scale between the two
extremes of order and disorder—a concept known as ‘self-organized criticality’ (Beggs
45
and Plenz 2003). A system with high order (low entropy) exhibits dynamics that
resemble ‘petrification’ and are relatively inflexible but more stable, while a system
with low order (high entropy) exhibits dynamics that resemble ‘formlessness’ and
are more flexible but less stable. The notion of ‘criticality’ describes the transition
zone in which the brain remains poised between order and disorder. Physical systems
at criticality exhibit increased transient ‘metastable’ states, increased sensitivity
to perturbation, and increased propensity for cascading ‘avalanches’ of metastable
activity. Importantly, EBT points out that these characteristics are consistent with
psychedelic phenomenology, e.g., hypersensitivity to external stimuli, broadened range
of experiences, or rapidly shifting perceptual and mental contents. Furthermore, EBT
uses the notion of criticality to characterize the difference between psychedelic states
and normal waking states as it “describes cognition in adult modern humans as ‘near
critical’ but ‘sub-critical’—meaning that its dynamics are poised in a position between
the two extremes of formlessness and petrification where there is an optimal balance
between order and flexibility” (Carhart-Harris et al. 2014, 12). EBT hypothesizes
that psychedelic drugs interfere with ‘entropy-suppression’ brain mechanisms which
normally sustain sub-critical brain dynamics, thus bringing the brain “closer to
criticality in the psychedelic state” (Carhart-Harris et al. 2014, 12).
Entropic Brain Theory further characterizes psychedelic neurodynamics using a
neo-psychoanalytic framework proposed in an earlier paper by Carhart-Harris and Fris-
ton (2010, 1265) where they “recast some central Freudian ideas in a mechanistic and
biologically informed fashion.” Freud’s primary process (renamed “primary conscious-
ness”) is hypothesized to be a high-entropy brain dynamic which operates at criticality,
while Freud’s secondary process (renamed “secondary consciousness”) is hypothesized
to involve a lower-entropy brain state which sustains a sub-critical dynamic via a key
neurobiological entropy-suppression mechanism—the ego—which exerts an organizing
influence in order to constrain the criticality-like dynamic of primary consciousness.
EBT argues that these ego functions have a signature neural footprint; namely, the
DMN’s intrinsic functional connectivity and DMN coupling of medial temporal lobes
(MTLs) in particular. Furthermore, EBT argues that DMN/ego develops ontoge-
46
netically in adult humans and plays an adaptive role in which it sustains secondary
consciousness and associated metacognitive abilities (Shimamura 2000; Fleming, Dolan,
and Frith 2012) along with an “integrated sense of self” (Carhart-Harris et al. 2014,
9).
Importantly, this hypothesis maps onto the subjective phenomenology of psychedelic
effects, particularly ego dissolution. As psychedelics weaken the oscillatory power and
intrinsic functional connectivity of the DMN, the normally constrained activity of
subordinate DMN nodes—MTLs in particular—becomes “freely mobile” allowing the
emergence of more uncertain (higher entropy) primary consciousness. This view, based
on Freudian metapsychology, is also consistent with filtration accounts, like those of
Bergson and Huxley, who hypothesized that psychedelic drug effects are the result
of a pharmacological inhibition of inhibitory brain mechanisms. EBT recasts these
theoretical features using the quantitative terms of physical entropy and informational
uncertainty as measured via “the repertoire of functional connectivity motifs that
form and fragment across time” (Carhart-Harris et al. 2014, 1). In normal waking
states, the DMN constrains the activity of its cortical and subcortical nodes and
prohibits simultaneous co-activation with TPNs. By interfering with DMN integration,
psychedelics permit a larger repertoire of brain activity, a wider variety of explored
functional connectivity motifs, co-activation of normally mutually exclusive brain
networks, increased levels of between-network functional connectivity, and an overall
more diverse set of neural interactions.
Carhart-Harris et al. (2014) point out a number of implications of EBT. First,
they map the feelings of ‘uncertainty’ that often accompany psychedelic effects onto
the fact that a more entropic brain dynamic is the information-theoretic equivalent to
a more ‘uncertain’ brain dynamic. “Thus, according to the entropic brain hypothesis,
just as normally robust principles about the brain lose definition in primary states, so
confidence is lost in ‘how the world is’ and ‘who one is’ as a personality” (Carhart-Harris
et al. 2014, 16).
Second, like Huxley’s cerebral reducing valve and Freud’s ego, EBT argues that
the DMN’s organizational stronghold over brain activity can be both an evolutionary
47
advantage and a source of pathology. “It is argued that this entropy-suppressing
function of the human brain serves to promote realism, foresight, careful reflection and
an ability to recognize and overcome wishful and paranoid fantasies. Equally however,
it could be seen as exerting a limiting or narrowing influence on consciousness” (Carhart-
Harris et al. 2014, 7). Carhart-Harris et al. (2014) point out that neuroimaging studies
have implicated increased DMN activity and RSFC with various aspects of depressive
rumination, trait neuroticism, and depression. “The suggestion is that increased DMN
activity and connectivity in mild depression promotes concerted introspection and
an especially diligent style of reality-testing. However, what may be gained in mild
depression (i.e., accurate reality testing) may be offset by a reciprocal decrease in
flexible or divergent thinking (and positive mood)” (Carhart-Harris et al. 2014, 10).
Third, consistent with both psychoanalytic and filtration theory, is the notion that
psychedelic drugs’ capacity to temporarily weaken, collapse, or disintegrate the normal
ego/DMN stronghold underpins their therapeutic utility. “Specifically, it is proposed
that psychedelics work by dismantling reinforced patterns of negative thought and
behavior by breaking down the stable spatiotemporal patterns of brain activity upon
which they rest” (Carhart-Harris et al. 2014, 1).
Fourth and finally, EBT sheds light on the shared descriptive elements between
psychedelic effects and psychotic symptoms by characterizing both in terms of elevated
levels of entropy and uncertainty in brain activity which lead to a “regression” into
primary consciousness. The collapse of the organizing effect of DMN coupling and
anticorrelation patterns, according to EBT, point to “system-level mechanics of the
psychedelic state as an exemplar of a regressive style of cognition that can also be
observed in REM sleep and early psychosis” (Carhart-Harris et al. 2014, 5)
Thus, EBT formulates all four of the theoretical features identified in filtration and
psychoanalytic accounts, but does so using 21st-century empirical data plugged into the
quantitative concepts of entropy, uncertainty, criticality, and functional connectivity.
EBT hints at possible ways to close the gaps in understanding by offering quantitative
concepts that link phenomenology to brain activity and pathogenesis to therapeutic
mechanisms.
48
1.5.2 Integrated Information Theory
Integrated Information Theory (IIT) is a general theoretical framework which describes
the relationship between consciousness and its physical substrates (Oizumi, Albantakis,
and Tononi 2014; Tononi 2004, 2008). While EBT is already loosely consistent with
the core principles of IIT, Gallimore (2015) demonstrates how EBT’s hypotheses
can be operationalized using the technical concepts of the IIT framework. Using
EBT and recent neuroimaging data as a foundation, Gallimore develops an IIT-based
model of psychedelic effects. Consistent with EBT, this IIT-based model describes the
brain’s continual challenge of minimizing entropy while retaining flexibility. Gallimore
formally restates this problem using IIT parameters: brains attempt to optimize the
give-and-take dynamic between cause-effect information and cognitive flexibility. In
IIT, a (neural) system generates cause-effect information when the mechanisms which
make up its current state constrain the set of states which could casually precede
or follow the current state. In other words, each mechanistic state of the brain: (1)
limits the set of past states which could have causally given rise to it, and (2) limits
the set of future states which can causally follow from it. Thus, each current state of
the mechanisms within a neural system (or subsystem) has an associated cause-effect
repertoire which specifies a certain amount of cause-effect information as a function
of how stringently it constrains the unconstrained state repertoire of all possible
system states. Increasing the entropy within a cause-effect repertoire will in effect
constrain the system less stringently as the causal possibilities are expanded in both
temporal directions as the system moves closer to its unconstrained repertoire of
all possible states. Moreover, increasing the entropy within a cause-effect repertoire
equivalently increases the uncertainty associated with its past (and future) causal
interactions. Using this IIT-based framework, Gallimore (2015) argues that, compared
with normal waking states, psychedelic brain states exhibit higher entropy, higher
cognitive flexibility, but lower cause-effect information (Figure 1.4).
Neuroimaging data suggests that human brains exhibit a larger overall repertoire of
neurophysiological states under psychedelic drugs, exploring a greater diversity of states
49
Figure 1.4: “Increasing neural entropy elevates cognitive flexibility at the expense
of a decrease in the cause-effect information specified by individual mechanisms”
(Gallimore 2015, 10).
50
in a more random fashion. For example, in normal waking states, DMN activity ‘rules
out’ the activity of TPNs, and vice versa, due to their relatively strict anticorrelation
patterns. Brain network anticorrelation generates cause-effect information because
it places constraints on the possible causal interactions within and between brain
mechanisms; for example, DMN-TPN anticorrelation patterns ‘rule out’ the DMN
activity in the presence of activated TPNs. However, psychedelic drugs ‘dissolve’
DMN-TPN (and other) network anticorrelation patterns, which permits simultaneous
activation of brain networks which are normally mutually exclusive. The cause-effect
repertoire of brain mechanisms thus shifts closer to the unconstrained repertoire of all
possible past and future states. This has the effect of “increasing the probability of
certain states from zero or, at least, from a very low probability” (Gallimore 2015,
7). Therefore the subjective contents perception and cognition become more diverse,
more unusual, and less predictable. This increases flexibility but decreases precision
and control as the subjective boundaries which normally demarcate distinct cognitive
concepts and perceptual objects dissolve. Gallimore leverages IIT in an attempt unify
these phenomena under a formalized framework.
However, as Gallimore notes, “this model does not explain how neural entropy is
increased by (psychedelic drugs), but predicts consequences of the entropy increase
revealed by functional imaging data” (Gallimore 2015, 7). How do psychedelic drugs
increase neural entropy?
1.5.3 Predictive Processing
The first modern brain imaging measurements in humans under psilocybin yielded
somewhat unexpected results: reductions in oscillatory power (MEG) and cerebral
blood flow (fMRI) correlated with the intensity of subjective psychedelic effects
(Carhart-Harris, Erritzoe, et al. 2012; Muthukumaraswamy et al. 2013). In their
discussion, the authors suggest that their findings, although surprising through the lens
of commonly held beliefs about how brain activity maps to subjective phenomenology,
may actually be consistent with a theory of brain function known as the free energy
principle (FEP; Friston 2010).
51
In one model of global brain function based on the free-energy principle (Friston
2010), activity in deep-layer projection neurons encodes top-down inferences
about the world. Speculatively, if deep-layer pyramidal cells were to become
hyperexcitable during the psychedelic state, information processing would be
biased in the direction of inference—such that implicit models of the world
become spontaneously manifest—intruding into consciousness without prior
invitation from sensory data. This could explain many of the subjective effects
of psychedelics (Muthukumaraswamy et al. 2013, 15181).
What is FEP? “In this view, the brain is an inference machine that actively predicts
and explains its sensations. Central to this hypothesis is a probabilistic model that
can generate predictions, against which sensory samples are tested to update beliefs
about their causes” (Friston 2010). FEP is a formulation of a broader conceptual
framework emerging in cognitive neuroscience known as predictive processing11 (PP;
A. Clark 2013). PP has links to bayesian brain hypothesis (Knill and Pouget 2004),
predictive coding (Rao and Ballard 1999), and earlier theories of perception and
cognition (MacKay 1956; Neisser 1967; Gregory 1968) dating back to Helmholtz (1867)
who was inspired by Kant (1787) (see Swanson (2016)). At the turn of the 21st
century, the ideas of Helmholtz catalyzed innovations in machine learning (Dayan et
al. 1995), new understandings of cortical organization (Mumford 1992; Friston 2005),
and theories of how perception works (Kersten and Yuille 2003; Lee and Mumford
2003).
PP subsumes key elements from these efforts (see Clark, 2013) to describe a uni-
versal principle of brain function captured by the idea of prediction error minimization
(PEM; Hohwy 2013). What does it mean to say that the brain works to minimize
its own prediction error? Higher-level areas of the nervous system (i.e., higher-order
cortical structures) generate top-down synaptic ‘predictions’ aimed at matching the
expected bottom-up synaptic activity at lower-level areas, all the way down to ‘input’
activity at sense organs. Top-down signals encode a kind of ‘best guess’ about the
most likely (hidden)12 causes of bodily sensations. In this multi-level hierarchical
11See also A. Clark (2015) and Wiese and Metzinger (2017) for introductory reviews conceptual
overviews.
12The causes of our bodily sensations cannot be directly observed by the brain: an organism’s
brain is ‘skull-bound’ (Hohwy 2013) and limited to a ‘view from inside the black box’ (A. Clark
2013).
52
cascade of neural activity, high-level areas attempt to ‘explain’ the states of levels
below via synaptic attempts to inhibit lower-level activity—“high-level areas tell lower
levels to ‘shut up’ ” (Kersten, Mamassian, and Yuille 2004, 297). But lower levels
will not ‘shut up’ until they receive top-down feedback (inference) signals that ade-
quately fit (explain) the bottom-up (evidence) signals. Mismatches between synaptic
‘expectation’ and synaptic ‘evidence’ generate prediction error signals which ‘carry
the news’ by propagating the ‘surprise’ upward to be ‘explained away’ by yet higher
levels of hierarchical cortical processing anatomy (see A. Clark 2015). This recurrent
neural processing scheme approximates (empirical) Bayesian inference (Friston et
al. 2007) as the brain continually maps measured bodily effects to different sets of
possible causes and attempts to select the set of possible causes that can best ‘explain
away’ the measured bodily effects. Crucially, the sets of possible causes must be
narrowed in order for the system to settle on an explanation (Tenenbaum et al. 2011).
Prior constraints which allow the system to narrow the hypothesis space are known
as ‘inductive biases’ or priors (Kemp, Perfors, and Tenenbaum 2007; Tenenbaum
et al. 2011; A. Clark 2013). Efforts in Bayesian statistics and machine learning
have demonstrated that improvements in inductive capabilities occur when priors are
linked in a multi-level hierarchy, with “not just a single level of hypotheses to explain
the data but multiple levels: hypothesis spaces of hypothesis spaces, with priors on
priors” (Tenenbaum et al. 2011, 1282). Certain priors in the hierarchy, known as
‘hyperpriors’ (Friston, Lawson, and Frith 2013) or ‘overhypotheses’ (Goodman 1983;
Kemp, Perfors, and Tenenbaum 2007) are more abstract and allow the system to ‘rule
out’ large swaths of possibilities, drastically narrowing the hypothesis space, making
explanation more tractable (Blokpoel, Kwisthout, and Rooij 2012). For example, the
brute constraints of space and time act as hyperpriors; e.g., prior knowledge “that
there is only one object (one cause of sensory input) in one place, at a given scale, at a
given moment,” or the fact that “we can only perform one action at a time, choosing
the left turn or the right but never both at once” (A. Clark 2013, 196).
Thus, PP states that brains are neural generative models built from linked hierar-
chies of priors where higher levels continuously attempt to ‘guess’ and explain activity
53
at lower levels. The entire process can be characterized as the agent’s attempt to
optimize its own internal model of the sensorium (and the world) over multiple spatial
and temporal scales (Friston 2010).
Interestingly, PP holds that our perceptions of external objects recruit the same
synaptic pathways that enable our capacity for mental imagery, dreaming, and hal-
lucination. The brain’s ability to ‘simulate’ its own ‘virtual reality’ using internal
(generative) models of the world’s causal structure is thus crucial to its ability to
perceive the external world. “[A] fruitful way of looking at the human brain, therefore,
is as a system which, even in ordinary waking states, constantly hallucinates at the
world, as a system that constantly lets its internal autonomous simulational dynamics
collide with the ongoing flow of sensory input, vigorously dreaming at the world and
thereby generating the content of phenomenal experience” (Metzinger 2003).
How do psychedelic molecules perturb predictive processing? If normal perception
is a kind of ‘controlled hallucination’ (see A. Clark 2015) where top-down simulation
is constrained by bottom-up sensory input colliding with priors upon priors, then, as
the above quotation from (Muthukumaraswamy et al. 2013) suggests, psychedelic
drugs essentially cause perception to be less controlled hallucination. The idea is
that psychedelic drugs perturb the (learned and innate) prior constraints on internal
generative models. Via their 5-HT2A agonism, psychedelic drugs cause hyperexcitation
in layer V pyramidal neurons, which might cause endogenous simulations to ‘run
wild’ so that awareness becomes more imaginative, dreamlike, and hallucinatory. This
hypothesis could in principle still be consistent with observed reductions in brain
activity under psychedelics; recall from above that, in PP schemes, the higher-level
areas ‘explain away’ lower-level excitation by suppressing it with top-down inhibitory
signals. “Here, explaining away just means countering excitatory bottom-up inputs to
a prediction error neuron with inhibitory synaptic inputs that are driven by top-down
predictions” (Friston 2010, 130).
How does PP tie into filtration theories and psychoanalytic accounts? Carhart-
Harris, Leech, et al. (2012) link Huxley with Friston to interpret their initially
surprising fMRI scans of humans under psilocybin (see also Zizo 2013). One objection
54
to this linkage might be that Huxley often describes psychedelic opening of the cerebral
reducing valve as revealing more of the world. At first glance this seems at odds
with the above PP account of psychedelic effects, which describes psychedelic drugs
causing rampant internal simulations of reality, not revealing more of the external
world. However, this apparent tension might be resolved in light of active inference,
a key principle of FEP (Friston 2010). Active inference shows how internal models
do not merely generate top-down (inference) signals but also shape the sampling
and accumulation of bottom-up sensory (evidence) signals. “In short, the agent will
selectively sample the sensory inputs that it expects. This is known as active inference.
An intuitive example of this process (when it is raised into consciousness) would be
feeling our way in darkness: we anticipate what we might touch next and then try to
confirm those expectations” (Friston 2010, 129). The principle of active inference hints
at a resolution to the apparent tensions between Osmond’s ‘mind-manifesting’ model
and Huxley’s ‘world-manifesting’ model. Psychedelics manifest mind by perturbing
prior constraints on internal generative models, thereby expanding the possibilities in
our inner world of feelings, thoughts, and mental imagery. Importantly, this could also
manifest normally ignored aspects of world by altering active inference, which would
in effect expand the sampling of sensory data to include samples that are normally
routinely ‘explained away.’ Potentially, this understanding goes some way in explaining
the perception-hallucination continuum of psychedelic drug effects (reviewed above)
as it shows how perceptual intensifications, on the one hand, and distortions and
hallucinations, on the other hand, could both be caused by a synaptic disruption of
hierarchically linked priors in internal generative models.
The brief speculative remark by (Muthukumaraswamy et al. 2013) is not the
only PP-based account of psychedelic drug effects. The PP framework describes a
recurrent back-and-forth give-and-take between colliding top-down and bottom-up
signals, where internal models serve to shape experience and experience serves to build
internal models, so this leaves room for rival PP-based accounts that diverge regarding
where exactly the psychedelic drug perturbs the system. For example, increased
top-down activity could be the result of pharmacological hyperactivation of top-down
55
synaptic transmission; yet equally plausible is the hypothesis that increased top-down
activity is a compensatory response to pharmacological attenuations or distortions of
bottom-up signal.
For example, Corlett et al. (2009, 521) hypothesize that LSD hallucinations result
from “noisy, unpredictable bottom-up signaling in the context of preserved and perhaps
enhanced top-down processing.” In contrast to the PP-based account outlined above,
which focuses on changes to top-down signals, the strategy of Corlett, Frith, and
Fletcher (2009) is to map various psychedelic effects to disturbances of top-down
and/or bottom-up signals. The issue of what is primary and what is compensatory
illustrates the vast possibilities in the hypothesis space of PP-based accounts.
While most PP-based accounts point to changes in top-down signaling, even within
this hypothesis space there are contrasting conceptions of exactly how psychedelic
molecules perturb top-down processing. Briefly, these differing hypotheses include: (1)
hyperactivation or heavier weighting of top-down signaling (Muthukumaraswamy et
al. 2013; described above), (2) reduced influence of signals from higher cortical areas
(Carhart-Harris and Friston 2010; McKenna and Riba 2015), (3) interference with
multisensory integration processes and PP-based binding of sensory signals (Carhart-
Harris and Friston 2010; Letheby and Gerrans 2017; Millière 2017), and (4) changes
in the composition and level of detail specified by top-down signals (Pink-Hashkes,
Rooij, and Kwisthout 2017).
Carhart-Harris and Friston (2010) argue that the Freudian conception of ego, with
its organizing influence over the primary process, is consistent with PP descriptions
of higher-level cortical structures predicting and suppressing the excitation in lower
levels in the hierarchy (i.e., limbic regions). Freud hypothesized that the secondary
process binds, integrates, and organizes the ‘lower’ and more chaotic neural activity
of the primary process into the broader and more cohesive composite structure of the
ego. Carhart-Harris and Friston (2010) argue that when large-scale intrinsic networks
become dis-integrated, the activity at lower levels can no longer be ‘explained away’
(suppressed) by certain higher-level systems, causing conscious awareness to take
on hallmark characteristics of the primary process. In normal adult waking states,
56
networks based in higher-level areas can successfully predict and explain (suppress and
control) the activity of lower level areas. “In non-ordinary states, this function may
be perturbed (e.g., in the case of hallucinogenic drugs, through actions at modulatory
post-synaptic receptors), compromising the hierarchical organization and suppressive
capacity of the intrinsic networks” (Carhart-Harris and Friston 2010, 1274).
Similar PP-based theories of psychedelic ego dissolution have been proposed
without invoking Freud (Letheby and Gerrans 2017; Millière 2017). PP posits that
the brain explains self-generated stimuli by attributing its causes to a coherent and
persisting entity (i.e., the self), much like how it predicts and explains external
stimuli by attributing their causes to coherent and persisting external objects (see
also Limanowski and Blankenburg 2013; M. Allen and Friston 2016). Letheby and
Gerrans (2017) use the PP framework to recast the psychoanalysis-based theories of
LSD ego effects proposed by Savage (1955) 13 and Klee (1963) described in Section
1.3.3. The core idea is that psychedelic drugs interfere with processes that bind and
integrate stimuli according to probabilistic estimates of how relevant the stimuli are
to the organism’s (self) goals. Letheby and Gerrans (2017, 7) point out that ego
dissolution under psychedelic drugs is correlated with the desynchronization (reductions
in intrinsic functional connectivity) of brain networks implicated in “one aspect or
another of self-representation”—specifically the salience network (SLN) and the DMN
(Tagliazucchi et al. 2016). This causes an ‘unbinding’ of stimuli that are normally
processed according to self-binding multisensory integration mechanisms. “Attention
is no longer guided exclusively by adaptive and egocentric goals and agendas; salience
attribution is no longer bound to personal concern” (Letheby and Gerrans 2017, 6).
This description echoes Huxley’s cerebral reducing valve “in which the brain with its
associated normal self, acts as a utilitarian device for limiting, and making selections
from, the enormous possible world of consciousness, and for canalizing experience
into biologically profitable channels” (Huxley 1953, 29; emphasis mine). Letheby and
Gerrans’ PP-based account elucidates how psychedelic drugs could perturb the brain’s
13“Disturbances in perception caused by LSD make it impossible for the ego to integrate the
evidence of the senses and to coordinate its activities . . . ” (Savage 1955, 14).
57
“associated normal self” preventing the usual self-binding of internal and external
stimuli.
Pink-Hashkes et al. (2017, 2907) argue that under psychedelic drugs “top-down
predictions in affected brain areas break up and decompose into many more overly
detailed predictions due to hyper activation of 5-HT2A receptors in layer V pyramidal
neurons.” Pink-Hashkes, Rooij, and Kwisthout (2017) state that when internal genera-
tive models are described as categorical probability distributions rather than Gaussian
densities (Friston et al. 2015; Kwisthout, Bekkering, and Rooij 2017), “the state space
granularity (how detailed are the generative models and the predictions that follow
from them) is crucial” Kwisthout and Rooij (2015). Categorical predictions that are
less detailed will ‘explain’ more bottom-up data (because they cover more ground) and
thus produce less prediction error. Categorical predictions that are more detailed, by
contrast, will carry less precision and thus potentially generate more prediction error
(Kwisthout and Rooij 2015; Kwisthout, Bekkering, and Rooij 2017). Pink-Hashkes
et al. (2017, 2908) propose that psychedelic drugs cause brain structures at certain
levels of the cortical hierarchy to issue more detailed (less abstract) ‘decomposed’
predictions that “fit less data than the ‘usual’ broad prediction.” They argue that
many psychedelic effects stem from the brain’s attempts to compensate for these
decomposed top-down predictions as it responds to the increase in prediction errors
that result from overly detailed predictions.
In summary, the current state of PP-based theories of psychedelic effects reveals
a divergent mix of heterogeneous ideas and conflicting hypotheses. Do psychedelic
molecules perturb top-down (feedback) signaling, or bottom-up (feedforward) signaling,
or both? Do the subjective phenomenological effects result from direct neurophar-
macological changes or compensatory mechanisms responding to pharmacological
perturbations? Yet there seems to be a core intuition that transcends the conceptual
variance here: psychedelic drugs (somehow) interfere with established priors that
normally constrain the brain’s internal generative models.
Predictive processing-based accounts, consistent with EBT and IIT (and filtration
and psychoanalytic accounts), propose that psychedelic drugs disrupt neural mecha-
58
nisms (priors on internal generative models) which normally constrain perception and
cognition. Perturbing priors causes subjective phenomenology to present a wider range
of experiences with increased risk of perceptual instability and excessive cognitive
flexibility. As prior constraints on self and world are loosened, the likelihood of
psychosis-like phenomena increases. At the same time, novel thinking is increased
and the brain becomes more malleable and conducive to therapeutic cognitive and
behavioral change.
1.6 Conclusion
The four key features identified in filtration and psychoanalytic accounts from the late
19th and early 20th century continue to operate in 21st-century cognitive neuroscience:
(1) psychedelic drugs produce their characteristic diversity of effects because they
perturb adaptive mechanisms which normally constrain perception, emotion, cognition,
and self-reference, (2) these adaptive mechanisms can develop pathologies rooted in
either too much or too little constraint (3) psychedelic effects appear to share elements
with psychotic symptoms because both involve weakened constraints (4) psychedelic
drugs are therapeutically useful precisely because they offer a way to temporarily
inhibit these adaptive constraints. It is on these four points that EBT, IIT, and PP
seem consistent with each other and with earlier filtration and psychoanalytic accounts.
EBT and IIT describe psychedelic brain dynamics and link them to phenomenological
dynamics, while PP describes informational principles and plausible neural information
exchanges which might underlie the larger-scale dynamics described by EBT and IIT.
Certain descriptions of neural entropy-suppression mechanisms (EBT), cause-effect
information constraints (IIT), or prediction-error minimization strategies (PP, FEP)
are loosely consistent with Freud’s ego and Huxley’s cerebral reducing valve.
In surveying the literature for this review I can confidently conclude that 21st-
century psychedelic science has yet to approach a unifying theory linking the diverse
range of phenomenological effects with pharmacology and neurophysiology while
tying these to clinical efficacy. However, the historically necessary ingredients for
59
successful theory unification—formalized frameworks and unifying principles (Morrison
2000)—seem to be taking shape. Formal models are an integral part of 21st-century
neuroscience (Forstmann et al. 2011) where they help to describe natural principles in
the brain and aid explanation and understanding (Kay 2017).14 Here I have reviewed
a handful of formalized frameworks—EBT, IIT, PP—which are just beginning to
be used to account for psychedelic effects. I have also highlighted the fact that all
of the accounts reviewed here, from the 19th-century to the 21st-century, propose
that psychedelic drugs inhibit neurophysiological constraints in order to produce their
diverse phenomenological, psychotomimetic, and therapeutic effects.
Why should we pursue a unified theory of psychedelic drug effects at all? To date,
theories of brain function and mind in general have resisted the kind of unification
that has occurred in other areas of science (Huang 2008; Edelman 2012). Because the
human brain has evolved disparate and complex layers under diverse environmental
circumstances, many doubt the possibility of and debate the merits of seeking ‘grand
unified theories’ (GUTs) of brain function. “There is every reason to think that there
can be no grand unified theory of brain function because there is every reason to
think that an organ as complex as the brain functions according to diverse principles”
(M. L. Anderson and Chemero 2013, 205). Indeed, Anderson and Chemero (2013,
205) caution that “we should be skeptical of any GUT of brain function” and charge
that PP in particular, when taken as a unified theory as outlined by (A. Clark 2013),
“threatens metaphysical disaster.”
Given these understandable critical reservations about seeking after GUTs of brain
function (and therefore any truly unifying theory of psychedelic drug effects), it is
perhaps safer to aspire for theories that feature “broad explanatory frameworks” and
offer “conceptual breadth” allowing us to “paint the big picture” (Edelman 2012). PP
and FEP, at the very least, offer a broad explanatory framework that emcompasses
a large swath of perceptual and cognitive phenomena (Huang 2008; Friston 2010;
A. Clark 2015). Psychedelic drugs offer a unique way to iteratively develop and
14This remains true regardless of the outcome of healthy debates about the nature and proper use
of models in science (Frigg and Hartmann 2017).
60
test such big-picture explanatory frameworks: these molecules can be used to probe
the links between neurochemistry and neural computation across multiple layers of
neuroanatomy and phenomenology. Meeting the challenge of predicting and explaining
psychedelic drug effects is the ultimate acid test for any unified theory of brain
function.
61
Chapter 2
Psychedelic visuals in context
“If we analyze the visual phenomena produced by mescaline . . . the same mech-
anisms may be operative, no matter whether an object is perceived, imagined,
or hallucinated. The mescaline experiments demonstrate, therefore, that we
must go beyond the level of visual hallucinations to determine hallucinatory
constants. In fact, we must even go beyond the visual mechanisms that cut
across distinctions between perception, imagery, and hallucination and raise the
question whether similar mechanisms are operative in nonvisual spheres.”
—Heinrich Klüver (1942), Mechanisms of Hallucinations
“A psychedelic experience is a period of intensely heightened reactivity to sensory
stimuli from within and without.”
—Ralph Metzner and Timothy Leary (1967), “On programming psychedelic
experiences”
62
ABSTRACT
The visual effects of psychedelic drugs are well-known but not well understood.
Here, I situate psychedelic visual phenomenology within the context of what we
know about everyday visual perception. I describe three types of open-eye visuals
(OEVs)—common alterations to external visual appearances under psychedelic
drugs—and argue that they are caused by hypersensitivity to contextual cues. I
provide evidence and analysis to support the notion that psychedelics selectively
impact contextual modulation processes at multiple levels of mental function. I
then apply this idea to explain both visual and non-visual effects, and how they
might link to therapeutic benefits. I note that contextual modulation can be
modeled by a canonical neural computation known as divisive normalization.
Finally, I explore the practical implications of these ideas for psychedelic therapy,
cognitive neuroscience, and philosophy.1
2.1 Introduction
Some researchers argue that subjective psychedelic effects are entirely epiphenomenal to
the therapeutic mechanisms of psychedelic therapy—i.e., play no role whatsoever—and
hope to firmly demarcate the therapeutic benefits from all acute subjective effects
(Hesselgrave et al. 2021; McClure-Begley and Roth 2022; Berg et al. 2022; Cao et al.
2022). Others disagree, arguing that mystical-type experiences (Yaden and Griffiths
2020) or experiences of enhanced existential insight (Letheby 2021) are critically
involved in engendering beneficial outcomes. Amidst this recent bout of attention
on subjective psychedelic phenomenology, visual effects have been overlooked. This
is surprising, as the visual effects “are the most frequent and robust features of the
psychedelic experience” (Vollenweider and Preller 2020) for which these drugs are
“famous” (Kometer and Vollenweider 2016; Vollenweider and Preller 2020; Letheby
2021). Rating scale items that measure visual changes have consistent dose-response
relative to other effects under lysergic acid diethylamide (LSD) (Schmid et al. 2015;
Liechti, Dolder, and Schmid 2017; Holze et al. 2020, 2022), N,N-dimethyltryptamine
(DMT) (Strassman et al. 1994; Pallavicini et al. 2021), and psilocybin (Hasler et al.
1This chapter was written as an invited chapter (under review) to be included in a forthcoming
edited volume Philosophical Perspectives on the Psychedelic Renaissance from Oxford University
Press (Letheby and Gerrans, n.d.).
63
2004; Studerus et al. 2010; Carbonaro, Johnson, and Griffiths 2020; Hirschfeld and
Schmidt 2021; Holze et al. 2022).
Considering their central place in psychedelic experiences, along with the central
role that visual perception itself plays in everyday human mental function, under-
standing the visual effects of psychedelics should be central to understanding how
psychedelics impact the mind. Yet visuals remain relatively under-examined in recent
discussions. At best, their role is treated as vehicular—that is, as ‘vehicles’ that deliver
the properly therapeutic psychological experiences. For example, although Letheby
(2021) acknowledges that the emotional shifts and psychological insights that occur
during psychedelic therapy sessions “are often deeply intertwined with perceptual
alterations” (Letheby 2021, 47), he nonetheless argues that
perceptual experiences such as listening to music or visual imagery assume
importance insofar as they act as a vehicle for experiences of this [psychological
insight] kind. Thus, although perceptual and emotional/existential/noetic
changes are intertwined, the importance of the former often derives greatly from
their intertwinement with the latter (Letheby 2021, 46–47)
This sentiment is not uncommon; recently, visual effects have been sidelined by
some authors either as epiphenomenal or as mere means to an end. This is unsurprising,
as Anglo-European cultures have long devalued and demonized drug-induced visual
alterations, treating ‘hallucinations’ as a sign of pathology (in psychiatry) and epistemic
failure (in philosophy); symptomatic of a more general “negative attitude toward any
distortion of normal sensory and cognitive perception” (Wallace 1959), dating back
at least to the Inquisition and Christian colonialism.2 Psychiatry labeled psychedelic
effects as ‘psychotomimetic’ partly because of their visual effects. The War on Drugs
appealed to cultural fear of hallucination in anti-drug (echoing anti-witch) propaganda
rhetoric (Curtis and Texas Alcohol Narcotics Education 1968 is an early example).
Thus, the downplaying of visual effects in contemporary discussions might be an
(unconscious) effort to avoid the cultural stigma and negative bias associated with
visual hallucinations.
2I thank Dr. Kathryn Swanson for illuminating the thematic similarities here. Her insights are
supported by Carod-Artal (2015) and George et al. (2021), as well the perspectives found in Collins
(2018).
64
By contrast, visual effects continue to be central to the ritualistic use of psychedelics
by indigenous cultures around the world, who place high value on the visual effects
of DMT-containing ayahuasca, mescaline-containing cacti, and psilocybin-containing
mushrooms (Schultes 1969; Schultes, Hofmann, and Rätsch 2006; GebhartSayer
1985; Carod-Artal 2015; George et al. 2021). As Hartogsohn (2017) notes, “in such
societies hallucinations are often cherished and regarded as potentially valuable for
the individual and the culture” (see also Wallace 1959). Furthermore, in modern
illicit markets, visual effects continue to be a subjective benchmark of the authenticity,
quality, and potency of psychedelic drug material (see, e.g., Weasel 1994) and are
among the top effects that account for human self-administration of classic psychedelics
(Carbonaro, Johnson, and Griffiths 2020).
Here, I address three knowledge gaps in our current understanding of psychedelic
visuals. First, with a few notable exceptions (Klüver 1928; Kometer and Vollenweider
2016; Aday et al. 2021), analysis of psychedelic visuals is lacking relative to non-visual
effects. Even where visuals are addressed, attention tends to focus on closed-eye visuals
(CEVs)—visual imagery that occurs behind closed eyelids, in the ‘mind’s eye’, akin to
imagination but with extra vivacity and force (Erowid 1995; Swanson 2018), which have
been subdivided into ‘elementary’ and ‘complex’ (Stoll 1947; Dittrich 1998; Studerus,
Gamma, and Vollenweider 2010; Kometer and Vollenweider 2016; Swanson 2018). In
comparison, there have been fewer analyses of open-eye visuals (OEVs)—psychedelic
alterations of external visual perception. Here, I introduce a taxonomy to classify
three distinct types of OEVs: (1) enhancements, (2) transformations, and (3) overlays.
Following Klüver (1942), I demonstrate how examining OEV phenomenology offers
insights into how psychedelics affect mental functions.
Second, relatively few accounts attempt to explain why and how psychedelics
affect visual perception, as contrasted with, for example, the care that has gone into
accounts aimed at explaining ego dissolution (Letheby and Gerrans 2017; Millière
2017). Existing accounts that do address visuals lack compelling explanations for
the particular phenomenology of OEVs. For example, the thalamic gating model
(Vollenweider and Smallridge 2022) might account for “sensory overload”, but this
65
notion falls short of explaining why OEVs have the particular phenomenology that
they do. Similarly, predictive processing accounts that explain OEVs as resulting
from disrupted Bayesian priors—“decomposed predictions” (Pink-Hashkes, Rooij,
and Kwisthout 2017) or “relaxed beliefs” (Carhart-Harris and Friston 2019)—do not
explain why OEVs involve only certain kinds of visual alterations: If psychedelics
interrupted all visual ‘priors’ then we would expect complete perceptual chaos, which
does not capture the phenomenology of OEVs.
The third knowledge gap I address here is our lack of understanding of how
psychedelic visuals link, if at all, to therapeutic mechanisms. Are visuals entirely
epiphenomenal to therapeutic mechanisms, or do they play a causal role in the
therapeutic process? If they play no role, then why do drugs that cause OEVs also
have therapeutic benefits?
Visual perception is perhaps the most well-characterized and extensively stud-
ied mental function in psychology and neuroscience. Moreover, visual experiences
dominate debates in philosophy compared to other sense modalities. Our efforts to
understand psychedelic drugs can advantageously leverage this foundation of knowl-
edge. Psychedelic OEVs can serve as specimens of the way in which psychedelics affect
this important aspect of the mind. We can analyze psychedelic visual phenomenology
from the platform of existing knowledge about everyday visual perception. Conversely,
psychedelics offer pharmacological probes for understanding everyday visual percep-
tion (Carter, Burr, et al. 2005; Bayne and Carter 2018). Visual effects are thus
‘low-hanging fruit’ waiting to be harvested in service of improving our understanding
of how psychedelic drugs affect our minds.
In this chapter, I situate psychedelic visual phenomenology within the context
of what we know about everyday perceptual and cognitive functions. I think of
this approach as a phenomenological-functional analysis. Starting with an informal
taxonomic classification of some common OEV phenomenology, I describe a canonical
principle that I argue captures the essence of many types of psychedelic visuals, which
I call hypercontextual modulation (HCM). OEVs, I argue, are ordinary visual context
effects ‘on drugs’—drugs that I argue cause hypersensitivity to sensory contextual cues
66
even at the lowest levels of visual processing. From OEVs I apply the notion of HCM
to non-visual psychedelic effects in cognition such as expanded semantic activation and
symbolic thinking. I note that contextual modulation can be modeled by a canonical
neural computation known as divisive normalization, which might provide a plausible
neurocomputational account of HCM under psychedelic drugs. Finally, I cash out
the notion of HCM to arrive at some practical implications for psychedelic therapy,
cognitive neuroscience, and philosophy.
2.2 What it’s like to see OEVs
Here, I introduce a general taxonomy that distinguishes three kinds of OEV phe-
nomenology commonly reported under classic psychedelics. Importantly, my analysis
is restricted to what are conventionally known as ‘classic’ psychedelic molecules: LSD,
DMT, mescaline, and psilocybin. The following OEVs are reliably induced by these
drugs.
2.2.1 Enhancements
Even at low doses, the first detectable visual changes are often subtle ‘enhancements’
in the ordinary visual field; e.g., edges appear sharper, textures more pronounced, hues
intensified. These changes are what I call enhancement OEVs. They are commonly
measured by rating scale items such as “Change in visual distinctiveness” and “Change
in brightness of objects in room” (Strassman et al. 1994; Carbonaro et al. 2018).
Visual enhancements were noted in a 1914 issue of Science magazine in a report from
a botanist who inadvertently consumed a psilocybin-containing mushroom species.
. . . objects took on peculiar bright colors . . . Real objects at this time appeared
in their true forms, but if colored they assumed far more intense or vivid colors
than natural; dull red becoming brilliant red, etc. (Verrill 1914, 409)
In 1925, neuroscientist Heinrich Klüver ingested peyote at the Psychological
Laboratory at the University of Minnesota and noted that “there was a general
increase in brightness; most colors seemed to be more deeply saturated” (Klüver 1926)
67
and subsequently collected numerous descriptions of enhancement OEVs from the
literature of the time.
With respect to colors, there are frequently changes in brightness and saturation
. . . the objects appear more solid than ever, the surface colors are more sharply
defined . . . Small differences in hue and brightness of real objects are often
noticed. . . . An enhancement of contrast phenomena seems to be the rule.
Very pronounced simultaneous contrast is found; the contours of the objects
become sharp and well-defined. . . . Objects normally seen in two dimensions
may appear tridimensional in the mescal state. Tridimensional objects may
seem still more voluminous than usual. . . . Human faces seem to undergo
certain changes . . . the features become more sharply defined (Klüver 1928).
Díaz (2010) refers to these phenomena as dishabituation of perception: “The
ordinary visual scene looks new, and everything seems as if seen for the first time.
Textures or colors are fascinating and are perceived as much more intense.” Huxley
(1954) describes how the ordinary textures looked to his eyes under mescaline.3 “Those
folds in the trousers—what a labyrinth of endlessly significant complexity! And the
texture of the gray flannel—how rich, how deeply, mysteriously sumptuous!”
Enhancement OEVs persist through the duration of the drug effects, often outlasting
other effects (Dittrich 1998; Díaz 2010; Kometer and Vollenweider 2016; Preller and
Vollenweider 2018). “The following day . . . after a period of sleep, there was still a
great sensitivity to colors, but visions could not be detected with closed or open eyes”
(Klüver 1926). Albert Hofmann, the inventor of LSD, noted that enhancement OEVs
remained the morning after taking LSD at 4:20 in the afternoon the day before.
When I later walked out into the garden, in which the sun shone now after a
spring rain, everything glistened and sparkled in a fresh light. The world was
as if newly created. All my senses vibrated in a condition of highest sensitivity,
which persisted for the entire day (Hofmann 1980).
2.2.2 Transformations
What I call transformation4 OEVs involve changes to the spatial properties or form of
objects—i.e., their perceived shape, size, and distance. Rating scales measure this type
3Check out Letheby (2021) for more fun with this quote.
4My term ‘transformation OEVs’ follows Klüver’s (1928, 34) remark that such effects are “what may
be called a ‘visionary transformation’ of the object.” I prefer this term to terms ‘pseudohallucination’,
‘distortions’ or ‘illusions’ used elsewhere to describe the phenomena (Hofmann 1980; Dittrich 1998;
Kometer and Vollenweider 2016; Preller and Vollenweider 2018; Vollenweider and Preller 2020).
68
of OEV with items such as “Room looks different”, “My sense of size and space was
distorted”, “Things in my surroundings appeared smaller or larger”, “Edges appeared
warped”, “I saw movement in things that weren’t actually moving”, and “With eyes
open visual field vibrating or jiggling” (Dittrich 1998; Strassman et al. 1994; Studerus,
Gamma, and Vollenweider 2010; Muthukumaraswamy et al. 2013; Carbonaro et al.
2018; Hirschfeld and Schmidt 2021; Holze et al. 2022; Lawrence et al. 2022).
Regarding size transformations, objects can appear either larger (macropsia) or
smaller (micropsia) than normal (Kometer and Vollenweider 2016).
The changes in the apparent size of real objects require special consideration.
To illustrate [from documented cases of mescaline experiences]: “When I moved
my hand towards me, it got enormous and bulky forms” . . . “I looked out of the
window and was particularly surprised at the changes in the size of the houses
. . . they seemed to have grown” . . . “The branches [of a tree] became longer
and shorter” (Klüver 1928).
Relatedly, Klüver reports from his own peyote experience how “Standing near
a corner of the room, the walls . . . seemed to move towards me” (Klüver 1926).
Similarly, Huxley remarks that under mescaline “the perspective looked rather odd,
and the walls of the room no longer seemed to meet in right angles” (Huxley 1954).
These reports highlight how transformation OEVs involve visual shifts in apparent
distance and perspective.
Huxley, describing a staple transformation OEV, notes that he looked at flowers
in a vase and “seemed to detect the qualitative equivalent of breathing . . . ” (Huxley
1954). Note that visually ‘breathing’ involves both movement and change of shape
(take a deep breath now, observe your chest, and it will both move and change shape).
Klüver characterized these as “the apparent movement of stationary objects,” but
also used descriptions that entail changes in object shape—“undulating movements . . .
movements which change the contours and dimensions of the object” (Klüver 1928,
41). “Looking at the radiator in the room, its divisions seemed to move rhythmically”
(Klüver 1926). Hofmann (under LSD) reported seeing both “continuous motion” and
changes in the shape of the inanimate objects he looked at.
Everything in my field of vision wavered and was distorted as if seen in a curved
mirror. . . . My surroundings had now transformed themselves . . . the familiar
69
objects and pieces of furniture . . . were in continuous motion, animated, as if
driven by an inner restlessness (Hofmann 1980, emphasis mine).
Similar phenomenology has been noted under psilocybin, an early example being
again from the 1914 Science magazine.
I noticed that the irregular figures on the wall-paper seemed to have creepy and
crawling motions, contracting and expanding continually, though not changing
their forms; finally they began to project from the wall and grew out toward
me from it with uncanny motions (Verrill 1914, 409).
2.2.3 Overlays
The final species of OEV phenomena I taxonomize here are what I call overlays:
geometric patterns that infuse the external visual scene. Often compared to decorative
patterns found on rugs, tapestries, and wallpaper, overlay OEVs appear as a layer,
on top of, or ‘covering’ ordinary objects. Rating scales capture overlay OEVs quite
explicitly: “Room overlaid with visual patterns” (Strassman et al. 1994; Carbonaro et
al. 2018). Klüver observed OEV overlays when he looked at the walls of the room
under peyote.
(Eyes open): the walls covered with squares (about 2 x 2 cm): shadowy dark
contours. The corners of the squares: red jewels (three-dimensional). This
design followed by similar mostly more complicated designs in various colors.
Sometimes not sure whether the phenomena are localized at the distance of the
walls (Klüver 1926).
As the effects climaxed, the overlays intensified. “(Eyes open): impossible to look
at the walls without seeing them covered with visionary phenomena. Various designs .
. . visionary phenomena on the walls” (Klüver 1926, emphasis mine). Klüver (1928)
later compiled descriptions from other subjects:
“transparent oriental rugs, but infinitely small” seen for example on the surface
of the soup at lunch time . . . “wallpaper designs” . . . “countless rugs with
such magnificent hues and such singular brilliancy” . . . localized on the walls,
on the floor or wherever the subject happens to look (Klüver 1928 emphasis
mine).
Klüver notes that “The visionary phenomena . . . lasted until retiring and could
always be seen in the dark room with open eyes” (Klüver 1926). After spending
70
time staring at his overlay-covered walls, Klüver went on to develop a taxonomy
of OEV overlay phenomenology that identifies four fundamental types of patterns
that he called “form constants”; namely, (1) lattices (checkerboards, honeycombs and
triangles), (2) tunnels, (3) spirals and (4) cobwebs (Klüver 1928). Klüver argued that
OEV overlays seen under psychedelic drugs “are upon close examination nothing but
variations of these form-constants” (Klüver 1928).
Overlay OEVs are common with all classic psychedelics. The following description of
viewing the night sky under psilocybin uses the term ‘overlay’ as well as ‘enhancement’:
Visual acuity is enhanced to the point where the sky becomes three-dimensional.
. . . Electromagnetic fields ebb and flow, much like a hyperactive aurora borealis.
. . . Overlaying this display of splendor are colorful, dancing, geometrical fractals
of infinite complexity (Stamets 1996).
2.3 Hypercontextual modulation in psychedelic vi-
sion
Everyday visual perception involves cascades of contextual modulation: each perceived
element of a visual scene is shaped by the spatial context (the other visual elements)
in which it is situated. Spatial context effects occur when the perceived properties of
a target element change based on the properties of neighboring elements (Schwartz,
Hsu, and Dayan 2007). Visual illusions demonstrate how perceived visual properties
can be altered simply by changing contextual cues, without changing the physical
attributes of the target stimulus. Consider the classic context effects presented in
Figure 2.1. In such cases, “the origin of the illusory effects is not based on physical
distal or physical proximal differences of target objects, but on the differences in their
contexts” (Todorović 2020). Indeed, more than a century of psychophysical studies has
established that “the perception of, and neurophysiological responses to, a target input
depend strongly on both its spatial context (what surrounds a given object or feature)
and its temporal context (what has been observed in the recent past)” (Schwartz,
Hsu, and Dayan 2007). Figure 2.2 presents the exact same target stimuli used in
Figure 2.1 without the contextual cues. The term contextual modulation refers to the
71
Figure 2.1: Examples of (spatial) visual context effects. A: The two diamonds have
the same shade of grey, but appear to have different shades (Chevreul 1860). B: The
top and bottom horizontal lines are the same length, but the bottom line appears
longer (Müller-Lyer 1889). C: The two (orange) central circles are the same size, but
the right one looks larger (Ebbinghaus 1902; Titchener 1905). D All strips have the
same orientation, but appear slanted in different directions (Zöllner 1860). E: The two
horizontal (red) lines are straight, but appear slightly curved (Hering 1861). F The
stripes in the center circle are completely vertical, but appear slightly tilted (Gibson
and Radner 1937). Figure 2.2 shows the same target stimuli without the contextual
cues.
72
processes by which contextual stimulus cues influence the perceived properties of a
target stimulus. For our purposes here, the specific neural mechanisms of contextual
modulation are irrelevant. All that is required is that contextual modulation occurs
in perception, as demonstrated in Figures 2.1 and 2.2.
The key insight of this section is that psychedelics might produce OEVs by inducing
hypersensitive contextual modulation. I motivate this analytically with a thought
experiment, followed by phenomenological and psychophysical evidence.
2.3.1 Thought experiment
Imagine that some fictional drug could selectively enhance contextual modulation.
Under this drug, the apparent contrast of the diamonds in Figure 2.1 A would
diverge even more—that is, the background shades (contextual cues) would have a
greater effect on the perceived shades of the diamonds (target stimulus). By inducing
hypercontextual modulation—amplified modulatory effects of contextual cues—the
drug would exaggerate perceived differences in line length (B), circle size (C), line
orientation (D; F), and curvature (E). Importantly, under this fictional drug, it is
only the magnitude of influence of contextual cues that is impacted—i.e., without the
contextual cues, (Figure 2.2) the drug would have less influence on percepts of the
target stimuli.
Now, imagine further that the strength of these drug-amplified contextual modula-
tions varied from one frame to the next, perhaps due to its pharmacokinetics, causing
a ‘rubber-banding’ effect, where each moment the contextual modulation was stronger,
then weaker, then stronger, relative to the previous moment.5 The net phenomenology,
I argue, would be exactly the kind of “breathing” (Huxley 1954), “wavering” (Hofmann
1980), “undulating movements” (Klüver 1926) seen in transformation OEVs. Our
fictional drug would be able to produce transformation OEVs solely by enhancing
5Alternatively, the context provided by the visual scene at one moment might unduly influence the
next scene as eye movements occur. The phenomenon of ‘tracers’ or ‘trails’—lingering afterimages in
moving objects—is consistent with the notion that hypersensitive temporal contextual modulation
occurs under psychedelics. In essence, the context of an object being at position A is carried over into
the next frame due to hypercontextual modulation of the previous frame. Thanks to Michael-Paul
Schallmo for raising this possibility.
73
Figure 2.2: The exact same target stimuli from Figure 2.1 but without the contextual
cues. A: The two diamonds have the same shade of grey. B: The top and bottom
horizontal lines are the same length. C: The two (orange) central circles are the same
size. D All strips have the same orientation. E: The two horizontal (red) lines are
straight. F The stripes in the center circle are completely vertical. Figure 2.1 shows
how contextual cues can alter the appearance of these stimuli.
74
the contextual modulation that occurs when you look at Figure 2.1. Furthermore,
your overall visual field would have richer variations among its elements, akin to
enhancement OEVs, just as the target stimuli in Figure 2.1 have more variation than
they do in Figure 2.2.
I argue that common psychedelic OEVs can be accounted for by what I call
hypercontextual modulation (HCM) in visual perception. The central notion is that
psychedelics could cause OEVs by amplifying the visual processes by which perception
of the target stimuli in Figure 2.2 are transformed by the contextual cues as in Figure
2.1.
2.3.2 OEV phenomenology as HCM
Consider that psychedelics increase scores on the rating scale item ‘Things in my sur-
roundings appeared smaller or larger’, phenomena known as micropsia and macropsia
(Kometer and Vollenweider 2016). In normal vision, the apparent sizes of objects are
perceived in relation to the spatial context of the visual scene—an effect illustrated by
the moon illusion (Berkeley 1709; Hershenson 2013), in which the apparent size of
the moon changes depending on its position in the sky. Thus, if psychedelics caused
HCM—i.e. disproportionate modulatory responses to contextual cues that ordinarily
determine perceived size and shape—we would expect shifts in the apparent size of
objects, as seen in transformation OEVs. Interestingly, transformation OEVs are often
restricted to only some objects in the visual scene—e.g., some objects might appear
to move or ‘breathe’, while others simultaneously remain static and appear normal,
consistent with my claim that OEVs stem from a selective impact on contextual
modulation processes rather than whole-scale distortions of the entire visual stream.
It is theoretically interesting that the occurrence of apparent movements in
the mescal state depends to a certain extent on the nature of the stimuli. . . .
objects which together with their surroundings form an optical “whole” and
which are so to speak definitely anchored in optical respects are less likely to
move than those which seem to be detached from their backgrounds; objects
the contours of which “suggest” movement are more likely to move than those
with definite, well-marked contours . . . (Klüver 1928, 42–43)
The context effects in Figure 2.1 match well to transformation OEVs. In a state
75
of HCM, a greater-than-usual effect of cues seen in Figure 2.1 B/C could lead to
“The branches [of a tree] became longer and shorter” (Klüver 1928). Hypersensitivity
to cues that modulate line orientation (panels D/F) could lead to “the walls of the
room no longer seemed to meet in right angles” (Huxley 1954). Amplification of the
modulation seen in panel E could lead to “my field of vision wavered and was distorted
as if seen in a curved mirror” (Hofmann 1980).
The same logic applies to enhancement OEVs. As seen in Figure 2.1 panel A,
amplified contextual modulation could produce intensification of colors, heightened
vivacity, and finer detail by increasing simultaneous contrast effects. Indeed, Klüver
(1928) observed that under mescaline “Very pronounced simultaneous contrast is found.”
In everyday perception, contextual cues modulate many low-level visual features,6
including perceived luminance (Adelson 2000; B. L. Anderson and Winawer 2005),
contrast (Ejima and Takahashi 1985; Chubb, Sperling, and Solomon 1989; Cannon and
Fullenkamp 1991; Snowden and Hammett 1998), and orientation (Zöllner 1860; Fraser
1908; Goddard, Clifford, and Solomon 2008; Qiu, Kersten, and Olman 2013; Clifford
2014), which underpin the perception of textures. Enhancement OEV phenomenology
is thus consistent with the notion that psychedelics cause HCM at the lowest levels of
visual processing.
Does HCM help us understand overlay OEVs? Klüver (1928) suggests that the
‘form constants’ in these OEVs arise from intrinsic functions of visual anatomy, and
Bressloff et al. (2002) build on this idea in an anatomical-computational account
suggesting that the “mechanisms that generate geometric visual hallucinations are
closely related to those used to process edges, contours, surfaces, and textures.”
Speculatively, the geometric patterns seen in overlay OEVs are proto-elements of
these higher-level visual features, which might become ‘orphaned’ or ‘detached’ from
any objects. In a state of HCM, low-level visual processes might generate extra
6Indeed, like in visual perception, contextual modulation is pervasive in auditory perception of
both speech (Stilp 2019) and music, where contextual interactions between loudness, pitch, and
timbre (Melara and Marks 1990; Marozeau and Cheveigné 2007; Borchert, Micheyl, and Oxenham
2011; E. J. Allen and Oxenham 2014; Wang, Kreft, and Oxenham 2015) determine perceived auditory
qualities. Enhanced subjective fidelity of auditory percepts under psychedelics, captured by the
rating scale item “Sounds in room sound different” (Strassman et al. 1994), could thus plausibly be
explained by HCM.
76
proto-elements of edges, contours, surfaces, and textures in (hyper)response to various
(low-level) contextual cues, resulting in overlay OEVs. Indeed, there is evidence
that the earliest/lowest visual areas generate proto-elements of naturalistic textures
(Freeman et al. 2013; Ziemba et al. 2016). Thus, it is plausible that overlay OEVs
result from hyper-recruitment of the response patterns in these early visual areas,
causing them to produce an overabundance of textures like those found in the natural
world.7
In summary, I propose that HCM is critically involved in all major types of OEVs in-
duced by classic psychedelics. The defining phenomenological features of enhancement
OEVs, transformation OEVs, and overlay OEVs are exactly the kinds of perceptual
consequences we might expect to see if the visual system became hypersensitive to con-
textual cues at multiple levels. So far, I have provided a phenomenological-functional
analysis supporting HCM as the cause of OEVs. I next present supporting empirical
evidence from studies that asked participants to complete psychophysical tasks under
psychedelics.
2.3.3 Psychedelic psychophysics
To support my argument that OEVs are hypercontextual modulations, I need psy-
chophysical evidence that psychedelics selectively impact how contextual cues are
processed; i.e., that a measured percept is not significantly impacted by the drug
when significant context-modulating cues are absent from the stimulus, such as in
Figure 2.2. Indeed, evidence indicates that visual performance is largely preserved
under psychedelic drugs, unless the stimuli are designed to elicit strong contextual
modulation. For instance, Barrett et al. (2018) found no significant differences in
performance on the Penn Line Orientation Test (PLOT) (Moore et al. 2015) under
psilocybin, a task that does not trigger significant contextual modulation at the level
of perception that it measures.8
7Thanks to Michael-Paul Schallmo for providing the extra neuroscience references on this point.
8“The participant is shown two lines on the computer screen that differ in length and orientation,
and must press a button to rotate one of the lines until its orientation (angle relative to a horizontal
line) is the same as the other (nonrotating) line” (Moore et al. 2015).
77
Further evidence can be found in Carter et al. (2004), who designed two motion
detection tasks—one “local motion” task, and one “global motion” task—and compared
task performance under psilocybin versus placebo. Psilocybin had no impact on the
local motion task, but significantly impacted the global motion task. Importantly,
contextual cues remained fixed across trials on the local motion task. The global
motion task, by contrast, presented contextual cues in the form of randomly moving
(non-target) dots intermixed with the target dots, so that the visual context varied
across trials. Psilocybin significantly impacted performance on this task. Furthermore,
“a number of subjects commented that subjectively the global motion task became
harder [under psilocybin] due to an increased salience of the randomly moving dots”
(Carter et al. 2004, emphasis mine). In a different study that measured the influence
of contextual cues on visual attention, Gouzoulis-Mayfrank et al. (2002) found that
psilocybin significantly increased the influence that visual cues had on performance on
the covert orienting of attention task (COVAT), consistent with the notion of HCM.
Taken together, these studies support HCM because they indicate that task
performance is largely preserved under psilocybin on tasks (and on ‘catch trials’) that
do not invoke or measure context effects, while performance is significantly impacted
on tasks that induce contextual modulation. This data squares nicely with the notion
that psychedelics selectively impact contextual modulation to produce OEVs. Note
that the notion of HCM suggests an empirical research programme for future studies
in psychedelic psychophysics, with testable hypotheses, which could confirm, disprove,
or amend the notion that psychedelic visual effects stem from HCM.
2.3.4 Divisive normalization, serotonin, and psychedelics
The link between everyday context effects and psychedelic visuals is a phenomenological-
functional insight that stands on its own, regardless of any proposed account of the
neural mechanisms by which psychedelic drugs might amplify contextual modulation.
Nonetheless, a computation known as divisive normalization (DN) stands out in
current neuroscience as a successful neurocomputational account of context effects
(Heeger 1992; Schwartz, Hsu, and Dayan 2007; Carandini and Heeger 2011; Schallmo
78
et al. 2018; Louie and Glimcher 2019; Aqil, Knapen, and Dumoulin 2021), so it
is worth exploring whether it could be used to model psychedelic visuals. DN is a
mathematical operation that, when used to model neural activity,
scales the activity of a given neuron by the activity of a larger neuronal pool.
This nonlinear transformation introduces an intrinsic contextual modulation into
information coding, such that the selective response of a neuron to features of
the input is scaled by other input characteristics. This contextual modulation
allows the normalization model to capture a wide array of neural and behavioral
phenomena not captured by simpler linear models of information processing”
(Louie and Glimcher 2019, emphasis mine).
In the simplest terms, DN models how one neuron’s activity is influenced by
(contextual) activity in other neurons. Classic visual context effects can be modeled
with DN (Schwartz, Hsu, and Dayan 2007; Louie and Glimcher 2019; Aqil, Knapen,
and Dumoulin 2021), including surround suppression and enhancement of perceived
contrast (Xing and Heeger 2001), visual salience (Itti and Koch 2000; Coen-Cagli,
Dayan, and Schwartz 2012), object boundaries (Zhou and Mel 2008; Walshe and
Geisler 2020), and motion (Quaia, Optican, and Cumming 2017; Schallmo et al. 2018).
I suggest here that DN might capture how psychedelics alter these visual features to
produce OEVs.
Is it neurobiologically plausible that psychedelics might alter DN computations
to cause HCM in visual perception? Indeed, DN responses in early visual cortex
have recently been linked to serotonergic (5-HT) neuromodulation in mouse models
(Michaiel, Parker, and Niell 2019; Azimi et al. 2020). The findings of Azimi et al.
(2020) provide preliminary evidence that serotonergic signaling is the neuromodulatory
substrate by which neurons implement DN-like mechanisms to scale their activity.
5-HT1A receptors promote divisive suppression of spontaneous activity, while
5-HT2A receptors act divisively on visual response gain and largely account
for normalization of population responses over a range of visual contrasts in
awake and anesthetized states. Thus, 5-HT input provides balanced but distinct
suppressive effects on ongoing and evoked activity components across neuronal
populations (Azimi et al. 2020).
All classic psychedelic drugs agonize 5-HT2A receptors (Glennon, Titeler, and
McKenney 1984; Halberstadt 2015; Vollenweider and Smallridge 2022). We know
79
that this agonism is is critically involved in engendering psychedelic effects because
the effects can be blocked by blocking 5-HT2A agonism (i.e., by co-administering the
drug ketanserin, a selective 5-HT2A antagonist) under psilocybin, LSD, and DMT in
humans (Vollenweider et al. 1998; Kometer et al. 2011, 2012; Valle et al. 2016; Preller
et al. 2017; Barrett, Preller, et al. 2017; Kraehenmann, Pokorny, Aicher, et al. 2017).
Importantly, there is also evidence (Strassman 1995; T. Pokorny et al. 2016; Lawn et
al. 2022) “suggesting a modulatory effect of the 5-HT1AR system on 5-HT2-mediated
psychedelic effects” (Vollenweider and Smallridge 2022). As noted above, Azimi et al.
(2020) found that 5-HT2A and 5-HT1A might serve to balance different components of
the DN computation in mice. Thus, the same 5-HT1A/5-HT2A activity that is critical
for psychedelic visuals has also been implicated in supporting DN computations that
accurately model visual context effects.
A question arises: How would the computational structure of DN need to be altered
to yield the hypercontextual modulation that I argue underpins psychedelic effects? A
normalization model “computes a ratio between the response of an individual neuron
and the summed activity of a pool of neurons” (Carandini and Heeger 2011) such
that “the selective response of a neuron to features of the input is scaled by other
input characteristics” (Louie and Glimcher 2019). Roughly, the kind of HCM seen in
OEVs might arise if the normalization pool were broadened such that the output of
each neuron was computed in relation to responses from a larger-than-usual pool of
neurons, the net effect being greater-than-usual contextual modulation.9
Importantly, my proposal that DN might model psychedelic OEVs is broadly
consistent with existing theories based on hierarchical predictive coding (Corlett,
Frith, and Fletcher 2009; Pink-Hashkes, Rooij, and Kwisthout 2017; Letheby and
Gerrans 2017; Swanson 2018; Carhart-Harris and Friston 2019) insofar as predictive
coding and DN computations are not mutually exclusive (Marino 2022; Srinivasan,
Laughlin, and Dubs 1982). However, HCM and DN have advantages over existing
Bayesian accounts for two reasons. First, DN is more parsimonious than predictive
processing; it can yield understanding without requiring us to grasp the whole lot of
9My full treatment of this idea is forthcoming as a neurocomputational modeling paper.
80
esoteric neurocomputational concepts positied by hierarchical predictive processing.10
Second, as mentioned in Section 2.1, while predictive processing accounts do make
some attempt to explain OEVs—“such as seeing walls breathing” (Carhart-Harris
and Friston 2019)—they do not offer principled reasons why walls so commonly
appear to “breathe” rather than, say, crumble, catch fire, or morph into pink rats.
For instance, Carhart-Harris and Friston (2019) argue that psychedelics ‘relax’ the
normally heavily-weighted belief (or Bayesian prior) that “walls don’t breathe” thus
permitting the percept of breathing walls to emerge. But if psychedelics work this
way, why would they not also ‘relax’ all sorts of other perceptual priors that normally
constrain our perception of corporeal properties? Presumably ‘relaxed beliefs’ at such
a fundamental level would cause complete perceptual chaos, which is not the case
under classic psychedelics, as psychophysical studies readily show. By comparison, the
notion of HCM can explain ‘breathing’ OEVs in terms of hypersensitivity to contextual
cues, causing exaggeration of the same context effects known to operate at multiple
levels in everyday visual perception, known to induce illusory curvature (Hering 1861),
line orientation (Zöllner 1860; Gibson and Radner 1937), and size (Ebbinghaus 1902;
Müller-Lyer 1889)—as seen in Figure 2.1.
Finally, DN is a “canonical computation”—an information processing strategy
repeated “across brain regions and modalities to apply similar operations to different
problems” (Carandini and Heeger 2011). Indeed, DN can model the contextual
modulation involved in multisensory integration (Ohshiro, Angelaki, and DeAngelis
2011), visual attention (Reynolds and Heeger 2009), and value-coding on decision-
making tasks (Louie et al. 2014; Louie and Glimcher 2019), all of which also happen
to be impacted by psychedelics.11 It is plausible that psychedelics produce their effects
10Such concepts include top-down/bottom-up hierarchical message-passing, unconscious perceptual
inference, Bayesian priors, probability density functions, precision-weighting of prediction error, free-
energy minimization, or a neurocomputation-specific notion of “relaxed beliefs” (e.g., Carhart-Harris
and Friston 2019).
11While decision-making has not been studied under psychedelics, Metzner and Leary (1967)
anecdotally observe that it “can lead to interminable distracting deliberations.” Furthermore, the
5D-ASC includes the items “I felt incapable of making even the smallest decision” and “I had
difficulties in distinguishing important from unimportant” (Dittrich 1998). Potentially, normalization
models of decision-making (Louie et al. 2014) might be able to model how psychedelics cause
“interminable distracting deliberations.”
81
by impacting a canonical computation, given the cross-modal effects of psychedelics
across perception and cognition. A DN model of psychedelic effects could be highly
appealing for these reasons.
2.4 HCM, multisensory integration, and cognition
Next, I apply the idea of HCM beyond OEVs to explain other common psychedelic
effects in modalities that have also been modeled with DN.
2.4.1 Audio-visual context effects
Auditory stimuli influence visual percepts in unusual ways under psychedelics, measured
by the rating scale items ‘Sounds seemed to influence what I saw’, ‘Shapes seemed to be
changed by sounds or noises’, and ‘The colors of things seemed to be altered by sounds
or noises’ (Studerus, Gamma, and Vollenweider 2010). Anecdotally, Hofmann noted
these effects in his early experiences with LSD. “It was particularly remarkable how
every acoustic perception, such as the sound of a door handle or a passing automobile,
became transformed into optical perceptions” (Hofmann 1980). This phenomenology is
consistent with the general idea of HCM. In everyday perception, auditory sensations
provide contextual cues that can influence visual percepts (McGurk and MacDonald
1976; Shams, Kamitani, and Shimojo 2000) and may even enhance deficient visual
abilities (Caclin et al. 2011).
Consistent with this idea, psychedelics can amplify responses to contextual cues in
musical stimuli (Bonny and Pahnke 1972; Barrett, Preller, et al. 2017), captured by
the rating scale item “Increase in the beauty and significance of music” (Carbonaro
et al. 2018), which can manifest as increased CEVs (Kaelen et al. 2016), emotions
(Kaelen et al. 2015), and perceived meaning (Preller et al. 2017). One subject
describes what it is like to listen to music under LSD as follows.
The most beautiful array of fabrics and trimmings in fantastic inter-weaving
of designs and in delicate colors of pastel to more intense in hue followed the
inter-weaving movements of the music—visualizations of the voice timbre—ever
82
changing in complex and unusual ways . . . When the music stopped, the flow
of imagery stopped (Bonny and Pahnke 1972, 71–74).
Musical stimuli provide a rich source of abstract, ambiguous, open-ended auditory
cues, especially instrumental music, which is preferred over lyrical music for evoking
meaningful associations under psychedelics (Barrett, Robbins, et al. 2017; Kaelen et al.
2018). Enhanced responses to music under psychedelics is thus plausibly due to HCM,
where the contextual cues in music might produce greater-than-usual modulations
of emotions (Kaelen et al. 2015), mental imagery (Kaelen et al. 2016), and meaning
(Preller et al. 2017). Indeed, in everyday auditory perception, interactions between
contextual cues determine perceived auditory qualities such as loudness, pitch, and
timbre (Melara and Marks 1990; Marozeau and Cheveigné 2007; Borchert, Micheyl,
and Oxenham 2011; E. J. Allen and Oxenham 2014; Wang, Kreft, and Oxenham 2015)
and aid auditory scene analysis (Lewicki et al. 2014), semantic salience, and perceptual
‘pop out’ of meaningful sounds (Auerbach and Gritton 2022). Thus, augmentation of
these everyday contextual modulation processes might explain why music has peculiar
auditory and visual phenomenology under psychedelics.
2.4.2 Semantic activation
As with visual tasks, psychedelics impact performance on certain types of cognitive
tasks but not others. Barrett et al. (2018) report that psilocybin did not impact
performance on measures of episodic memory or executive function. By contrast,
psilocybin and LSD both have significant impact on tasks that measure or induce
semantic context effects, indicating that the characteristic psychedelic effects on
cognition also involve a form of HCM. Using the Forward Flow Task (FFT) (Gray et
al. 2019), in which subjects produce word associations from a seed word,12 Wiessner
et al. (2021) found that “LSD increased forward flow, flow distance and flow steps but
not semantic spread, indicating that semantic distances of words were meaningfully
increased in relation to their predecessors, successors and neighbors but not overall,
12“For this, the participant receives a seed word and is asked to type the first word that comes to
mind from this seed word, followed by the first word that comes to mind from this first written word,
and so on until reaching 20 words” (Wiessner et al. 2021).
83
randomly spread” (Wiessner et al. 2021, emphasis mine). These results are broadly
congruent with other studies using cognitive tasks that measure and manipulate
semantic context. Spitzer et al. (1996) found that psilocybin enhanced semantic
priming for indirectly related (but not directly related or unrelated) word pairs in
a lexical decision task, suggesting that psilocybin “leads to an increased availability
of remote associations and thereby may bring cognitive contents to mind that under
normal circumstances remain nonactivated” (Spitzer et al. 1996, 1057). Broadly
consistent with these results, Family et al. (2016) found increased semantic spread
under LSD in a picture-naming task (Vigliocco et al. 2002; Snodgrass and Vanderwart
1980) for semantically similar (but not for dissimilar) pictures. Relatedly, in an early
study (albeit with some methodological limitations), Barr, Langs, and Holt (1972)
asked subjects under LSD or placebo to list the connotations that came to mind as
they viewed both fictional words and drawings of faces, presented separately as well
as in word-face pairs. Connotations of the words and the faces were largely distinct
under placebo, while “in the LSD-altered states, the usual connotations of the word
were modified [when presented with the face], presumably by taking on some of the
face’s qualities” (Barr, Langs, and Holt 1972, 61).
Taken together, these results suggest that psychedelics alter thinking in a specific
way, in which thoughts are more readily modulated by remote (but not random)
contextual information, producing stronger associations between distantly-related
(but not directly-related) concepts as well as between concepts and incoming sensory
cues. Existing predictive processing accounts face challenges explaining these effects
for reasons similar to the case of OEVs. Reducing the precision-weighting of priors
(‘relaxed beliefs’) would presumably impact all kinds of cognitive tasks, including those
that use concrete prompts and direct semantic associations. However, psychedelics did
not cause significant differences on these kinds of tasks; their influence was selective
to tasks that used abstract, indirectly-related cues and prompts. By contrast, such
effects are consistent with HCM because enhanced sensitivity to contextual cues
would plausibly lead to a positive correlation between cue ambiguity and the resulting
semantic activation. In a state of HCM, trials using concrete, unambiguous prompts
84
would yield the usual semantic activations, while trials with abstract, ambiguous cues
would trigger greater semantic activation that would spread more widely, just as these
studies measured under psilocybin and LSD.
Relatedly, consider the “continuous gales of laughter” (Isbell 1959, 32), or “unmo-
tivated laughter” (Preller and Vollenweider 2016) that can occur under psychedelics,
an effect captured by the rating scale item “Many things seemed incredibly funny
to me” (Dittrich 1998). Humor and laughter have long been linked to contextual
shifts in semantic meaning (Beattie 1776; Morreall 2020). It is thus plausible that
psychedelics cause excessive laughter by inducing HCM of semantic processing. Since,
as we saw above, there is evidence that abstract or ambiguous contextual cues produce
greater semantic activation under psychedelics, then, insofar as humor involves unusual
semantic shifts, a cognitive form of HCM could increase the likelihood of laughter in
response to cues that normally are not funny.
2.4.3 Symbolic thinking
Wiessner et al. (2022) found increased symbolic thinking on the pattern meaning
task (PMT),13 the alternate uses task (AUT),14 and the figural creativity task15
under LSD compared with placebo, characterized by “non-concreteness as common
denominator . . . indicating that abstract, more than concrete, input stimulates the
generation of semantically distinct thinking under LSD” (Wiessner et al. 2022). It
is worth noting that the PMT and AUT tasks used visual stimuli (abstract line
patterns, everyday objects) as task prompts. Relatedly, a construct in psychology
known as ‘primary-process thinking’ (Rapaport 1950) defines a mode of cognition
marked by increased symbolic thinking linked to mental imagery (Stigler and Pokorny
2001). Under LSD, Kraehenmann, Pokorny, Aicher, et al. (2017) measured increased
primary-process thinking using a rating scale consisting of nine categories (Auld,
13The pattern meaning task (PMT) involves writing as many creative interpretations as possible
for abstract line patterns (8 patterns, 2 min each) (Claridge and McDonald 2009).
14The alternate uses task (AUT) involves writing as many uncommon uses as possible for everyday
objects (2 objects, 3 min each) (Guilford 1967).
15The figural creativity task (FIG) involves producing drawings based on simple line patterns on a
sheet of paper and writing creative titles for them (2 patterns, 10 min in total) (Artola et al. 2012).
85
Goldenberg, and Weiss 1968), which include ‘visual representation’, ‘symbolism’, ‘fluid
transformations’, and ‘unlikely combinations or events’. Similarly, the Rorschach
(inkblot) projective test—another task that uses an abstract visual prompt—measured
increased primary-process thinking under LSD (Barr, Langs, and Holt 1972; Holt
2002), “further supporting the notion that abstract and imagined stimuli promote
LSD-induced symbolic thinking” (Wiessner et al. 2022). Relatedly, psychedelics
increase scores on the rating scale items “Some everyday things acquired special
meaning”, “Things in my environment had a new strange meaning”, and “Objects in
my surroundings engaged me emotionally much more than usual” (Studerus, Gamma,
and Vollenweider 2010; Schmid et al. 2015; Liechti, Dolder, and Schmid 2017; Holze
et al. 2020, 2022; Hasler et al. 2004).
HCM can make sense of increased symbolic thinking under psychedelics. If
contextual cues trigger increased modulations at multiple levels of perception and
cognition—perhaps underpinned by individual neurons scaling their responses accord-
ing to activity from a larger-than-usual pool of other neurons (DN)—then we might
expect increased attribution of meaning to various external and internal cues.
2.4.4 Set, setting, and HCM
The term ‘set and setting’ is a slogan coined by Leary, Litwin, and Metzner (1963) to
capture a phenomenon (noted as early16 as Moreau (1845; Hartogsohn 2017)) in which
extra-pharmacological factors (non-drug variables) influence the subjective effects of
psychedelic drugs to a degree that is disproportionate to other psychoactive drugs
(WHO 1958; Abramson 1960; Zinberg 1984; Hartogsohn 2017). The variables in ‘set’
(mindset) include “personality, preparation, expectation, and intention of the person
having the experience,” while the variables in ‘setting’ (external environment) include
“the physical, social, and cultural environment in which the experience takes place”
(Hartogsohn 2017). Importantly, the reasons for this influence remain unclear within
16Arguably the ritualistic practices surrounding the use of psychedelics by indigenous cultures
reflects a deep and sophisticated understanding of how the variables of set and setting impact
subjective drug effects.
86
certain mechanistic assumptions in pharmacology; namely, “that drugs exert basically
conform effects on their users (DeGrandpre 2006). It would seem nonsensical to claim
that a drug experience could differ fundamentally depending on the place in which
the drug is taken or the people present” (Hartogsohn 2017).
However, set and setting makes more sense if psychedelic effects stem from HCM.
HCM entails that subjective effects are dependent on set and setting, as the amount
of contextual modulations in any given moment depends on the nature of the external
and internal contextual cues. Thus, the broad hypersensitivity to surroundings elicited
by psychedelics in humans (Eisner 1997) and other mammals (Halberstadt and Geyer
2016) is plausibly due to HCM impacting multiple levels of perception and cognition.
2.4.5 Bad HCM
Relatedly, the valence of psychedelic experiences—their ‘pleasantness’ or ‘unpleasant-
ness’—is highly variable. The same individual can have a positively-valenced ‘good
trip’ on one occasion, yet a separate occasion with the same drug at the same dosage
can yield a negatively-valenced ‘bad trip’ (Cohen 1960; Strassman 1984; Barrett et al.
2016; Carbonaro et al. 2016; Gashi, Sandberg, and Pedersen 2021), a fact that early
psychopharmacologists found puzzling and contradictory (Osmond 1957; WHO 1958;
Abramson 1960; Hartogsohn 2017). The contradiction is resolved, I argue, when we
view HCM as the primary action of psychedelics: descriptions of bad trips often detail
how a particular visual or auditory stimulus led to a cascade of negative thoughts
and feelings (Gashi, Sandberg, and Pedersen 2021). To mitigate such hypercontextual
cascades, researchers and illicit users alike have developed protocols for controlling set
and setting (Leary, Litwin, and Metzner 1963; Eisner 1997; Johnson, Richards, and
Griffiths 2008; Carbonaro et al. 2016; Gashi, Sandberg, and Pedersen 2021)—they
control the contextual cues. Amidst the cognitive effects described in previous sec-
tions—increased semantic activation, semantic spread, and symbolic thinking—it is
unsurprising that trains of thought can go ‘off the rails’ into startling mental territory
as one thought hypermodulates subsequent thoughts. Taken together, the phenomenol-
ogy and etiology of bad trips offer yet more reason to suspect that HCM is central to
87
psychedelic drug effects. As one illicit user put it, when it comes to understanding
what causes bad trips, “Context is everything” (Gashi, Sandberg, and Pedersen 2021).
2.5 Practical implications
In this section, I describe key takeaways whereby HCM might inform current debates
and philosophical questions about the therapeutic use of psychedelics.
2.5.1 Hallucination and HCM
Do psychedelic visuals count as hallucinations? Through the lens of HCM, psychedelic
visuals are perceptual consequences of hypersensitivity to contextual cues—normal
contextual modulation processes are augmented to produce exaggerated visual re-
sponses. Since we generally don’t say that you are hallucinating when you view
Figure 2.1, hypercontextual modulations of perception might be more appropriately
considered as illusions (Todorović 2020), not hallucinations. Importantly, our concepts
of illusion and hallucination are not synonymous, as the two concepts differ with
regard to the epistemic status or veridicality of the experiences. Macpherson and
Batty (2016), for example, argue that common features of everyday perception have
mixed veridicality. If I am right that psychedelic visuals stem from HCM, then we
might hold their epistemic status akin to how we regard perceptual context effects:
illusory, in a sense, but part of the normal way that everyday perception functions
(Schwartz, Hsu, and Dayan 2007; Macpherson and Batty 2016; Todorović 2020)—just
exaggerated.
The epistemic status of psychedelic visuals is thus improved by disentangling
them from the lousy epistemic reputation of hallucinations, seeing their status instead
as roughly on par with garden variety illusions, which brings them much closer
to the epistemic status of everyday perception, which, of course, is infused with
‘illusory’ elements at every turn (Klüver 1966; Purves, Morgenstern, and Wojtach
2015; Macpherson and Batty 2016). Thus, psychedelic visuals are nothing to fear,
epistemically speaking.
88
2.5.2 New context for old cues
Letheby (2021) provides an argument for what he sees as a chief epistemic virtue of
psychedelics:
1. Psychedelics often facilitate the apprehension of already known facts under
new and distinctive modes of presentation.
2. If one apprehends an already known fact under a new and distinctive mode
of presentation, then one thereby acquires new knowledge of an old fact.
3. Therefore, psychedelics often facilitate the acquisition of new knowledge
of old facts (Letheby 2021).
In terms of HCM, we might say that psychedelics allow old cues to trigger new con-
textual modulations. Furthermore, HCM offers a new way to describe and understand
the old fact that psychedelics can cause people to see things anew, adding greater
specificity to the claim that “psychedelics facilitate the representation of old facts in
new modes by disrupting the brain’s functional architecture” (Letheby 2021). Many
classes of drugs (among other things) can disrupt the brain’s functional architecture.
Psychedelics are uniquely valuable, I argue, because they cause hypersensitivity to
contextual cues, allowing the brain to modulate itself in new ways.
Another epistemic benefit highlighted by Letheby (2021) is that psychedelics might
engender knowledge by acquaintance (Russell 1910); namely, acquaintance with the
fact that our minds have “vast, normally unrealized potential” (Letheby 2021, 184).
Here, I argue that the potential we are acquainted with is the mind’s capacity for
nearly limitless contextual modulation—to combine cues in infinite different ways
producing infinite varieties of percepts and thoughts—and that psychedelics grant
direct acquaintance with this at multiple levels of mental function. HCM also offers a
framework for understanding how psychedelics facilitate epistemic gains that take the
form of knowledge how and knowledge that (Letheby 2021). Within a new “context of
discovery” (Letheby 2021), new insights are made available (knowledge that). Insofar
as learning new (psychological and emotional) skills arises from enhanced contextual
modulation, knowledge how might also be gained via HCM.
89
2.5.3 HCM and psychedelic therapy
HCM can be cashed out in pursuit of a naturalistic, neurocognitive understanding
(Shanon 2002; Letheby 2021) of why psychedelics can have therapeutic benefits. Con-
sider that patients with major depressive disorder (MDD) show reduced (hyposensitive)
contextual modulation in the processing of visual (Salmela et al. 2021; X. M. Song et
al. 2021; Golomb et al. 2009), and emotional (Rottenberg, Gross, and Gotlib 2005)
stimuli. Relatedly, a common symptom of MDD is physical anhedonia (Chapman,
Chapman, and Raulin 1976), the reduction of interest and enjoyment in sensory
stimuli (Shankman et al. 2010; Sagud et al. 2020). Furthermore, MDD patients
show blunted responses to everyday stimuli that convey contextual emotional cues,
including body movements (Kaletsch et al. 2014), speech (Pang et al. 2014), music
(Naseri et al. 2019), and faces (Krause et al. 2021; Bourke, Douglas, and Porter
2010). Conversely, psychedelics appear to do exactly the opposite. Responses to
music, as described above, are enhanced (Bonny and Pahnke 1972; Preller et al. 2017;
Kaelen et al. 2015). There is substantial evidence that psilocybin “revives emotional
responsiveness [to faces] on a neural and psychological level” (Mertens et al. 2020) in
MDD patients (Stroud et al. 2017; Roseman et al. 2018; Mertens et al. 2020; Gill et
al. 2022) as well as healthy volunteers (Grimm et al. 2018). Altered visual perception
of faces under mescaline was reported early on by Klüver (1928): “Human faces seem
to undergo certain changes . . . the features become more sharply defined.” HCM
might thus provide a key insight into the general trajectory by which psychedelics
move patients out of depression; namely, that MDD involves hyposensitive contextual
modulation, while psychedelics perhaps counteract this by inducing hypersensitive
contextual modulation.
As argued above, this neurocognitive understanding has the potential to free
psychedelic therapy from the lousy epistemic reputation of hallucinations. It might
also defuse what Letheby (2021) calls the “Comforting Delusion Objection,” which
holds that therapeutic benefits derive from false beliefs. HCM offers a naturalistic
account of therapeutic benefits that does not rest on the induction of non-naturalistic
90
beliefs.
2.5.4 Vision(s), symbols, and insight
As Letheby (2021) points out, testimonies from patients suggest that psychological
insights and emotional shifts often occur concomitantly with unusual visual experiences,
sometimes taking the form of symbolic imagery or ‘visual parables’: “Visual parables
and metaphors constitute an alteration to perceptual experience, but also an experience
of apparent insight . . . they engender novel understanding or comprehension, or at
least feelings thereof” (Letheby 2021, 43). Recall that the most significant increases in
symbolic thought measured by Wiessner et al. (2022) under LSD came in response to
tasks that used abstract prompts with visual stimuli, which the authors explicitly link
to the therapeutic role of symbolic visions reported by early psychedelic therapists.
There is wide agreement that music listening is critical to psychedelic therapy
(Bonny and Pahnke 1972; Kaelen et al. 2018; Barrett et al. 2018). Moreover, indige-
nous cultures continue to use sound and music—often simple, repetitive motifs and
rhythms—to drive and optimize visual effects and concomitant revelations (Schultes
1969; Schultes, Hofmann, and Rätsch 2006; Carod-Artal 2015; George et al. 2021).
Less discussed in the literature, but relevant to our concerns here, is the role of
external visual stimuli.17 Furnishings and decorations in the treatment room are
standard ‘setting’ protocol in virtually all recent clinical trials (Johnson, Richards,
and Griffiths 2008).18 In many protocols, “the environment in which the treatment is
conducted commonly includes mystical themes, use of psychedelic imagery such as
mandalas” (Hosanagar, Cusimano, and Radhakrishnan 2021). Interestingly, mandalas
were prescribed by Metzner and Leary (1967) to visually drive psychological insight
under psychedelics. “The various symbolic figures on the mandala, with letters, colors
etc., serve as additional anchoring points of associations. The idea is to get as much of
17I thank Dr Kathyrn Swanson for pointing out the importance of physical objects in shaping
psychedelic experience.
18Note that these protocols also encourage the use of eyeshades. This confounds analyses such as
Roseman, Nutt, and Carhart-Harris (2017) which examine the degree to which visual alterations
correlate with positive clinical outcomes.
91
the mental contents on to the two-dimensional surface . . . ” (Metzner and Leary 1967,
emphasis mine). Moreover, indigenous psychedelic rituals place ‘sacred objects’ within
the ritual space, often arranged as shrines; peyote rituals, for example, use an altar
“over which thoughts and visions understood travel to and from God” (Stewart 1961),
while ayahuasca rituals are often held in spaces with intentional geometric layouts
and filled with “visually pleasing objects such as flowers, colorful icons, crystals, and
glowing candles, but also by the calculated use of symbology” (Hartogsohn 2021).
Letheby (2021) characterizes perceptual effects as “vehicles,” the importance of
which, he argues, is “derivative” from the insights they can deliver. However, if the
insights are important, then understanding the vehicles that deliver them would be
even more important. This calls into question the recent tendency to downplay the
importance of psychedelic visuals. It also casts doubt on what Letheby (2021) terms
“Pure Neuroplasticity Theory,” which hopes that all acute subjective psychedelic
effects are epiphenomenal to what are putatively purely neurobiological therapeutic
mechanisms (Hesselgrave et al. 2021; McClure-Begley and Roth 2022; Berg et al.
2022). If therapeutically beneficial experiences come from HCM, perhaps by impacting
the serotonergic substrates of DN, then the visuals might come with the territory.
At the very least, visuals are a perceptual marker that the mind has entered the
therapeutically beneficial mode of functioning.
Metzner and Leary (1967) stated that “A psychedelic experience is a period of
intensely heightened reactivity to sensory stimuli from within and without.” This
perspective appears to have faded, as the role of visual stimuli, external objects, and
visual imagery in psychedelic therapy continues to be downplayed. HCM offers an
antidote to the professional blind eye19 that has been turned to psychedelic visuals,
because it is a naturalistic, neurocognitive explanation capable of linking visuals to
therapeutic benefits.
19Pun intended (wink).
92
2.6 Conclusion
Visual perception is a central element of human mental function. We routinely
speak about our mental operations using visual metaphors.20 Psychedelic OEVs thus
offer an important clue to how psychedelics impact the mind. I have argued that the
phenomenology of OEVs suggests that psychedelic drugs impact contextual modulation
such that percepts are hypermodulated in response to the usual contextual cues.
The implications of the HCM understanding of psychedelics are threefold. First,
HCM suggests that psychedelic effects on visual perception are congruent with effects
on cognition. Second, understanding psychedelics in terms of HCM can serve as a basis
for a neurocognitive account of psychedelic therapy, whereby psychological insights
are more likely to arise from a sea of exaggerated responses to external and internal
cues. Third, HCM might relieve the epistemic stigma associated with visual effects,
as both visual and non-visual psychedelic effects can be understood within a unified,
naturalistic, neurocognitive framework.
HCM also sketches an empirical research programme, with OEVs as the phenomena
of interest, in which the fundamentals of psychedelic effects on low-level perceptual
processes could be studied using classic paradigms and modeled using canonical neural
computations like divisive normalization to understand how psychedelics alter vision
and other mental functions.
There is much to be gained by seeing psychedelic visuals in context.
20We focus on a topic and illustrate our ideas to show how they make sense; when we have insight,
we see things in a new light and are enlightened; ideas can be illuminated making their relevance
apparent; we view facts through the lens of a conceptual construct; a shift in understanding activates
a new perspective, outlook, or worldview that reveals things previously overlooked. I trust that you
see my point, or at least the general picture, because seeing is believing!
93
Image credits
Figures 1 & 2 panel A are adapted from Todorović (2020).
Figures 1 & 2 panel B are adapted from Wikimedia commons
https://commons.wikimedia.org/wiki/File:M%C3%BCller-Lyer_illusion.svg (Licensed
under Creative Commons CC BY-SA 3.0)
Figures 1 & 2 panel C are adapted from Wikimedia commons
https://commons.wikimedia.org/wiki/File:Mond-vergleich.svg (Public Domain).
Figures 1 & 2 panel D adapted from Wikimedia commons
https://commons.wikimedia.org/wiki/File:Zollner_illusion.svg (Licensed under Cre-
ative Commons CC BY-SA 3.0)
Figures 1 & 2 panel E adapted from Wikimedia commons
https://commons.wikimedia.org/wiki/File:Hering_illusion.svg (Licensed under Cre-
ative Commons CC BY-SA 3.0)
Figures 1 & 2 panel F adapted from Schwartz, Hsu, and Dayan (2007).
94
Chapter 3
Enhanced visual contrast
suppression during peak psilocybin
effects: A psychophysical study
“Most investigators have been more interested in the vision-producing effects . . .
than in the sensory changes caused by the drug. But an analysis of only the
visions would leave our account of the optical effects incomplete . . . we should
not arrive at a proper understanding of the way the visual world appears to
the subjects. . . . An enhancement of contrast phenomena seems to be the rule.
Very pronounced simultaneous contrast is found; the contours of the objects
become sharp and well-defined.”
–Heinrich Klüver (1928, 33–35)
95
ABSTRACT
In visual psychophysics, an effect known as surround suppression occurs when
the apparent contrast of a center stimulus is reduced when presented within
a surrounding higher-contrast stimulus. Many key aspects of visual percep-
tion invoke surround suppression—texture segregation, perceptual constan-
cies, figure-ground segmentation, contour integration, motion detection, and
depth perception—yet the neuromodulatory processes involved remain unclear.
Psilocybin, a drug garnering renewed interest for its clinical potential, is a
serotonergic psychedelic compound known for its robust effects on visual per-
ception, particularly texture, color, object, and motion perception. We asked
whether surround suppression is altered under peak effects of psilocybin. Using
a contrast-matching task with different center-surround stimulus configurations,
we measured surround suppression after a high dose of psilocybin compared
with placebo. Our results, while preliminary, suggest that psilocybin enhanced
surround suppression. Psilocybin produced no change in contrast discrimination
when stimuli were presented without a surround, suggesting that psilocybin
selectively impacted center-surround modulation of perceived contrast, which
is thought to reflect cortical rather than retinal aspects of visual perception.
Furthermore, we found that the intensity of subjective ‘psychedelic visuals’
induced by psilocybin correlated positively with the magnitude of surround
suppression. We note the potential impact of our findings for psychiatry, given
that recent studies have demonstrated weakened visual surround suppression
in patients with major depressive disorder, for which psilocybin has recently
been identified as a breakthrough therapy. Our finding is thus immediately
relevant to understanding the linkages between serotonin, surround suppression,
the visual effects of psilocybin, mental health disorders, and the mechanisms of
psychedelic therapies.1
3.1 Introduction
In visual perception, the appearance of a given object is influenced by neighboring
objects (Schwartz, Hsu, and Dayan 2007). These interactions, known as spatial
context effects, produce many well-known visual illusions (Ebbinghaus 1902; Gibson
and Radner 1937; Fraser 1908). Surround suppression of apparent contrast—when
the perceived contrast of a target stimulus is reduced (suppressed) by the presence
1Link Swanson and Michael-Paul Schallmo designed the experiment. Jessica Nielson, Sophia
Jungers, and Link Swanson carried out the experiment. Kathryn Cullen and Ranji Varghese helped
with participant safety monitoring. Link Swanson analyzed the data and wrote the manuscript.
96
of higher-contrast surrounding stimuli—has been robustly demonstrated using psy-
chophysics (Ejima and Takahashi 1985; Chubb, Sperling, and Solomon 1989; Cannon
and Fullenkamp 1991; Snowden and Hammett 1998; Olzak and Laurinen 1999; Petrov
and McKee 2006; Xing and Heeger 2000, 2001; C. Yu, Klein, and Levi 2001; Schallmo
and Murray 2016). However, it remains unclear which neuromodulator systems are
critically involved in surround suppression. One approach to investigate the role of
neuromodulators in visual perception is to use drugs as pharmacological probes paired
with visual tasks. Previous inquiries into surround suppression have probed GABAer-
gic pathways with the drugs ethanol (Read et al. 2015) and lorazepam (Schallmo et
al. 2018); cholinergic pathways with donepezil (Gratton et al. 2017; Kosovicheva et al.
2012) and caffeine (Nguyen et al. 2018); dopaminergic pathways with bromocriptine
(Gratton et al. 2017); and noradrenergic pathways with guanfacine (Gratton et al.
2017). The GABA system has been the target of many studies of surround suppression
in humans (Cook, Hammett, and Larsson 2016; Read et al. 2015; C. Song et al. 2017;
Loon et al. 2012; Yoon et al. 2010; Schallmo et al. 2018, 2020) and animal models
(Adesnik et al. 2012; Haider et al. 2010; Ma et al. 2010; Nienborg et al. 2013; Ozeki
et al. 2004, 2009b; Sato et al. 2016). However, recent work has cast doubt on the
hypothesis that surround suppression is directly mediated by GABA (Read et al. 2015;
Schallmo et al. 2018; Schach, Surges, and Helmstaedter 2021). To our knowledge,
no studies have paired psychophysics with serotonin (5-HT) agonist drugs to probe
serotonergic pathways for their possible role in surround suppression in humans. The
present study aims to address this gap in knowledge.
The psychedelic drug psilocybin (4-phosphoryloxy-N,N-dimethyltryptamine) is a
naturally-occurring tryptamine compound (Figure 3.1A) found in at least 144 different
species of mushrooms worldwide (Gastón Guzmán 2005; Gaston Guzmán, Allen, and
Gartz 1998) (see Figure 3.1 C). Psilocybin, which is rapidly metabolized into psilocin
(Figure 3.1 B) in the body, is a serotonin 2A (5-HT2A) receptor agonist that can alter
perception, emotion, cognition, and sense of self (Studerus, Gamma, and Vollenweider
2010; Hirschfeld and Schmidt 2021; Holze et al. 2022). Visual effects (‘psychedelic
visuals’) are the most common and prominent dose-dependent subjective effects of
97
Figure 3.1: Psilocybin (A) and psilocin (B) are found in more than one-hundred
species of mushrooms (examples shown in C). Psilocybin is rapidly metabolized into
psilocin during metabolism. Mushroom illustrations by Elmer W. Smith (Schultes
and Smith 1976)
98
psilocybin (Studerus, Gamma, and Vollenweider 2010; Carbonaro et al. 2018; Bayne
and Carter 2018; Vollenweider and Preller 2020; Hirschfeld and Schmidt 2021; Aday
et al. 2021; Holze et al. 2022). Relatively few studies have investigated psilocybin
using visual psychophysics (Aday et al. 2021). Psilocybin appears to impact only
select visual processes, while others are left intact (Carter et al. 2004; Kometer et al.
2011; Carbonaro et al. 2018; Aday et al. 2021). The phenomenology of psilocybin’s
visual effects can include alterations in the appearance of textures, the shapes/sizes of
objects, motion, and colors—the visual field can take on a kind of ‘fluidity’, exhibiting
animated dynamics described as ‘pulsing’, ‘glowing’, ‘breathing’, ‘shifting’, ‘dancing’,
etc (see Chapters 1 and 2). Given this visual phenomenology, it is plausible that
center-surround interactions are in some way impacted by psilocybin, as surround
suppression plays a critical role in certain visual processes (Nurminen and Angelucci
2014; Angelucci et al. 2017), including visual saliency and pop-out (Knierim and
Essen 1992), perception of object boundaries (Nothdurft, Gallant, and Van Essen
2000), perceptual constancies (Allman, Miezin, and McGuinness 1985), figure-ground
segmentation (Lamme 1995; Supèr, Romeo, and Keil 2010), contour integration (Field,
Hayes, and Hess 1993; Kapadia et al. 1995; Polat et al. 1998; Hess and Field 1999),
and motion detection (Jones et al. 2001). To our knowledge, no previous study has
measured visual contrast suppression under psilocybin in humans.2 Thus, a closer
examination of the links between surround suppression and the subjective effects of
psilocybin is warranted.
Psilocybin-assisted therapy has rapid and enduring antidepressant effects in patents
with major depressive disorder (MDD) (Davis et al. 2021; Ross et al. 2016; Griffiths
et al. 2016; Carhart-Harris, Bolstridge, et al. 2018; Gukasyan et al. 2022). Visual
surround suppression is known to be weaker in patients with MDD (Golomb et al. 2009;
Salmela et al. 2021), a difference that normalizes somewhat with remission (Salmela
et al. 2021). The neuromodulatory causes of weakened surround suppression in MDD
are unknown. The links between serotonin and MDD are widely assumed but not well
2Michaiel, Parker, and Niell (2019) found reduced surround suppression in mice after administration
of the psychedelic drug DOI. Relatedly, Azimi et al. (2020) provide evidence for the role of 5-HT2A
in mouse visual gain control.
99
understood (Cowen and Browning 2015; Carhart-Harris and Nutt 2017). Knowing the
impact of psilocybin on surround suppression could thus address multiple knowledge
gaps in our understanding of the links between MDD, serotonin, visual surround
suppression, and the antidepressant mechanisms of psilocybin-assisted therapy.
In spite of its therapeutic promise, psilocybin’s subjective effects have paradoxically
(Osmond and Smythies 1952) been characterized as “a schizophrenia-like psychosis”
(Vollenweider et al. 1998, 3897). This claim has remained controversial for nearly
a century (see Chapter 1) and has been called into question by recent comparisons
(Leptourgos et al. 2020). Multiple investigations have found weakened visual surround
suppression in schizophrenic patients compared with healthy controls (Dakin, Carlin,
and Hemsley 2005; Tadin et al. 2006; Yoon et al. 2009; Yang et al. 2012; Tibber et al.
2013; Serrano-Pedraza et al. 2014; Schallmo, Sponheim, and Olman 2015; V. J. Pokorny
et al. 2019; Linares et al. 2020). Understanding the impact of psilocybin on surround
suppression could thus illuminate the debates over the exact relationship between
psychedelics and psychosis, as well as the possible role of serotonergic neuromodulation
in schizophrenia.
Taken together, the above gaps in knowledge indicate a pressing need to study the
impact of psilocybin on visual surround suppression. Here, we addressed this need
using a psychophysical contrast-matching task to examine suppression of perceived
contrast using different center-surround stimulus configurations under peak effects of
a high dose of psilocybin in healthy human participants. These results were compared
with placebo in a double-blind crossover (i.e., within-subjects) experimental design.
Our results indicated that psilocybin selectively enhanced visual surround suppression
without impacting generalized contrast discrimination. Furthermore, we found that
the magnitude of surround suppression correlated significantly with the intensity of
the subjective psychedelic visual effects.
100
3.2 Methods
This study was conducted as part of a double-blind, randomized, placebo-controlled
clinical trial at The University of Minnesota Health Clinical Research Unit (M Health
CRU) in Minneapolis, Minnesota.3
3.2.1 Participants
Adults aged 25 to 38 years with no current or previous mental health diagnosis, not
currently using any prescription medications, with at least one reported previous
experience taking a moderate to high dose of psilocybin, and without histories of
psychotic disorder were eligible to participate. Six healthy observers with normal or
corrected-to-normal vision (3 male and 3 female, mean age 33 years) were included in
this study. All participants gave written informed consent and were compensated at a
rate of $20 per hour. Experimental protocols were approved by the Institutional Review
Board of the University of Minnesota. All experiments were performed in accordance
with approved guidelines and regulations, including approval from the United States
Food and Drug Administration (FDA) and Drug Enforcement Administration (DEA).
3.2.2 Crossover design
Participants completed two drug dosing sessions—one day with active drug (25mg
psilocybin); one day with placebo (100mg niacin)—two weeks apart. Doses of psilocy-
bin and placebo were administered in identical opaque gelatin capsules in the context
of a comfortable, decorated hospital room (approximately 9 hours). Participants were
randomized into groups where group A received psilocybin on dosing day 1 and placebo
on dosing day 2, while group B received the drugs in the reverse order. Participants,
experimenters, and all study staff were blind to which drug was administered on any
given experimental session. See Table 3.1 for an overview of the study design.
3ClinicalTrials.gov Identifier: NCT04424225
101
Table 3.1: The within-subjects crossover design. Each dosing session was separated
by two weeks. For the Dosing 1 and Dosing 2 sessions, participants received either
the active drug (psilocybin, 25mg, oral) or the placebo (niacin, 100mg, oral). A
within-subjects crossover design, all participants completed all sessions. Participants
in Group A were administered psilocybin on their Dosing 1 session. Participants
in Group B were administered psilocybin on their Dosing 2 session. Participants
were assigned to the groups using an adaptive minimization randomization method
to balance age and gender between the groups. Participants, experimenters, and all
study staff were blind to the group assignments.
Group A Group B
Session
Dosing 1 psilocybin plaebo
Dosing 2 placebo psilocybin
3.2.3 Apparatus
Visual stimuli were presented on a Dell P2319H 23-in LED-backlit monitor (1920
× 1080 pixels, 60 Hz refresh rate) at a viewing distance of 60cm. The monitor was
calibrated using a spectrophotometer. Mean luminance was 111.2 cd/m2. Stimuli
were generated and presented using PsychoPy (Peirce et al. 2019) version 2021.2.3.
3.2.4 Stimuli
Stimuli consisted of annulus sinusoidal luminance modulation gratings presented on a
mean gray background at 3° eccentricity to the left and right of fixation. The central
fixation mark was constructed according to Thaler et al. (2013) (shown in Figure
3.2). The contrast of one grating (target) was fixed at 50%, while the contrast of the
other grating (reference) varied across trials using an adaptive staircase (detailed in
Section 3.2.5). Both gratings had an outer diameter of 2°, a spatial frequency of 1.1
cycles/°, and always shared the same orientation, which varied from trial-to-trial in
counterbalanced randomized instances of 0°, 45°, 90°, or 135° orientation. In a subset
of stimulus conditions (see Table 3.2 and Figure 3.2), the target grating was presented
within a larger (4° outer diameter) annular grating, referred to as the surround. The
contrast of the surround was fixed at 100% and its spatial frequency and orientation
were identical to the target grating. A 0.5° mean luminance ‘gap’ was placed between
102
the target grating and the surround grating to prevent brightness induction effects
(see C. Yu, Klein, and Levi 2001; Schallmo, Sponheim, and Olman 2015) as well as to
help the observers attend only to the central grating in making their judgments (Xing
and Heeger 2001). The reference grating was always presented with no surround.
There were 3 experimental conditions: No Surround (NS), Orthogonal Surround
(OS), and Parallel Surround (PS). See Figure 3.2 and Table 3.2 for details.
3.2.5 Paradigm
Participants completed the following visual tasks after a 3-hour wash-in period post
drug administration. We chose the 3-hour timepoint because it roughly corresponds to
the latter half of peak plasma concentration (Brown et al. 2017; Holze et al. 2022) and
subjective effects (Carbonaro et al. 2018; Holze et al. 2022) of psilocybin, allowing
participants to acclimate to peak subjective effects before starting the task.
We used a two-alternative forced-choice (2AFC) visual psychophysics task designed
to measure the point of subjective equality (PSE) at which the difference in contrast
between the target and reference gratings was imperceptible to the observer. Partici-
pants were asked to choose which circular grating appeared higher in contrast (target
or reference, ignoring the surround). Each trial began with a fixation mark presented
alone for 500 ms, after which the grating stimuli appeared on either side of fixation.
Each trial ended when the participant made a key press to indicate which stimulus
was higher contrast. Response time was not limited.
Two staircases per condition (target left and target right; 40 trials each) were
presented in a randomized, intermixed order. The adaptive staircase (implemented
using PsychoPy’s (Peirce et al. 2019) default StairHandler() class configured with
a one-up, one-down rule) adjusted the contrast of the reference grating on every trial
in order to converge on the contrast level at which the observer produced 50% correct
responses. See Figure 3.2.
To ensure task comprehension, subjects verbally confirmed their understanding of
the task and completed 7 practice trials (one sample trial from each staircase plus
one catch trial) before beginning the main experiment. In addition, 40 catch trials
103
Figure 3.2: Samples of stimuli used in the experiment. A is an example of a trial from
the no-surround staircases. B is an example of a catch trial, in which the reference
stimulus appeared with 80% Michelson contrast. (C-D) are example trials from the
orthogonal surround staircases. (E-F) are example trials from the parallel surround
staircases.
104
Table 3.2: The six staircases comprising the three stimulus conditions. Forty trials
of each type were presented during one full experimental run. An adaptive staircase
dynamically adjusted the contrast of the reference stimulus on every trial. All trials
from all staircases were presented intermixed and counterbalanced in random order.
The target and surround stimuli were presented in four different orientations (0°, 45°,
90°, or 135°; ten trials of each orientation in each staircase) in random order.
Target position Surround Example N trials
Condition
NS-L left absent Figure 3.2 A 40
NS-R right absent Figure 3.2 A 40
OS-L left orthogonal Figure 3.2 C 40
OS-R right orthogonal Figure 3.2 D 40
PS-L left parallel Figure 3.2 F 40
PS-R right parallel Figure 3.2 E 40
(large contrast difference, low difficulty) were included to assess off-task performance.
Reference contrast in the catch trials was fixed at 80% (i.e., no staircase was used).
Catch trial response accuracy was analyzed to ensure that the participant understood
and performed the task correctly. See Section 3.3.2 for detailed catch trial results.
Total task duration was approximately 30 minutes.
3.2.6 Subjective drug effects
The Altered States of Consciousness questionnaire (5D-ASC) scale (Dittrich 1998;
Studerus, Gamma, and Vollenweider 2010) was used to assess the overall effects of
drug on various elements of subjective experience. 5D-ASC is a well-validated 94-item
self-rating visual analog scale (VAS) that assesses five “dimensions” of altered states of
consciousness: (1) Oceanic Boundlessness, (2) Anxious Ego Dissolution, (3) Visionary
Restructuralization, (4) Auditory Alterations and (5) Vigilance Reduction (Dittrich
1998). Studerus, Gamma, and Vollenweider (2010) used confirmatory factor analysis
to identify 11 subscales of 5D-ASC (11D-ASC): (1) Experience of Unity, (2) Spiritual
Experience, (3) Blissful State, (4) Insightfulness, (5) Disembodiment, (6) Impaired
Control and Cognition, (7) Anxiety, (8) Complex Imagery, (9) Elementary Imagery,
(10) Audio-Visual Synesthesia, and (11) Changed Meaning of Percepts. Both the
105
5D-ASC and its 11D-ASC subset are routinely used to assess subjective effects in
studies that use psychedelic drugs (Studerus, Gamma, and Vollenweider 2010, 2010;
Preller et al. 2017; Liechti, Dolder, and Schmid 2017; Carbonaro et al. 2018; Holze
et al. 2019, 2020, 2022; Hirschfeld and Schmidt 2021). Importantly, the 5D-ASC
detects the unique mind- and perception-altering effects of serotonergic psychedelic
drug effects in particular, as it is sensitive enough to distinguish these from other
psychoactive effects such as those produced by D-amphetamine or MDMA (Holze
et al. 2020). Of particular interest to the present study is the 5D-ASC Visionary
Restructuralization dimension and its 11D-ASC subscales (8) Complex Imagery, (9)
Elementary Imagery, (10) Audio-Visual Synesthesia, and (11) Changed Meaning of
Percepts, which are comprised of questions that assess alterations to visual experience.
All participants in this study completed all 94 items of the 5D-ASC questionnaire
to retrospectively rate drug effects 24h after their psilocybin and placebo dosing
sessions.
3.2.7 First-person written narratives
Participants submitted written ‘digital journal’ entries via webform in response to the
following prompt 24 hours after each dosing session.
Please fill out this digital journal to help us understand what your experience
during the dosing session was like, from your perspective. In your own words,
please spend 30-60 minutes writing about your experience during the testing
session you participated in this week. This is similar to a “trip report” detailing
the content of your experience, and any thoughts or feelings this experience
brought up for you.
3.2.8 Statistical analyses
The point of subjective equality (PSE) for each observer was calculated independently
for each of the six staircases by fitting a Logistic function to the response data.4
Surround suppression illusion strength was quantified by subtracting the veridical
4We obtained the PSE threshold using the data.FitLogistic() and
data.functionFromStaircase() functions provided by PsychoPy (Peirce and MacAskill
2018). Guess rate and lapse rate were set at 4%.
106
contrast of the target stimulus grating (50.0%) from the PSE value. Data from
placebo and psilocybin sessions were analyzed separately, and then compared within
participants. Differences in PSE values between psilocybin and placebo were compared
in a repeated measures analysis of variance (ANOVA) using drug and stimulus condition
as within-subjects factors. 5D-ASC ratings between psilocybin and placebo were
compared in a repeated measures ANOVA using drug and ASC subscale/dimension
as within-subjects factors. Both ANOVAs were followed-up with post hoc pairwise t
test comparisons.
Correlation between PSE and 5D-ASC ratings were calculated using the rmcorr
technique (Bakdash and Marusich 2017; Bland and Altman 1995a, 1995b) and followed
up with pairwise biweight midcorrelation tests (Langfelder and Horvath 2012) using
the difference (psilocybin minus placebo) for threshold and rating score pairs for each
participant. We chose these methods because they do not require first averaging the
data and avoid violating independence assumptions, making them ideal (and more
sensitive) for paired repeated-measures data (Bakdash and Marusich 2017).
All statistical analyses were performed using the Pingouin package for Python
(Vallat 2018). The criterion for significance was p < 0.05.
3.3 Results
To examine the impact of psilocybin on surround suppression of perceived contrast, we
measured PSEs in observers under psilocybin and placebo in three stimulus conditions
(NS, OS, PS; see Section 3.2 Figure 3.2, Table 3.2, and Table 3.1). In addition,
we measured the subjective effects of psilocybin and placebo using the 5D-ASC
psychometric instrument (see Section 3.2.6). The following subsections provide results
from the different comparisons and correlations across these data points.
3.3.1 Effect of drug on PSE
Repeated measures ANOVA performed on the PSE values revealed a main effect of
drug that reached significance (F 1,5 = 7.294, p = 0.043, nG2 = 0.128). The main
107
Table 3.3: Results from a two-way repeated measures ANOVA comparing the effect of
drug, stimulus condition, and their interaction on measured PSE values.
Sum Sq. df1 df2 Mean Sq. F p ng2
Source
drug 180.66 1 5 180.66 7.29 0.043 0.128
condition 759.56 2 10 379.78 8.82 0.006 0.381
drug * condition 73.67 2 10 36.84 3.00 0.095 0.056
effect of stimulus condition reached significance, consistent with surround suppression
(F 2,10 = 8.822, p = 0.006, nG2 = 0.381). The interaction effect of drug × condition
did not reach significance; however, a notable trend effect was observed (F 2,10 = 3.0,
p = 0.095, nG2 = 0.056).
A Shapiro-Wilk test did not show evidence of non-normality in the placebo (W =
0.983, p = 0.848) or the psilocybin (W = 0.971, p = 0.459) PSE data.
Subsequent post hoc pairwise t test (two-sided) comparisons, in which the PSEs
from all stimulus conditions were compared between psilocybin and placebo confirmed
the significant ANOVA main effect of drug on PSE (t5 = 2.701, p = 0.043, Bayes factor
= 2.342). Further t test comparisons were performed using PSEs from each stimulus
condition. For the No Surround (NS) condition, drug did not have a significant
effect on PSE, indicating that psilocybin did not significantly impact general contrast
perception when stimuli were presented without a surround (t5 = 0.665, p = 0.535,
Bayes factor = 0.446). Post hoc comparisons revealed a trend effect in the Orthogonal
Surround (OS) condition (t5 = 2.467, p = 0.057, Bayes factor = 1.905) and a trend
effect in the Parallel Surround (PS) condition (t5 = 2.268, p = 0.073, Bayes factor =
1.594).
Mean PSE data from psilocybin compared with placebo sessions indicate that
psilocybin enhanced surround suppression. Mean PSE values were more negative
under psilocybin (i.e., the strength of the illusion was greater, observers perceived
contrast less veridically, compared with placebo) for the OS and PS, but not for the
NS, stimulus conditions. Figure 3.3 A displays the magnitude of illusory suppression
of PSE (defined as the PSE minus the target contrast of 50.0% Michelson contrast).
108
Figure 3.3: Surround suppression under psilocybin (blue) and placebo (orange), shown
as the difference between observers’ PSEs and the veridical contrast of the target
stimulus (PSE minus 50.0%) for the No Surround (NS), Orthogonal Surround (OS),
and Parallel Surround (PS) stimulus conditions. Panel A shows suppression for
individual observers in each stimulus condition. Panel B shows means that have been
normalized according to the technique in Morey (2008) for within-subjects data. Error
bars represent standard error of the mean (SEM).
Table 3.4: Mean PSEs in each stimulus condition measured under psilocybin and
placebo.
mean ± SEM
placebo psilocybin
condition
NS 48.732 ± 2.102 48.105 ± 2.086
OS 45.844 ± 1.033 40.507 ± 1.322
PS 40.914 ± 1.854 33.437 ± 2.748
109
The PSE thresholds measured in the no-surround (NS) condition fell close to the
target stimulus veridical value of 50.0% Michelson contrast. Importantly, this was true
for placebo (NS = 48.73% contrast) as well as for psilocybin (NS = 48.1% contrast),
suggesting that psilocybin did not significantly impact perceived contrast when no
surrounding stimuli were present. We further confirmed this using a two-sided paired
t-test, which found no significant differences between psilocybin and placebo in the
no-surround (NS) condition (t5 = -0.6655, p = 0.5352, d = 0.2876). This supports
the notion that the trends we observed in the OS and PS surround conditions are the
result of psilocybin’s selective impact on center-surround interactions and not due to
other perturbations in contrast discrimination, motor function, or cognitive functions.
For the orthogonal surround (OS) condition under placebo, the presence of the
surround stimulus reduced the mean PSE by approximately 4.16% contrast (sup-
pressing the perceived contrast from veridical 50.0% contrast to a mean PSE of
45.84% contrast). Under psilocybin, the magnitude of OS suppression was larger than
placebo—the orthogonal surround reduced the mean PSE by approximately 9.5%
contrast (suppressing the perceived contrast from veridical 50.0% contrast to a mean
PSE of 40.5% contrast).
For the parallel surround (PS) condition under placebo, the presence of the surround
stimuli reduced the mean PSE by approximately 9.09% contrast (suppressing the
perceived contrast from veridical 50.0% contrast to a PSE of 40.91% contrast). Under
psilocybin, the magnitude of PS suppression was larger than placebo—the parallel
surround reduced the mean PSE by approximately 16.57% contrast (suppressing the
perceived contrast from veridical 50.0% contrast to a mean PSE of 33.43% contrast).
The effect of psilocybin on PSE for each stimulus condition was quantified as the
psilocybin PSE minus the placebo PSE. The mean effect of psilocybin was a shift
in the PSE of -7.48% contrast in the parallel surround (PS) condition and -5.34%
contrast in the orthogonal surround (OS) condition. By comparison, when stimuli
were presented with no surrounds (NS), the difference in the PSE from placebo to
psilocybin was -0.63%. Figure 3.4 summarizes our results in terms of the effect of
psilocybin on PSE (drug minus placebo) for each stimulus condition. These results
110
Figure 3.4: Within-subjects effects of the drug were calculated by subtracting the PSE
measured under placebo from the PSE measured under psilocybin for each stimulus
condition. Panel A shows the effect of psilocybin on PSE for each individual observer.
Panel B shows the mean effect for each stimulus condition. Error bars represent
standard error of the mean (SEM).
111
suggest that psilocybin enhanced the magnitude of visual surround suppression while
general contrast discrimination remained unaffected by the drug.
3.3.2 Catch trials
Figure 3.5: Catch trial scores. Values represent the percentage of correct responses
to 40 catch trials. A: Catch trial scores for each individual observer. B: Mean catch
trial scores. Error bars represent standard error of the mean (SEM).
Each observer completed 40 ‘catch’ trials intermixed with all of the other trails under
both placebo and psilocybin (see Section 3.2 and Figure 3.2 F). Catch trials—contrast
discrimination tasks with very low difficulty—were used to measure whether the
observer was completing trials with sufficient attention, understanding of the task,
and ability to judge visual contrast at a basic level.
Catch trial accuracy was high in both placebo (mean = 99.17%, SD = 1.29%) and
drug sessions (mean = 97.92%, SD = 1.88%); paired t-test (two-sided), t5 = 2.24, p =
0.08—indicating that psilocybin did not significantly interfere with the participants’
ability to perform the task correctly. Figure 3.5 summarizes the results of catch trials.
112
3.3.3 Subjective effects of drug
Figure 3.6: Subjective effects of drug as measured by the 5-Dimensional Altered
States of Consciousness (5D-ASC) scale and its lower-level 11D-ASC subscales. Panels
A and B show individual participant rating scores for 5D-ASC dimensions and
11D-ASC subscales, respectively, in reference to their psilocybin (blue lines) and
placebo (orange lines) dosing sessions. Panels C and D show mean scores. Error
bars represent standard error of the mean (SEM). Post hoc pairwise comparisons of
scores under each drug revealed significant (p < 0.05) differences for the 5D-ASC
dimensions Oceanic Boundlessness and Visionary Restructuralization; and the 11D-
ASC subscales Blissful State, Spiritual Experience, Complex Imagery, Experience of
Unity, Insightfulness, Disembodiment, Audio-Visual Synesthesia, Changed Meaning of
Percepts, and Elementary Imagery.
Psilocybin produced robust effects on subjective experience, as indicated by a
significant main effect of drug (F 1,5 = 20.109, p = 0.006, nG2 = 0.518) in a repeated
measures (drug × subscale) ANOVA on 5D-ASC score. There was also a significant
main effect of subscale (F 4,20 = 14.051, p < 0.001, nG2 = 0.364) and a significant drug
× subscale interaction (F 4,20 = 8.484, p < 0.001, nG2 = 0.342). Subsequent post hoc
pairwise comparisons revealed significantly (p < 0.05) higher ratings under psilocy-
113
bin compared with placebo for the 5D-ASC dimensions Oceanic Boundlessness and
Visionary Restructuralization. When comparing along the lower-level 11D subscales
(see Section 3.2.6), significant (p < 0.05) differences were found for scores in Blissful
State, Spiritual Experience, Complex Imagery, Experience of Unity, Insightfulness,
Disembodiment, Audio-Visual Synesthesia, Changed Meaning of Percepts, and Ele-
mentary Imagery, affirming that psilocybin produced marked alterations of mind and
perception. See Figure 3.6 for data visualization.
3.3.4 Subjective drug effects and PSE
Next, we asked whether the magnitude of surround suppression correlated in with the
intensity of the psychedelic effects reported by the participants. To assess correlation
between rating scale scores and PSEs, we used repeated measures correlation (rmcorr),
“a statistical technique for determining the common within-individual association for
paired measures assessed on two or more occasions for multiple individuals” (Bakdash
and Marusich 2017; Bland and Altman 1995a, 1995b).5 We performed rmcorr analyses
by pairing each participant’s PSE thresholds with their 5D-ASC ratings of subjective
effects from each dosing session. Rmcorr allowed us to assess the links between surround
suppression and particular subjective drug effects in a drug-agnostic fashion, regardless
of which drug (psilocybin or placebo) induced the effects. We reasoned that such an
analysis would be informative, given that the subjective effects of psilocybin span
perceptual, emotional, and cognitive domains (Studerus, Gamma, and Vollenweider
2010; Carbonaro et al. 2018; Holze et al. 2022) and can be dependent on many
extra-pharmacological factors (Studerus et al. 2012).
On the 5D-ASC scale, rmcorr revealed statistically significant inverse correlations
between PSEs and 5D-ASC scores for the data points (OS, Visionary Restructuraliza-
tion) (r = -0.856, p = 0.014) and (PS, Visionary Restructuralization) (r = -0.836, p
= 0.019); higher rating scores (more intense subjective drug effect) were associated
5“Rmcorr accounts for non-independence among observations using analysis of covariance (AN-
COVA) to statistically adjust for inter-individual variability. By removing measured variance
between-participants, rmcorr provides the best linear fit for each participant using parallel regression
lines (the same slope) with varying intercepts” (Bakdash and Marusich 2017).
114
Table 3.5: Results from rmcorr repeated measures within-participants correlation of
PSE threshold and ASC subscale scores under psilocybin and placebo.
dof r pval
condition subscale
OS Visionary Restructuralization 5 -0.856 0.014
Elementary Imagery 5 -0.824 0.023
Audio-Visual Synesthesia 5 -0.822 0.023
Complex Imagery 5 -0.770 0.043
Changed Meaning of Percepts 5 -0.759 0.048
PS Audio-Visual Synesthesia 5 -0.942 0.001
Visionary Restructuralization 5 -0.836 0.019
Elementary Imagery 5 -0.802 0.030
Complex Imagery 5 -0.784 0.037
Blissful State 5 -0.772 0.042
Changed Meaning of Percepts 5 -0.757 0.049
Figure 3.7: Rmcorr plot showing each individual’s 5D-ASC dimension scores from
placebo and psilocybin as a function of their PSE thresholds in each stimulus condition.
The lines show the rmcorr slope fit in relation to each participant’s data points.
115
with greater surround suppression (lower PSEs). At the level of 5D-ASC dimensions,
PSE did not significantly correlate with any other type of subjective effects, nor with
the overall Total Score.
We performed an additional rmcorr analysis using the 11D-ASC scoring method
(Studerus, Gamma, and Vollenweider 2010), which includes eleven lower-level subscales
comprised of items from three of the five dimensions of the 5D-ASC scale (see Methods
section 3.2.6). This analysis revealed significant inverse correlations at the data points
(PS, Audio-Visual Synesthesia) (r = -0.942, p = 0.001), (OS, Elementary Imagery) (r
= -0.824, p = 0.023), (OS, Audio-Visual Synesthesia) (r = -0.822, p = 0.023), (PS,
Elementary Imagery) (r = -0.802, p = 0.03), (PS, Complex Imagery) (r = -0.784,
p = 0.037), (PS, Blissful State) (r = -0.772, p = 0.042), (OS, Complex Imagery) (r
= -0.77, p = 0.043), (OS, Changed Meaning of Percepts) (r = -0.759, p = 0.048),
and (PS, Changed Meaning of Percepts) (r = -0.757, p = 0.049). Taken together,
these results indicate that, only for these specific 11D-ASC subscales, higher rating
scores (more intense subjective drug effect) were associated with greater surround
suppression (lower PSEs in the OS or PS, but not the NS, stimulus conditions).
For each correlation that reached significance in the rmcorr analysis, subsequent
post hoc pairwise correlations were performed using robust biweight midcorrelation
(Langfelder and Horvath 2012) on the (psilocybin minus placebo) difference values
for each data point, which reached significance for the data points (PS, Audio-Visual
Synesthesia) (r = -0.898, p = 0.008) and (PS, Complex Imagery) (r = -0.732, p =
0.049). The data points for (OS, Visionary Restructuralization) (r = -0.645, p =
0.084), (PS, Visionary Restructuralization) (r = -0.633, p = 0.089), (OS, Elementary
Imagery) (r = -0.618, p = 0.095), (OS, Audio-Visual Synesthesia) (r = -0.704, p =
0.059), (PS, Elementary Imagery) (r = -0.584, p = 0.112), (PS, Blissful State) (r =
-0.524, p = 0.143), (OS, Complex Imagery) (r = -0.598, p = 0.105), (OS, Changed
Meaning of Percepts) (r = -0.439, p = 0.192), and (PS, Changed Meaning of Percepts)
(r = -0.458, p = 0.18) did not reach significance in the post-hoc biweight midcorrelation
tests. This discrepency is not unexpected, as rmcorr is considered more sensitive than
other available correlations when using within-participants repeated-measures data
116
Figure 3.8: Rmcorr plot showing PSE as a function of subjective drug effects rating
scores on subscales from the Visionary Restructuralization dimension for each subject
in each stimulus condition. The lines show the rmcorr slope fit in relation to each
participant’s two data points (placebo and psilocybin).
117
Table 3.6: Questionnaire items from subscales in the Visionary Restructuralization
dimension that reached significant correlation with at least one stimulus condition in
the rmcorr analysis. Mean scores for placebo (Pla) and psilocybin (Psil) are shown in
the right-hand columns.
text Pla Psil
subscale item
Visionary Restructuralization 7 I saw things I knew were not real. 0 83
58 Things came to my mind that I
thought long forgotten.
2 47
70 Many things seemed incredibly
funny to me.
0 73
77 I had very original thoughts. 2 50
83 Things in my surroundings ap-
peared smaller or larger.
0 33
90 I was able to remember certain
events with exceeding clarity.
1 35
Audio-Visual Synesthesia 20 Sounds seemed to influence what
I saw.
9 72
23 Shapes seemed to be changed by
sounds or noises.
2 52
75 The colors of things seemed to be
altered by sounds or noises.
0 44
Changed Meaning of Percepts 28 Some everyday things acquired spe-
cial meaning.
2 44
31 Things in my environment had a
new strange meaning.
0 35
54 Objects in my surroundings en-
gaged me emotionally much more
than usual.
0 53
Complex Imagery 39 I saw whole scenes roll by with
closed eyes or in complete dark-
ness.
5 85
72 I could see images from my mem-
ory or imagination with extreme
clarity.
9 63
82 My imagination was extremely
vivid.
9 85
Elementary Imagery 14 I saw regular patterns with closed
eyes or in complete darkness.
20 82
22 I saw colors with closed eyes or in
complete darkness.
22 83
33 I saw brightness or flashes of light
with closed eyes or in complete
darkness.
14 44
118
Figure 3.9: Rmcorr plot showing PSE as a function of subjective drug effects rating
scores on subscales from the Oceanic Boundlessness dimension for each subject in each
stimulus condition. The lines show the rmcorr slope fit in relation to each participant’s
two data points (placebo and psilocybin).
Table 3.7: Questionnaire items from subscales in the Oceanic Boundlessness dimension
that reached significant correlation with at least one stimulus condition in the rmcorr
analysis. Mean scores for placebo (Pla) and psilocybin (Psil) are shown in the
right-hand columns.
text Pla Psil
subscale item
Blissful State 12 I experienced boundless pleasure. 2 58
86 I experienced profound inner
peace.
3 75
91 I experienced an all-embracing
love.
3 80
119
(Bakdash and Marusich 2017).
3.3.5 Qualitative first-person narratives
The following sections are excerpts from the digital journal entries of participants (see
Methods Section 3.2.7), provided for supplementary insight into the subjective effects
of the drug.
3.3.5.1 Performing the visual task under peak psilocybin effects
Many participants shared descriptions of what it was like to complete the visual
psychophysics task at the computer screen under peak effects of the drug. A few
participants reported that the computer monitor appeared to them overlaid with
psychedelic visuals or that the stimuli appeared more “visually interesting” or with
“much more humor” or even that the computer screen appeared “beautiful” under
psilocybin.
Interestingly, a few participants shared that in spite of these perceptual changes,
they were nonetheless able to perform the task without much additional difficulty.
I recall seeing shapes moving in a circular pattern in the grayscale background
of the monitor. Recording answers of which circle had a higher contrast than
the other had much more humor to the process than when performed previously,
but I did not feel a definitive level of increased ease or difficulty with the task
itself.
—Participant sub-2f0e76
When I took the tests on the computer [during the placebo session] they were
not nearly as visually interesting [as the psilocybin session].
—Participant sub-be6e2a
I never look at screens while tripping so this was a fun experience. . . . [the
stimuli were] moving and breathing slightly . . . This was enjoyable and common
for me.
—Participant sub-d2a5cc
I remember seeing an intricate web of geometric patterns and shapes very lightly
woven into the background of the computer screen when I was looking at it . . .
geometric mandala type grid . . . made the testing more interesting. I recall
120
feeling like there was more contrast between the testing circles/rings and the
background of the screen. I felt like there was more depth between the images
and the background and I noticed more separation between the rings and the
inner circles when they came up together. I also had instances where the black
lines would suddenly pop more on certain circles and could see some of the color
bleeding between the lines or outside the circle.
—Participant sub-d2a5cc
Some participants shared that, while they were able to perform the contrast-
matching task, the psychedelic visuals and other subjective effects tended to distract
their attention away from the task at hand.
The testing was strangely tolerable and the screen was beautiful. The shaded
circles were completely fixed but everything around them swirled—they looked
like portals. Staying focused on the task was challenging and I was continuously
sucked into the world around the circles and losing my sense of self to a point
where I became worried that it was getting so deeply immersive that I wouldn’t
be able to come back . . . I would frequently become absorbed in the swirly
goo—losing myself and forgetting about the task at hand.
—Participant sub-7bdb3b
I started worrying that I wouldn’t be able to perform the test. I couldn’t picture
myself moving or sitting down, and holding on to consciousness seemed really
difficult . . . I had to read and think about the instructions, to remind myself
which finger was which. . . . I could see a honey comb pattern in the blank
screen, and I saw wiggly neon lines on the outer striped circles. Fun to look at,
but made the test difficult.
—Participant sub-b623c1
It was common for participants to see psychedelic visuals within the mean gray
background of the stimulus presentation, consistent with previous visual psychophysics
studies conducted under peak psilocybin effects, as Carter, Pettigrew, et al. (2005)
report: “Subjects reported that the computer keyboard and monitor generally remained
stable while dynamic textures and patterns would sometimes be seen both within the
background grey component of the display and the target stimulus” (Carter, Pettigrew,
et al. 2005). Nearly a century earlier, Klüver (1926), under mescaline, noted this
phenomena when viewing paper stimuli: “During the experiment the gray background
was covered most of the time with ever-changing designs in various forms and color.”
121
Notwithstanding the ongoing open-eyed psychedelic visuals that pervaded their
perceptual field during the contrast-matching task, all participants completed the
task in a reasonable timeframe and with accuracy comparable to that of their placebo
session. Importantly, our psychophysics data suggest that psilocybin did not adversely
impair task performance becase: (1) scores on catch trials were highly accurate under
psilocybin (mean = 97.92%, SD = 1.88; see Section 3.3.2), (2) contrast judgments in
the NS condition under psilocybin were near-veridical and not significantly different
from placebo (Figure 3.3), and (3) surround suppression in the OS and PS conditions
under psilocybin is proportionate to, albeit stronger than, the placebo data (Figure
3.3).
3.3.5.2 Audio-visual synesthesia, elementary, and complex imagery
Three of the six participants noted that their visual experiences responded dynamically
to music6—i.e., the subjective effect measured by the 11D-ASC subscale Audio-Visual
Synesthesia. Moreover, the below phenomenological descriptions also include attributes
of 11D-ASC subscales Elementary Imagery, Complex Imagery, and Changed Meaning
of Percepts (see Table 3.6).
. . . sometimes I think the [music] tracks created or guided the scenes that I was
seeing.
—Participant sub-b623c1
. . . the psychedelic tapestry was alive, mushroom gills taking breaths, paisleys
swirling in and out, fronds growing and then shrinking back down. When I
covered my eyes again, I could see colors swirling and pulsating in rhythm with
the music. Always a type of abstract landscape, different with every song. So
many emotions overtook me—this was a very cathartic experience.
—Participant sub-be6e2a
The music was a big driver of my experience as I felt emotionally connected to
each song and had strong visuals in my mind that were directed by the music.
It felt very cinematic and I thought of things like Fantasia, where the art is
tied to each song and there is a mix of specific images and almost narrative
structure as well as purely abstract colors and shapes . . . it felt very beautiful
and magical to me.
6Participants did not listen to music during the psychophysics task.
122
—Participant sub-d2a5cc
. . . the music that I was hearing was spectacularly radiant, captivating and I
was completely riveted to every song that came next. I’m not sure which came
first, the visions or the tears, but both were plentiful.
—Participant sub-be6e2a
Interestingly, we found that surround suppression PSE values correlated with
11D-ASC scores that measure several of the elements in the above phenomenological
accounts; namely, Audio-Visual Synesthesia, Elementary Imagery, Complex Imagery,
and Changed Meaning of Percepts (see Section 3.3.4 for quantitative statistical
analysis).
3.4 Discussion
We examined the extent to which the psychedelic 5-HT2A agonist psilocybin impacted
surround suppression using a contrast discrimination task in healthy observers. Our
results, while preliminary and limited by the small sample size of our pilot study,
indicate that psilocybin enhanced visual surround suppression. Furthermore, we
found that the magnitude of visual surround suppression correlated with intensity of
subjective psychedelic visuals significantly more than other subjective drug effects.
3.4.1 Psilocybin, surround suppression, and visual context
processing
We found that surrounding stimuli had a greater influence on the perceived contrast
of a center stimulus under psilocybin compared with placebo. During peak effects of
psilocybin, the strength of the illusion was greater (observers perceived the apparent
contrast of the center stimulus less veridically). We did not find strong evidence to
suggest that this effect depends on stimulus orientation—when the surround had a
parallel (collinear) orientation to the center, the effect of psilocybin on suppression
was only slightly greater than when the surround orientation was orthogonal (see
Figure 3.4).
123
Importantly, psilocybin reduced PSEs only on the trials that presented a sur-
round—–contrast discrimination remained largely unaffected by psilocybin in the No
Surround (NS) condition. This suggests that psilocybin’s impact on vision may be
restricted to modulations in mechanisms that underpin processing of visual context,
specifically. Moreover, on catch trials—where the reference grating was presented
within a surround, but the target grating was fixed at 80% contrast—psilocybin did
not significantly impair task performance (see Section 3.3.2 and Figure 3.2).
Taken together, our findings suggest that psilocybin may specifically impact
processing of contextual stimuli in the visual system. This interpretation is consistent
with previous findings from Carter et al. (2004), who used a psychophysical motion
processing experiment conducted under peak effects of psilocybin and concluded that
psilocybin “impairs high-level but not low-level motion perception”. However, an
alternative interpretation of the ‘impaired’ high-level motion processing in Carter
et al. (2004) is that psilocybin amplified the processing of contextual information
in the visual motion stimuli, which is supported by the observation that “a number
of subjects commented that subjectively the global motion task became harder due
to an increased salience of the randomly moving dots” leading the authors to note
that this raises “the question of whether the coherent motion deficit induced by the
psilocybin reflects a reduction in sensitivity to motion signals or reduced inhibition
of the non-coherent motion signals” (Carter et al. 2004). In other words, increased
influence of visual context, consistent with our results as well as with observations of
how psilocybin impacts other domains of mental function (Carhart-Harris, Roseman,
et al. 2018).
3.4.2 Serotonin, divisive normalization, and surround sup-
pression
Our findings indicate that serotonergic neuromodulation may be a critical mechanism
enabling visual surround suppression in everyday vision. Furthermore, consistent with
recent findings (Azimi et al. 2020), our results suggest that serotonin systems play a
124
role in divisive normalization (Heeger 1992), a canonical neural computation linked to
a wide range of contextual functions in perception and cognition, including suround
suppression (Schallmo et al. 2018). Azimi et al. (2020) combined optogenetics,
wide-field optical imaging, electrophysiology, and pharmacological manipulations to
demonstrate that “5-HT1A receptors promote divisive suppression of spontaneous
activity, while 5-HT2A receptors act divisively on visual response gain and largely
account for normalization of population responses over a range of visual contrasts”
(Azimi et al. 2020). Linking psychedelic drug effects to altered neural divisive
normalization processes via perturbations in serotonergic signaling offers a novel
unified explanation for why psychedelics impact both perception and cognition in the
way that they do: divisive normalization is at play in many brain functions, including
visual context processing (Schallmo et al. 2018), auditory spectrotemporal contrast
(Rabinowitz et al. 2011), multisensory integration (Ohshiro, Angelaki, and DeAngelis
2011), perceptual attention (Reynolds and Heeger 2009), and context-dependent
decision making (Louie, Khaw, and Glimcher 2013). We note that the subjective
effects of psychedelic drugs involve qualitative shifts in nearly all mental functions
currently modeled by divisive normalization.
3.4.3 Surround suppression and psychedelic visual phe-
nomenology
Scores in the Visionary Restructuralization dimension correlated significantly with
magnitude of surround suppression in the OS condition (when the orientation of the
surround grating was orthogonal to the center grating; see Figure 3.2). We note that
this correlation was found exclusively for the Visionary Restructuralization dimension.
Interestingly, the Oceanic Boundlessness dimension had the highest mean scores for
psilocybin, as well as the largest difference in score between psilocybin and placebo,
yet surround suppression did not significantly correlate with this dimension. Similarly,
the 5D-ASC Total Score was not predictive of the magnitude of surround suppression.
Taken together, these findings indicate that the subjective effects measured by the
125
Visionary Restructuralization dimension (see Table 3.6) arise concomitantly with
visual surround suppression (Figure 3.7). This finding suggests that there is a link
between surround suppression, serotonergic signaling, and the phenomenology of
classic ‘psychedelic visuals’ induced by psilocybin.
The 11D-ASC scoring defines four lower-level subscales from items in the Visionary
Restructuralization dimension, namely: Audio-Visual Synesthesia, Changed Meaning
of Percepts, Elementary Imagery, and Complex Imagery (refer to Table 3.6 for a
complete list of items in the Visionary Restructuralization dimension). These subscales
are of interest to the present study. The Audio-Visual Synesthesia subscale includes
the items “Sounds seemed to influence what I saw”, “Shapes seemed to be changed
by sounds or noises”, and “The colors of things seemed to be altered by sounds
or noises”. Intriguingly, Audio-Visual Synesthesia scores correlated strongly with
magnitude of surround suppression in both the OS and PS conditions; furthermore,
correlation in the PS condition was far stronger than that of the other significant
correlations and was confirmed in robust biweight midcorrelation tests. How should
we interpret this finding? Speculatively, this finding indicates that psilocybin might
induce a hypersensitivity to contextual cues from the visual context in the stimulus
(the surround) as well as from contexts outside of vision (sounds, noises). It also
provides some evidence that perceptual context processing, including visual surround
suppression, is influenced by serotonergic neuromodulation.
The Elementary Imagery subscale includes “I saw [regular patterns/colors/brightness
or flashes of light] with closed eyes or in complete darkness”. Rmcorr revealed
significant correlations between the Elementary Imagery rating scores and surround
suppression in both the OS and PS conditions. Weaker correlations were found
with items in the Complex Imagery subscale, which includes the items “I saw whole
scenes roll by with closed eyes or in complete darkness,” “I could see images from my
memory or imagination with extreme clarity,” and “My imagination was extremely
vivid”. Taken together, the correlations we found suggests that the neural processes
leading to enhanced surround suppression under peak effects of psilocybin are linked
with the characteristic psychedelic visual effects of the drug.
126
The Changed Meaning of Percepts subscale includes the items “Some everyday
things acquired special meaning”, “Things in my environment had a new strange
meaning”, and “Objects in my surroundings engaged me emotionally much more than
usual”. Higher ratings in this scale were significantly correlated with greater magnitude
of surround suppression in the PS condition. This finding suggests that psilocybin-
induced changes in visual context processing span from the cognitive-perceptual
(“Things in my environment had a new strange meaning”) to the emotional (“Objects
in my surroundings engaged me emotionally”) to the most basic visual functions—i.e.,
surround suppression of visual contrast, as measured by our psychophysical task.
Considered alongside the Audio-Visual Synesthesia correlations, this finding lends
further support to the idea that psilocybin selectively modulates contextual processing
concomitantly with subjective effects. Our finding that surround suppression correlated
with the Changed Meaning of Percepts subscale in particular may have implications
for understanding how psilocybin works to treat major depressive disorder.
3.4.4 Depression, surround suppression, and psilocybin
Psilocybin-assisted psychotherapy has recently shown promise in the treatment of
major depressive disorder (MDD) (Davis et al. 2021; Ross et al. 2016; Griffiths et
al. 2016; Carhart-Harris, Bolstridge, et al. 2018; Gukasyan et al. 2022). Davis et
al. (2021) found “substantial rapid and enduring antidepressant effects of psilocybin-
assisted therapy among patients with MDD.” However, the exact mechanisms are
unclear, and current debate is centered around the question of which subjective
psychedelic effects (if any) are critically involved in positive clinical outcomes—some
teams strongly emphasize the critical role of mystical-type experiences (Griffiths et al.
2016; Ross et al. 2016) while others question the importance of mystical experiences
(Letheby 2021). Meanwhile, some researchers (and corporations) hope to discover
novel drugs that could deliver psilocybin’s antidepressant effect without producing
hallucinogenic effects (McClure-Begley and Roth 2022; Berg et al. 2022). The role of
psychedelic visuals receives little attention or is downplayed (Letheby 2021). Roseman,
Nutt, and Carhart-Harris (2017) found that positive MDD treatment outcomes did
127
correlated with intensity visual effects measured with the 5D-ASC or 11D-ASC, but
not as strongly as the effects measured by the Blissful State dimension. However,
the use of eyeshades in Roseman, Nutt, and Carhart-Harris (2017) placed limits
on measuring the subjective effects on open-eyed visual perception. Our findings
warrant further investigation into the links between psilocybin’s visual effects and its
antidepressant properties.
During major depressive episodes (MDEs), MDD patients in previous studies show
weakened surround suppression (Golomb et al. 2009; Salmela et al. 2021). Using
stimuli similar to those in the current study, Salmela et al. (2021) measured surround
suppression of perceived contrast in patients with unipolar MDD, bipolar disorder,
and borderline personality disorder who were actively undergoing MDEs—“[w]hen
comparing the patient group with [healthy] controls, we observed a strong and highly
significant reduction in contrast suppression . . . patients perceived the contrast
suppression illusion more veridically . . . Furthermore, contrast suppression was
similarly reduced in patients with unipolar MDD, bipolar disorder and borderline
personality disorder” [Salmela et al. (2021); emphasis added]. Importantly, Salmela et
al. (2021) found that as some of these patients went into remission (no MDE criterion
symptoms), follow-up measurements revealed their weakened surround suppression had
normalized (moved closer to levels measured in controls)—“a decrease in depression
symptoms was associated with an increase in contrast suppression” (Salmela et al.
2021), which suggests that weakened visual surround suppression tracks depressive
symptoms. Speculatively, our results hint by corollary that enhanced visual surround
suppression measured under psilocybin might similarly track the neural shifts that
underpin the antidepressant effects of the drug. Future studies could explore this by
measuring visual surround suppression along with MDE symptoms before, during,
and after psilocybin administration in MDD patients.
Further exploring this line of thought from a phenomenological viewpoint, we note
that physical anhedonia (Chapman, Chapman, and Raulin 1976)—a reduction of inter-
est and enjoyment in sensory stimuli—is widespread among MDD patients (Shankman
et al. 2010; Sagud et al. 2020). Interestingly, psilocybin consistently produces high
128
scores on the 11D-ASC Changed Meaning of Percepts subscale (Studerus, Gamma,
and Vollenweider 2010; Holze et al. 2022), which includes the items: “Objects in my
surroundings engaged me emotionally much more than usual”, “Some everyday things
acquired special meaning”, and “Things in my environment had a new strange meaning”.
The phenemenology of perception under acute psilocybin is thus antithetical to the
phenomenology of sensory anhedonia in MDD. This phenomenological observation has
substantial neurophysiological support. The active phase of MDD is associated with
deficits (blunted responses) in processing emotional stimuli, including faces (Krause
et al. 2021; Bourke, Douglas, and Porter 2010), body movements (Kaletsch et al.
2014), speech (Pang et al. 2014), and music (Naseri et al. 2019). Psilocybin has been
found to enhance processing of emotional face stimuli in healthy volunteers (Grimm
et al. 2018) and can have a restorative effect on deficient responses to emotional
stimuli in patients with MDD (Stroud et al. 2017; Roseman et al. 2018; Mertens et al.
2020; Gill et al. 2022)—evidence which suggests that psilocybin “revives emotional
responsiveness on a neural and psychological level” (Mertens et al. 2020). In 1928,
Klüver noted this about the subjective visual effects of the psychedelic drug mescaline:
“Human faces seem to undergo certain changes; they become more ‘expressive’, the
features become more sharply defined . . . Some subjects feel that their ability to
infer certain traits of personality from facial expressions becomes increased” (Klüver
1928, 37–38). We speculate that the neural mechanisms underpinning the enhanced
processing of emotional stimuli are also those that underpin subjective experiences
captured by the Changed Meaning of Percepts subscale (specifically item 54, “Objects
in my surroundings engaged me emotionally much more than usual”). Notably, we
found that surround suppression correlated significantly with Changed Meaning of
Percepts scores.7
In summary, our results highlight surround suppression as a potential trans-
diagnostic neuropsychological mechanism which could be leveraged toward better
7Magaraggia, Kuiperes, and Schreiber (2021) argue that the antidepressant effects of psychedelics
can be understood in light of their ability to enhance pattern separation. It is currently unclear
how pattern separation relates to surround suppression, other than that both involve spatial context
processing. The connection is intriguing nonetheless.
129
understanding the use of serotonergic psychedelic drugs in treating MDD.
3.4.5 Set and setting, suggestibility, flexibility, and surround
suppression
Psilocybin and other classic psychedelic drugs’ effects are uniquely sensitive to non-
drug factors, such as pre-dose mood, drug session environment, and external stimuli
(see Chapter 1 Figure 1.1) (Leary, Litwin, and Metzner 1963; Zinberg 1984; Studerus
et al. 2012; Hartogsohn 2016, 2017). Known as set and setting, the internal mental
context or ‘mindset’ (set) and external context or environment (setting) of the drug
taker can drastically influence the magnitude, character, and content of the effects
that psychedelic drugs produce. Speculatively, our results can be thought of as the
set and setting phenomenon playing out on a basic perceptual level: the surround
(setting) has greater influence on the percept under psilocybin.
Relatedly, psychedelic drugs can increase suggestibility (Leary 1961; Sjoberg and
Hollister 1965; Grob and Rios 1992; Carhart-Harris et al. 2015; Timmermann et al.
2021), where suggestibility is defined in the minimal sense of “a person’s propensity
to respond positively to suggested communications, i.e., thinking and acting following
the suggestions of others” (Dupuis 2021; Sidis 1898). Speculatively, it is interesting to
view surround suppression itself as a kind of ‘perceptual suggestibility’ and to see our
results as consistent with psilocybin-induced enhancements in suggestibility.
Cognitive flexibility (Scott 1962) “is broadly defined as the mental ability to switch
between thinking about two different concepts according to the context of a situation”
(Uddin 2021; emphasis added). Increases in cognitive flexibility have been measured
at the 1-week and 4-week timepoints after a single dose of psilocybin in patients
with MDD (Doss et al. 2021)—however, LSD reduced cognitive flexibility scores
during acute peak effects (T. Pokorny et al. 2019) and 24 hours after administration
(Wießner et al. 2022). While the links between surround suppression and cognitive
flexibility are unknown, we note that both involve contextual processing. Speculatively,
psilocybin-induced enhancement of surround suppression and alterations to cognitive
130
flexibility may share an underlying neuromodulatory mechanism.
3.4.6 Schizophrenic psychosis vs psilocybin effects
In line with conclusions from recent comparisons (Leptourgos et al. 2020), our
findings warrant caution when comparing psilocybin’s visual phenomenology with the
perceptual abnormalities found in schizophrenic psychosis. As suggested in Chapter
1 the word ‘psychotomimetic’ may be apt for psilocybin only in the sense that it
suggests mimicry and mimicry implies merely superficial prima facie likeness rather
than meaningful shared properties. Numerous studies have found weakened surround
suppression in schizophrenic patients compared to healthy controls (Dakin, Carlin, and
Hemsley 2005; Tadin et al. 2006; Yoon et al. 2009; Yang et al. 2012; Tibber et al. 2013;
Serrano-Pedraza et al. 2014; Schallmo, Sponheim, and Olman 2015; V. J. Pokorny et
al. 2019; Linares et al. 2020). Importantly, we found that psilocybin shifted surround
suppression in the opposite direction (enhancement). This finding is at odds with
long-held assumptions that psychedelic drug effects ‘resemble’ or ‘model’ schizophrenic
psychosis; assumptions that have been used to justify the ‘model psychoses’ theories
that began in the late 1800s and to classify the drugs as psychotomimetic (see Chapter
1). In a study titled “Psilocybin induces schizophrenia-like psychosis in humans
via a serotonin-2 agonist action” psilocybin is described as a drug that “produces
a psychosis-like syndrome in humans that resembles first episodes of schizophrenia
. . . perceptual alterations are especially common features of psilocybin-induced
and early acute schizophrenic stages” (Vollenweider et al. 1998, 3897). However, our
results suggest the opposite, at least for visual surround suppression. Importantly,
we found that the subjective visual effects of psilocybin correlated significantly with
the magnitude of surround suppression, indicating that the neural mechanisms from
which psychedelic visuals emerge may not be the same as those that give rise to the
perceptual anomalies found in schizophrenic patients.
Furthermore, our findings are at odds with the theoretical claims of Carhart-Harris
and Friston (2019), who argue that in both schizophrenic and psychedelic states “the
brain’s highest-level priors are ineffective, meaning bottom-up prediction errors travel
131
more freely up the hierarchy”. While the schizophrenia side of their claim is indeed
supported by findings of weakened surround suppression in schizophrenic patients
(Dakin, Carlin, and Hemsley 2005; Tadin et al. 2006; Yoon et al. 2009; Yang et al.
2012; Tibber et al. 2013; Serrano-Pedraza et al. 2014; Schallmo, Sponheim, and
Olman 2015; V. J. Pokorny et al. 2019; Linares et al. 2020), our findings do not
support the hypothesis that psychedelic drugs induce a similar breakdown of top-down
feedback in perceptual processing.
3.4.7 Neural information processing and psychedelic effects
Our results highlight a central ambiguity lurking in recent attempts that use ‘predictive
processing’ (PP; see Chapters 1 and Swanson (2016)) to explain psychedelic effects
(Corlett, Frith, and Fletcher 2009; Pink-Hashkes, Rooij, and Kwisthout 2017; Letheby
and Gerrans 2017; Carhart-Harris and Friston 2019; Stoliker, Egan, and Razi 2022).
The critical question raised in Chapter 1 is: “Do psychedelic molecules perturb top-
down (feedback) signaling, or bottom-up (feedforward) signaling, or both?” (Chapter
1 Section 1.5.3). The leading proposals argue that psychedelic effects stem from a
‘decomposition’ (Pink-Hashkes, Rooij, and Kwisthout 2017) or ‘relaxing’ (Carhart-
Harris and Nutt 2010; Carhart-Harris and Friston 2019) of top-down feedback signaling,
“which concomitantly liberates bottom-up signaling” (Carhart-Harris and Friston 2019).
However, we found that the surround suppression illusion, which we can reasonably
assume is underpinned by top-down feedback signaling from cortex, was strengthened
under peak effects of psilocybin. Our finding is therefore in tension with the central
hypothesis of Carhart-Harris and Friston (2019) who argue that psychedelic effects
stem from a “weakening the precision weighting of high-level priors”.8 Furthermore,
Carhart-Harris and Friston (2019) state that “the relaxation of high-level priors [under
psychedelics] necessarily implies a liberation of bottom-up information flow”. Yet if
this scenario were true, we would expect to see a weakening, not an enhancement, of
surround suppression under psilocybin—greater bottom-up information flow would
translate to a weaker visual illusion; i.e., a more veridical percept that matches the
8For a primer on the concept of precision in Bayesian theories, see (Yon and Frith 2021).
132
external stimuli with greater accuracy. However, we found the opposite result in our
experiment. One way to address this tension would be to attempt a more nuanced
interpretation of our findings, in which psilocybin-induced enhancements of surround
suppression are understood as the result of increased weighting of bottom-up cues
that trigger top-down feedback, whereby the ‘liberated’ bottom-up signals (i.e., the
surround stimulus) are more likely to trigger stronger top-down effects (i.e., the
suppression illusion) under psychedelics. However, this interpretation would still be
in conflict with the core hypotheses of Carhart-Harris and Friston (2019) and Pink-
Hashkes, Rooij, and Kwisthout (2017) who clearly argue that psychedelics weaken
top-down processes.
In summary, our results warrant further inquiry into the behavior of perceptual sys-
tems under psychedelics, as well as a closer theoretical examination of how psychedelics
might impact the brain’s balance of bottom-up (feedforward) signals against top-down
(feedback) modulation.
3.5 Limitations
The results presented here are preliminary findings from the first six (n = 6) participants
in our pilot study. We plan to expand our sample size to include a total of 40
participants, which will necessarily yield greater statistical power than in our current
sample. Furthermore, we did not correct for multiple comparisons (i.e., across the
correlations) which limits the conclusions that can be drawn from the results presented
here.
Maintaining an effective blind in psychedelic drug investigations is notoriously
complicated (Muthukumaraswamy, Forsyth, and Lumley 2021; Burke and Blumberger
2021; Olson et al. 2020) and our study was no exception. We attempted to minimize
placebo effects using Niacin as an ‘active’ placebo; nonetheless, it is likely that
unblinding occurred for some experimenters and participants alike. We take seriously
the possibility that placebo/nocebo and expectancy could influence PSEs. This
possibility is somewhat mitigated by our within-subjects crossover design, in which
133
half of our participants received psilocybin first, and the other half placebo first (see
Table 3.1).
Our inclusion criteria excluded drug-naive subjects (we required at least one prior
personal experience with psilocybin), which may have increased the likelihood of
expectancy effects in both the psilocybin and placebo conditions.
The dose of psilocybin was a fixed 25mg (oral) for every participant regardless
of body mass. Thus, it is possible that dose-response differences occurred between
participants, as the intensity of psilocybin’s visual effects can be dose-dependent
(Hasler et al. 2004; Carter, Pettigrew, et al. 2005; Holze et al. 2022). To mitigate
this, future studies could dose psilocybin using a body-mass-relative (mg/kg) ratio.
The role of specific g-protein coupled receptors (GPCRs; e.g., the 5-HT2A receptor)
in causing our findings is unknown, as psilocybin has affinity for a variety of serotonergic
and dopaminergic receptors (Ray 2010) and we did not employ techniques to isolate
pharmacological action. However, previous studies that paired the selective 5-HT2A
antagonist ketanserin with psilocybin indicate that 5-HT2A activation is required for
psilocybin’s subjective visual effects (Vollenweider et al. 1998; Kometer et al. 2013).
However, although the ketanserin can block these effects (Vollenweider et al. 1998;
Kometer et al. 2013), it does not always block psilocybin’s impact on visual functions
measured via psychophysics (e.g., Carter et al. 2007). Future studies could use selective
5-HT2A antagonist ketanserin to probe the specific role of 5-HT2A in psilocybin-induced
alterations of surround suppression, as well as to test the correlations we found between
enhanced surround suppression and subjective effects.
3.6 Conclusion
We found that psilocybin enhanced visual surround suppression in a contrast-matching
psychophysics task. Furthermore, the magnitude of surround suppression correlated
significantly with self-rated intensity of subjective visual effects, but not with overall
psychedelic alterations in consciousness. Interestingly, subjective effects measured
by the Audio-Visual Synesthesia subscale had the strongest correlation with mag-
134
nitude of surround suppression. Our results suggest that (1) surround suppression
is not weakened and may instead be enhanced under peak effects of psilocybin, (2)
serotonergic neuromodulation may be critically involved in surround suppression,
(3) enhancements in surround suppression appear to correlate with the intensity of
subjective visual effects, (4) psilocybin may shift visual context processing in the
opposite direction of that found in patients experiencing major depressive episodes, (5)
the visual effects of psychedelics appear to emerge from processes distinct from those
underlying abnormal visual perception in schizophrenia, and (6) we may have reason
to question the theoretical notion that psychedelic drugs weaken top-down feedback.
135
Chapter 4
Stronger surround suppression of
ERP responses under psilocybin: A
pharmaco-EEG study
“Pharmaco-EEG, a neuroscience that was discarded well before its full potential
was achieved . . . is a quantitative science that yields direct images of ongoing
biochemical and neurophysiologic brain events that are intimately connected to
human behavior.”
–Max Fink (2010, 161–71) “Remembering the Lost Neuroscience of Pharmaco-
EEG”
“Drugs with effects comparable to those of LSD-25, for example, have been
known for generations, but it is only recently that we have been in a position to
study them meaningfully. The long awaited millennium in which biochemical,
physiological, and psychological processes can be freely correlated still seems
a great distance off. This should not discourage us from responding to the
occasional opportunities for observing parallels between these different orders
of phenomena as are afforded us in experimental studies.”
–Gerald Klee (1963, 473), “Lysergic Acid Diethylamide (LSD-25) and Ego
Functions”
136
ABSTRACT
The perceived contrast and neural responses to a target stimulus can be reduced
by surrounding it with higher-contrast stimuli. In this experiment we examined
center-surround modulation of visual event-related potential (ERP) amplitude in
EEG recordings from humans under the psychedelic drug psilocybin. We found
that: (1) under psilocybin, the surround stimulus had a greater suppression
effect on the magnitude of the N1 ERP response compared with placebo; (2)
this effect was feature-selective, being strongest when the surround stimulus
had an orientation parallel to the orientation of the target; and (3) the ERP
amplitude in response to center stimuli presented alone (no surround stimulus)
was not significantly impacted by psilocybin. The finding suggests that serotonin
plays a functional role in suppressing neural responses to visual stimuli, and
that psychedelic drugs enhance contextual modulation of neural responses to
center-surround stimulus configurations.1
4.1 Introduction
Surround suppression in contrast perception is a visual illusion wherein the perceived
contrast of a stimulus is reduced (suppressed) by neighboring stimuli in its (sur-
rounding) visual context (see Chapter 3 Section 3.1). In addition to psychophysical
measurements, changes in neural activity that occur when a target stimulus is sur-
rounded by contextual stimuli can be measured with neuroimaging techniques. In
electroencephalography (EEG) and magnetoencephalography (MEG) recordings, this
surround suppression is measured by analyzing the event-related potential (ERP)
produced by various center-surround stimulus configurations (Ohtani et al. 2002;
Haynes et al. 2003; Appelbaum et al. 2006; Joo, Boynton, and Murray 2012; Joo and
Murray 2014; Chen et al. 2016; Vanegas, Blangero, and Kelly 2015; Schallmo, Kale,
and Murray 2019). These studies indicate that neural responses in human occipital
lobe differ when viewing a center stimulus within the context of a surrounding stimulus
versus viewing the center stimulus alone, and that these differences depend on the
configuration (e.g., orientation) of the center stimulus in relation to its surround.
1Link Swanson and Michael-Paul Schallmo designed the experiment. Jessica Nielson, Sophia
Jungers, and Link Swanson carried out the experiment. Kathryn Cullen and Ranji Varghese helped
with participant safety monitoring. Link Swanson analyzed the data and wrote the manuscript.
137
Surrounding stimuli have been shown to alter neural responses in both early (~80
ms) and later (~130 ms) response components in the ERP time course. Generally,
the strength of the neural response mirrors the strength of the perceptual illusion for
different center-surround configurations, i.e., a surround with parallel orientation will
have greater influence on ERP response components (especially the later ~130 ms
component) than a surround that is oriented orthogonally in relation to the target
stimulus (Ohtani et al. 2002; Haynes et al. 2003; Schallmo, Kale, and Murray 2019).
While this phenomenon is empirically well-established, its neural origin within the
visual processing hierarchy remains unclear, and the underlying biochemical neuro-
modulatory mechanisms have not been identified (Schwartz, Hsu, and Dayan 2007;
Schallmo et al. 2018; Schallmo, Kale, and Murray 2019).
Pharmacological interventions incorporated into ERP paradigms provide a means
for investigating and isolating specific neuromodulator mechanisms (Fink 1969, 2010).
The benzodiazepine drug lorazepam, a positive allosteric modulator at the GABAA
receptor, has been found to weaken surround suppression (Schallmo et al. 2018),
suggesting that the mechanisms of suppression are not directly mediated by GABAergic
inhibition. Rather than direct GABAergic inhibition, an alternative hypothesis is
that surround suppression occurs via withdrawal of excitation (Ozeki et al. 2009a;
Sato et al. 2016; Schallmo et al. 2018). Serotonergic (5-HT) neurotransmission is
known to regulate neuronal excitability in cortical networks, primarily via 5-HT1A
and 5-HT2A receptor subtypes (Andrade 2011). In mouse V1, Azimi et al. (2020)
found evidence that 5-HT2A receptors serve to (divisively) modulate visual evoked
response gain. Visual perception is significantly altered by psychedelic drugs, and these
effects critically depend on the agonist activation at 5-HT2A receptors (Vollenweider
et al. 1998; Kometer et al. 2011, 2013; Halberstadt and Nichols 2020). The distinct
phenomenology of psychedelic visual alterations—which includes “very pronounced
simultaneous contrast” (Klüver 1928) as well as other illusory object properties such
as “breathing” (Huxley 1954), “wavering” (Hofmann 1980), “undulating movements”
(Klüver 1926)—indicates that serotonergic psychedelics may impact mechanisms that
regulate gain control of visual evoked responses (Vollenweider and Smallridge 2022).
138
However, the impact of classic psychedelic drugs on surround suppression in humans
has not been investigated.
Our findings reported in Chapter 3 suggest that perceptual surround suppression
is enhanced (i.e., stronger, as measured by our psychophysical paradigm) under
psilocybin, a classic psychedelic drug that agonizes 5-HT2A and 5-HT1A receptors
(Nichols 2004; Ray 2010). We found that when a target stimulus was presented alone,
perceptual judgements did not differ under psilocybin vs placebo. By comparison, when
the target stimulus was presented within a higher-contrast surround, the perceived
contrast of the target stimulus was more greatly suppressed (i.e., the illusion was
stronger) under psilocybin, and the magnitude of psilocybin’s impact was dependent
on surround orientation (stronger for parallel vs orthogonal orientation). Psilocybin
thus appears to induce greater contextual modulation, as it had no effect on perceptual
judgments in the No Surround condition, and had the strongest effect in the Parallel
Surround condition, when the target stimulus was most similar to its surround. This
finding is broadly consistent with previous findings that used visual stimuli, where
psilocybin was found to impact performance on visual tasks that evoke contextual
modulation (Carter et al. 2004; Carter, Burr, et al. 2005; Gouzoulis-Mayfrank et al.
2002; Kometer et al. 2011, 2013), while performance on other tasks was no different
from placebo (Carter et al. 2004; Carter, Burr, et al. 2005; Barrett et al. 2018) (see
Chapter 2 for more discussion of this point).
The impact of psilocybin (and other psychedelic drugs) on neural responses re-
flected in visual-evoked event-related potentials (vERPs; as measured by EEG) is
largely unknown—only two previous investigations have paired vERP paradigms with
psychedelic drugs in humans. The N1/N170 component (Bentin et al. 1996) is a
common vERP associated with object recognition and with the perception of illusory
object boundaries (Murray 2004), such as Kanizsa’s (1979) illusory triangle shape
induced by ‘pacman’ stimulus configurations. Kometer et al. (2011) recorded EEG
responses to Kanizsa figures under psilocybin and found that the magnitude of the N1
component was attenuated, and that this attenuation correlated with the intensity
of the visual experiences produced by the drug (Kometer et al. 2011). A later study
139
found that pharmacologically blocking psilocybin’s 5-HT2A receptor agonism blocked
the effect on the N1 and eliminated all subjective visual effects of the drug (Kometer
et al. 2013), indicating that the 5-HT2A receptor was involved in psilocybin’s effect
on the N1 vERP component.
Phenomenologically, as noted above and in Chapter 2, psilocybin produces illusory
shifts in the appearance object forms, often with illusory motion in real objects,
and these subjective effects were found to correlate with surround suppression of
perceived contrast (Chapter 3) and with decreased N1 amplitude evoked by Kanizsa
stimuli (Kometer et al. 2011), which was dependent on psilocybin’s 5-HT2A activation
(Kometer et al. 2013). Thus, we reasoned that the visual phenomenology characteristic
of psilocybin might involve increases in surround suppression, as surround suppression
plays a role in the everyday perception of object boundaries, figure-ground segregation,
and visual saliency (Angelucci et al. 2017; H. Yu et al. 2022). Furthermore, variations
in contrast perception are crucial for perceiving object form, size, and motion features
(H. Yu et al. 2022), all of which are altered under psilocybin. Moreover, surround
suppression involves shifts in excitation-inhibition balance, which is modulated by
serotonergic signaling (Andrade 2011; Azimi et al. 2020). Taken together, we reason
that 5-HT2A/5-HT1A signaling may be critically involved in surround suppression
ERP responses to center-surround stimulus configurations, and that the visual effects
of psilocybin are in part due to its ability to impact this mechanism. This highlights
the critical need for investigations that measure context-modulated ERP responses
under psilocybin and other serotonergic psychedelic drugs.
Here, we paired psilocybin with a vERP surround suppression paradigm using
nearly identical stimuli as in our psychophysical paradigm reported in Chapter 3.
The current study was designed to measure psilocybin’s impact on ERP responses to
differing center-surround stimulus configurations as measured by EEG. Using a double-
blind, placebo-controlled within-subjects crossover design, we found that psilocybin
increased the surround suppression ERP response in the N1 component time window
(~120-250 ms) relative to placebo. Furthermore, the strength of psilocybin’s impact
on ERP responses depended on stimulus orientation, with the strongest effect being
140
in the parallel center-surround orientation, and the orthogonal orientation impacted
to a lesser degree.
4.2 Methods
4.2.1 Participants
Adults aged 30 to 38 years with no current or previous mental health diagnosis, not
currently using any prescription medications, with at least one reported previous
experience taking a moderate to high dose of psilocybin, and without histories of
psychotic disorder were eligible to participate. Five healthy observers with normal or
corrected-to-normal vision (3 male and 2 female, mean age 35 years) were included in
this study. A total of 6 completed the experiments; however, data from one participant
was excluded due to excessive signal artifacts in the EEG recording. The participants
in this experiment were the same individuals who completed the psychophysical task
reported in Chapter 3. All participants gave written informed consent and were
compensated at a rate of $20 per hour. Experimental protocols were approved by
the Institutional Review Board of the University of Minnesota. All experiments were
performed in accordance with approved guidelines and regulations, including approval
from the United States Food and Drug Administration (FDA) and Drug Enforcement
Administration (DEA).
4.2.2 Drug administration and timing
For this experiment, participants completed the EEG task 5 hours after psilocybin
was administered, which means the measurements were taken after drug-plasma
concentrations had likely peaked, as the strongest ‘peak’ subjective drug effects had
begun to gradually diminish. This timing differs substantially from our psychophysical
measurements (Chapter 3), which were performed three hours post-drug administration,
during typical peak subjective drug effects (Carbonaro et al. 2018; Holze et al. 2022)
and typical peak plasma concentrations (Brown et al. 2017; Holze et al. 2022). Thus,
141
the present experiment reflects psilocybin’s effect on ERP responses post-peak, during
the transition out of the psilocybin state returning to the baseline waking state. This
should be kept in mind when considering all findings presented here.
4.2.3 Crossover design
Every participant completed two drug dosing sessions—one day with active drug
(25mg psilocybin); one day with placebo (100mg niacin)—two weeks apart. EEG
recordings were also obtained at separate non-drug baseline and follow-up sessions,
but these data are not reported here as they will be analyzed for a future report, along
with fMRI and psychophysical measurements from baseline and follow-up sessions.
Participants were randomized into groups where group A received psilocybin on dosing
day 1 and placebo on dosing day 2, while group B received the drugs in the reverse
order. Participants, experimenters, and all study staff were blind to which drug was
administered on any given experimental session. See Chapter 3 for more detail on the
study design.2
4.2.4 Apparatus
EEG data were recorded using a Wearable Sensing DSI-24 dry electrode EEG headset
at a sampling rate of 600 Hz, except for three sessions, which were inadvertently
recorded at 300 Hz. All recordings were downsampled to 300 Hz to achieve a uniform
sampling rate across all recordings included in the analysis. The DSI-24 has 21
channels in a fixed position compliant with the 10-20 International System.3 The CZ
sensor was positioned halfway between the nasion and inion points, which positions
the remaining fixed-position sensors of the DSI-24 headset as shown in Figure 4.1. The
DSI-24 was used in wired mode for all data acquisition sessions. Data were captured
using WearableSensing’s DSI-Streamer software running on Windows 10.
Visual stimuli were presented on a Dell P2319H 23-in LED-backlit monitor (1920
2ClinicalTrials.gov Identifier: NCT04424225
321 sensors at 10-20 locations: Fp1, Fp2, Fz, F3, F4, F7, F8, Cz, C3, C4, T7/T3, T8/T4, Pz, P3,
P4, P7/T5, P8/T6, O1, O2, A1, A2.
142
× 1080 pixels, 60 Hz refresh rate) at a viewing distance of 60cm. The monitor was
calibrated using a spectrophotometer. Mean luminance was 111.2 cd/m2. Stimuli
were generated and presented using PsychoPy (Peirce et al. 2019) version 2021.2.3.
Event codes (triggers) were transmitted from PsychoPy over LTP (parallel) port into a
WearableSensing Trigger Hub device, and from there sent to the DSI-24 headset where
they were recorded into the final data stream as a dedicated channel alongside the
electrode channels. Latency differences between software triggers sent by PsychoPy
and actual on-screen stimulus presentation time were quantified using a photodiode,
resulting in a latency offset of 48.387 ms. A correction for this offset was applied to
the EEG data as described in Section 4.2.8 below.
Figure 4.1: DSI-24 EEG headset sensor positions.
4.2.5 Stimuli
Visual stimuli were the same as in the psychophysics experiment in Chapter 3 Section
3.2.4, except for the following differences. Two circular gratings (“centers”) were
presented simultaneously at 2.8° to the left and right, and 1.1° above fixation, with
fixed 50% contrast. The central fixation marker was constructed according to Thaler
143
Table 4.1: The three different stimulus conditions. One set of 105 trials was presented
for each stimulus condition intermixed with the other sets in counterbalanced random
order. Four different orientation variations were used (0°, 45°, 90°, or 135°; 105 trials
of each) presented in random order.
Surround stimulus Example N trials
Condition
NS None Figure 3.2 A 105
OS Orthogonal Figure 3.2 C–D 105
PS Parallel Figure 3.2 E–F 105
et al. (2013) (shown in Figure 4.2). On a subset of the trials, the centers appeared
within larger gratings–referred to as “surrounds”—that were 4° in diameter, were
spatially in-phase with the center gratings, with 100% contrast. Surround orientation
was either parallel or orthogonal to the center (see below).
4.2.6 Paradigm
The experiment was designed to measure the effect of psilocybin versus placebo on
the ERP evoked by the center stimuli as measured by the EEG recording. Three
stimulus conditions (No Surround, Orthogonal Surround, Parallel Surround) mirrored
the psychophysics experiment in Chapter 3. A fourth condition (“catch”) was included
in which the center gratings were replaced with plaid (checkered) gratings. The
participants’ task was to report (via key press) whether centers were stripes or plaids
on each trial. When present, surrounds were presented 1.5-2.5 s (randomized) before,
during, and 500 ms after presentation of centers (200 ms; see Figure 4.3), so that the
ERP response to the centers could be distinguished in time from the response to the
surrounds (see Ohtani et al. 2002; Haynes et al. 2003; Joo, Boynton, and Murray
2012; Joo and Murray 2014; Schallmo, Kale, and Murray 2019). The No Surround
condition provided an isolated centers-only reference ERP response. 105 trials per
condition were collected (a total of 420 trials), with breaks about every 5 minutes for
a total duration of about 50 minutes.
At the beginning of each trial, the fixation mark changed from blue to red, and
participants were instructed avoid blinking and responding until fixation marker turned
144
Figure 4.2: Samples of stimuli used in the experiment. A is an example of the
no-surround (NS) condition. B is an example of the plaid grating (‘catch’ trial) to
which participants were instructed to respond with a specific key press. (C–D) are
examples of the orthogonal surround (OS) condition. (E–F) are examples of the
parallel surround (PS) condition. All conditions appeared in four orientation variants
(0°, 45°, 90°, or 135°) in counterbalanced randomized order.
145
back to blue (see Figure 4.3).
4.2.7 Subjective drug effects
The Altered States of Consciousness questionnaire (5D-ASC) scale (Dittrich 1998;
Studerus, Gamma, and Vollenweider 2010) was used to assess the overall effects of
drug on various elements of subjective experience. See Chapter 3 Section 3.2.6 for a
full description of the measure.
4.2.8 Data processing
EEG recordings were processed using the open-source EEGLAB toolbox software
package (Delorme and Makeig 2004) version 2022.1 integrated with MATLAB (The
Mathworks, Inc., Natick, MA, USA) version 2021b. A high-pass filter (0.05 Hz) and
notch filter (58–62 Hz) were applied sequentially using the pop_eegfiltnew() filter
function in EEGLAB toolbox. Each continuous recording was inspected visually and
any non-trial (i.e., beginning, end, breaks) sections were removed. Next, an independent
components analysis (using EEGLAB) was used to identify signal artifacts produced
by eye blinks, eye movements, muscle movements, and channel noise, which were
reviewed manually, flagged for removal, and removed using EEGLAB’s pop_subcomp()
function. Signals from each electrode were re-referenced from the hardware vendor’s
Pz reference electrode to a more common Cz reference using EERLAB’s pop_reref()
function.
ERP analyses were performed with the open-source ERPLAB toolbox (Lopez-
Calderon and Luck 2014) version 9.0 in MATLAB. Trials were examined within
time-locked epochs from -200 to +500 ms relative to target stimulus onset. Baseline
correction was performed by subtracting the average voltage from the -200 to 0
ms period from all time points within each trial. Epochs were then screened for
artifacts using a peak-to-peak algorithm via ERPLAB’s pop_artmwppth() function,
and excluded from analysis if activity exceeded a 100µV threshold.4 Included trial
4The mean number of excluded trials per condition per participant is reported in Table 4.2.
146
Figure 4.3: Presentation timing for a single trial. Each trial begins after a 1–2 s
(duration randomized) inter-trial interval. The trial begins with the onset of the
surrounds, which are presented continuously until the trial ends. Centers appear after
a randomized duration of 1.5–2.5s, are presented for 200ms. Surrounds remain for an
additional 500ms after center offset, at which point the trial ends.
147
Table 4.2: Descriptive statistics on trials excluded from analysis due to artifacts.
Values represent number of trials removed for each subject in each stimulus condition
under each drug.
mean max std
drug
NS placebo 3.20 5 1.30psilocybin 5.20 10 4.60
OS placebo 3.80 6 1.48psilocybin 5.20 11 5.40
PS placebo 3.40 4 0.89psilocybin 5.60 11 4.39
epochs were then separated into bins grouped by stimulus condition (NS, OS, PS).
Catch trials were not examined in the current study, as the ERP responses to the
oddball plaid stimuli would introduce complications in interpreting these results. ERP
amplitudes from all trials in each condition were then averaged for each participant
using ERPLAB’s pop_averager() function.
We identified locations O1 and O2 (occipital) as a priori electrodes of interest for
the analysis, based on previous studies (V. P. Clark, Fan, and Hillyard 1994; Joo,
Boynton, and Murray 2012; Joo and Murray 2014; Schallmo, Kale, and Murray 2019).
Scalp maps of grand average mean amplitude for each electrode at successive 20ms
latencies over the entire trial epoch (-200 ms pre- to +500 ms post-stimulus onset;
Figure 4.4) support our a priori choice of electrodes.
Four ERP components were selected a priori for analyses: P1, N1, and P2. To
identify the separate ERP component time windows, we grand-averaged O1 & O2
amplitudes from all from all 5 subjects in all three surround stimulus conditions (NS,
OS, PS) combined, plotted the grand average ERP waveform, and visually demarcated
the start and end points for the P1, N1, and P2 components (Figure 4.5).
We then computed average response magnitudes within each ERP time window,
using the average time course across trials within each condition for each participant,
for psilocybin and placebo separately.
148
Figure 4.4: Grand average amplitudes from all 20 electrodes placed across the scalp are
shown in 20 ms segments across the epoch time window from 0 to 400 ms post-stimulus
onset.
149
Figure 4.5: The grand average ERP waveform (all included trials in all conditions
form all participants). Mean amplitudes from the O1 and O2 electrodes are plotted in
time, with zero being the stimulus onset. Dashed lines indicate the time windows we
chose to demarcate each component in our analyses. Shaded area shows SEM.
150
Table 4.3: Time windows for ERP components (ms). These time windows were defined
qualitatively from the grand average ERP waveform.
N1 P1 P2
Start 120.0 90.0 244.3
End 243.0 119.3 283.3
4.2.9 Statistical analyses
Data from placebo and psilocybin sessions were analyzed separately, then compared
within participants. ERP responses for each component (mean voltage across the time
window) were compared in separate repeated-measures analyses of variance (ANOVAs)
using drug and stimulus condition as within-subjects factors. ANOVAs that reached
significance were followed-up with post hoc pairwise t-test comparisons.
5D-ASC subjective drug effects ratings were compared in a repeated measures
ANOVA using drug and ASC subscale/dimension as within-subjects factors (results
reported in Chapter 3 Section 3.3.3).
Correlation between ERP component amplitudes and 5D-ASC ratings were cal-
culated using RMcorr, “a statistical technique for determining the common within-
individual association for paired measures assessed on two or more occasions for
multiple individuals” (Bakdash and Marusich 2017; Bland and Altman 1995a, 1995b).5
and followed up with pairwise biweight midcorrelation tests (Langfelder and Horvath
2012) using the difference (psilocybin minus placebo) for ERP amplitude and rating
score pairs for each participant. We chose these correlation methods because they do
not require first averaging the data and avoid violating independence assumptions,
making them ideal (and more sensitive) for paired repeated-measures data (Bakdash
and Marusich 2017).
All statistical analyses were performed using the Pingouin package for Python
(Vallat 2018). The criterion for significance was p < 0.05.
5“Rmcorr accounts for non-independence among observations using analysis of covariance (AN-
COVA) to statistically adjust for inter-individual variability. By removing measured variance
between-participants, rmcorr provides the best linear fit for each participant using parallel regression
lines (the same slope) with varying intercepts” (Bakdash and Marusich 2017).
151
4.3 Results
Each of five participants completed the EEG task under psilocybin and placebo on
separate days. To examine the impact of psilocybin on visual-evoked ERP amplitudes,
we analyzed four distinct ERP waveform components—P1, N1, and P2—from EEG
recordings under psilocybin and placebo in three stimulus conditions (NS, OS, PS;
see Section 4.2 Figures 4.2). Repeated measures ANOVA analyses were performed
separately for each ERP component latency time window. The full ERP waveform
showing mean amplitude time course over the epoch time window for all three stimulus
conditions is shown in Figure 4.6 for psilocybin (A) and placebo (B).
To assess correlation between subjective rating scale item scores and ERP compo-
nent responses, we performed rmcorr analyses by pairing each participant’s (psilocybin
minus placebo) ERP component amplitudes with their 5D-ASC ratings of subjective
effects from each dosing session.
We chose the N1 component as our ERP time window of interest based on previous
findings that the N1 component is sensitive to center-surround stimulus configurations
in normal healthy adults (Schallmo, Kale, and Murray 2019), together with evidence
that psilocybin impacts ERP at the N1 time window (Kometer et al. 2013). Although
the N1 component was our time window of interest, we nonetheless performed separate
statistical analyses on each of the P1, N1, and P2 components, which are provided in
the sections below, in chronological order (relative to stimulus onset time).
4.3.1 P1 component (90.0 - 119.3 ms)
Our ANOVA analysis of P1 amplitudes found no significant main effect of drug (p
= 0.592) or stimulus condition (p = 0.236). The drug × condition interaction effect
likewise did not reach significance (p = 0.851).
A Shapiro-Wilk test did not show evidence of non-normality in the placebo (W =
0.942, p = 0.41) or the psilocybin (W = 0.952, p = 0.549) amplitudes data.
152
Figure 4.6: Full ERP waveform showing mean amplitude time course over the epoch
time window for all three stimulus conditions under placebo (A) and psilocybin (B).
153
Figure 4.7: Mean amplitude of all electrodes during three temporal segments within
the P1 time course in each stimulus condition under placebo (A) and psilocybin (B).
Table 4.4: Mean ERP amplitude for the P1 component in each stimulus condition
measured under psilocybin and placebo.
mean ± SEM
placebo psilocybin
condition
NS 1.506 ± 0.642 1.451 ± 0.426
OS 0.977 ± 0.347 0.553 ± 0.455
PS 0.853 ± 0.382 0.381 ± 0.427
154
Figure 4.8: Within-subjects effects of the drug were calculated by subtracting the
P1 amplitude measured under placebo from that measured under psilocybin for each
stimulus condition. Panel A shows the effect of psilocybin on P1 amplitude for each
individual participant. Panel B shows the mean effect for each stimulus condition.
Error bars represent standard error of the mean (SEM).
155
Figure 4.9: P1 amplitudes under psilocybin (blue) and placebo (orange) for the No
Surround (NS), Orthogonal Surround (OS), and Parallel Surround (PS) stimulus
conditions. Panel A shows amplitudes for individual participants in each stimulus
condition. Panel B shows means that have been normalized according to the technique
in Morey (2008) for within-subjects data. Error bars represent standard error of the
mean (SEM).
156
4.3.2 N1 component (120.0 - 243.0 ms)
Figure 4.10: Mean amplitude of all electrodes during three temporal segments within
the N1 time course in each stimulus condition under placebo (A) and psilocybin (B).
Main effect of drug on N1 amplitudes did not reach significance (F 1,4 = 0.477, p =
0.528, nG2 = 0.016). The main effect of stimulus condition did not reach significance,
likely due to insufficient statistical power (F 2,8 = 0.192, p = 0.829, nG2 = 0.001). The
drug × condition interaction effect reached significance (F 2,8 = 4.611, p = 0.047, nG2
= 0.011).
A Shapiro-Wilk test did not show evidence of non-normality in the placebo (W =
0.9, p = 0.096) or the psilocybin (W = 0.925, p = 0.229) amplitudes data.
Subsequent post hoc pairwise t test (two-sided) comparisons in which the N1
amplitudes from each stimulus condition were compared between psilocybin and
placebo did not reveal a main effect of drug on N1 amplitude (t4 = -0.69, p = 0.528,
Bayes factor = 0.48). Further t test comparisons were performed using N1 mean
amplitudes from each stimulus condition. For the No Surround (NS) condition, drug
did not have a significant effect on N1 amplitude (t4 = -0.055, p = 0.959, Bayes factor
= 0.398). Post hoc comparisons did not reveal an effect in the Orthogonal Surround
157
Figure 4.11: Within-subjects effects of the drug were calculated by subtracting the
N1 amplitude measured under placebo from that measured under psilocybin for each
stimulus condition. Panel A shows the effect of psilocybin on N1 amplitude for each
individual participant. Panel B shows the mean effect for each stimulus condition.
Error bars represent standard error of the mean (SEM).
Table 4.5: Results from a two-way repeated measures ANOVA comparing the effect
of drug, stimulus condition, and their interaction on mean amplitudes in theN1
component time window.
Sum Sq. df1 df2 Mean Sq. F p ng2
Source
drug 6.11 1 4 6.11 0.48 0.528 0.016
condition 0.47 2 8 0.24 0.19 0.829 0.001
drug * condition 4.15 2 8 2.07 4.61 0.047 0.011
158
(OS) condition (t4 = -0.539, p = 0.619, Bayes factor = 0.447). No significant effect
was found in the Parallel Surround (PS) condition (t4 = -1.936, p = 0.125, Bayes
factor = 1.167).
Figure 4.12: N1 amplitudes under psilocybin (blue) and placebo (orange) for the
No Surround (NS), Orthogonal Surround (OS), and Parallel Surround (PS) stimulus
conditions. Panel A shows amplitudes for individual participants in each stimulus
condition. Panel B shows means that have been normalized according to the technique
in Morey (2008) for within-subjects data. Error bars represent standard error of the
mean (SEM).
The mean N1 amplitude measured in the No Surround condition under placebo
was -3.49µV and under psilocybin was -3.4µV, suggesting that the N1 component was
not impacted by psilocybin when no surrounding stimuli were present.
We further confirmed this using a two-sided paired t-test, which found no significant
differences between psilocybin and placebo in the no-surround (NS) condition (t4 =
0.0552, p = 0.9587, d = 0.0202). This supports the notion that the mean trends we
observed in the OS and PS surround conditions for the N1 component are specific to
stimulus-driven center-surround interactions.
The effect of psilocybin on N1 amplitude for each stimulus condition was quantified
159
Table 4.6: Mean ERP amplitude for the N1 component in each stimulus condition
measured under psilocybin and placebo.
mean ± SEM
placebo psilocybin
condition
NS -3.489 ± 0.95 -3.398 ± 0.801
OS -4.109 ± 0.751 -3.38 ± 0.669
PS -4.594 ± 0.45 -2.707 ± 0.691
as the psilocybin N1 amplitude minus the placebo N1 amplitude. The mean effect of
psilocybin was a shift in the N1 amplitude of 1.887µV in the parallel surround (PS)
condition and 0.729µV in the orthogonal surround (OS) condition. By comparison,
when stimuli were presented with no surrounds (NS), the difference in the N1 amplitude
from placebo to psilocybin was 0.091µV. Figure 4.11 summarizes our results in terms
of the effect of psilocybin on N1 amplitude (drug minus placebo) for each stimulus
condition.
4.3.2.1 Subjective drug effects and the N1 component
Since ANOVA found some significant effects of drug on N1 amplitudes, we performed
rmcorr analyses to assess whether these differences correlated with ratings of sub-
jective drug effects on the 5D-ASC questionnaire. Our analyses found no significant
correlations between N1 amplitudes and 5D-ASC subjective rating scores.
4.3.3 P2 component (244.3 - 283.3 ms)
Main effect of drug on P2 amplitudes showed trend effects (F 1,4 = 5.831, p = 0.073,
nG2 = 0.378). The main effect of stimulus condition did not reach significance, likely
due to insufficient statistical power (F 2,8 = 2.28, p = 0.165, nG2 = 0.019). The drug
× condition interaction effect did not reach significance (F 2,8 = 0.647, p = 0.549, nG2
= 0.007).
A Shapiro-Wilk test did not show evidence of non-normality in the placebo (W =
0.926, p = 0.241) or the psilocybin (W = 0.925, p = 0.231) amplitudes data.
160
Figure 4.13: Mean amplitude of all electrodes during three temporal segments within
the P2 time course in each stimulus condition under placebo (A) and psilocybin (B).
Table 4.7: Results from a two-way repeated measures ANOVA comparing the effect of
drug, stimulus condition, and their interaction on mean amplitudes in theP2 component
time window.
Sum Sq. df1 df2 Mean Sq. F p ng2
Source
drug 71.66 1 4 71.66 5.83 0.073 0.378
condition 2.29 2 8 1.15 2.28 0.165 0.019
drug * condition 0.87 2 8 0.44 0.65 0.549 0.007
161
Figure 4.14: Within-subjects effects of the drug were calculated by subtracting the
P2 amplitude measured under placebo from that measured under psilocybin for each
stimulus condition. Panel A shows the effect of psilocybin on P2 amplitude for each
individual participant. Panel B shows the mean effect for each stimulus condition.
Error bars represent standard error of the mean (SEM).
162
Figure 4.15: P2 amplitudes under psilocybin (blue) and placebo (orange) for the
No Surround (NS), Orthogonal Surround (OS), and Parallel Surround (PS) stimulus
conditions. Panel A shows amplitudes for individual participants in each stimulus
condition. Panel B shows means that have been normalized according to the technique
in Morey (2008) for within-subjects data. Error bars represent standard error of the
mean (SEM).
163
Table 4.8: Mean ERP amplitude for the P2 component in each stimulus condition
measured under psilocybin and placebo.
mean ± SEM
placebo psilocybin
condition
NS 3.182 ± 0.759 0.051 ± 0.95
OS 3.478 ± 0.556 -0.009 ± 0.512
PS 2.425 ± 0.795 -0.23 ± 0.497
The mean P2 amplitude measured in the No Surround condition under placebo
was 3.18µV and under psilocybin was 0.05µV, suggesting that the P2 component was
impacted by psilocybin even when no surrounding stimuli were present.
The effect of psilocybin on P2 amplitude for each stimulus condition was quantified
as the psilocybin P2 amplitude minus the placebo P2 amplitude. The mean effect of
psilocybin was a shift in the P2 amplitude of -2.655µV in the parallel surround (PS)
condition and -3.487µV in the orthogonal surround (OS) condition. By comparison,
when stimuli were presented with no surrounds (NS), the difference in the P2 amplitude
from placebo to psilocybin was -3.131µV. Figure 4.14 summarizes our results in terms
of the effect of psilocybin on P2 amplitude (drug minus placebo) for each stimulus
condition.
4.3.3.1 Subjective drug effects and the P2 component
Since ANOVA found some trend effects of drug on P2 amplitudes, we performed
rmcorr analyses to assess whether these differences correlated with ratings of subjective
drug effects on the 5D-ASC questionnaire.
On the 5D-ASC scoring scale, rmcorr revealed statistically significant correlations
between P2 amplitudes and 5D-ASC scores for the data points (OS, Visionary Re-
structuralization) (r = -0.866, p = 0.026) and (OS, Oceanic Boundlessness) (r =
-0.836, p = 0.038); higher rating scores (more intense subjective drug effect) for the
psychedelic effects in these subscales were associated with P2 amplitudes.
Subsequent post hoc pairwise biweight midcorrelation tests did not reach significance
164
Table 4.9: Results from rmcorr repeated measures within-participants correlation of
P2 amplitude and ASC subscale scores under psilocybin and placebo.
dof r pval
condition subscale
OS Visionary Restructuralization 4 -0.866 0.026
Experience of Unity 4 -0.842 0.036
Oceanic Boundlessness 4 -0.836 0.038
Blissful State 4 -0.828 0.042
Audio-Visual Synesthesia 4 -0.825 0.043
PS Experience of Unity 4 -0.827 0.043
Figure 4.16: Rmcorr plot showing each individual’s 5D-ASC dimension scores from
placebo and psilocybin as a function of their P2 amplitude in each stimulus condition.
The lines show the rmcorr slope fit in relation to each participant’s data points.
165
for any of the significant rmcorr ERP/ASC results for ERP responses in the P2
component.
Figure 4.17: Rmcorr plot showing P2 amplitudes as a function of subjective drug
effects rating scores on subscales from the Oceanic Boundlessness dimension for each
subject in each stimulus condition. The lines show the rmcorr slope fit in relation to
each participant’s two data points (placebo and psilocybin).
Rmcorr analysis revealed significant correlations between P2 amplitudes and 11D-
ASC subscale items at the data points (OS, Experience of Unity) (r = -0.842, p =
0.036), (OS, Blissful State) (r = -0.828, p = 0.042), (PS, Experience of Unity) (r
= -0.827, p = 0.043), and (OS, Audio-Visual Synesthesia) (r = -0.825, p = 0.043).
For these data points, higher rating scores (more intense subjective drug effect) were
associated with P2 amplitudes.
Subsequent post hoc pairwise biweight midcorrelation tests reached significance for
the data points (OS, Blissful State) (r = -0.85, p = 0.034). The data points for (OS,
Experience of Unity) (r = -0.53, p = 0.179), (PS, Experience of Unity) (r = -0.712, p
= 0.089), and (OS, Audio-Visual Synesthesia) (r = -0.49, p = 0.201) did not reach
significance in the post-hoc tests.
166
Figure 4.18: Rmcorr plot showing P2 amplitudes as a function of subjective drug
effects rating scores on subscales from the Visionary Restructuralization dimension
for each subject in each stimulus condition. The lines show the rmcorr slope fit in
relation to each participant’s two data points (placebo and psilocybin).
Table 4.10: Questionnaire items from 11D-ASC subscale Audio-Visual Synesthesia
that reached significant correlation with P2 amplitudes in at least one stimulus
condition in the rmcorr analysis. Mean scores for placebo (Pla) and psilocybin (Psil)
are shown in the right-hand columns.
text Pla Psil
item
20 Sounds seemed to influence what I saw. 9 72
23 Shapes seemed to be changed by sounds or noises. 2 52
75 The colors of things seemed to be altered by sounds or noises. 0 44
Table 4.11: Questionnaire items from 11D-ASC subscale Blissful State that reached
significant correlation with P2 amplitudes in at least one stimulus condition in the
rmcorr analysis. Mean scores for placebo (Pla) and psilocybin (Psil) are shown in the
right-hand columns.
text Pla Psil
item
12 I experienced boundless pleasure. 2 58
86 I experienced profound inner peace. 3 75
91 I experienced an all-embracing love. 3 80
167
Table 4.12: Questionnaire items from 11D-ASC subscale Experience of Unity that
reached significant correlation with P2 amplitudes in at least one stimulus condition
in the rmcorr analysis. Mean scores for placebo (Pla) and psilocybin (Psil) are shown
in the right-hand columns.
text Pla Psil
item
18 Everything seemed to unify into a oneness. 3 50
34 I felt one with my surroundings. 7 80
41 I experienced a touch of eternity. 6 59
42 Conflicts and contradictions seemed to dissolve. 5 60
52 I experienced past, present, and future as a oneness. 7 47
4.4 Limitations
This was a pilot study with a small sample size (n = 5). A total of six participants
completed the study; however we excluded data from one participant in this analysis
due to excessive artifacts in all of their EEG recordings. This skewed the counterbal-
ancing of sex (M = 3; F = 2) and the order in which the participants received the
drug (placebo first = 3; psilocybin first = 2).
Our analysis does not include signal source localization analyses to identify the
anatomical regions of origin for the ERP responses, other than our choice of including
only occipital electrode locations O1 and O2. While we feel that this is appropriate
for the early P1 and N1 components (Schallmo, Kale, and Murray 2019), excluding
data from the other electrodes likely limits the conclusions that can be drawn from
our analyses regarding the P2 component, as the neural responses in this component
are usually stronger in higher brain regions. Furthermore, our vendor EEG hardware
had 21 channels, which is relatively fewer channels than other ERP studies.
While the visual effects of psilocybin are linked to its 5-HT2A receptor agonism,
we did not implement any method for determining whether our findings result from
psilocybin’s 5-HT2A agonism versus its affinity at other receptor sites. While there
are pharmacological methods for investigating this (e.g., by administering ketanserin
to block psilocybin’s 5-HT2A agonism in a separate experimental condition), we did
not employ this method. It will be important to do so in future studies, as Carter,
168
Burr, et al. (2005) found that at least some cognitive-perceptual effects of psilocybin
are not blocked when ketanserin is used to block its 5-HT2A agonism, suggesting that
its 5-HT1A agonist properties might also play a role in some of its effects. Taken
together, these points illustrate that our findings are inclonclusive about whether
5-HT2A agonism, 5-HT1A agonism, or other pharmacological properties of psilocybin
caused the effects on ERP that we observed.
As noted in Section 4.2.2, the EEG task began exactly 5 hours post-drug ad-
ministration, meaning that the strongest ‘peak’ drug effects (and plasma levels) had
diminished by the time the EEG task started. As a result, all EEG findings should
be considered ‘post-peak’ psilocybin effects. The differences between psilocybin and
placebo reported here would likely be stronger if the task had occurred during peak
drug effects, but we cannot be sure until future experiments are done at the peak
effects (~2.5 hours) timepoint. Furthermore, due to individual differences in drug
metabolism rates, there is greater individual variability in the intensity of drug effects
at this later time-point. We reason that measurements obtained at the peak-effects
time point would show less individual variability.
4.5 Discussion
We obtained EEG recordings while participants viewed visual stimuli with differing
center-surround configurations under psilocybin and placebo, and compared ERP
responses under each drug. Our analyses of ERP responses were segmented into four
time windows (known as ERP components)—P1, N1, and P2 (Table 4.3). We asked
whether psilocybin impacted surround suppression, a form of contextual modulation
where neural responses differ depending on the center-surround configurations of the
stimuli. Previous studies have found that the strength of the N1 response is reduced
(amplitude is less negative) when the target stimulus appears within a surround versus
when it appears alone, and that this effect is strongest in parallel vs orthogonal
surrounds (Schallmo, Kale, and Murray 2019). Our main finding was that psilocybin
increased surround suppression in the N1 ERP component time window; i.e., the
169
magnitude of stimulus-evoked N1 responses was lower for the Parallel Surround
stimulus under psilocybin compared with placebo, indicating that psilocybin increased
surround suppression (as measured by ERP) in an orientation-selective manner.
4.5.1 Enhanced ERP surround suppression in the N1 com-
ponent
We observed a marked effect of psilocybin on the average N1 response in the Parallel
Surround condition (Figures 4.6, 4.12, and 4.11). ANOVA results revealed a significant
interaction effect of drug × condition (F 2,8 = 4.611, p = 0.047, nG2 = 0.011). While
subsequent post hoc pairwise t test (two-sided) comparisons did not confirm the effect
of psilocybin on N1 ERP response in the Parallel Surround condition, the statistical
effect in the t test for the PS condition (t4 = -1.936, p = 0.125, Bayes factor = 1.167)
was much stronger than in the OS condition (t4 = -0.539, p = 0.619, Bayes factor =
0.447) or the NS condition (t4 = -0.055, p = 0.959, Bayes factor = 0.398), indicating
that the effect we observed in the PS condition is specific to the parallel orientation
of the surround and could possibly reach significance in future research with larger
sample sizes.
Due to our small sample size and limited statistical power, the effect we observed on
the ERP response in the N1 component should be considered a preliminary trend effect.
Nonetheless, it suggests that psilocybin led to an increase in contextual modulation:
when the target stimulus appeared within a parallel-oriented surround, the mean N1
response (-2.71µV) was less strong (less negative) than it was in the No Surround
(-3.4µV) and Orthogonal Surround (-3.38µV) conditions (see Figures 4.6, 4.12, and
4.11). Taken together, this trend suggests that psilocybin may increase neural surround
suppression in the N1 ERP response, possibly reflecting a strengthening of contextual
modulation during neural processing after about 120 ms post-stimulus—the Parallel
Surround condition having stronger contextual cues than the OS and NS stimuli.
This trend mirrors the behavioral results from the psychophysics experiment in
Chapter 3. Using a perceived contrast matching task, we found that surround stimuli
170
produced a stronger illusion under psilocybin than they did under placebo, leading
subjects to perceive the contrast of the target stimulus less veridically compared
with placebo. Furthermore, the impact of psilocybin was strongest in the Parallel
Surround condition, less strong in the Orthogonal Surround condition, and psilocybin
had no impact on contrast perception in the No Surround condition. Thus, the present
ERP findings (in N1 responses) are generally consistent with our psychophysical
measurements of contrast suppression in perception reported in Chapter 3.
Chapter 2 argues that the visual effects of psychedelic drugs occur because the
brain becomes hypersensitive to contextual cues. Section 2.3.3 reviewed multiple
studies (Barrett et al. 2018; Carter et al. 2004; Gouzoulis-Mayfrank et al. 2002)
which suggest that psilocybin impacts performance on visual tasks if and only if the
task induces contextual modulation.
Taken together with our behavioral findings reported in Chapter 3, the present
ERP findings lend further support to this theory. The trends we found in the present
ERP study provide some evidence that, like task performance, neural responses display
greater spatial context-sensitivity under psilocybin. We found that ERP responses
evoked by our stimuli under psilocybin were most different from placebo when the
stimulus induced strong contextual modulation (i.e., the Parallel Surround). As
Figures 4.6, 4.12, and 4.11 show, psilocybin’s impact on ERP responses increased
as a function of the level of contextual information in the stimuli, suggesting that
participants became hypersensitive to visual contextual cues under psilocybin.
4.5.2 The general effect of psilocybin on ERP responses
We found reduced mean amplitudes across all components in the ERP time course
under psilocybin. This finding was expected, as MEG and EEG studies have found
consistent reductions in amplitudes and oscillatory power across a broad frequency
range under psilocybin (Muthukumaraswamy et al. 2013; Kometer et al. 2015;
Schartner et al. 2017), as well as ayahuasca (Riba et al. 2002, 2004; Schenberg et al.
2015; Valle et al. 2016) and LSD (Carhart-Harris, Muthukumaraswamy, et al. 2016;
Schartner et al. 2017).
171
As shown in Figure 4.6, all ERP components were less pronounced (the peaks of
the waveform are ‘flattened’) under psilocybin compared with placebo. The significant
correlations we found between P2 amplitudes and intensity of subjective effects is likely
due to the general reduced amplitude in all ERP components under psilocybin, which
would trigger significant correlations with higher scores on the 5D-ASC in response to
the psilocybin experience. Furthermore, our analyses are not ideal for probing the
effect of psilocybin on the later ERP components (e.g., the P2), as we confined our
analyses to data in the occipital electrodes of interest (O1 & O2); the later-latency
components should be investigated using a different (and broader) selection of electrode
sites. We are therefore reluctant to draw conclusions from the correlations we reported
between subjective effects and reduced ERP responses in the P2 component time
window. Nonetheless, the results have been included here to inform future hypotheses
and study designs.
4.5.3 Contextual modulation, top-down feedback, and 5-
HT2A
Schallmo, Kale, and Murray (2019) argue that feature-selective N1 surround sup-
pression reflects top-down feedback modulation from higher to lower visual areas,
consistent with findings from electrophysiology in primates Webb et al. (2005). When
our result is considered within this line of reasoning, the (preliminary) conclusion
is that psilocybin enhanced top-down modulation, because the magnitude of the
N1 response was suppressed in a feature-selective manner, consistent with stronger
top-down feedback on V1 responses.
Our finding is thus inconsistent with the theoretical tenets of Carhart-Harris and
Friston (2019), who argue:
that psychedelics act preferentially via stimulating 5-HT2ARs on deep pyramidal
cells within the visual cortex as well as at higher levels of the cortical hierarchy
. . . Computationally, this process corresponds to reducing the precision of
higher-level prior beliefs . . . It is our view that psychedelics affect not just
high-level priors but intermediate-level priors also (e.g., those instantiated within
the visual cortex) (Carhart-Harris and Friston 2019).
172
The “REBUS” model thus proposes a fundamental tenet of “reduced top-down
processing under psychedelics” (Carhart-Harris and Friston 2019, emphasis added)
including feedback from higher to lower visual areas. However, our findings suggest the
opposite. The enhanced feature-selective N1 surround suppression under psilocybin
we observed here suggests that psilocybin increased top-down feedback from higher
to lower visual areas. The differences between our stimulus conditions (no surround
vs. parallel vs. orthogonal surround) are reasonably assumed to depend on top-down
priors recruited during stimulus processing. As the enhanced N1 surround suppression
we observed was feature-selective, basic visual priors appear to be preserved—and
their influence strengthened—under psilocybin. Taken together, these considerations
illustrate how our result might contribute to the ongoing debates, (Corlett, Frith, and
Fletcher 2009; Pink-Hashkes, Rooij, and Kwisthout 2017; Letheby and Gerrans 2017;
Swanson 2018; Carhart-Harris and Friston 2019), which currently present conflicting
heterogenous interpretations (see Chapter 1, Section 1.5.3 and also Chapter 2 Section
2.3.4) about how to characterize serotonergic psychedelic effects within a theoretical
framework of predictive processing.
4.5.4 Future research
Future ERP studies with psychedelics are needed to more precisely characterize
the impact of psychedelics on visual stimulus-evoked ERPs. The present study
demonstrates that differences in responses across the ERP time course can be measured
under psychedelics using carefully designed stimulus conditions. Moreover, theoretically
impactful predictions can be tested—regarding visual processing, serotonin, and
psychedelic effects—using such pharmaco-EEG paradigms.
173
Conclusion
In this dissertation, I examined the effects that psychedelic drugs have on visual
perception. In this final chapter I summarize the key findings, discuss their implications,
and suggest ways in which this work might contribute to our knowledge of visual
perception, psychedelic drugs, and mental health. I also note important limitations
and suggest areas of future research.
Main findings
The research question of my historical literature review in Chapter 1 was: What is the
best explanation of psychedelic drug effects to date? After examining and comparing
early and more recent theories of psychedelic effects, I concluded that a truly unifying
understanding has yet to emerge. I identified some important universal themes—in
particular, the notion that psychedelics ‘loosen constraints’ on neural activity, widening
the ‘filter’ or opening up the ‘reducing valve’ of the mind. I noted that this general
idea, which inspired Osmond and Huxley to coin the word ‘psychedelic’, has roots
in the ideas of James (1890), Bergson (1911), Broad (1923), and Freud (1895). This
idea is often leveraged to explain why psychedelic effects overlap with symptoms of
early psychosis, which prima facie appear to share presentation of hallucinatory and
mystical themes. The reasoning is that psychedelics can ‘model psychoses’ because
they ‘loosen constraints’ on the mind (Osmond and Smythies 1952). I pointed out that
contemporary theoretical neuroscience continues to think along these lines. Entropic
Brain Theory characterizes the “system-level mechanics of the psychedelic state as an
exemplar of a regressive style of cognition that can also be observed in REM sleep and
174
early psychosis” (Carhart-Harris et al. 2014, 5). These system-level mechanics have
been characterized in terms of hierarchical predictive coding as a reduced influence
of higher over lower cortical areas (Carhart-Harris and Friston 2019), an idea linked
to Freud’s neural theory of ego function (Carhart-Harris and Friston 2010; Savage
1955; Klee 1963). A central idea in predictive coding theories of brain function is that
“high-level areas tell lower levels to ‘shut up’ ” (Kersten, Mamassian, and Yuille 2004,
297). Carhart-Harris and Friston (2010) argue that under psychedelics higher-level
cortical structures are less likely to suppress activity in lower areas, and in a later
paper Carhart-Harris and Friston (2019) see this as a property that psychedelic states
share with schizophrenia, stating that “our view of the etiology of schizophrenia is that
. . . the brain’s highest-level priors are ineffective, meaning bottom-up prediction errors
travel more freely up the hierarchy . . . This much is consistent with the psychedelic
experience”. Taken together, the idea is that top-down feedback modulation is reduced
under psychedelics, and that this process is also at play in the processing characteristic
of schizophrenic patients.
Thus, on the above model, surround suppression should be weaker in schizophrenic
patients. Indeed, weakened surround suppression has been found in schizophrenic
patients (Dakin, Carlin, and Hemsley 2005; Tadin et al. 2006; Yoon et al. 2009; Yang
et al. 2012; Tibber et al. 2013; Serrano-Pedraza et al. 2014; Schallmo, Sponheim,
and Olman 2015; V. J. Pokorny et al. 2019; Linares et al. 2020). Based on these
findings, taken together with the idea that psychedelic states follow a comparable
pattern of ‘weakened priors’ and reduced top-down feedback modulation of higher over
lower cortical areas, our initial hypothesis was that we would find weaker surround
suppression under psilocybin. However, this hypothesis was not confirmed, as both
the behavioral results from our psychophysics experiment and the ERP results from
our EEG experiment found stronger surround suppression under psilocybin.
Our psychophysical experiment in Chapter 3 found that surround suppression was
stronger under peak effects of a high dose of psilocybin in healthy human participants.
In this experiment, some trials involved context-modulating (surround) stimuli, while
other trials did not. For the trials using stimuli not designed to modulate spatial
175
context, psilocybin had no impact on task performance compared with placebo. By
comparison, psilocybin significantly impacted task performance on trials that presented
the target stimulus within a context-modulating surround stimulus. The direction
of impact was that psilocybin increased the influence that the surround had over
the percept of the target stimulus. In other words, under peak effects of psilocybin,
surround suppression was enhanced; the strength of the illusion was greater; the
percept of the target stimuli was less veridical. These results suggest that some of the
visual effects of psilocybin and other classic serotonergic psychedelic drugs may reflect
increased contextual modulation in perception. The findings presented in Chapter 3
addressed my third research objective—To measure the impact of a psychedelic drug
on visual contrast perception and surround suppression in humans—as well as my
third research question: Does psilocybin impact contrast perception and surround
suppression in humans? The results indicate that contrast perception is preserved
under psilocybin when the target stimuli appear in isolation, but the influence of
higher-contrast surrounding stimuli is stronger, suggesting that the drug selectively
targets contextual modulation in visual contrast perception.
In Chapter 4 we used similar stimuli to measure evoked neural responses with EEG
equipment, and analyzed the event-related potentials (ERPs) that occurred under
psilocybin and placebo. We found a preliminary result indicating that psilocybin
increased the effect that the surround stimulus had on the magnitude of the N1
component of the ERP wave form. In other words, psilocybin enhanced surround
suppression in the ERP, mirroring the psychophysical results we reported in Chapter
3. Furthermore, the effect of psilocybin on N1 ERP surround suppression was stimulus
feature-selective, in that it occurred only in the condition when the surround had the
same orientation as the target stimulus. In other words, when the contextual cues
were stronger, the visual-evoked ERP response showed the greatest difference under
psilocybin compared with placebo.
Returning to theoretical considerations, our finding calls into question the posit
(e.g., Carhart-Harris and Friston 2019) that psychedelics weaken the kind of top-down
feedback that surround suppression is thought to stem from. Our findings, while
176
preliminary, are thus anomalous to the idea that psychedelics work by weakening top-
down feedback in perceptual processing. They also challenge the century-old ‘model
psychoses’ assumption that psychedelics produce modes of brain function comparable
to psychosis, as our surround suppression finding (enhanced under psilocybin) is the
opposite of what is found in schizophrenia (weakened surround suppression).
I thus began to question the theoretical notion that psychedelic effects result
from weakened influence of priors, asking: What alternative mechanism would cause
psilocybin to enhance surround suppression? One way to address the tension between
our findings and Carhart-Harris and Friston (2019), who state that “the relaxation
of high-level priors [under psychedelics] necessarily implies a liberation of bottom-
up information flow” would be to consider the possibility that psilocybin-induced
enhancements of surround suppression might be the result of increased weighting of
bottom-up cues that trigger top-down feedback, whereby the ‘liberated’ bottom-up
signals (i.e., the surround stimulus) are more likely to trigger stronger top-down effects
(i.e., the suppression illusion) under psychedelics. However, this interpretation would
still be in conflict with the core hypotheses of Carhart-Harris and Friston (2019)
and Pink-Hashkes, Rooij, and Kwisthout (2017) who clearly argue that psychedelics
weaken top-down processes. The alternative I propose is that the brain becomes more
sensitive to bottom-up cues, and in response recruits stronger top-down feedback from
higher-level priors.
In Chapter 2 I argued in support of the notion that many hallmark psychedelic
effects might be understood as resulting from psychedelic molecules inducing a hy-
persensitivity to contextual cues. The same mechanisms of contextual modulation
that produce classic visual illusions in everyday perception—which are thought to
involve top-down feedback from higher-level priors, triggered by specific stimulus
properties—may be selectively impacted by psychedelic drugs, exaggerating everyday
visual context effects to produce characteristic psychedelic visuals. I then argued that,
if the mechanisms of contextual modulation that produce classic everyday illusions
were exaggerated pharmacologically, the resulting phenomenology would potentially
match that of classic open-eyed psychedelic visuals. Importantly, this theoretical
177
principle has advantages over existing theories reviewed in Chapter 1 because it
can be operationalized both empirically (using well-known psychophysical paradigms
that induce and measure contextual modulation) as well as neurocomputationally
(using divisive normalization and/or predictive coding models of visual illusions). I
named this idea hypercontextual modulation (HCM) and applied it to explain not
only psychedelic visuals, but also classic psychedelic effects in non-visual modalities,
including music listening, cognitive semantic activation, and symbolic thinking. Taken
together, the analysis presented in Chapter 2 addressed my second research objec-
tive—To taxonomize and analyze the phenomenology of psychedelic visuals—as well
as my second research question: Do psychedelic drugs affect contextual modulation in
visual processes?
In summary, the main takeaways from this dissertation are as follows. First,
existing theories of psychedelic effects present conflicting and heterogenous posits
(Chapter 1). Second, our (preliminary) results found that psilocybin enhanced sur-
round suppression in perception and in neural responses (Chapters 3 & 4). Third, our
findings are in tension with the idea that psychedelic effects result from weakened
influence of top-down priors, and challenge the assumption that psychedelic states
share patterns of neural processing with psychosis. Fourth, an alternative view of
psychedelics was proposed to resolve these tensions; namely the idea of hypercontex-
tual modulation, which understands psychedelic effects as resulting from over-active
contextual modulation in response to bottom-up contextual cues (Chapter 2). Fifth
and more generally, I hope that this dissertation demonstrates the promise of focused
thinking about the visual effects of psychedelics. I argue that treating psychedelic
visuals as specimens to be examined toward understanding psychedelic action in the
brain is a pragmatic approach. Visual perception is relatively well-understood (in
comparison with other mental functions) and thus experiments can be designed to
characterize the ways in which psychedelics alter neural and perceptual responses to
visual stimuli, providing scoped characterizations of high scientific value.
178
Contributions and significance
The most straightforward contribution of this dissertation is to the field of visual
neuroscience. The experimental findings in Chapters 3 and 4, which suggest that
psilocybin selectively enhances surround suppression in human contrast perception,
indicate that serotonergic (5-HT2A/5-HT1A) signaling is used by the visual system
to shape neural responses according to contextual cues. Because the visual effects of
psilocybin are critically linked to activation of 5-HT2A/5-HT1A receptors, our findings
hint that serotonergic mechanisms may underlie the suppression (and possibly also
facilitation) of neural responses to visual stimuli. Moreover, the analysis in Chapter 2
provides rationale to suspect that serotonin might be a canonical sensory neuromodu-
lator that shapes experience in response to contextual cues in vision, possibly other
perceptual modalities, and even cognition. The general logic is this: (1) psychedelics
alter perception and cognition in characteristic ways, (2) these alterations depend on
5-HT2A/5-HT1A agonism, (3) therefore, 5-HT2A/5-HT1A signaling may be critically
involved in certain everyday perceptual functions. The present work demonstrates
how probing the serotonin-perception relationship using targeted analytical and em-
pirical methods can have significant scientific payoff toward understanding how visual
perception is implemented in the brain.
The second contribution is to the field of psychedelic research. Psychedelic experi-
ences are unique mental phenomena that the cognitive sciences and philosophy have
largely missed out on—and have almost completely ignored—since 1970. As a result,
works that apply modern methods to psychedelic phenomena are currently in high de-
mand. Chapter 1 was published in Frontiers in Pharmacology in 2018. The essence of
this contribution is in its synthesis—it has served as a conceptual anchor-point within
which researchers have framed their questions and interpreted their results. Citing
publications often reference Chapter 1 for its synthesis of theoretical concepts and its
analysis of open research questions. It is also commonly referenced as an overview of
the phenomenology of acute psychedelic drug effects. As Chapter 2 provides analyses,
literature review, and descriptions of visual phenomenology, it has the potential to
179
have a similar impact on psychedelic research.
As mentioned in the Introduction chapter, the visual effects of psychedelics have
been largely understudied for the past 100 years, since the early work of Klüver (1928),
and remain in the sidelines as recent work has focused instead on task-free (resting-
state) neuroimaging and subjective ego-dissolution and mystical experiences. Thus,
the visual-focused analysis in Chapter 2, the psychophysical experiment in Chapter
3, and the visual task-oriented EEG experiment in Chapter 4 all address important
gaps in contemporary knowledge of how psychedelics impact visual perception. The
empirical chapters are likely to spur more studies into how psychedelics alter contextual
modulation in perception. The theory that I propose in Chapter 2 will hopefully
contribute new ways of understanding psychedelic drug mechanisms; namely, that
many effects might be due to selective exaggeration of contextual modulation processes
in neural responses.
The third contribution is to the field of psychiatry. Currently, the arguably
impressive therapeutic efficacy of psilocybin remains scientifically mysterious and
actively debated (Yaden and Griffiths 2020; Letheby 2021; Hesselgrave et al. 2021;
McClure-Begley and Roth 2022; Berg et al. 2022). A central contribution to psychiatry
that I make with the present work is the suggestion that psilocybin (and other classic
serotonergic psychedelics) shift sensory processing in the opposite direction of the
sensory deficits found in the disorders that psilocybin is effective at treating. For
instance, a recent study found that patients actively experiencing major depressive
episodes show weakened surround suppression in visual contrast perception relative
to healthy controls (Salmela et al. 2021), while our results in Chapter 3 found
that psilocybin strengthened surround suppression relative to placebo in healthy
participants, and the same stimuli evoked greater surround suppression in EEG/ERP
responses as reported in 4. Meanwhile, other studies have shown that depressed
patients show blunted reactions to emotional face stimuli, while psilocybin has been
shown to enhance reactions to faces. In Chapter 2 I argue that the hypersensitivity
induced by psychedelics might serve to counteract the hyposensitivity to sensory cues
found in patients with active depressive episodes (i.e., anhedonia, reduced appreciation
180
for music, reduced sense of richness in sensory phenomenology). Thus, the contribution
to psychiatry and psychedelic therapy that I make in the present work is the notion
that hypercontextual modulation is a candidate mechanism by which these drugs
confer therapeutic benefits for certain patients. Importantly, this notion offers greater
scientific tractability than alternative theories that currently emphasize ego-dissolution
and mystical experiences as causal therapeutic mechanisms, as these concepts are by
comparison more challenging to operationalize clinically. Furthermore, the empirical
and analytical contributions I make in this dissertation, while preliminary, have
the potential to spur innovations in clinical practices that use psychedelic drugs to
improve mental health, perhaps by inspiring methodologies that better harness the
context-modulating properties of these drugs.
Limitations
The main empirical limitation of the experiments in Chapters 3 and 4 is the small
number of participants (n = 6). Psychedelic research is resource-intensive, requiring
significant amounts of funding for administrative staff, facilities, and large amounts
of time for navigating complex regulatory requirements of the research institution
and the federal government. While I feel lucky to have been able to collect data
from this many participants for the present dissertation, the results of the empirical
chapters should be considered preliminary findings from a pilot study. I am hopeful
that the future will bring further opportunities to confirm the findings, refine the
methodologies, and test alternative hypotheses.
The empirical chapters excluded drug-naive participants. This decision was made
due to the demanding nature of completing the visual tasks under a high dose of
psilocybin, and we felt that some prior experience with the effects of psilocybin would
reduce the likelihood of participants becoming overwhelmed by the stimuli and the task.
This limits the generalizability of our findings; the measurements may have turned out
differently if we had included drug-naive participants. A second limitation with the
EEG experiment, as noted in Chapter 4, is that the task was performed ‘post-peak’, 5
181
hours after psilocybin administration, while the most intense subjective effects had
diminished (the psychophysics experiment, by contrast, was completed during peak
effects). Thus, the neural responses we measured with EEG are not reflective of brain
activity during peak drug effect. A third limitation that applies to both experiments
is that we did not attempt to isolate 5-HT2A from other receptor agonism produced
by psilocybin, so we cannot determine which pharmacological properties of psilocybin
were responsible for the effect we measured.
Chapter 2 is my own theoretical contribution. It is based on first-principles analysis,
taking subjective phenomenology as a starting point, then applying analytical reasoning
to interpret previous empirical findings to arrive at a theory of how psychedelics alter
perception and other mental processes. Although philosophy has preceded scientific
progress throughout history, this type of approach has its limitations. The arguments I
offer demand empirical vetting. Though the findings from the experiments in Chapters
3 and 4 are highly consistent with the theoretical conclusions I draw in Chapter 2,
further inquiry is needed.
While the insights and findings presented here have bearing on understanding how
psychedelics facilitate therapy and mental health—and I hope they will do so—they
are not intended to serve as prescriptive therapeutic practices, and it is important
to emphasize that the mechanisms and efficacy of psychedelics in treating mental
health disorders remain largely unknown. The passages in which I discuss therapeutic
mechanisms should be considered entirely speculative. They are offered solely as
conceptual navigation tools for future research.
Finally, I wish to point out that analytical reasoning and scientific methods have
upper limits in what they can offer toward improving our understanding of psychedelic
experiences. Experiments, models, theories, and verbal descriptions of subjective
experience do not capture the complete nature of the rich subjective phenomena that
psychedelics produce in human consciousness. The experiences that these molecules
engender remain qualitatively mysterious and elusive.
182
Future research
The central finding of the present work is that psychedelics impact visual processes in
a feature-selective and context-sensitive manner. We found that both perceptual and
neural responses to visual stimuli differed most from placebo when the stimulus was
designed to elicit contextual modulation. This finding is consistent with the theoretical
framework of hypercontextual modulation proposed in Chapter 2 to explain psychedelic
visuals and other hallmark psychedelic effects in perception and cognition. While the
findings are tentative and preliminary, and the theory is highly speculative, I believe
that together they offer a highly promising line of research.
The first task will be to carry out a full-scale study on visual surround suppression
under psilocybin. As the present study demonstrates, this paradigm is an effective
means of measuring differences in contextual modulation in response to visual stimuli
under psychedelic drugs. I envision such a study as involving four key components.
First, it should collect at least two separate baseline measurements before any drug is
taken. Second, it should be multimodal, collecting three data points under peak drug
effects: (1) psychophysical measurements of perception, (2) EEG/MEG measurements
for temporal resolution, and (3) fMRI measurements for spatial/anatomical resolution.
Third, it should implement dose-response techniques, comparing low-medium with
higher drug doses. Fourth, it should implement a separate experimental condition in
which a 5-HT2A antagonist drug (such as ketanserin) is administered simultaneously
with psilocybin, in order to enable scientific inferences about the neuromodulator
mechanisms that are causally involved in surround suppression. Finally, follow-up
measurements should be conducted on at least two separate time-points post-drug.
A further expansion of the surround suppression under psychedelics research
programme will be to investigate a variety of psychedelic drugs. LSD, DMT, and
mescaline each have unique phenomenological profiles and pharmacological properties
distinct from psilocybin. It would thus be informative to characterize their impact on
visual contextual modulation using the same multimodal paradigm described above.
The next task will be to measure surround suppression in a clinical population of
183
MDD patients who undergo psilocybin therapy. I envision such a study as measuring
surround suppression pre- and post-psilocybin therapy (not during acute drug effects),
with a control group of patients who receive non-psilocybin therapy, and another
control group of healthy participants. Such a study would provide insight into
the relationship between MDD, contextual modulation in visual processing, and
the therapeutic mechanisms of psilocybin. Previous work that used stimuli almost
identical to ours (Salmela et al. 2021) found reduced surround suppression in MDD
patients while they were experiencing a major depressive episode (MDE), and that
their levels of visual surround suppression returned closer to healthy controls once
they were in remission and no longer experiencing an MDE. A future study measuring
visual surround suppression in MDD patients at timepoints before and after psilocybin
therapy would thus be of great scientific value. It could directly test the hypothesis that
blunted contextual modulation in MDD is restored by the hypercontextual modulation
induced by psilocybin.
Beyond surround suppression, contextual modulation paradigms in auditory per-
ception, verbal processing, and cognitive tasks should be carried out in healthy
participants under acute psychedelic effects. Such experiments would expand the
characterization of how psychedelics impact contextual processing across the brain
and in different dimensions of mental function.
Once the above data is collected, computational modeling for observed neural
and behavioral responses to contextual stimuli will aid the development of theoretical
understanding. As mentioned in Chapter 2, divisive normalization is a promising
computational model that captures contextual modulation as a canonical computation
carried out in multiple neural processes. Future modeling work using data collected
from neural responses to context-modulating stimuli under psychedelic drugs could
contribute significantly to understanding how the brain modulates its responses using
contextual cues, and the neuromodulatory substrates that underpin normalization
computations.
184
Final thoughts
Our minds are fluid manifolds of contextual interactions. Sensory stimuli are continu-
ously bound together into patterns that become subjectively meaningful via processes
that have long been elusive. The visual effects of psychedelics magnify this process in
a spectacular manner. In the present work, I have used these phenomena as a handle
to get a grip on what psychedelics do to human minds. As Metzner and Leary (1967)
put it, “A psychedelic experience is a period of intensely heightened reactivity to
sensory stimuli from within and without.” It is my hope that readers will see the value
in taking psychedelic visuals seriously as important phenomena with the potential to
enhance our understanding of psychedelics, visual perception, and the way in which
our minds transform contextual cues into the subjective stream of meaningful contents
that we experience in perception and cognition.
185
Bibliography
Abbott, L F. 2008. “Theoretical Neuroscience Rising.” Neuron 60 (3): 489–95.
https://doi.org/10.1016/j.neuron.2008.10.019.
Abraham, H. 1996. “The Psychopharmacology of Hallucinogens.” Neuropsychophar-
macology 14 (4): 285–98. https://doi.org/10.1016/0893-133x(95)00136-2.
Abramson, Harold A. 1956. Neuropharmacology: Transactions of the Second Confer-
ence, May 25, 26, and 27, 1955, Princeton, NJ. Josiah Macy, Jr. Foundation.
———. 1960. The Use of LSD in Psychotherapy : Transactions of a Conference on
d-Lysergic Acid Diethylamide (LSD-25), April 22, 23, and 24, 1959, Princeton,
n.j. New York, N.Y.
Aday, Jacob S., Julia R. Wood, Emily K. Bloesch, and Christopher C. Davoli. 2021.
“Psychedelic Drugs and Perception: A Narrative Review of the First Era of
Research.” Reviews in the Neurosciences 32 (5): 559–71. https://doi.org/10.1515/
revneuro-2020-0094.
Adelson, Edward H. 2000. “Lightness Perception and Lightness Illusions. The New
Cognitive Neurosciences.” Cambridge, MA: MIT Press.
Adesnik, Hillel, William Bruns, Hiroki Taniguchi, Z Josh Huang, and Massimo
Scanziani. 2012. “A Neural Circuit for Spatial Summation in Visual Cortex.”
Nature 490 (7419): 226–31. https://doi.org/10.1038/nature11526.
Allen, Emily J., and Andrew J. Oxenham. 2014. “Symmetric Interactions and
Interference Between Pitch and Timbre.” The Journal of the Acoustical Society of
America 135 (3): 1371–79. https://doi.org/10.1121/1.4863269.
Allen, Micah, and Karl J Friston. 2016. “From Cognitivism to Autopoiesis: Towards
a Computational Framework for the Embodied Mind.” Synthese, December, 1–24.
https://doi.org/10.1007/s11229-016-1288-5.
Allman, J, F Miezin, and E McGuinness. 1985. “Stimulus Specific Responses from
Beyond the Classical Receptive Field: Neurophysiological Mechanisms for Local-
Global Comparisons in Visual Neurons.” Annual Review of Neuroscience 8 (1):
407–30. https://doi.org/10.1146/annurev.ne.08.030185.002203.
Alonso, Joan Francesc, Sergio Romero, Miquel Àngel Mañanas, and Jordi Riba. 2015.
“Serotonergic Psychedelics Temporarily Modify Information Transfer in Humans.”
The International Journal of Neuropsychopharmacology / Official Scientific Journal
of the Collegium Internationale Neuropsychopharmacologicum 18 (8). https:
//doi.org/10.1093/ijnp/pyv039.
Anderson, Barton L., and Jonathan Winawer. 2005. “Image Segmentation and
186
Lightness Perception.” Nature 434 (7029): 79–83. https://doi.org/10.1038/nature
03271.
Anderson, Michael L, and Tony Chemero. 2013. “The Problem with Brain GUTs:
Conflation of Different Senses of ‘Prediction’ Threatens Metaphysical Disaster.”
The Behavioral and Brain Sciences 36 (03): 204–5. https://doi.org/10.1017/s014
0525x1200221x.
Andrade, R. 2011. “Serotonergic Regulation of Neuronal Excitability in the Prefrontal
Cortex.” Neuropharmacology 61 (3): 382–86. https://doi.org/10.1016/j.neuropha
rm.2011.01.015.
Angelucci, Alessandra, Maryam Bijanzadeh, Lauri Nurminen, Frederick Federer, Sam
Merlin, and Paul C. Bressloff. 2017. “Circuits and Mechanisms for Surround
Modulation in Visual Cortex.” Annual Review of Neuroscience 40 (1): 425–51.
https://doi.org/10.1146/annurev-neuro-072116-031418.
Angelucci, Alessandra, Jonathan B. Levitt, Emma J. S. Walton, Jean-Michel Hupé,
Jean Bullier, and Jennifer S. Lund. 2002. “Circuits for Local and Global Signal
Integration in Primary Visual Cortex.” Journal of Neuroscience 22 (19): 8633–46.
https://doi.org/10.1523/JNEUROSCI.22-19-08633.2002.
Appelbaum, L. G., A. R. Wade, V. Y. Vildavski, M. W. Pettet, and A. M. Norcia.
2006. “Cue-Invariant Networks for Figure and Background Processing in Human
Visual Cortex.” Journal of Neuroscience 26 (45): 11695–708. https://doi.org/10.1
523/jneurosci.2741-06.2006.
Aqil, Marco, Tomas Knapen, and Serge O. Dumoulin. 2021. “Divisive Normalization
Unifies Disparate Response Signatures Throughout the Human Visual Hierarchy.”
Proceedings of the National Academy of Sciences 118 (46). https://doi.org/10.107
3/pnas.2108713118.
Araujo, Draulio B de, Sidarta Ribeiro, Guillermo A Cecchi, Fabiana M Carvalho,
Tiago A Sanchez, Joel P Pinto, Bruno S de Martinis, Jose A Crippa, Jaime E C
Hallak, and Antonio C Santos. 2012. “Seeing with the Eyes Shut: Neural Basis
of Enhanced Imagery Following Ayahuasca Ingestion.” Human Brain Mapping 33
(11): 2550–60.
Argento, Elena, Steffanie A Strathdee, Kenneth Tupper, Melissa Braschel, Evan Wood,
and Kate Shannon. 2017. “Does Psychedelic Drug Use Reduce Risk of Suicidality?
Evidence from a Longitudinal Community-Based Cohort of Marginalised Women in
a Canadian Setting.” BMJ Open 7 (9): e016025. https://doi.org/10.1136/bmjopen-
2017-016025.
Arminjon, Mathieu. 2011. “The Four Postulates of Freudian Unconscious Neurocogni-
tive Convergences.” Frontiers in Psychology 2 (June): 125. https://doi.org/10.338
9/fpsyg.2011.00125.
Artola, T., P. Mosteiro, B. Poveda, J. Barraca, I. Ancillo, and N. Sánchez. 2012.
“Prueba de Imaginación Creativa Para Adultos.” Madrid: TEA Ediciones.
Atasoy, Selen, Gustavo Deco, Morten L Kringelbach, and Joel Pearson. 2017. “Har-
monic Brain Modes: A Unifying Framework for Linking Space and Time in Brain
Dynamics.” The Neuroscientist: A Review Journal Bringing Neurobiology, Neurol-
ogy and Psychiatry, September, 1073858417728032. https://doi.org/10.1177/1073
858417728032.
187
Atasoy, Selen, Leor Roseman, Mendel Kaelen, Morten L Kringelbach, Gustavo Deco,
and Robin L Carhart-Harris. 2017. “Connectome-Harmonic Decomposition of
Human Brain Activity Reveals Dynamical Repertoire Re-Organisation Under LSD.”
https://doi.org/10.1038/s41598-017-17546-0.
Auerbach, Benjamin D., and Howard J. Gritton. 2022. “Hearing in Complex En-
vironments: Auditory Gain Control, Attention, and Hearing Loss.” Frontiers in
Neuroscience 16 (February). https://doi.org/10.3389/fnins.2022.799787.
Auld, Frank, Gary M. Goldenberg, and Jutta V. Weiss. 1968. “Measurement of
Primary-Process Thinking in Dream Reports.” Journal of Personality and Social
Psychology 8 (4, Pt.1): 418–26. https://doi.org/10.1037/h0025488.
Azimi, Zohre, Ruxandra Barzan, Katharina Spoida, Tatjana Surdin, Patric Wollenwe-
ber, Melanie D Mark, Stefan Herlitze, and Dirk Jancke. 2020. “Separable Gain
Control of Ongoing and Evoked Activity in the Visual Cortex by Serotonergic
Input.” Edited by Eran Lottem, Joshua I Gold, and Magor Lorincz. eLife 9 (April):
e53552. https://doi.org/10.7554/eLife.53552.
Baggott, Matthew J. 2015. “Psychedelics and Creativity: A Review of the Quantitative
Literature.” e1468. PeerJ PrePrints; PeerJ Inc. https://doi.org/10.7287/peerj.prep
rints.1202.
Bakdash, Jonathan Z., and Laura R. Marusich. 2017. “Repeated Measures Correla-
tion.” Frontiers in Psychology 8 (April). https://doi.org/10.3389/fpsyg.2017.00456.
Barr, Harriet L., Robert. Langs, and Robert R. Holt. 1972. LSD: Personality and
Experience. New York: Wiley-Interscience.
Barrett, Frederick S, Matthew P Bradstreet, Jeannie-Marie S Leoutsakos, Matthew W
Johnson, and Roland R Griffiths. 2016. “The Challenging Experience Question-
naire: Characterization of Challenging Experiences with Psilocybin Mushrooms.”
Journal of Psychopharmacology 30 (12): 1279–95. https://doi.org/10.1177/026988
1116678781.
Barrett, Frederick S, Theresa M Carbonaro, Ethan Hurwitz, Matthew W Johnson, and
Roland R Griffiths. 2018. “Double-Blind Comparison of the Two Hallucinogens
Psilocybin and Dextromethorphan: Effects on Cognition.” Psychopharmacology
235 (10): 2915–27. https://doi.org/10.1007/s00213-018-4981-x.
Barrett, Frederick S, and Roland R Griffiths. 2017. “Classic Hallucinogens and
Mystical Experiences: Phenomenology and Neural Correlates.” Current Topics in
Behavioral Neurosciences, March. https://doi.org/10.1007/7854_2017_474.
Barrett, Frederick S, Matthew W Johnson, and Roland R Griffiths. 2015. “Validation
of the Revised Mystical Experience Questionnaire in Experimental Sessions with
Psilocybin.” Journal of Psychopharmacology 29 (11): 1182–90. https://doi.org/10
.1177/0269881115609019.
———. 2017. “Neuroticism Is Associated with Challenging Experiences with Psilocybin
Mushrooms.” Personality and Individual Differences 117 (October): 155–60. https:
//doi.org/10.1016/j.paid.2017.06.004.
Barrett, Frederick S, Katrin H Preller, Marcus Herdener, Petr Janata, and Franz
X Vollenweider. 2017. “Serotonin 2a Receptor Signaling Underlies LSD-induced
Alteration of the Neural Response to Dynamic Changes in Music.” Cerebral Cortex,
September, 1–12. https://doi.org/10.1093/cercor/bhx257.
188
Barrett, Frederick S, Hollis Robbins, David Smooke, Jenine L. Brown, and Roland
R. Griffiths. 2017. “Qualitative and Quantitative Features of Music Reported
to Support Peak Mystical Experiences During Psychedelic Therapy Sessions.”
Frontiers in Psychology 8 (July). https://doi.org/10.3389/fpsyg.2017.01238.
Baumeister, Roy F, and Julie J Exline. 2002. “Mystical Self Loss: A Challenge for
Psychological Theory.” The International Journal for the Psychology of Religion
12 (1): 15–20. https://doi.org/10.1207/s15327582ijpr1201_02.
Bayne, Tim, and Olivia Carter. 2018. “Dimensions of Consciousness and the
Psychedelic State.” Neuroscience of Consciousness 2018 (1). https://doi.or
g/10.1093/nc/niy008.
Beattie, James. 1776. An Essay on Laughter and Ludicrous Composition, Written in
the Year 1764. [Edinburgh]: [Printed for W. Creech; E.; C. Dilly].
Beggs, John M, and Dietmar Plenz. 2003. “Neuronal Avalanches in Neocortical
Circuits.” The Journal of Neuroscience: The Official Journal of the Society for
Neuroscience 23 (35): 11167–77. https://doi.org/10.1523/jneurosci.23-35-
11167.2003.
Belser, Alexander B, Gabrielle Agin-Liebes, T Cody Swift, Sara Terrana, Neşe Devenot,
Harris L Friedman, Jeffrey Guss, Anthony Bossis, and Stephen Ross. 2017.
“Patient Experiences of Psilocybin-Assisted Psychotherapy: An Interpretative
Phenomenological Analysis.” Journal of Humanistic Psychology 57 (4): 354–88.
https://doi.org/10.1177/0022167817706884.
Bentin, Shlomo, Truett Allison, Aina Puce, Erik Perez, and Gregory McCarthy. 1996.
“Electrophysiological Studies of Face Perception in Humans.” Journal of Cognitive
Neuroscience 8 (6): 551–65. https://doi.org/10.1162/jocn.1996.8.6.551.
Benton, Arthur L. 2016. “Bergson and Freud on Aphasia: A Comparison.” In Bergson
and Modern Thought, edited by Pete A Y Gunter.
Berg, Manon van den, Igor Magaraggia, Rudy Schreiber, Todd M. Hillhouse, and
Joseph H. Porter. 2022. “How to Account for Hallucinations in the Interpretation
of the Antidepressant Effects of Psychedelics: A Translational Framework.” Psy-
chopharmacology 239 (6): 1853–79. https://doi.org/10.1007/s00213-022-06106-8.
Bergson, Henri. 1911. Matter and Memory. New York: Macm. https://doi.org/10.1
037/13803-000.
———. 1931. The Two Sources of Morality and Religion. Garden City: Doubleday.
https://doi.org/10.1097/00005053-193608000-00042.
Beringer, Kurt. 1927. Der Meskalinrausch (Mescaline Intoxication). Monographien
Aus Dem Gesamtgebiete Der Neurologie Und Psychiatrie. Springer Berlin Heidel-
berg.
Berkeley, George. 1709. An Essay Towards a New Theory of Vision. Aaron Rhames.
Bland, J M., and D. G Altman. 1995a. “Statistics Notes: Calculating Correlation
Coefficients with Repeated Observations: Part 1–Correlation Within Subjects.”
BMJ 310 (6977): 446–46. https://doi.org/10.1136/bmj.310.6977.446.
———. 1995b. “Statistics Notes: Calculating Correlation Coefficients with Repeated
Observations: Part 2–Correlation Between Subjects.” BMJ 310 (6980): 633–33.
https://doi.org/10.1136/bmj.310.6980.633.
Blokpoel, Mark, Johan Kwisthout, and Iris van Rooij. 2012. “When Can Predictive
189
Brains Be Truly Bayesian?” Frontiers in Psychology 3 (November): 406. https:
//doi.org/10.3389/fpsyg.2012.00406.
Bogenschutz, Michael P., Alyssa A. Forcehimes, Jessica A. Pommy, Claire E. Wilcox, P.
C. R. Barbosa, and Rick J. Strassman. 2015. “Psilocybin-Assisted Treatment for
Alcohol Dependence: A Proof-of-Concept Study.” Journal of Psychopharmacology
29 (3): 289–99. https://doi.org/10.1177/0269881114565144.
Bogenschutz, Michael P, and Matthew W Johnson. 2016. “Classic Hallucinogens in
the Treatment of Addictions.” Progress in Neuro-Psychopharmacology & Biological
Psychiatry 64 (January): 250–58. https://doi.org/10.1016/j.pnpbp.2015.03.002.
Bonny, Helen L, and Walter N Pahnke. 1972. “The Use of Music in Psychedelic (LSD)
Psychotherapy.” Journal of Music Therapy 9 (2): 64–87. https://doi.org/10.1093/
jmt/9.2.64.
Borchert, Elizabeth M. O., Christophe Micheyl, and Andrew J. Oxenham. 2011. “Per-
ceptual Grouping Affects Pitch Judgments Across Time and Frequency.” Journal
of Experimental Psychology: Human Perception and Performance 37 (1): 257–69.
https://doi.org/10.1037/a0020670.
Bourke, Cecilia, Katie Douglas, and Richard Porter. 2010. “Processing of Facial
Emotion Expression in Major Depression: A Review.” Australian and New Zealand
Journal of Psychiatry 44 (8): 681–96. https://doi.org/10.3109/00048674.2010.4963
59.
Bouso, José Carlos, Josep Maria Fábregas, Rosa Maria Antonijoan, Antoni Rodríguez-
Fornells, and Jordi Riba. 2013. “Acute Effects of Ayahuasca on Neuropsychological
Performance: Differences in Executive Function Between Experienced and Occa-
sional Users.” Psychopharmacology 230 (3): 415–24. https://doi.org/10.1007/s002
13-013-3167-9.
Bouso, José Carlos, Fernanda Palhano-Fontes, Antoni Rodríguez-Fornells, Sidarta
Ribeiro, Rafael Sanches, José Alexandre S Crippa, Jaime E C Hallak, Draulio
B de Araujo, and Jordi Riba. 2015. “Long-Term Use of Psychedelic Drugs
Is Associated with Differences in Brain Structure and Personality in Humans.”
European Neuropsychopharmacology: The Journal of the European College of
Neuropsychopharmacology 25 (4): 483–92. https://doi.org/10.1016/j.euroneuro.20
15.01.008.
Bouso, José Carlos, Eduardo José Pedrero-Pérez, Sam Gandy, and Miguel Ángel
Alcázar-Córcoles. 2016. “Measuring the Subjective: Revisiting the Psychometric
Properties of Three Rating Scales That Assess the Acute Effects of Hallucinogens.”
Human Psychopharmacology 31 (5): 356–72. https://doi.org/10.1002/hup.2545.
Bressloff, Paul C., Jack D. Cowan, Martin Golubitsky, Peter J. Thomas, and Matthew
C. Wiener. 2002. “What Geometric Visual Hallucinations Tell Us about the Visual
Cortex.” Neural Computation 14 (3): 473–91. https://doi.org/10.1162/0899766023
17250861.
Broad, C D. 1923. The Mind and Its Place in Nature. London: Routledge & K. Paul.
https://doi.org/10.4324/9781315824147.
Brogaard, Berit. 2013. “Serotonergic Hyperactivity as a Potential Factor in Develop-
mental, Acquired and Drug-Induced Synesthesia.” Frontiers in Human Neuroscience
7 (October): 657. https://doi.org/10.3389/fnhum.2013.00657.
190
Brook, Andrew. 2007. The Prehistory of Cognitive Science. Palgrave Macmillan.
Brown, Randall T, Christopher R Nicholas, Nicholas V Cozzi, Michele C Gassman,
Karen M Cooper, Daniel Muller, Chantelle D Thomas, et al. 2017. “Pharma-
cokinetics of Escalating Doses of Oral Psilocybin in Healthy Adults.” Clinical
Pharmacokinetics, March. https://doi.org/10.1007/s40262-017-0540-6.
Buckner, Randy L, Jessica R Andrews-Hanna, and Daniel L Schacter. 2008. “The
Brain’s Default Network: Anatomy, Function, and Relevance to Disease.” Annals
of the New York Academy of Sciences 1124 (March): 1–38. https://doi.org/10.119
6/annals.1440.011.
Burke, Matthew J., and Daniel M. Blumberger. 2021. “Caution at Psychiatry’s
Psychedelic Frontier.” Nature Medicine 27 (10): 1687–88. https://doi.org/10.1038/
s41591-021-01524-1.
Buzsáki, György, and Andreas Draguhn. 2004. “Neuronal Oscillations in Cortical
Networks.” Science 304 (5679): 1926–29.
Caclin, Anne, Patrick Bouchet, Farida Djoulah, Elodie Pirat, Jacques Pernier, and
Marie-Hélène Giard. 2011. “Auditory Enhancement of Visual Perception at
Threshold Depends on Visual Abilities.” Brain Research 1396 (June): 35–44.
https://doi.org/10.1016/j.brainres.2011.04.016.
Cannon, Mark W., and Steven C. Fullenkamp. 1991. “Spatial Interactions in Ap-
parent Contrast: Inhibitory Effects Among Grating Patterns of Different Spatial
Frequencies, Spatial Positions and Orientations.” Vision Research 31 (11): 1985–98.
https://doi.org/10.1016/0042-6989(91)90193-9.
Cao, Dongmei, Jing Yu, Huan Wang, Zhipu Luo, Xinyu Liu, Licong He, Jianzhong
Qi, et al. 2022. “Structure-Based Discovery of Nonhallucinogenic Psychedelic
Analogs.” Science 375 (6579): 403–11. https://doi.org/10.1126/science.abl8615.
Carandini, Matteo, and David J Heeger. 2011. “Normalization as a Canonical
Neural Computation.” Nature Reviews. Neuroscience 13 (1): 51–62. https:
//doi.org/10.1038/nrn3136.
Carandini, Matteo, David J. Heeger, and Walter Senn. 2002. “A Synaptic Explanation
of Suppression in Visual Cortex.” Journal of Neuroscience 22 (22): 10053–65.
https://doi.org/10.1523/JNEUROSCI.22-22-10053.2002.
Carbonaro, Theresa M, Matthew P Bradstreet, Frederick S Barrett, Katherine A
MacLean, Robert Jesse, Matthew W Johnson, and Roland R Griffiths. 2016.
“Survey Study of Challenging Experiences After Ingesting Psilocybin Mushrooms:
Acute and Enduring Positive and Negative Consequences.” Journal of Psychophar-
macology 30 (12): 1268–78. https://doi.org/10.1177/0269881116662634.
Carbonaro, Theresa M, Matthew W. Johnson, and Roland R. Griffiths. 2020. “Sub-
jective Features of the Psilocybin Experience That May Account for Its Self-
Administration by Humans: A Double-Blind Comparison of Psilocybin and Dex-
tromethorphan.” Psychopharmacology 237 (8): 2293–2304. https://doi.org/10.100
7/s00213-020-05533-9.
Carbonaro, Theresa M, Matthew W Johnson, Ethan Hurwitz, and Roland R Grif-
fiths. 2018. “Double-Blind Comparison of the Two Hallucinogens Psilocybin
and Dextromethorphan: Similarities and Differences in Subjective Experiences.”
Psychopharmacology 235 (2): 521–34.
191
Carhart-Harris, Robin L, Mark Bolstridge, James Rucker, Camilla M J Day, David
Erritzoe, Mendel Kaelen, Michael Bloomfield, et al. 2016. “Psilocybin with
Psychological Support for Treatment-Resistant Depression: An Open-Label Fea-
sibility Study.” The Lancet. Psychiatry, May. https://doi.org/10.1016/s2215-
0366(16)30065-7.
Carhart-Harris, Robin L, M Bolstridge, C M J Day, J Rucker, R Watts, D E Erritzoe,
M Kaelen, et al. 2018. “Psilocybin with Psychological Support for Treatment-
Resistant Depression: Six-month Follow-up.” Psychopharmacology 235 (2): 399–408.
https://doi.org/10.1007/s00213-017-4771-x.
Carhart-Harris, Robin L, David Erritzoe, Tim Williams, James M Stone, Laurence J
Reed, Alessandro Colasanti, Robin J Tyacke, et al. 2012. “Neural Correlates of the
Psychedelic State as Determined by fMRI Studies with Psilocybin.” Proceedings of
the National Academy of Sciences of the United States of America 109 (6): 2138–43.
https://doi.org/10.1073/pnas.1119598109.
Carhart-Harris, Robin L, D Erritzoe, E Haijen, Mendel Kaelen, and R Watts. 2017.
“Psychedelics and Connectedness.” Psychopharmacology, August. https://doi.org/
10.1007/s00213-017-4701-y.
Carhart-Harris, Robin L, and Karl Friston. 2010. “The Default-Mode, Ego-Functions
and Free-Energy: A Neurobiological Account of Freudian Ideas.” Brain: A Journal
of Neurology 133 (Pt 4): 1265–83. https://doi.org/10.1093/brain/awq010.
Carhart-Harris, Robin L, and Karl J Friston. 2019. “REBUS and the Anarchic Brain:
Toward a Unified Model of the Brain Action of Psychedelics.” Pharmacological
Reviews 71 (3): 316–44. https://doi.org/10.1124/pr.118.017160.
Carhart-Harris, Robin L, and Guy M Goodwin. 2017. “The Therapeutic Potential
of Psychedelic Drugs: Past, Present, and Future.” Neuropsychopharmacology:
Official Publication of the American College of Neuropsychopharmacology, April.
https://doi.org/10.1038/npp.2017.84.
Carhart-Harris, Robin L, Mendel Kaelen, M Bolstridge, T M Williams, L T Williams,
R Underwood, A Feilding, and D J Nutt. 2016. “The Paradoxical Psychological
Effects of Lysergic Acid Diethylamide (LSD).” Psychological Medicine, February,
1–12. https://doi.org/10.1017/s0033291715002901.
Carhart-Harris, Robin L, Mendel Kaelen, M G Whalley, M Bolstridge, A Feilding,
and D J Nutt. 2015. “LSD Enhances Suggestibility in Healthy Volunteers.”
Psychopharmacology 232 (4): 785–94.
Carhart-Harris, Robin L, Robert Leech, David Erritzoe, Tim M Williams, James M
Stone, John Evans, David J Sharp, Amanda Feilding, Richard G Wise, and David
J Nutt. 2013. “Functional Connectivity Measures After Psilocybin Inform a Novel
Hypothesis of Early Psychosis.” Schizophrenia Bulletin 39 (6): 1343–51.
Carhart-Harris, Robin L, Robert Leech, Peter J Hellyer, Murray Shanahan, Amanda
Feilding, Enzo Tagliazucchi, Dante R Chialvo, and David Nutt. 2014. “The
Entropic Brain: A Theory of Conscious States Informed by Neuroimaging Research
with Psychedelic Drugs.” Frontiers in Human Neuroscience 8 (February): 20.
https://doi.org/10.3389/fnhum.2014.00020.
Carhart-Harris, Robin L, R Leech, T M Williams, D Erritzoe, N Abbasi, T Bargiotas,
P Hobden, et al. 2012. “Implications for Psychedelic-Assisted Psychotherapy:
192
Functional Magnetic Resonance Imaging Study with Psilocybin.” The British
Journal of Psychiatry: The Journal of Mental Science 200 (3): 238–44. https:
//doi.org/10.1192/bjp.bp.111.103309.
Carhart-Harris, Robin L, Suresh Muthukumaraswamy, Leor Roseman, Mendel Kaelen,
Wouter Droog, Kevin Murphy, Enzo Tagliazucchi, et al. 2016. “Neural Correlates
of the LSD Experience Revealed by Multimodal Neuroimaging.” Proceedings of the
National Academy of Sciences 113 (17): 4853–58. https://doi.org/10.1073/pnas.1
518377113.
Carhart-Harris, Robin L, and D J Nutt. 2010. “User Perceptions of the Benefits and
Harms of Hallucinogenic Drug Use: A Web-Based Questionnaire Study.” Journal
of Substance Use 15 (4): 283–300. https://doi.org/10.3109/14659890903271624.
———. 2017. “Serotonin and Brain Function: A Tale of Two Receptors.” Journal of
Psychopharmacology 31 (9): 1091–1120. https://doi.org/10.1177/02698811177259
15.
Carhart-Harris, Robin L, Leor Roseman, Eline Haijen, David Erritzoe, Rosalind Watts,
Igor Branchi, and Mendel Kaelen. 2018. “Psychedelics and the Essential Impor-
tance of Context.” Journal of Psychopharmacology, February, 269881118754710.
https://doi.org/10.1177/0269881118754710.
Carod-Artal, F. J. 2015. “Hallucinogenic Drugs in Pre-Columbian Mesoamerican
Cultures.” Neurología (English Edition) 30 (1): 42–49. https://doi.org/10.1016/j.
nrleng.2011.07.010.
Carter, Olivia L, David C Burr, John D Pettigrew, Guy M Wallis, Felix Hasler, and
Franz X Vollenweider. 2005. “Using Psilocybin to Investigate the Relationship
Between Attention, Working Memory, and the Serotonin 1a and 2a Receptors.”
Journal of Cognitive Neuroscience 17 (10): 1497–1508. https://doi.org/10.1162/08
9892905774597191.
Carter, Olivia L, Felix Hasler, John D Pettigrew, Guy M Wallis, Guang B Liu, and
Franz X Vollenweider. 2007. “Psilocybin Links Binocular Rivalry Switch Rate to
Attention and Subjective Arousal Levels in Humans.” Psychopharmacology 195 (3):
415–24. https://doi.org/10.1007/s00213-007-0930-9.
Carter, Olivia L, John D Pettigrew, David C Burr, David Alais, Felix Hasler, and Franz
X Vollenweider. 2004. “Psilocybin Impairs High-Level but Not Low-Level Motion
Perception.” Neuroreport 15 (12): 1947–51. https://doi.org/10.1097/00001756-
200408260-00023.
Carter, Olivia L, John D Pettigrew, Felix Hasler, Guy M Wallis, Guang B Liu,
Daniel Hell, and Franz X Vollenweider. 2005. “Modulating the Rate and
Rhythmicity of Perceptual Rivalry Alternations with the Mixed 5-Ht2a and
5-Ht1a Agonist Psilocybin.” Neuropsychopharmacology: Official Publication of
the American College of Neuropsychopharmacology 30 (6): 1154–62. https:
//doi.org/10.1038/sj.npp.1300621.
Chapman, Loren J., Jean P. Chapman, and Michael L. Raulin. 1976. “Scales for
Physical and Social Anhedonia.” Journal of Abnormal Psychology 85 (4): 374–82.
https://doi.org/10.1037/0021-843x.85.4.374.
Chen, Juan, Qing Yu, Ziyun Zhu, Yujia Peng, and Fang Fang. 2016. “Spatial
Summation Revealed in the Earliest Visual Evoked Component C1 and the Effect
193
of Attention on Its Linearity.” Journal of Neurophysiology 115 (1): 500–509.
https://doi.org/10.1152/jn.00044.2015.
Chevreul, M. A. 1860. The Laws of Contrast of Colour. Routledge.
Chubb, Charles, George Sperling, and Joshua A Solomon. 1989. “Texture Interactions
Determine Perceived Contrast.” Proceedings of the National Academy of Sciences
86 (23): 9631–35. https://doi.org/10.1073/pnas.86.23.9631.
Claridge, Gordon, and Anna McDonald. 2009. “An Investigation into the Relationships
Between Convergent and Divergent Thinking, Schizotypy, and Autistic Traits.”
Personality and Individual Differences 46 (8): 794–99. https://doi.org/10.1016/j.
paid.2009.01.018.
Clark, Andy. 2013. “Whatever Next? Predictive Brains, Situated Agents, and the
Future of Cognitive Science.” The Behavioral and Brain Sciences 36 (3): 181–204.
https://doi.org/10.1017/s0140525x12000477.
———. 2015. Surfing Uncertainty: Prediction, Action, and the Embodied Mind. Oxford
University Press. https://doi.org/10.1093/acprof:oso/9780190217013.001.0001.
Clark, Vincent P., Silu Fan, and Steven A. Hillyard. 1994. “Identification of Early
Visual Evoked Potential Generators by Retinotopic and Topographic Analyses.”
Human Brain Mapping 2 (3): 170–87. https://doi.org/10.1002/hbm.460020306.
Clifford, Colin W G. 2014. “The Tilt Illusion: Phenomenology and Functional
Implications.” Vision Research 104 (November): 3–11. https://doi.org/10.1016/j.
visres.2014.06.009.
Coen-Cagli, Ruben, Peter Dayan, and Odelia Schwartz. 2012. “Cortical Surround
Interactions and Perceptual Salience via Natural Scene Statistics.” Edited by Olaf
Sporns. PLoS Computational Biology 8 (3): e1002405. https://doi.org/10.1371/jo
urnal.pcbi.1002405.
Coen-Cagli, Ruben, Adam Kohn, and Odelia Schwartz. 2015. “Flexible Gating of
Contextual Influences in Natural Vision.” Nature Neuroscience 18 (11): 1648–55.
https://doi.org/10.1038/nn.4128.
Cohen, Sidney. 1960. “LYSERGIC ACID DIETHYLAMIDE.” The Journal of Nervous
and Mental Disease 130 (1): 30–40. https://doi.org/10.1097/00005053-196001000-
00005.
———. 1965. The Beyond Within : The LSD Story. New York: Atheneum.
Cohen, Sidney, and Betty Eisner. 1959. “Use of Lysergic Acid Diethylamide in a
Psychotherapeutic Setting.” A.M.A. Archives of Neurology and Psychiatry 81 (5):
615–19. https://doi.org/10.1001/archneurpsyc.1959.02340170081008.
Collins, David J. 2018. “The Cambridge History of Magic and Witchcraft in the West
from Antiquity to the Present.” https://www.vlebooks.com/vleweb/product/open
reader?id=none&isbn=9781139043021.
Cook, Emily, Stephen T Hammett, and Jonas Larsson. 2016. “GABA Predicts Visual
Intelligence.” Neuroscience Letters 632 (October): 50–54.
Corlett, P R, C D Frith, and P C Fletcher. 2009. “From Drugs to Deprivation: A
Bayesian Framework for Understanding Models of Psychosis.” Psychopharmacology
206 (4): 515–30. https://doi.org/10.1007/s00213-009-1561-0.
Cowen, Philip J., and Michael Browning. 2015. “What Has Serotonin to Do with
Depression?” World Psychiatry 14 (2): 158–60. https://doi.org/10.1002/wps.20229.
194
Crick, Francis. 1998. “Consciousness and Neuroscience.” Cerebral Cortex 8 (2): 97–107.
https://doi.org/10.1093/cercor/8.2.97.
Crick, Francis, and Christof Koch. 1998. “Feature Article Consciousness and Neuro-
science.” Cerebral Cortex Mar 8: 97–107.
Curtis, Lindsay R., and Inc. Texas Alcohol Narcotics Education. 1968. LSD: Trip or
Trap? Dallas: Texas Alcohol Narcotics Education.
Dakin, Steven, Patricia Carlin, and David Hemsley. 2005. “Weak Suppression of
Visual Context in Chronic Schizophrenia.” Current Biology 15 (20): R822–24.
https://doi.org/10.1016/j.cub.2005.10.015.
Davis, Alan K., Frederick S. Barrett, Darrick G. May, Mary P. Cosimano, Nathan D.
Sepeda, Matthew W. Johnson, Patrick H. Finan, and Roland R. Griffiths. 2021.
“Effects of Psilocybin-Assisted Therapy on Major Depressive Disorder.” JAMA
Psychiatry 78 (5): 481. https://doi.org/10.1001/jamapsychiatry.2020.3285.
Dayan, P, G E Hinton, R M Neal, and R S Zemel. 1995. “The Helmholtz Machine.”
Neural Computation 7 (5): 889–904. https://doi.org/10.1162/neco.1995.7.5.889.
Deco, Gustavo, Viktor K Jirsa, and Anthony R McIntosh. 2011. “Emerging Concepts
for the Dynamical Organization of Resting-State Activity in the Brain.” Nature
Reviews. Neuroscience 12 (1): 43–56.
DeGrandpre, Richard. 2006. The Cult of Pharmacology. Duke University Press.
https://doi.org/10.1215/9780822388197.
Delorme, Arnaud, and Scott Makeig. 2004. “EEGLAB: An Open Source Toolbox
for Analysis of Single-Trial EEG Dynamics Including Independent Component
Analysis.” Journal of Neuroscience Methods 134 (1): 9–21. https://doi.org/10.101
6/j.jneumeth.2003.10.009.
Díaz, José Luis. 2010. “Sacred Plants and Visionary Consciousness.” Phenomenology
and the Cognitive Sciences 9 (2): 159–70. https://doi.org/10.1007/s11097-010-
9157-z.
Dittrich, A. 1998. “The Standardized Psychometric Assessment of Altered States of
Consciousness (ASCs) in Humans.” Pharmacopsychiatry 31 Suppl 2 (July): 80–84.
https://doi.org/10.1055/s-2007-979351.
Dittrich, A, D Lamparter, and M Maurer. 2010. “5d-ASC: Questionnaire for the
Assessment of Altered States of Consciousness.” A Short Introduction. Zurich,
Switzerland: PSIN.
Dolder, Patrick C, Yasmin Schmid, Felix Müller, Stefan Borgwardt, and Matthias E
Liechti. 2016. “LSD Acutely Impairs Fear Recognition and Enhances Emotional
Empathy and Sociality.” Neuropsychopharmacology: Official Publication of the
American College of Neuropsychopharmacology 41 (11): 2638–46. https://doi.org/
10.1038/npp.2016.82.
Dos Santos, Rafael G, Flávia L Osório, José Alexandre S Crippa, Jordi Riba, An-
tônio W Zuardi, and Jaime E C Hallak. 2016. “Antidepressive, Anxiolytic,
and Antiaddictive Effects of Ayahuasca, Psilocybin and Lysergic Acid Diethy-
lamide (LSD): A Systematic Review of Clinical Trials Published in the Last 25
Years.” Therapeutic Advances in Psychopharmacology 6 (3): 193–213. https:
//doi.org/10.1177/2045125316638008.
Doss, Manoj K., Michal Považan, Monica D. Rosenberg, Nathan D. Sepeda, Alan
195
K. Davis, Patrick H. Finan, Gwenn S. Smith, et al. 2021. “Psilocybin Therapy
Increases Cognitive and Neural Flexibility in Patients with Major Depressive
Disorder.” Translational Psychiatry 11 (1). https://doi.org/10.1038/s41398-021-
01706-y.
Dupuis, David. 2021. “Psychedelics as Tools for Belief Transmission. Set, Setting,
Suggestibility, and Persuasion in the Ritual Use of Hallucinogens.” Frontiers in
Psychology 12 (November). https://doi.org/10.3389/fpsyg.2021.730031.
Ebbinghaus, H. 1902. “Grundzu «Ge Der Psychologie Volumes i and II (Leipzig:
Verlag von Viet & Co.).”
Edelman, Shimon. 2012. “Six Challenges to Theoretical and Philosophical Psychology.”
Frontiers in Psychology 3 (July): 219. https://doi.org/10.3389/fpsyg.2012.00219.
Eisner, Betty. 1997. “Set, Setting, and Matrix.” Journal of Psychoactive Drugs 29 (2):
213–16. https://doi.org/10.1080/02791072.1997.10400190.
Eisner, Betty, and Sidney Cohen. 1958. “Psychotherapy with Lysergic Acid Diethy-
lamide.” The Journal of Nervous and Mental Disease 127 (6): 528–39. https:
//doi.org/10.1097/00005053-195812000-00006.
Ejima, Y, and S Takahashi. 1985. “Apparent Contrast of a Sinusoidal Grating in the
Simultaneous Presence of Peripheral Gratings.” Vision Research 25 (9): 1223–32.
https://doi.org/10.1016/0042-6989(85)90036-7.
Ellis, Havelock. 1898. Mescal: A New Artificial Paradise. US Government Printing
Office.
Erowid. 1995. 1995. https://www.erowid.org/.
Evarts, Edward V. 1957. “A REVIEW OF THE NEUROPHYSIOLOGICAL EF-
FECTS OF LYSERGIC ACID DIETHYLAMIDE (LSD) AND OTHER PSY-
CHOTOMIMETIC AGENTS.” Annals of the New York Academy of Sciences 66
(3): 479–95. https://doi.org/10.1111/j.1749-6632.1957.tb40744.x.
Family, Neiloufar, David Vinson, Gabriella Vigliocco, Mendel Kaelen, Mark Bolstridge,
David J Nutt, and Robin L Carhart-Harris. 2016. “Semantic Activation in LSD:
Evidence from Picture Naming.” Language, Cognition and Neuroscience 0 (0): 1–8.
https://doi.org/10.1080/23273798.2016.1217030.
Field, David J., Anthony Hayes, and Robert F. Hess. 1993. “Contour Integration
by the Human Visual System: Evidence for a Local “Association Field”.” Vision
Research 33 (2): 173–93. https://doi.org/10.1016/0042-6989(93)90156-q.
Fink, Max. 1969. “EEG and Human Psychopharmacology.” Annual Review of
Pharmacology 9 (1): 241–58. https://doi.org/10.1146/annurev.pa.09.040169.00132
5.
———. 2010. “Remembering the Lost Neuroscience of Pharmaco-EEG.” Acta
Psychiatrica Scandinavica 121 (3): 161–73. https://doi.org/10.1111/j.1600-
0447.2009.01467.x.
Fleming, Stephen M, Raymond J Dolan, and Christopher D Frith. 2012. “Metacog-
nition: Computation, Biology and Function.” Philosophical Transactions of the
Royal Society of London. Series B, Biological Sciences 367 (1594): 1280–86.
https://doi.org/10.1098/rstb.2012.0021.
Forman, Robert K C (ed). 1998. The Innate Capacity: Mysticism, Psychology, and
Philosophy. Oxford University Press. https://doi.org/10.2307/1387614.
196
Forstmann, Birte U, Eric-Jan Wagenmakers, Tom Eichele, Scott Brown, and John
T Serences. 2011. “Reciprocal Relations Between Cognitive Neuroscience and
Formal Cognitive Models: Opposites Attract?” Trends in Cognitive Sciences 15
(6): 272–79. https://doi.org/10.1016/j.tics.2011.04.002.
Fox, Michael D, and Marcus E Raichle. 2007. “Spontaneous Fluctuations in Brain
Activity Observed with Functional Magnetic Resonance Imaging.” Nature Reviews.
Neuroscience 8 (9): 700–711. https://doi.org/10.1038/nrn2201.
Fraser, James. 1908. “A New Visual Illusion of Direction.” British Journal of
Psychology 2 (3): 307. https://doi.org/10.1111/j.2044-8295.1908.tb00182.x.
Frecska, E, K D White, and L E Luna. 2004. “Effects of Ayahuasca on Binocular
Rivalry with Dichoptic Stimulus Alternation.” Psychopharmacology 173 (1-2):
79–87. https://doi.org/10.1007/s00213-003-1701-x.
Freeman, Jeremy, Corey M Ziemba, David J Heeger, Eero P Simoncelli, and J Anthony
Movshon. 2013. “A Functional and Perceptual Signature of the Second Visual
Area in Primates.” Nature Neuroscience 16 (7): 974–81. https://doi.org/10.1038/
nn.3402.
Freud, Sigmund. 1895. “Project for a Scientific Psychology.” In Standard Edition Vol.
1. London: Vintage.
———. 1900. The Interpretation of Dreams. London: Penguin. https://doi.org/10.1
037/10561-000.
———. 1915. “The Unconscious.” In Standard Edition Vol. 14. Vol. 14. London:
Vintage. https://doi.org/10.4324/9781351226387-21.
———. 1940. An Outline of Psycho-Analysis. London: Hogarth.
Friedman, Michael. 1983. Foundations of Space-Time Theories : Relativistic Physics
and Philosophy of Science. https://doi.org/10.1515/9781400855124.
Frigg, Roman, and Stephan Hartmann. 2017. “Models in Science.” In The Stanford
Encyclopedia of Philosophy, edited by Edward N Zalta, Spring 2017. Metaphysics
Research Lab, Stanford University.
Friston, Karl. 2005. “A Theory of Cortical Responses.” Philosophical Transactions
of the Royal Society of London. Series B, Biological Sciences 360 (1456): 815–36.
https://doi.org/10.1098/rstb.2005.1622.
———. 2010. “The Free-Energy Principle: A Unified Brain Theory?” Nature Reviews.
Neuroscience 11 (2): 127–38. https://doi.org/10.1038/nrn2787.
Friston, Karl, Rebecca Lawson, and Chris Frith. 2013. “On Hyperpriors and Hypopri-
ors: Comment on Pellicano and Burr.” Trends in Cognitive Sciences 17 (1): 1.
https://doi.org/10.1016/j.tics.2012.11.003.
Friston, Karl, Jérémie Mattout, Nelson Trujillo-Barreto, John Ashburner, and Will
Penny. 2007. “Variational Free Energy and the Laplace Approximation.” NeuroIm-
age 34 (1): 220–34. https://doi.org/10.1016/j.neuroimage.2006.08.035.
Friston, Karl, Francesco Rigoli, Dimitri Ognibene, Christoph Mathys, Thomas Fitzger-
ald, and Giovanni Pezzulo. 2015. “Active Inference and Epistemic Value.” Cognitive
Neuroscience 6 (4): 187–214. https://doi.org/10.1080/17588928.2015.1020053.
Gaddum, J H. 1953. “Antagonism Between Lysergic Acid Diethylamide and 5-
Hydroxytryptamine.” The Journal of Physiology 121 (1): 15P.
Gaddum, J H, and K A Hameed. 1954. “Drugs Which Antagonize 5-
197
Hydroxytryptamine.” British Journal of Pharmacology and Chemotherapy
9 (2): 240–48. https://doi.org/10.1111/j.1476-5381.1954.tb00848.x.
Gallimore, Andrew R. 2015. “Restructuring Consciousness -the Psychedelic State
in Light of Integrated Information Theory.” Frontiers in Human Neuroscience 9
(June): 346. https://doi.org/10.3389/fnhum.2015.00346.
Gashi, Liridona, Sveinung Sandberg, and Willy Pedersen. 2021. “Making “Bad Trips”
Good: How Users of Psychedelics Narratively Transform Challenging Trips into
Valuable Experiences.” International Journal of Drug Policy 87 (January): 102997.
https://doi.org/10.1016/j.drugpo.2020.102997.
Gasser, Peter, Dominique Holstein, Yvonne Michel, Rick Doblin, Berra Yazar-Klosinski,
Torsten Passie, and Rudolf Brenneisen. 2014. “Safety and Efficacy of Lysergic Acid
Diethylamide-Assisted Psychotherapy for Anxiety Associated with Life-Threatening
Diseases.” The Journal of Nervous and Mental Disease 202 (7): 513–20. https:
//doi.org/10.1097/nmd.0000000000000113.
GebhartSayer, Angelika. 1985. “The Geometric Designs of the Shipibo-Conibo in
Ritual Context (Peruvian Indian Art).” Journal of Latin American Lore 11 (2):
143–75.
George, Daniel R., Ryan Hanson, Darryl Wilkinson, and Albert Garcia-Romeu. 2021.
“Ancient Roots of Today’s Emerging Renaissance in Psychedelic Medicine.” Culture,
Medicine, and Psychiatry, September. https://doi.org/10.1007/s11013-021-09749-
y.
Gibson, J J, and M Radner. 1937. “Adaptation, After-Effect and Contrast in the
Perception of Tilted Lines. I. Quantitative Studies.” Journal of Experimental
Psychology 20 (5): 453–67. https://doi.org/10.1037/h0059826.
Gill, Hartej, Parnian Puramat, Pankti Patel, Barjot Gill, CéAnn A. Marks, Nelson
B. Rodrigues, David Castle, et al. 2022. “The Effects of Psilocybin in Adults
with Major Depressive Disorder and the General Population: Findings from
Neuroimaging Studies.” Psychiatry Research 313 (July): 114577. https://doi.org/
10.1016/j.psychres.2022.114577.
Glennon, Richard A, Milt Titeler, and J D McKenney. 1984. “Evidence for 5-Ht2
Involvement in the Mechanism of Action of Hallucinogenic Agents.” Life Sciences
35 (25): 2505–11. https://doi.org/10.1016/0024-3205(84)90436-3.
Goddard, Erin, Colin W. G. Clifford, and Samuel G. Solomon. 2008. “Centre-Surround
Effects on Perceived Orientation in Complex Images.” Vision Research 48 (12):
1374–82. https://doi.org/10.1016/j.visres.2008.02.023.
Golomb, Julie D, Jenika R B McDavitt, Barbara M Ruf, Jason I Chen, Aybala
Saricicek, Kathleen H Maloney, Jian Hu, Marvin M Chun, and Zubin Bhagwagar.
2009. “Enhanced Visual Motion Perception in Major Depressive Disorder.” The
Journal of Neuroscience: The Official Journal of the Society for Neuroscience 29
(28): 9072–77. https://doi.org/10.1523/jneurosci.1003-09.2009.
Goodman, N. 1983. Fact, Fiction, and Forecast. Harvard University Press.
Gouzoulis-Mayfrank, E, B Thelen, S Maier, K Heekeren, K-A Kovar, H Sass, and
M Spitzer. 2002. “Effects of the Hallucinogen Psilocybin on Covert Orienting
of Visual Attention in Humans.” Neuropsychobiology 45 (4): 205–12. https:
//doi.org/10.1159/000063672.
198
Gratton, Caterina, Sahar Yousef, Esther Aarts, Deanna L Wallace, Mark D’Esposito,
and Michael A Silver. 2017. “Cholinergic, but Not Dopaminergic or Noradrenergic,
Enhancement Sharpens Visual Spatial Perception in Humans.” The Journal of
Neuroscience: The Official Journal of the Society for Neuroscience 37 (16): 4405–15.
https://doi.org/10.1523/jneurosci.2405-16.2017.
Gray, Kurt, Stephen Anderson, Eric Evan Chen, John Michael Kelly, Michael S.
Christian, John Patrick, Laura Huang, Yoed N. Kenett, and Kevin Lewis. 2019.
“Forward Flow: A New Measure to Quantify Free Thought and Predict Creativity.”
American Psychologist 74 (5): 539–54. https://doi.org/10.1037/amp0000391.
Green, A R. 2008. “Gaddum and LSD: The Birth and Growth of Experimental and
Clinical Neuropharmacology Research on 5-HT in the UK.” British Journal of
Pharmacology 154 (8): 1583–99. https://doi.org/10.1038/bjp.2008.207.
Gregory, Richard L. 1968. “Perceptual Illusions and Brain Models.” Proceedings of
the Royal Society of London. Series B. Biological Sciences 171 (1024): 279–96.
https://doi.org/10.1098/rspb.1968.0071.
Griffiths, Roland R, Matthew W Johnson, Michael A Carducci, Annie Umbricht,
William A Richards, Brian D Richards, Mary P Cosimano, and Margaret A
Klinedinst. 2016. “Psilocybin Produces Substantial and Sustained Decreases in
Depression and Anxiety in Patients with Life-Threatening Cancer: A Randomized
Double-Blind Trial.” Journal of Psychopharmacology 30 (12): 1181–97. https:
//doi.org/10.1177/0269881116675513.
Griffiths, Roland R, Matthew W Johnson, William A Richards, Brian D Richards,
Una McCann, and Robert Jesse. 2011. “Psilocybin Occasioned Mystical-Type
Experiences: Immediate and Persisting Dose-Related Effects.” Psychopharmacology
218 (4): 649–65. https://doi.org/10.1007/s00213-011-2358-5.
Griffiths, Roland R, Wa Richards, Matthew W Johnson, Ud McCann, and R Jesse.
2008. “Mystical-Type Experiences Occasioned by Psilocybin Mediate the Attribu-
tion of Personal Meaning and Spiritual Significance 14 Months Later.” Journal of
Psychopharmacology 22 (6): 621–32. https://doi.org/10.1177/0269881108094300.
Grimm, O, R Kraehenmann, Katrin H Preller, E Seifritz, and Franz X Vollenweider.
2018. “Psilocybin Modulates Functional Connectivity of the Amygdala During
Emotional Face Discrimination.” European Neuropsychopharmacology: The Journal
of the European College of Neuropsychopharmacology 28 (6): 691–700. https:
//doi.org/10.1016/j.euroneuro.2018.03.016.
Grinspoon, Lester, and James B Bakalar. 1979. Psychedelic Drugs Reconsidered. New
York: Basic Books. https://doi.org/10.2307/1387601.
Grob, Charles, Alicia L Danforth, Gurpreet S Chopra, Marycie Hagerty, Charles R
McKay, Adam L Halberstadt, and George R Greer. 2011. “Pilot Study of Psilocybin
Treatment for Anxiety in Patients with Advanced-Stage Cancer.” Archives of
General Psychiatry 68 (1): 71–78. https://doi.org/10.1001/archgenpsychiatry.2010
.116.
Grob, Charles, and Marlene Dobkin de Rios. 1992. “Adolescent Drug Use in Cross-
Cultural Perspective.” Journal of Drug Issues 22 (1): 121–38. https://doi.org/10.1
177/002204269202200108.
Grof, Stanislav. 1976. Realms of the Human Unconscious : Observations from LSD
199
Research. New York: E.P. Dutton. https://doi.org/10.2307/1385643.
———. 1980. LSD Psychotherapy. Pomona, Calif: Hunter House.
Guilford, Joy Paul. 1967. “The Nature of Human Intelligence.”
Gukasyan, Natalie, Alan K Davis, Frederick S Barrett, Mary P Cosimano, Nathan D
Sepeda, Matthew W Johnson, and Roland R Griffiths. 2022. “Efficacy and Safety
of Psilocybin-Assisted Treatment for Major Depressive Disorder: Prospective
12-Month Follow-up.” Journal of Psychopharmacology 36 (2): 151–58. https:
//doi.org/10.1177/02698811211073759.
Guttmann, Erich. 1936. “Artificial Psychoses Produced by Mescaline.” The British
Journal of Psychiatry: The Journal of Mental Science 82 (338): 203–21. https:
//doi.org/10.1192/bjp.82.338.203.
Guzmán, Gastón. 2005. “Species Diversity in the Genus Psilocybe (Basidiomycotina,
Agaricales, Strophariaceae) of World Mycobiota, with Special Attention to Hal-
lucinogenic Properties.” International Journal of Medicinal Mushrooms 7 (1/2):
305.
Guzmán, Gaston, John W Allen, and Jochen Gartz. 1998. “A Worldwide Geographical
Distribution of the Neurotropic Fungi, an Analysis and Discussion.” Ann. Mus.
Civ. Rovereto 14: 189–280.
Haider, Bilal, Matthew R Krause, Alvaro Duque, Yuguo Yu, Jonathan Touryan, James
A Mazer, and David A McCormick. 2010. “Synaptic and Network Mechanisms of
Sparse and Reliable Visual Cortical Activity During Nonclassical Receptive Field
Stimulation.” Neuron 65 (1): 107–21. https://doi.org/10.1016/j.neuron.2009.12.0
05.
Halberstadt, Adam L. 2015. “Recent Advances in the Neuropsychopharmacology
of Serotonergic Hallucinogens.” Behavioural Brain Research 277 (Supplement C):
99–120. https://doi.org/10.1016/j.bbr.2014.07.016.
Halberstadt, Adam L, and Mark A. Geyer. 2016. “Effect of Hallucinogens on
Unconditioned Behavior.” In Behavioral Neurobiology of Psychedelic Drugs, 159–99.
Springer Berlin Heidelberg. https://doi.org/10.1007/7854_2016_466.
Halberstadt, Adam L, and David E. Nichols. 2020. “Serotonin and Serotonin
Receptors in Hallucinogen Action.” In Handbook of Behavioral Neuroscience,
edited by Christian P. Müller and Kathryn A. Cunningham, 843–63. Elsevier.
https://doi.org/10.1016/b978-0-444-64125-0.00043-8.
Hartogsohn, Ido. 2016. “Set and Setting, Psychedelics and the Placebo Response:
An Extra-Pharmacological Perspective on Psychopharmacology.” Journal of Psy-
chopharmacology 30 (12): 1259–67. https://doi.org/10.1177/0269881116677852.
———. 2017. “Constructing Drug Effects: A History of Set and Setting.” Drug Science,
Policy and Law 3 (January): 205032451668332. https://doi.org/10.1177/20503245
16683325.
———. 2021. “Set and Setting in the Santo Daime.” Frontiers in Pharmacology 12
(May). https://doi.org/10.3389/fphar.2021.651037.
Hasler, Felix, Ulrike Grimberg, Marco A Benz, Theo Huber, and Franz X Vollenweider.
2004. “Acute Psychological and Physiological Effects of Psilocybin in Healthy
Humans: A Double-Blind, Placebo-Controlled Dose–Effect Study.” Psychopharma-
cology 172 (2): 145–56. https://doi.org/10.1007/s00213-003-1640-6.
200
Haynes, J. D., G. Roth, M. Stadler, and H. J. Heinze. 2003. “Neuromagnetic Correlates
of Perceived Contrast in Primary Visual Cortex.” Journal of Neurophysiology 89
(5): 2655–66. https://doi.org/10.1152/jn.00820.2002.
Heeger, David J. 1992. “Normalization of Cell Responses in Cat Striate Cortex.”
Visual Neuroscience 9 (2): 181–97. https://doi.org/10.1017/s0952523800009640.
Heffter, A. 1898. “Ueber Pellote.” Archiv für Experimentelle Pathologie Und Phar-
makologie 40 (5-6): 385–429. https://doi.org/10.1007/bf01825267.
Heimann, H. 1963. “OBSERVATIONS ON DISTURBED TIME PERCEPTION IN
MODEL PSYCHOSIS.” Europepmc.org.
Helmholtz, Heinrich von. 1867. Treatise on Physiological Optics Vol. III. Dover
Publications.
Hendricks, Peter S, Christopher B Thorne, C Brendan Clark, David W Coombs, and
Matthew W Johnson. 2015. “Classic Psychedelic Use Is Associated with Reduced
Psychological Distress and Suicidality in the United States Adult Population.”
Journal of Psychopharmacology 29 (3): 280–88. https://doi.org/10.1177/02698811
14565653.
Hering, Ewald. 1861. Beitrage Zur Physiologie. W. Engelmann.
Hershenson, Maurice, ed. 2013. The Moon Illusion. Psychology Press. https:
//doi.org/10.4324/9780203771303.
Hess, Robert, and David Field. 1999. “Integration of Contours: New Insights.”
Trends in Cognitive Sciences 3 (12): 480–86. https://doi.org/10.1016/s1364-
6613(99)01410-2.
Hesselgrave, Natalie, Timothy A. Troppoli, Andreas B. Wulff, Anthony B. Cole, and
Scott M. Thompson. 2021. “Harnessing Psilocybin: Antidepressant-like Behavioral
and Synaptic Actions of Psilocybin Are Independent of 5-HT2R Activation in
Mice.” Proceedings of the National Academy of Sciences 118 (17). https://doi.org/
10.1073/pnas.2022489118.
Himwich, H E. 1959. “Neuropharmacology : Transactions of the Second Conference.”
American Journal of Psychiatry 116 (1): 88–88. https://doi.org/10.1176/ajp.116.
1.88.
Hintzen, Annelie, and Torsten Passie. 2010. The Pharmacology of LSD : A Critical
Review. Oxford: Oxford University Press : Beckley Foundation Press.
Hirschfeld, Tim, and Timo T Schmidt. 2021. “Dose–response Relationships of
Psilocybin-Induced Subjective Experiences in Humans.” Journal of Psychopharma-
cology 35 (4): 384–97. https://doi.org/10.1177/0269881121992676.
Hoffer, A, H Osmond, and J Smythies. 1954. “Schizophrenia; a New Approach. II.
Result of a Year’s Research.” The Journal of Mental Science 100 (418): 29–45.
https://doi.org/10.1192/bjp.100.418.29.
Hofmann, Albert. 1980. LSD, My Problem Child. New York: McGraw-Hill. https:
//doi.org/10.2307/4638477.
Hohwy, Jakob. 2013. The Predictive Mind. OUP Oxford. https://doi.org/10.1093/ac
prof:oso/9780199682737.001.0001.
Holt, Robert R. 2002. “Quantitative Research on the Primary Process: Method and
Findings.” Journal of the American Psychoanalytic Association 50 (2): 457–82.
https://doi.org/10.1177/00030651020500021501.
201
Holze, Friederike, Laura Ley, Felix Müller, Anna M. Becker, Isabelle Straumann,
Patrick Vizeli, Sebastian Silva Kuehne, et al. 2022. “Direct Comparison of the
Acute Effects of Lysergic Acid Diethylamide and Psilocybin in a Double-Blind
Placebo-Controlled Study in Healthy Subjects.” Neuropsychopharmacology 47 (6):
1180–87. https://doi.org/10.1038/s41386-022-01297-2.
Holze, Friederike, Patrick Vizeli, Laura Ley, Felix Müller, Patrick Dolder, Melanie
Stocker, Urs Duthaler, et al. 2020. “Acute Dose-Dependent Effects of Lysergic Acid
Diethylamide in a Double-Blind Placebo-Controlled Study in Healthy Subjects.”
Neuropsychopharmacology 46 (3): 537–44. https://doi.org/10.1038/s41386-020-
00883-6.
Holze, Friederike, Patrick Vizeli, Felix Müller, Laura Ley, Raoul Duerig, Nimmy Vargh-
ese, Anne Eckert, Stefan Borgwardt, and Matthias E. Liechti. 2019. “Distinct
Acute Effects of LSD, MDMA, and d-Amphetamine in Healthy Subjects.” Neu-
ropsychopharmacology 45 (3): 462–71. https://doi.org/10.1038/s41386-019-0569-3.
Hood, Ralph W. 2001. “Review: Cleansing the Doors of Perception: The Religious
Significance of Entheogenic Plants and Chemicals.” The International Journal for
the Psychology of Religion 11 (4): 285–86. https://doi.org/10.1207/s15327582ijpr1
104_08.
Hosanagar, Avinash, Joseph Cusimano, and Rajiv Radhakrishnan. 2021. “Therapeutic
Potential of Psychedelics in Treatment of Psychiatric Disorders, Part 2.” The
Journal of Clinical Psychiatry 82 (3). https://doi.org/10.4088/jcp.20ac13787.
Huang, Gregory. 2008. “Is This a Unified Theory of the Brain.” New Scientist 2658:
30–33.
Huxley, Aldous. 1945. The Perennial Philosophy. New York; London: Harper & Bros.
https://doi.org/10.2307/3706996.
———. 1953. “Letters to Dr. Humphrey Osmond.” In Moksha : Aldous Huxley’s
Classic Writings on Psychedelics and the Visionary Experience, edited by Michael
Horowitz and Cynthia Palmer.
———. 1954. The Doors of Perception. New York: Harper.
———. 1956. “History of Tension.” In Moksha : Aldous Huxley’s Classic Writings
on Psychedelics and the Visionary Experience, edited by Michael Horowitz and
Cynthia Palmer, 117–28.
———. 1961. “Visionary Experience.” In Moksha : Aldous Huxley’s Classic Writings
on Psychedelics and the Visionary Experience, edited by Michael Horowitz and
Cynthia Palmer.
Isbell, H. 1959. “Comparison of the Reactions Induced by Psilocybin and LSD-25 in
Man.” Psychopharmacologia 1: 29–38. https://doi.org/10.1007/bf00408109.
Itti, Laurent, and Christof Koch. 2000. “A Saliency-Based Search Mechanism for Overt
and Covert Shifts of Visual Attention.” Vision Research 40 (10-12): 1489–1506.
https://doi.org/10.1016/s0042-6989(99)00163-7.
James, William. 1882. “Subjective Effects of Nitrous Oxide.”
———. 1890. “The Principles of Psychology.” In. Henry Holt; Company. https:
//doi.org/10.1037/11059-000.
———. 1902. The Varieties of Religious Experience : A Study in Human Nature. New
York: Longmans, Green. https://doi.org/10.1037/10004-000.
202
Johnson, Matthew W, Albert Garcia-Romeu, Mary P Cosimano, and Roland R
Griffiths. 2014. “Pilot Study of the 5-Ht2ar Agonist Psilocybin in the Treatment
of Tobacco Addiction.” Journal of Psychopharmacology 28 (11): 983–92. https:
//doi.org/10.1177/0269881114548296.
Johnson, Matthew W, Wa Richards, and Roland R Griffiths. 2008. “Human Hallu-
cinogen Research: Guidelines for Safety.” Journal of Psychopharmacology 22 (6):
603–20. https://doi.org/10.1177/0269881108093587.
Jones, H. E., K. L. Grieve, W. Wang, and A. M. Sillito. 2001. “Surround Suppression
in Primate V1.” Journal of Neurophysiology 86 (4): 2011–28. https://doi.org/10.1
152/jn.2001.86.4.2011.
Joo, Sung Jun, Geoffrey M. Boynton, and Scott O. Murray. 2012. “Long-Range,
Pattern-Dependent Contextual Effects in Early Human Visual Cortex.” Current
Biology 22 (9): 781–86. https://doi.org/10.1016/j.cub.2012.02.067.
Joo, Sung Jun, and Scott O. Murray. 2014. “Contextual Effects in Human Visual
Cortex Depend on Surface Structure.” Journal of Neurophysiology 111 (9): 1783–91.
https://doi.org/10.1152/jn.00671.2013.
Kaelen, Mendel, F S Barrett, Leor Roseman, R Lorenz, N Family, M Bolstridge, H V
Curran, A Feilding, D J Nutt, and Robin L Carhart-Harris. 2015. “LSD Enhances
the Emotional Response to Music.” Psychopharmacology 232 (19): 3607–14. https:
//doi.org/10.1007/s00213-015-4014-y.
Kaelen, Mendel, Bruna Giribaldi, Jordan Raine, Lisa Evans, Christopher Timmerman,
Natalie Rodriguez, Leor Roseman, Amanda Feilding, David Nutt, and Robin L
Carhart-Harris. 2018. “The Hidden Therapist: Evidence for a Central Role of
Music in Psychedelic Therapy.” Psychopharmacology 235 (2): 505–19.
Kaelen, Mendel, Leor Roseman, Joshua Kahan, Andre Santos-Ribeiro, Csaba Orban,
Romy Lorenz, Frederick S Barrett, et al. 2016. “LSD Modulates Music-Induced
Imagery via Changes in Parahippocampal Connectivity.” European Neuropsy-
chopharmacology. https://doi.org/10.1016/j.euroneuro.2016.03.018.
Kaletsch, Morten, Sebastian Pilgramm, Matthias Bischoff, Stefan Kindermann, Isabell
Sauerbier, Rudolf Stark, Stefanie Lis, et al. 2014. “Major Depressive Disorder
Alters Perception of Emotional Body Movements.” Frontiers in Psychiatry 5.
https://doi.org/10.3389/fpsyt.2014.00004.
Kanizsa, Gaetano. 1979. Organization in Vision: Essays on Gestalt Perception.
Praeger Publishers.
Kant, Immanuel. 1787. Critique of Pure Reason, Second Edition. Edited by W S
Pluhar. Hackett Classics. Hackett Publishing Company.
Kapadia, Mitesh K., Minami Ito, Charles D. Gilbert, and Gerald Westheimer. 1995.
“Improvement in Visual Sensitivity by Changes in Local Context: Parallel Studies
in Human Observers and in V1 of Alert Monkeys.” Neuron 15 (4): 843–56. https:
//doi.org/10.1016/0896-6273(95)90175-2.
Kay, Kendrick N. 2017. “Principles for Models of Neural Information Processing.”
NeuroImage, August. https://doi.org/10.1016/j.neuroimage.2017.08.016.
Kemp, Charles, Amy Perfors, and Joshua B Tenenbaum. 2007. “Learning Over-
hypotheses with Hierarchical Bayesian Models.” Developmental Science 10 (3):
307–21. https://doi.org/10.1111/j.1467-7687.2007.00585.x.
203
Kersten, Daniel, Pascal Mamassian, and Alan Yuille. 2004. “Object Perception as
Bayesian Inference.” Annual Review of Psychology 55: 271–304. https://doi.org/10
.1146/annurev.psych.55.090902.142005.
Kersten, Daniel, and Alan Yuille. 2003. “Bayesian Models of Object Perception.”
Current Opinion in Neurobiology 13 (2): 150–58. https://doi.org/10.1016/s0959-
4388(03)00042-4.
Kitcher, Philip. 1981. “Explanatory Unification.” Philosophy of Science 48 (4): 507–31.
https://doi.org/10.1086/289019.
———. 1989. “Explanatory Unification and the Causal Structure of the World.”
In Scientific Explanation, edited by Philip Kitcher and Wesley Salmon, 410–505.
Minneapolis: University of Minnesota Press.
Klee, G D. 1963. “Lysergic Acid Diethylamide (LSD-25) and Ego Functions.” Archives
of General Psychiatry 8 (May): 461–74. https://doi.org/10.1001/archpsyc.1963.01
720110037005.
Klüver, Heinrich. 1926. “Mescal Visions and Eidetic Vision.” The American Journal
of Psychology 37 (4): 502–15. https://doi.org/10.2307/1414910.
———. 1928. Mescal: The ’Divine’ Plant and Its Psychological Effects. London:
Kegan Paul, Trench, Trubner & Co.
———. 1942. “Mechanisms of Hallucinations.” In Studies in Personality., edited
by M. A. Q. McNemar & Merrill, 175–207. New York, NY, US: McGraw-Hill.
https://doi.org/10.1037/13305-015.
———. 1966. Mescal and Mechanisms of Hallucinations. Chicago: Univ. of Chicago
Press.
Knauer, Alwyn, and William J M A Maloney. 1913. “A Preliminary Note on
the Psychic Action of Mescalin, with Special Reference to the Mechanism of
Visual Hallucinations.” The Journal of Nervous and Mental Disease 40 (7): 425.
https://doi.org/10.1097/00005053-191307000-00001.
Knierim, J. J., and D. C. van Essen. 1992. “Neuronal Responses to Static Texture
Patterns in Area V1 of the Alert Macaque Monkey.” Journal of Neurophysiology
67 (4): 961–80. https://doi.org/10.1152/jn.1992.67.4.961.
Knill, David C, and Alexandre Pouget. 2004. “The Bayesian Brain: The Role of
Uncertainty in Neural Coding and Computation.” Trends in Neurosciences 27 (12):
712–19. https://doi.org/10.1016/j.tins.2004.10.007.
Kometer, Michael, B Rael Cahn, David Andel, Olivia L Carter, and Franz X Vollenwei-
der. 2011. “The 5-Ht2a/1a Agonist Psilocybin Disrupts Modal Object Completion
Associated with Visual Hallucinations.” Biological Psychiatry 69 (5): 399–406.
https://doi.org/10.1016/j.biopsych.2010.10.002.
Kometer, Michael, Thomas Pokorny, Erich Seifritz, and Franz X Volleinweider. 2015.
“Psilocybin-Induced Spiritual Experiences and Insightfulness Are Associated with
Synchronization of Neuronal Oscillations.” Psychopharmacology 232 (19): 3663–76.
https://doi.org/10.1007/s00213-015-4026-7.
Kometer, Michael, André Schmidt, Rosilla Bachmann, Erich Studerus, Erich Seifritz,
and Franz X Vollenweider. 2012. “Psilocybin Biases Facial Recognition, Goal-
Directed Behavior, and Mood State Toward Positive Relative to Negative Emo-
tions Through Different Serotonergic Subreceptors.” Biological Psychiatry 72 (11):
204
898–906. https://doi.org/10.1016/j.biopsych.2012.04.005.
Kometer, Michael, André Schmidt, Lutz Jäncke, and Franz X Vollenweider. 2013.
“Activation of Serotonin 2a Receptors Underlies the Psilocybin-Induced Effects on
α Oscillations, N170 Visual-Evoked Potentials, and Visual Hallucinations.” The
Journal of Neuroscience: The Official Journal of the Society for Neuroscience 33
(25): 10544–51. https://doi.org/10.1523/jneurosci.3007-12.2013.
Kometer, Michael, and Franz X Vollenweider. 2016. “Serotonergic Hallucinogen-
Induced Visual Perceptual Alterations.” In, 257–82. Springer Berlin Heidelberg.
https://doi.org/10.1007/7854_2016_461.
Kosovicheva, Anna A., Summer L. Sheremata, Ariel Rokem, Ayelet N. Landau,
and Michael A. Silver. 2012. “Cholinergic Enhancement Reduces Orientation-
Specific Surround Suppression but Not Visual Crowding.” Frontiers in Behavioral
Neuroscience 6. https://doi.org/10.3389/fnbeh.2012.00061.
Kraehenmann, Rainer, Dan Pokorny, Helena Aicher, Katrin H Preller, Thomas Pokorny,
Oliver G Bosch, Erich Seifritz, and Franz X Vollenweider. 2017. “LSD Increases
Primary Process Thinking via Serotonin 2a Receptor Activation.” Frontiers in
Pharmacology 8: 814. https://doi.org/10.3389/fphar.2017.00814.
Kraehenmann, Rainer, Dan Pokorny, Leonie Vollenweider, Katrin H Preller, Thomas
Pokorny, Erich Seifritz, and Franz X Vollenweider. 2017. “Dreamlike Effects of
LSD on Waking Imagery in Humans Depend on Serotonin 2a Receptor Activation.”
Psychopharmacology 234 (13): 2031–46. https://doi.org/10.1007/s00213-017-4610-
0.
Kraepelin, Emil. 1892. Ueber Die Beeinflussung Einfacher Psychischer Vorgänge
Durch Einige Arzneimittel: Experimentelle Untersuchungen. G. Fischer.
Krause, Fernando C., Eftihia Linardatos, David M. Fresco, and Michael T. Moore.
2021. “Facial Emotion Recognition in Major Depressive Disorder: A Meta-Analytic
Review.” Journal of Affective Disorders 293 (October): 320–28. https://doi.org/
10.1016/j.jad.2021.06.053.
Krebs, Teri S, and Pål-Ørjan Johansen. 2013. “Psychedelics and Mental Health: A
Population Study.” PloS One 8 (8): e63972. https://doi.org/10.1371/journal.pone
.0063972.
Kuypers, K P C, Jordi Riba, M de la Fuente Revenga, S Barker, E L Theunissen,
and J G Ramaekers. 2016. “Ayahuasca Enhances Creative Divergent Thinking
While Decreasing Conventional Convergent Thinking.” Psychopharmacology 233
(18): 3395–3403. https://doi.org/10.1007/s00213-016-4377-8.
Kwisthout, Johan, Harold Bekkering, and Iris van Rooij. 2017. “To Be Precise, the
Details Don’t Matter: On Predictive Processing, Precision, and Level of Detail of
Predictions.” Brain and Cognition 112 (March): 84–91. https://doi.org/10.1016/j.
bandc.2016.02.008.
Kwisthout, Johan, and Iris van Rooij. 2015. “Free Energy Minimization and Infor-
mation Gain: The Devil Is in the Details.” Cognitive Neuroscience 6 (4): 216–18.
https://doi.org/10.1080/17588928.2015.1051014.
Lamme, VA. 1995. “The Neurophysiology of Figure-Ground Segregation in Primary
Visual Cortex.” The Journal of Neuroscience 15 (2): 1605–15. https://doi.org/10.1
523/jneurosci.15-02-01605.1995.
205
Langfelder, Peter, and Steve Horvath. 2012. “Fast r Functions for Robust Correlations
and Hierarchical Clustering.” Journal of Statistical Software 46 (23050260): i11.
Lawn, Timothy, Ottavia Dipasquale, Alexandros Vamvakas, Ioannis Tsougos, Mitul A.
Mehta, and Matthew A. Howard. 2022. “Differential Contributions of Serotonergic
and Dopaminergic Functional Connectivity to the Phenomenology of LSD.” Psy-
chopharmacology 239 (6): 1797–1808. https://doi.org/10.1007/s00213-022-06117-5.
Lawrence, David Wyndham, Robin Carhart-Harris, Roland Griffiths, and Christo-
pher Timmermann. 2022. “Phenomenology and Content of the Inhaled n, n-
Dimethyltryptamine (n, n-DMT) Experience.” Scientific Reports 12 (1). https:
//doi.org/10.1038/s41598-022-11999-8.
Leary, Timothy. 1961. “Drugs, Set & Suggestibility.” In Annual Meeting of the
American Psychological Association. Vol. 6.
Leary, Timothy, George H Litwin, and Ralph Metzner. 1963. “REACTIONS TO
PSILOCYBJN ADMINISTERED IN a SUPPORTIVE ENVIRONMENT.” The
Journal of Nervous and Mental Disease 137 (6): 561. https://doi.org/10.1097/00
005053-196312000-00007.
Leary, Timothy, Ralph Metzner, and Richard Alpert. 1964. The Psychedelic Experience
: A Manual Based on the Tibetan Book of the Dead. New York: University Books.
Lebedev, A V, Mendel Kaelen, M Lövdén, J Nilsson, A Feilding, D J Nutt, and
Robin L Carhart-Harris. 2016. “LSD-induced Entropic Brain Activity Predicts
Subsequent Personality Change.” Human Brain Mapping, May. https://doi.org/10
.1002/hbm.23234.
Lee, Tai Sing, and David Mumford. 2003. “Hierarchical Bayesian Inference in the
Visual Cortex.” Journal of the Optical Society of America. A, Optics, Image
Science, and Vision 20 (7): 1434–48. https://doi.org/10.1364/JOSAA.20.001434.
Leptourgos, Pantelis, Martin Fortier-Davy, Robin L Carhart-Harris, Philip R. Corlett,
David Dupuis, Adam L Halberstadt, Michael Kometer, et al. 2020. “Hallucinations
Under Psychedelics and in the Schizophrenia Spectrum: An Interdisciplinary
and Multiscale Comparison.” Schizophrenia Bulletin 46 (6): 1396–1408. https:
//doi.org/10.1093/schbul/sbaa117.
Letheby, Chris. 2021. Philosophy of Psychedelics. Oxford University Press. https:
//doi.org/10.1093/med/9780198843122.001.0001.
Letheby, Chris, and Philip Gerrans. 2017. “Self Unbound: Ego Dissolution in
Psychedelic Experience.” Neuroscience of Consciousness 3 (1). https://doi.org/10
.1093/nc/nix016.
———. n.d. Philosophical Perspectives on the Psychedelic Renaissance. Oxford
University Press.
Lewicki, Michael S, Bruno A Olshausen, Annemarie Surlykke, and Cynthia F Moss.
2014. “Scene Analysis in the Natural Environment.” Frontiers in Psychology 5
(February). https://doi.org/10.3389/fpsyg.2014.00199.
Lewin, Louis. 1894. “On Anhalonium Lewinii and Other Cacti.” Archiv für Experi-
mentelle Pathologie Und Pharmakologie.
———. 1927. Phantastica, Narcotic and Stimulating Drugs. Stilke Berlin. https:
//doi.org/10.1097/00000441-193209000-00032.
Lieberman, Jeffrey A, and Daniel Shalev. 2016. “Back to the Future: Research Re-
206
newed on the Clinical Utility of Psychedelic Drugs.” Journal of Psychopharmacology
30 (12): 1198–1200. https://doi.org/10.1177/0269881116675755.
Liechti, Matthias E, Patrick C Dolder, and Yasmin Schmid. 2017. “Alterations
of Consciousness and Mystical-Type Experiences After Acute LSD in Humans.”
Psychopharmacology 234 (9-10): 1499–1510.
Limanowski, Jakub, and Felix Blankenburg. 2013. “Minimal Self-Models and the
Free Energy Principle.” Frontiers in Human Neuroscience 7 (September): 547.
https://doi.org/10.3389/fnhum.2013.00547.
Linares, Daniel, Silvia Amoretti, Rafael Marin-Campos, André Sousa, Laia Prades,
Josep Dalmau, Miquel Bernardo, and Albert Compte. 2020. “Spatial Suppression
and Sensitivity for Motion in Schizophrenia.” Schizophrenia Bulletin Open 1 (1).
https://doi.org/10.1093/schizbullopen/sgaa045.
Loon, Anouk M van, H Steven Scholte, Simon van Gaal, Björn J J van der Hoort,
and Victor A F Lamme. 2012. “GABAA Agonist Reduces Visual Awareness: A
masking-EEG Experiment.” Journal of Cognitive Neuroscience 24 (4): 965–74.
https://doi.org/10.1162/jocn_a_00197.
Lopez-Calderon, Javier, and Steven J. Luck. 2014. “ERPLAB: An Open-Source Tool-
box for the Analysis of Event-Related Potentials.” Frontiers in Human Neuroscience
8 (April). https://doi.org/10.3389/fnhum.2014.00213.
Louie, Kenway, and Paul W. Glimcher. 2019. “Normalization Principles in Computa-
tional Neuroscience.” Oxford University Press. https://doi.org/10.1093/acrefore/9
780190264086.013.43.
Louie, Kenway, Mel W. Khaw, and Paul W. Glimcher. 2013. “Normalization Is a
General Neural Mechanism for Context-Dependent Decision Making.” Proceedings
of the National Academy of Sciences 110 (15): 6139–44. https://doi.org/10.1073/
pnas.1217854110.
Louie, Kenway, T. LoFaro, R. Webb, and P. W. Glimcher. 2014. “Dynamic Divisive
Normalization Predicts Time-Varying Value Coding in Decision-Related Circuits.”
Journal of Neuroscience 34 (48): 16046–57. https://doi.org/10.1523/jneurosci.2851-
14.2014.
Luke, David P, and Devin B Terhune. 2013. “The Induction of Synaesthesia with
Chemical Agents: A Systematic Review.” Frontiers in Psychology 4 (October):
753. https://doi.org/10.3389/fpsyg.2013.00753.
Ma, Wen-Pei, Bao-Hua Liu, Ya-Tang Li, Z Josh Huang, Li I Zhang, and Huizhong
W Tao. 2010. “Visual Representations by Cortical Somatostatin Inhibitory
Neurons–Selective but with Weak and Delayed Responses.” The Journal of Neuro-
science: The Official Journal of the Society for Neuroscience 30 (43): 14371–79.
https://doi.org/10.1523/jneurosci.3248-10.2010.
MacKay, D Mo. 1956. “The Epistemological Problem for Automata.” In Automata
Studies: Annals of Mathematics Studies. Number 34, edited by W R Ashby, C E
Shannon, and J McCarthy. Automata Studies, Princeton, NJ: Princeton University
Press. https://doi.org/10.1515/9781400882618-012.
MacLean, Katherine A, Matthew W Johnson, and Roland R Griffiths. 2011. “Mystical
Experiences Occasioned by the Hallucinogen Psilocybin Lead to Increases in the
Personality Domain of Openness.” Journal of Psychopharmacology 25 (11): 1453–61.
207
https://doi.org/10.1177/0269881111420188.
Maclean, Katherine A, Jeannie-Marie S Leoutsakos, Matthew W Johnson, and Roland
R Griffiths. 2012. “Factor Analysis of the Mystical Experience Questionnaire: A
Study of Experiences Occasioned by the Hallucinogen Psilocybin.” Journal for
the Scientific Study of Religion 51 (4): 721–37. https://doi.org/10.1111/j.1468-
5906.2012.01685.x.
Macpherson, Fiona, and Clare Batty. 2016. “REDEFINING ILLUSION AND HAL-
LUCINATION IN LIGHT OF NEW CASES.” Philosophical Issues 26: 263–96.
https://www.jstor.org/stable/26611239.
Magaraggia, Igor, Zilla Kuiperes, and Rudy Schreiber. 2021. “Improving Cognitive
Functioning in Major Depressive Disorder with Psychedelics: A Dimensional
Approach.” Neurobiology of Learning and Memory 183 (September): 107467. https:
//doi.org/10.1016/j.nlm.2021.107467.
Majić, Tomislav, Timo T Schmidt, and Jürgen Gallinat. 2015. “Peak Experiences
and the Afterglow Phenomenon: When and How Do Therapeutic Effects of Hallu-
cinogens Depend on Psychedelic Experiences?” Journal of Psychopharmacology 29
(3): 241–53. https://doi.org/10.1177/0269881114568040.
Marino, Joseph. 2022. “Predictive Coding, Variational Autoencoders, and Biological
Connections.” Neural Computation 34 (1): 1–44. https://doi.org/10.1162/neco_a
_01458.
Marozeau, Jeremy, and Alain de Cheveigné. 2007. “The Effect of Fundamental
Frequency on the Brightness Dimension of Timbre.” The Journal of the Acoustical
Society of America 121 (1): 383–87. https://doi.org/10.1121/1.2384910.
Marshall, Paul. 2005. “Mind Beyond the Brain: Reducing Valves and Metaphysics.”
In Mystical Encounters with the Natural World. Oxford: Oxford University Press.
Martindale, C, and R Fischer. 1977. “The Effects of Psilocybin on Primary Process
Content in Language.” Confinia Psychiatrica. Borderland of Psychiatry. Grenzge-
biete Der Psychiatrie. Les Confins de La Psychiatrie 20 (4): 195–202.
Masters, Robert E L, and Jean Houston. 1966. The Varieties of Psychedelic Experi-
ence,. New York: Holt, Rinehart; Winston. https://doi.org/10.2307/1141632.
McClure-Begley, Tristan D., and Bryan L. Roth. 2022. “The Promises and Perils of
Psychedelic Pharmacology for Psychiatry.” Nature Reviews Drug Discovery 21 (6):
463–73. https://doi.org/10.1038/s41573-022-00421-7.
McGurk, Harry, and John MacDonald. 1976. “Hearing Lips and Seeing Voices.”
Nature 264 (5588): 746–48. https://doi.org/10.1038/264746a0.
McKenna, Dennis J, D B Repke, L Lo, and S J Peroutka. 1990. “Differential
Interactions of Indolealkylamines with 5-Hydroxytryptamine Receptor Subtypes.”
Neuropharmacology 29 (3): 193–98. https://doi.org/10.1016/0028-3908(90)90001-
8.
McKenna, Dennis J, and Jordi Riba. 2015. “New World Tryptamine Hallucinogens
and the Neuroscience of Ayahuasca.” Current Topics in Behavioral Neurosciences,
February. https://doi.org/10.1007/7854_2015_368.
McKenna, Dennis J, G H Towers, and F Abbott. 1984. “Monoamine Oxidase In-
hibitors in South American Hallucinogenic Plants: Tryptamine and Beta-Carboline
Constituents of Ayahuasca.” Journal of Ethnopharmacology 10 (2): 195–223.
208
https://doi.org/10.1016/0378-8741(84)90003-5.
Melara, Robert D., and Lawrence E. Marks. 1990. “Interaction Among Auditory
Dimensions: Timbre, Pitch, and Loudness.” Perception & Psychophysics 48 (2):
169–78. https://doi.org/10.3758/bf03207084.
Merkur, Daniel. 1998. The Ecstatic Immagination : Psychedelic Experiences and the
Psychoanalysis of Self-Actualization. Albany: State University of New York Press.
Merriam-Webster. 2017. “Model.” In Merriam-Webster.com. Merriam-Webster.com.
Mertens, Lea J, Matthew B Wall, Leor Roseman, Lysia Demetriou, David J Nutt, and
Robin L Carhart-Harris. 2020. “Therapeutic Mechanisms of Psilocybin: Changes
in Amygdala and Prefrontal Functional Connectivity During Emotional Processing
After Psilocybin for Treatment-Resistant Depression.” Journal of Psychopharma-
cology 34 (2): 167–80. https://doi.org/10.1177/0269881119895520.
Metzinger, Thomas. 2003. Being No One : The Self-Model Theory of Subjectivity.
Cambridge; London: The MIT Press. https://doi.org/10.7551/mitpress/1551.001.
0001.
Metzner, Ralph, and Timothy Leary. 1967. “On Programming Psychedelic Experi-
ences.” Psychedelic Review 9: 5–19.
Michaiel, Angie M., Philip R. L. Parker, and Cristopher M. Niell. 2019. “A Hal-
lucinogenic serotonin-2A Receptor Agonist Reduces Visual Response Gain and
Alters Temporal Dynamics in Mouse V1.” Cell Reports 26 (13): 3475–3483.e4.
https://doi.org/10.1016/j.celrep.2019.02.104.
Miller, George A. 2003. “The Cognitive Revolution: A Historical Perspective.” Trends
in Cognitive Sciences 7 (3): 141–44. https://doi.org/10.1016/s1364-6613(03)00029-
9.
Millière, Raphaël. 2017. “Looking for the Self: Phenomenology, Neurophysiology and
Philosophical Significance of Drug-Induced Ego Dissolution.” Frontiers in Human
Neuroscience 11: 245. https://doi.org/10.3389/fnhum.2017.00245.
Mitchell, S W. 1896. “Remarks on the Effects of Anhelonium Lewinii (the Mescal
Button).” British Medical Journal 2 (1875): 1625–29. https://doi.org/10.1136/bm
j.2.1875.1625.
Montague, P Read, Raymond J Dolan, Karl J Friston, and Peter Dayan. 2012.
“Computational Psychiatry.” Trends in Cognitive Sciences 16 (1): 72–80. https:
//doi.org/10.1016/j.tics.2011.11.018.
Moore, Tyler M., Steven P. Reise, Raquel E. Gur, Hakon Hakonarson, and Ruben C.
Gur. 2015. “Psychometric Properties of the Penn Computerized Neurocognitive
Battery.” Neuropsychology 29 (2): 235–46. https://doi.org/10.1037/neu0000093.
Moreau, Jacques Joseph. 1845. Du Hachisch Et de l’aliénation Mentale: études
Psychologiques. Paris: Fortin, Masson.
Moreno, Francisco A, Christopher B Wiegand, E Keolani Taitano, and Pedro L
Delgado. 2006. “Safety, Tolerability, and Efficacy of Psilocybin in 9 Patients
with Obsessive-Compulsive Disorder.” The Journal of Clinical Psychiatry 67 (11):
1735–40. https://doi.org/10.4088/jcp.v67n1110.
Morey, Richard D. 2008. “Confidence Intervals from Normalized Data: A Correction
to Cousineau (2005).” Tutorials in Quantitative Methods for Psychology 4 (2):
61–64.
209
Morreall, John. 2020. “Philosophy of Humor.” In The Stanford Encyclopedia of
Philosophy, edited by Edward N. Zalta, Fall 2020. https://plato.stanford.edu/arc
hives/fall2020/entries/humor/; Metaphysics Research Lab, Stanford University.
Morrison, Margaret. 2000. Unifying Scientific Theories : Physical Concepts and
Mathematical Structures. Cambridge: Cambridge University Press. https://doi.or
g/10.1017/cbo9780511527333.
Müller, Ulrich, Paul C Fletcher, and Holger Steinberg. 2006. “The Origin of Pharma-
copsychology: Emil Kraepelin’s Experiments in Leipzig, Dorpat and Heidelberg
(1882-1892).” Psychopharmacology 184 (2): 131–38.
Müller-Lyer, F. C. 1889. “Optical Illusions.” In The Contributions of f c Müller-Lyer,
edited by Ross H Day.
Mumford, D. 1992. “On the Computational Architecture of the Neocortex.” Biological
Cybernetics 66 (3): 241–51. https://doi.org/10.1007/bf00198477.
Murray, M. M. 2004. “Setting Boundaries: Brain Dynamics of Modal and Amodal
Illusory Shape Completion in Humans.” Journal of Neuroscience 24 (31): 6898–6903.
https://doi.org/10.1523/jneurosci.1996-04.2004.
Muthukumaraswamy, Suresh, Robin L Carhart-Harris, Rosalyn J Moran, Matthew J
Brookes, Tim M Williams, David Errtizoe, Ben Sessa, et al. 2013. “Broadband
Cortical Desynchronization Underlies the Human Psychedelic State.” The Journal
of Neuroscience: The Official Journal of the Society for Neuroscience 33 (38):
15171–83. https://doi.org/10.1523/jneurosci.2063-13.2013.
Muthukumaraswamy, Suresh, Anna Forsyth, and Thomas Lumley. 2021. “Blinding
and Expectancy Confounds in Psychedelic Randomized Controlled Trials.” Expert
Review of Clinical Pharmacology 14 (9): 1133–52. https://doi.org/10.1080/175124
33.2021.1933434.
Myers, F W H. 1903. Human Personality and Its Survival of Bodily Death. London:
Longmans & Co. https://doi.org/10.1037/13858-000.
Naseri, Parisa, Hamid Alavi Majd, Mohammad Tabatabae, Naghmeh Khadembashi,
and Morteza Najibi. 2019. “Functional Brain Response to Emotional Musical
Stimuli in Depression, Using INLA Approach for Approximate Bayesian Inference.”
Basic and Clinical Neuroscience Journal, November. https://doi.org/10.32598/b
cn.9.10.480.
Natale, M, C C Dahlberg, and J Jaffe. 1978. “Effect of Psychotomimetics (LSD and
Dextroamphetamine) on the Use of Primary- and Secondary-Process Language.”
Journal of Consulting and Clinical Psychology 46 (2): 352–53. https://doi.org/10
.1037/0022-006x.46.2.352.
Natale, M, M Kowitt, Dahlberg -Journal of consulting, . . . C C, and 1978. 1978. “Effect
of Psychototmimetics (LSD and Dextroamphetamine) on the Use of Figurative
Language During Psychoanalysis.” Psycnet.apa.org. https://doi.org/10.1037/0022-
006x.46.6.1579.
Neisser, Ulric. 1967. “Cognitive Psychology.” https://doi.org/10.4324/9781315736174.
Nguyen, Bao N, Sui-Ann Hew, John Ly, Hee-Young Shin, Jessica C Wong, Emily
Yeung, and Allison M McKendrick. 2018. “Acute Caffeine Ingestion Affects
Surround Suppression of Perceived Contrast.” Journal of Psychopharmacology 32
(1): 81–88.
210
Nichols, David E. 2004. “Hallucinogens.” Pharmacology & Therapeutics 101 (2):
131–81. https://doi.org/10.1007/978-3-642-27772-6_44-2.
———. 2016. “Psychedelics.” Pharmacological Reviews 68 (2): 264–355. https:
//doi.org/10.1124/pr.115.011478.
Nienborg, Hendrikje, Andrea Hasenstaub, Ian Nauhaus, Hiroki Taniguchi, Z Josh
Huang, and Edward M Callaway. 2013. “Contrast Dependence and Differential
Contributions from Somatostatin- and Parvalbumin-Expressing Neurons to Spatial
Integration in Mouse V1.” The Journal of Neuroscience: The Official Journal of
the Society for Neuroscience 33 (27): 11145–54. https://doi.org/10.1523/jneurosci.
5320-12.2013.
Nothdurft, H. C., J. L. Gallant, and D. C. Van Essen. 2000. “Response Profiles
to Texture Border Patterns in Area V1.” Visual Neuroscience 17 (3): 421–36.
https://doi.org/10.1017/s0952523800173092.
Nour, Matthew M, Lisa Evans, David Nutt, and Robin L Carhart-Harris. 2016.
“Ego-Dissolution and Psychedelics: Validation of the Ego-Dissolution Inventory
(EDI).” Frontiers in Human Neuroscience 10 (May). https://doi.org/10.3389/fnhu
m.2016.00269.
Nurminen, Lauri, and Alessandra Angelucci. 2014. “Multiple Components of Surround
Modulation in Primary Visual Cortex: Multiple Neural Circuits with Multiple
Functions?” Vision Research 104 (November): 47–56. https://doi.org/10.1016/j.vi
sres.2014.08.018.
Ohshiro, Tomokazu, Dora E. Angelaki, and Gregory C. DeAngelis. 2011. “A Normal-
ization Model of Multisensory Integration.” Nature Neuroscience 14 (6): 775–82.
https://doi.org/10.1038/nn.2815.
Ohtani, Yoshio, Shoichi Okamura, Yoshikazu Yoshida, Keisuke Toyama, and
Yoshimichi Ejima. 2002. “Surround Suppression in the Human Visual Cortex:
An Analysis Using Magnetoencephalography.” Vision Research 42 (15): 1825–35.
https://doi.org/10.1016/s0042-6989(02)00103-7.
Oizumi, Masafumi, Larissa Albantakis, and Giulio Tononi. 2014. “From the Phe-
nomenology to the Mechanisms of Consciousness: Integrated Information Theory
3.0.” PLoS Computational Biology 10 (5): e1003588. https://doi.org/10.1371/jour
nal.pcbi.1003588.
Olson, Jay A., Léah Suissa-Rocheleau, Michael Lifshitz, Amir Raz, and Samuel P.
L. Veissière. 2020. “Tripping on Nothing: Placebo Psychedelics and Contextual
Factors.” Psychopharmacology 237 (5): 1371–82. https://doi.org/10.1007/s00213-
020-05464-5.
Olzak, Lynn A, and Pentti I Laurinen. 1999. “Multiple Gain Control Processes
in Contrast–contrast Phenomena.” Vision Research 39 (24): 3983–87. https:
//doi.org/10.1016/s0042-6989(99)00131-5.
Osborne, Hannah. 2017. “Scientists Show LSD and Ketamine Make the Brain Enter
a ’Higher State of Consciousness’.” http://www.newsweek.com/psychedelic-drugs-
lsd-ketamine-brain-higher-consciousness-586076.
Osmond, H. 1957. “A Review of the Clinical Effects of Psychotomimetic Agents.”
Annals of the New York Academy of Sciences 66 (3): 418–34. https://doi.org/10.1
111/j.1749-6632.1957.tb40738.x.
211
Osmond, H, and J Smythies. 1952. “Schizophrenia: A New Approach.” The Journal
of Mental Science 98 (411): 309–15. https://doi.org/10.1192/bjp.98.411.309.
Ott, U. 2007. “States of Absorption: In Search of Neurobiological Foundations.”
Hypnosis and Conscious States: The Cognitive.
Ozeki, Hirofumi, Ian M. Finn, Evan S. Schaffer, Kenneth D. Miller, and David Ferster.
2009a. “Inhibitory Stabilization of the Cortical Network Underlies Visual Surround
Suppression.” Neuron 62 (4): 578–92. https://doi.org/10.1016/j.neuron.2009.03.0
28.
Ozeki, Hirofumi, Ian M Finn, Evan S Schaffer, Kenneth D Miller, and David Ferster.
2009b. “Inhibitory Stabilization of the Cortical Network Underlies Visual Surround
Suppression.” Neuron 62 (4): 578–92. https://doi.org/10.1016/j.neures.2009.09.1
125.
Ozeki, Hirofumi, Osamu Sadakane, Takafumi Akasaki, Tomoyuki Naito, Satoshi
Shimegi, and Hiromichi Sato. 2004. “Relationship Between Excitation and Inhi-
bition Underlying Size Tuning and Contextual Response Modulation in the Cat
Primary Visual Cortex.” The Journal of Neuroscience: The Official Journal of the
Society for Neuroscience 24 (6): 1428–38. https://doi.org/10.1523/jneurosci.3852-
03.2004.
Pahnke, Walter N. 1966. “Drugs and Mysticism.” International Journal of Parapsy-
chology 8 (2): 295–313.
Palhano-Fontes, Fernanda, Katia C Andrade, Luis F Tofoli, Antonio C Santos, Jose
Alexandre S Crippa, Jaime E C Hallak, Sidarta Ribeiro, and Draulio B de Araujo.
2015. “The Psychedelic State Induced by Ayahuasca Modulates the Activity
and Connectivity of the Default Mode Network.” PloS One 10 (2): e0118143.
https://doi.org/10.1371/journal.pone.0118143.
Pallavicini, Carla, Federico Cavanna, Federico Zamberlan, Laura A de la Fuente,
Yayla Ilksoy, Yonatan S Perl, Mauricio Arias, et al. 2021. “Neural and Subjec-
tive Effects of Inhaled n,n-Dimethyltryptamine in Natural Settings.” Journal of
Psychopharmacology 35 (4): 406–20. https://doi.org/10.1177/0269881120981384.
Pang, Xiaomei, Jing Xu, Yi Chang, Di Tang, Ya Zheng, Yanhua Liu, and Yiming
Sun. 2014. “Mismatch Negativity of Sad Syllables Is Absent in Patients with
Major Depressive Disorder.” Edited by Piia Susanna Astikainen. PLoS ONE 9 (3):
e91995. https://doi.org/10.1371/journal.pone.0091995.
Peirce, Jonathan, Jeremy R. Gray, Sol Simpson, Michael MacAskill, Richard Höchen-
berger, Hiroyuki Sogo, Erik Kastman, and Jonas Kristoffer Lindeløv. 2019. “Psy-
choPy2: Experiments in Behavior Made Easy.” Behavior Research Methods 51 (1):
195–203. https://doi.org/10.3758/s13428-018-01193-y.
Peirce, Jonathan, and Michael MacAskill. 2018. Building Experiments in PsychoPy.
Petrov, Yury, and Suzanne P. McKee. 2006. “The Effect of Spatial Configuration
on Surround Suppression of Contrast Sensitivity.” Journal of Vision 6 (3): 4.
https://doi.org/10.1167/6.3.4.
Pink-Hashkes, Sarit, Iris van Rooij, and Johan Kwisthout. 2017. “Perception Is in
the Details: A Predictive Coding Account of the Psychedelic Phenomenon.” In
Proceedings of the 39th Annual Meeting of the Cognitive Science Society, 2907–12.
London, UK.
212
Pokorny, Thomas, Patricia Duerler, Erich Seifritz, Franz X. Vollenweider, and Katrin
H. Preller. 2019. “LSD Acutely Impairs Working Memory, Executive Functions,
and Cognitive Flexibility, but Not Risk-Based Decision-Making.” Psychological
Medicine 50 (13): 2255–64. https://doi.org/10.1017/s0033291719002393.
Pokorny, Thomas, Katrin H Preller, Michael Kometer, Isabel Dziobek, and Franz X
Vollenweider. 2017. “Effect of Psilocybin on Empathy and Moral Decision-Making.”
The International Journal of Neuropsychopharmacology / Official Scientific Journal
of the Collegium Internationale Neuropsychopharmacologicum 20 (9): 747–57.
https://doi.org/10.1093/ijnp/pyx047.
Pokorny, Thomas, Katrin H Preller, Rainer Kraehenmann, and Franz X Vollenweider.
2016. “Modulatory Effect of the 5-Ht1a Agonist Buspirone and the Mixed Non-
Hallucinogenic 5-Ht1a/2a Agonist Ergotamine on Psilocybin-Induced Psychedelic
Experience.” European Neuropsychopharmacology: The Journal of the European
College of Neuropsychopharmacology 26 (4): 756–66. https://doi.org/10.1016/j.eu
roneuro.2016.01.005.
Pokorny, Victor J., Timothy J. Lano, Michael-Paul Schallmo, Cheryl A. Olman, and
Scott R. Sponheim. 2019. “Reduced Influence of Perceptual Context in Schizophre-
nia: Behavioral and Neurophysiological Evidence.” Psychological Medicine 51 (5):
786–94. https://doi.org/10.1017/s0033291719003751.
Polat, Uri, Keiko Mizobe, Mark W. Pettet, Takuji Kasamatsu, and Anthony M. Norcia.
1998. “Collinear Stimuli Regulate Visual Responses Depending on Cell's Contrast
Threshold.” Nature 391 (6667): 580–84. https://doi.org/10.1038/35372.
Preller, Katrin H, Marcus Herdener, Thomas Pokorny, Amanda Planzer, Rainer
Kraehenmann, Philipp Stämpfli, Matthias E Liechti, Erich Seifritz, and Franz X
Vollenweider. 2017. “The Fabric of Meaning and Subjective Effects in LSD-Induced
States Depend on Serotonin 2a Receptor Activation.” Current Biology: CB 27 (3):
451–57. https://doi.org/10.1016/j.cub.2016.12.030.
Preller, Katrin H, and Franz X Vollenweider. 2016. “Phenomenology, Structure,
and Dynamic of Psychedelic States.” In, 221–56. Springer Berlin Heidelberg.
https://doi.org/10.1007/7854_2016_459.
———. 2018. “Phenomenology, Structure, and Dynamic of Psychedelic States.”
Current Topics in Behavioral Neurosciences 36: 221–56.
Prentiss, D W, and T P Morgan. 1895. “Anhalonium Lewinie (Mescal Buttons).”
Therapeutic Gazette 9: 577–85.
Purpura, D P. 1968. “Neurophysiological Actions of LSD.” In LSD, Man & Society.
Purves, Dale, Yaniv Morgenstern, and William T Wojtach. 2015. “Perception and
Reality: Why a Wholly Empirical Paradigm Is Needed to Understand Vision.”
Frontiers in Systems Neuroscience 9 (November): 156. https://doi.org/10.3389/fn
sys.2015.00156.
Qiu, Cheng, Daniel Kersten, and Cheryl A Olman. 2013. “Segmentation Decreases
the Magnitude of the Tilt Illusion.” Journal of Vision 13 (13): 19. https://doi.org/
10.1167/13.13.19.
Quaia, Christian, Lance M. Optican, and Bruce G. Cumming. 2017. “Suppression
and Contrast Normalization in Motion Processing.” The Journal of Neuroscience
37 (45): 11051–66. https://doi.org/10.1523/jneurosci.1572-17.2017.
213
Rabinowitz, Neil C., Ben D. B. Willmore, Jan W. H. Schnupp, and Andrew J. King.
2011. “Contrast Gain Control in Auditory Cortex.” Neuron 70 (6): 1178–91.
https://doi.org/10.1016/j.neuron.2011.04.030.
Rao, R P, and D H Ballard. 1999. “Predictive Coding in the Visual Cortex: A
Functional Interpretation of Some Extra-Classical Receptive-Field Effects.” Nature
Neuroscience 2 (1): 79–87. https://doi.org/10.1038/4580.
Rapaport, D. 1950. “On the Psycho-Analytic Theory of Thinking.” The International
Journal of Psycho-Analysis.
Ray, Thomas S. 2010. “Psychedelics and the Human Receptorome.” PloS One 5 (2):
e9019. https://doi.org/10.1371/journal.pone.0009019.
———. 2016. “Constructing the Ecstasy of MDMA from Its Component Mental
Organs: Proposing the Primer/Probe Method.” Medical Hypotheses 87 (February):
48–60. https://doi.org/10.1016/j.mehy.2015.12.018.
Read, Jenny C A, Renos Georgiou, Claire Brash, Partow Yazdani, Roger Whittaker,
Andrew J Trevelyan, and Ignacio Serrano-Pedraza. 2015. “Moderate Acute
Alcohol Intoxication Has Minimal Effect on Surround Suppression Measured
with a Motion Direction Discrimination Task.” Journal of Vision 15 (1): 1515.
https://doi.org/10.1167/15.1.5.
Reynolds, John H, and David J Heeger. 2009. “The Normalization Model of Attention.”
Neuron 61 (2): 168–85. https://doi.org/10.1016/j.neuron.2009.01.002.
Riba, Jordi, Peter Anderer, Francesc Jané, Bernd Saletu, and Manel J Barbanoj. 2004.
“Effects of the South American Psychoactive Beverage Ayahuasca on Regional
Brain Electrical Activity in Humans: A Functional Neuroimaging Study Using
Low-Resolution Electromagnetic Tomography.” Neuropsychobiology 50 (1): 89–101.
Riba, Jordi, Peter Anderer, Adelaida Morte, Gloria Urbano, Francesc Jané, Bernd
Saletu, and Manel J Barbanoj. 2002. “Topographic pharmaco-EEG Mapping of
the Effects of the South American Psychoactive Beverage Ayahuasca in Healthy
Volunteers.” British Journal of Clinical Pharmacology 53 (6): 613–28. https:
//doi.org/10.1046/j.1365-2125.2002.01609.x.
Riba, Jordi, A Rodriguez-Fornells, G Urbano, A Morte, R Antonijoan, M Montero,
J C Callaway, and M J Barbanoj. 2001. “Subjective Effects and Tolerability of
the South American Psychoactive Beverage Ayahuasca in Healthy Volunteers.”
Psychopharmacology 154 (1): 85–95. https://doi.org/10.1007/s002130000606.
Riba, Jordi, A. Rodríguez-Fornells, Rick J. Strassman, and M. J. Barbanoj. 2001.
“Psychometric Assessment of the Hallucinogen Rating Scale.” Drug and Alcohol
Dependence 62 (3): 215–23. https://doi.org/10.1016/s0376-8716(00)00175-7.
Richards, William. 2015. Sacred Knowledge. Columbia University Press. https:
//doi.org/10.7312/columbia/9780231174060.001.0001.
Rickli, Anna, Olivier D Moning, Marius C Hoener, and Matthias E Liechti. 2016.
“Receptor Interaction Profiles of Novel Psychoactive Tryptamines Compared with
Classic Hallucinogens.” European Neuropsychopharmacology: The Journal of the
European College of Neuropsychopharmacology 26 (8): 1327–37. https://doi.org/
10.1016/j.euroneuro.2016.05.001.
Roseman, Leor, Lysia Demetriou, Matthew BWall, David J Nutt, and Robin L Carhart-
Harris. 2018. “Increased Amygdala Responses to Emotional Faces After Psilocybin
214
for Treatment-Resistant Depression.” Neuropharmacology 142 (November): 263–69.
https://doi.org/10.1016/j.neuropharm.2017.12.041.
Roseman, Leor, David Nutt, and Robin L Carhart-Harris. 2017. “Quality of Acute
Psychedelic Experience Predicts Therapeutic Efficacy of Psilocybin for Treatment-
Resistant Depression.” Frontiers in Pharmacology 8: 974. https://doi.org/10.3389/
fphar.2017.00974.
Roseman, Leor, M I Sereno, R Leech, and Mendel Kaelen. 2016. “LSD Alters Eyes-
Closed Functional Connectivity Within the Early Visual Cortex in a Retinotopic
Fashion.” Wiley Online Library. https://doi.org/10.1002/hbm.23224.
Ross, Stephen, Anthony Bossis, Jeffrey Guss, Gabrielle Agin-Liebes, Tara Malone,
Barry Cohen, Sarah E Mennenga, et al. 2016. “Rapid and Sustained Symptom
Reduction Following Psilocybin Treatment for Anxiety and Depression in Patients
with Life-Threatening Cancer: A Randomized Controlled Trial.” Journal of Psy-
chopharmacology 30 (12): 1165–80. https://doi.org/10.1177/0269881116675512.
Rottenberg, Jonathan, James J. Gross, and Ian H. Gotlib. 2005. “Emotion Context
Insensitivity in Major Depressive Disorder.” Journal of Abnormal Psychology 114
(4): 627–39. https://doi.org/10.1037/0021-843x.114.4.627.
Rouhier, A. 1927. Le Peyotl. G. Doin.
Russ, S L, and M S Elliott. 2017. “Antecedents of Mystical Experience and Dread in
Intensive Meditation.” Psychology of Consciousness: Theory. https://doi.org/10.1
037/cns0000119.
Russell, Bertrand. 1910. “Knowledge by Acquaintance and Knowledge by Description.”
Proceedings of the Aristotelian Society 11: 108–28. http://www.jstor.org/stable/4
543805.
Sagud, Marina, Lucija Tudor, Lucija Šimunić, Dejana Jezernik, Zoran Madžarac,
Nenad Jakšić, Alma Mihaljević Peleš, et al. 2020. “Physical and Social Anhedonia
Are Associated with Suicidality in Major Depression, but Not in Schizophrenia.”
Suicide and Life-Threatening Behavior 51 (3): 446–54. https://doi.org/10.1111/sl
tb.12724.
Salmela, Viljami, Lumikukka Socada, John Söderholm, Roope Heikkilä, Jari Lahti,
Jesper Ekelund, and Erkki Isometsä. 2021. “Reduced Visual Contrast Suppression
During Major Depressive Episodes.” Journal of Psychiatry and Neuroscience 46
(2). https://doi.org/10.1503/jpn.200091.
Sandison, R A. 1954. “Psychological Aspects of the LSD Treatment of the Neuroses.”
The Journal of Mental Science 100 (419): 508–15. https://doi.org/10.1192/bjp.10
0.419.508.
Sandison, R A, and J D Whitelaw. 1957. “Further Studies in the Therapeutic Value
of Lysergic Acid Diethylamide in Mental Illness.” The Journal of Mental Science
103 (431): 332–43. https://doi.org/10.1192/bjp.103.431.332.
Sanz, Camila, and Enzo Tagliazucchi. 2018. “The Experience Elicited by Hallu-
cinogens Presents the Highest Similarity to Dreaming Within a Large Database
of Psychoactive Substance Reports.” Frontiers in Neuroscience 12: 7. https:
//doi.org/10.3389/fnins.2018.00007.
Sato, Tatsuo K, Bilal Haider, Michael Häusser, and Matteo Carandini. 2016. “An
Excitatory Basis for Divisive Normalization in Visual Cortex.” Nature Neuroscience
215
19 (4): 568–70. https://doi.org/10.1038/nn.4249.
Savage, C. 1955. “Variations in Ego Feeling Induced by d-Lysergic Acid Diethylamide
(LSD-25).” Psychoanalytic Review 42 (1): 1–16.
Schach, Sophia, Rainer Surges, and Christoph Helmstaedter. 2021. “Visual Sur-
round Suppression in People with Epilepsy Correlates with Attentional-Executive
Functioning, but Not with Epilepsy or Seizure Types.” Epilepsy & Behavior 121
(August): 108080. https://doi.org/10.1016/j.yebeh.2021.108080.
Schallmo, Michael-Paul, Alex M. Kale, and Scott O. Murray. 2019. “The Time Course
of Different Surround Suppression Mechanisms.” Journal of Vision 19 (4): 12.
https://doi.org/10.1167/19.4.12.
Schallmo, Michael-Paul, Alexander M Kale, Rachel Millin, Anastasia V Flevaris,
Zoran Brkanac, Richard Ae Edden, Raphael A Bernier, and Scott O Murray. 2018.
“Suppression and Facilitation of Human Neural Responses.” eLife 7 (January).
https://doi.org/10.7554/elife.30334.
Schallmo, Michael-Paul, Hannah R. Moser, Caroline Demro, Malgorzata Marjanska,
and Scott R. Sponheim. 2020. “The Role of GABA During Visual Contrast
Perception in Psychosis.” Journal of Vision 20 (11): 340. https://doi.org/10.1167/
jov.20.11.340.
Schallmo, Michael-Paul, and Scott O. Murray. 2016. “Identifying Separate Compo-
nents of Surround Suppression.” Journal of Vision 16 (1): 2–2. https://doi.org/10
.1167/16.1.2.
Schallmo, Michael-Paul, Scott R Sponheim, and Cheryl A Olman. 2015. “Reduced
Contextual Effects on Visual Contrast Perception in Schizophrenia and Bipolar
Affective Disorder.” Psychological Medicine 45 (16): 3527–37. https://doi.org/10.1
017/s0033291715001439.
Schartner, Michael M, Robin L Carhart-Harris, Adam B Barrett, Anil K Seth, and
Suresh Muthukumaraswamy. 2017. “Increased Spontaneous MEG Signal Diversity
for Psychoactive Doses of Ketamine, LSD and Psilocybin.” Scientific Reports 7
(April): 46421. https://doi.org/10.1038/srep46421.
Schenberg, Eduardo Ekman, João Felipe Morel Alexandre, Renato Filev, Andre Mas-
cioli Cravo, João Ricardo Sato, Suresh Muthukumaraswamy, Maurício Yonamine,
et al. 2015. “Acute Biphasic Effects of Ayahuasca.” PloS One 10 (9): e0137202.
https://doi.org/10.1371/journal.pone.0137202.
Schmid, Yasmin, Florian Enzler, Peter Gasser, Eric Grouzmann, Katrin H. Preller,
Franz X. Vollenweider, Rudolf Brenneisen, Felix Müller, Stefan Borgwardt, and
Matthias E. Liechti. 2015. “Acute Effects of Lysergic Acid Diethylamide in Healthy
Subjects.” Biological Psychiatry 78 (8): 544–53. https://doi.org/10.1016/j.biopsy
ch.2014.11.015.
Schmied, Lori A, Hannah Steinberg, and Elizabeth A B Sykes. 2006. “Psychopharma-
cology’s Debt to Experimental Psychology.” History of Psychology 9 (2): 144–57.
https://doi.org/10.1037/1093-4510.9.2.144.
Schultes, Richard Evans. 1969. “Hallucinogens of Plant Origin.” Science 163 (3864):
245–54. https://doi.org/10.1126/science.163.3864.245.
Schultes, Richard Evans, and Albert Hofmann. 1973. The Botany and Chemistry of
Hallucinogens. Springfield: Charles C. Thomas. https://doi.org/10.2307/2806315.
216
Schultes, Richard Evans, Albert Hofmann, and Christian Rätsch. 2006. Plants of the
Gods : Their Sacred, Healing and Hallucinogenic Powers. Rochester, Vt.: Healing
Arts Press.
Schultes, Richard Evans, and Elmer W. Smith. 1976. Hallucinogenic Plants. New
York: Golden Press.
Schwartz, Odelia, Anne Hsu, and Peter Dayan. 2007. “Space and Time in Visual
Context.” Nature Reviews. Neuroscience 8 (7): 522–35. https://doi.org/10.1038/
nrn2155.
Scott, William A. 1962. “Cognitive Complexity and Cognitive Flexibility.” Sociometry
25 (4): 405. https://doi.org/10.2307/2785779.
Serrano-Pedraza, Ignacio, VerÈ3nica Romero-Ferreiro, Jenny C. A. Read,
Teresa DiÃguez-Risco, Alexandra Bagney, Montserrat Caballero-GonzÃlez,
Javier RodrÃguez-Torresano, and Roberto Rodriguez-Jimenez. 2014. “Re-
duced Visual Surround Suppression in Schizophrenia Shown by Measuring
Contrast Detection Thresholds.” Frontiers in Psychology 5 (December).
https://doi.org/10.3389/fpsyg.2014.01431.
Sessa, B. 2008. “Is It Time to Revisit the Role of Psychedelic Drugs in Enhancing
Human Creativity?” Journal of Psychopharmacology 22 (8): 821–27. https:
//doi.org/10.1177/0269881108091597.
Seth, Anil. 2009. “Explanatory Correlates of Consciousness: Theoretical and Compu-
tational Challenges.” Cognitive Computation 1 (1): 50–63. https://doi.org/10.100
7/s12559-009-9007-x.
Shams, Ladan, Yukiyasu Kamitani, and Shinsuke Shimojo. 2000. “What You See Is
What You Hear.” Nature 408 (6814): 788–88. https://doi.org/10.1038/35048669.
Shankman, Stewart A., Brady D. Nelson, Martin Harrow, and Robert Faull. 2010.
“Does Physical Anhedonia Play a Role in Depression? A 20-Year Longitudinal
Study.” Journal of Affective Disorders 120 (1-3): 170–76. https://doi.org/10.1016/
j.jad.2009.05.002.
Shanon, Benny. 2002. The Antipodes of the Mind : Charting the Phenomenology of
the Ayahuasca Experience. New York: Oxford University Press.
Shaw, E, and D W Woolley. 1956. “Some Serotoninlike Activities of Lysergic Acid
Diethylamide.” Science 124 (3212): 121–22. https://doi.org/10.1126/science.124.
3212.121.
Shimamura, A P. 2000. “Toward a Cognitive Neuroscience of Metacognition.” Con-
sciousness and Cognition 9 (2 Pt 1): 313-23; discussion 324-6. https://doi.org/10
.1006/ccog.2000.0450.
Shulgin, Alexander T, and Ann Shulgin. 1997. Tihkal : The Continuation. Berkeley,
Calif.: Transform Press.
Sidis, Boris. 1898. The Psychology of Suggestion: A Research into the Subconscious
Nature of Man and Society. D Appleton & Company. https://doi.org/10.1037/10
578-000.
Siegel, Ronald K, and Louis Jolyon West. 1975. Hallucinations : Behavior, Experience,
and Theory. Vol. 37. New York: Wiley. https://doi.org/10.1037/031728.
Sinke, Christopher, John H Halpern, Markus Zedler, Janina Neufeld, Hinderk M
Emrich, and Torsten Passie. 2012. “Genuine and Drug-Induced Synesthesia: A
217
Comparison.” Consciousness and Cognition 21 (3): 1419–34. https://doi.org/10.1
016/j.concog.2012.03.009.
Sjoberg, B. M., and L. E. Hollister. 1965. “The Effects of Psychotomimetic Drugs on
Primary Suggestibility.” Psychopharmacologia 8 (4): 251–62. https://doi.org/10.1
007/bf00407857.
Smythies, John R. 1956. Analysis of Perception. London: Routledge; Paul. https:
//doi.org/10.4324/9781315009407.
Snodgrass, Joan G., and Mary Vanderwart. 1980. “A Standardized Set of 260
Pictures: Norms for Name Agreement, Image Agreement, Familiarity, and Visual
Complexity.” Journal of Experimental Psychology: Human Learning and Memory
6 (2): 174–215. https://doi.org/10.1037/0278-7393.6.2.174.
Snowden, Robert J, and Stephen T Hammett. 1998. “The Effects of Surround Contrast
on Contrast Thresholds, Perceived Contrast and Contrast Discrimination.” Vision
Research 38 (13): 1935–45. https://doi.org/10.1016/s0042-6989(97)00379-9.
Song, Chen, Kristian Sandberg, Lau Møller Andersen, Jakob Udby Blicher, and
Geraint Rees. 2017. “Human Occipital and Parietal GABA Selectively Influence
Visual Perception of Orientation and Size.” The Journal of Neuroscience: The
Official Journal of the Society for Neuroscience 37 (37): 8929–37. https://doi.org/
10.1523/jneurosci.3945-16.2017.
Song, Xue Mei, Xi-Wen Hu, Zhe Li, Yuan Gao, Xuan Ju, Dong-Yu Liu, Qian-Nan
Wang, et al. 2021. “Reduction of Higher-Order Occipital GABA and Impaired
Visual Perception in Acute Major Depressive Disorder.” Molecular Psychiatry 26
(11): 6747–55. https://doi.org/10.1038/s41380-021-01090-5.
Späth, Ernst. 1919. “Über dieAnhalonium-Alkaloide.” Monatshefte für Chemie Und
Verwandte Teile Anderer Wissenschaften 40 (2): 129–54. https://doi.org/10.1007/
bf01774466.
Speth, Jana, Clemens Speth, Mendel Kaelen, Astrid M Schloerscheidt, Amanda
Feilding, David J Nutt, and Robin L Carhart-Harris. 2016. “Decreased Mental
Time Travel to the Past Correlates with Default-Mode Network Disintegration
Under Lysergic Acid Diethylamide.” Journal of Psychopharmacology 30 (4): 344–53.
https://doi.org/10.1177/0269881116628430.
Spitzer, M, M Thimm, L Hermle, P Holzmann, K A Kovar, H Heimann, E Gouzoulis-
Mayfrank, U Kischka, and F Schneider. 1996. “Increased Activation of Indirect
Semantic Associations Under Psilocybin.” Biological Psychiatry 39 (12): 1055–57.
https://doi.org/10.1016/0006-3223(95)00418-1.
Srinivasan, Mandyam Veerambudi, Simon Barry Laughlin, and A. Dubs. 1982.
“Predictive Coding: A Fresh View of Inhibition in the Retina.” Proceedings of
the Royal Society of London. Series B. Biological Sciences 216 (1205): 427–59.
https://doi.org/10.1098/rspb.1982.0085.
Stace, W T. 1960. Mysticism and Philosophy. New York: MacMillan. https:
//doi.org/10.2307/2217211.
Stamets, Paul. 1996. Psilocybin Mushrooms of the World. Berkeley Calif: Ten Speed
Press.
Stewart, Omer Call. 1961. The Native American Church and the Law, with Description
of Peyote Religious Services.
218
Stigler, Michael, and Dan Pokorny. 2001. “Emotions and Primary Process in Guided
Imagery Psychotherapy: Computerized Text-Analytic Measures.” Psychotherapy
Research 11 (4): 415–31. https://doi.org/10.1093/ptr/11.4.415.
Stilp, Christian. 2019. “Acoustic Context Effects in Speech Perception.” WIREs
Cognitive Science 11 (1). https://doi.org/10.1002/wcs.1517.
Stockings, G T. 1940. “A Clinical Study of the Mescaline Psychosis, with Special
Reference to the Mechanism of the Genesis of Schizophenic and Other Psychotic
States.” RCP. https://doi.org/10.1192/bjp.86.360.29.
Stoliker, Devon, Gary F. Egan, and Adeel Razi. 2022. “Reduced Precision Underwrites
Ego Dissolution and Therapeutic Outcomes Under Psychedelics.” Frontiers in
Neuroscience 16 (March). https://doi.org/10.3389/fnins.2022.827400.
Stoll, W. 1947. “Lysergsäure-Diäthylamid, Ein Phantastikum Aus Der Mutterko-
rngruppe. [Lysergic Acid Diethylamide, a Phantasticant of the Ergot Class.].”
Schweizer Archiv Für Neurologie Und Psychiatrie, no. 60: 279–323.
Strassman, Rick J. 1984. “Adverse Reactions to Psychedelic Drugs: A Review
of the Literature.” The Journal of Nervous and Mental Disease 172 (10): 577.
https://doi.org/10.1097/00005053-198410000-00001.
———. 1995. “Human Psychopharmacology of n,n-Dimethyltryptamine.” Behavioural
Brain Research 73 (1-2): 121–24. https://doi.org/10.1016/0166-4328(96)00081-2.
Strassman, Rick J., C. R. Qualls, E. H. Uhlenhuth, and R. Kellner. 1994. “Dose-
Response Study of N,N-dimethyltryptamine in Humans. II. Subjective Effects and
Preliminary Results of a New Rating Scale.” Archives of General Psychiatry 51
(2): 98–108. https://doi.org/10.1001/archpsyc.1994.03950020022002.
Stroud, Jack, T. P. Freeman, R. Leech, C. Hindocha, W. Lawn, D. J. Nutt, H. V
Curran, and Robin L. Carhart-Harris. 2017. “Psilocybin with Psychological
Support Improves Emotional Face Recognition in Treatment-Resistant Depression.”
Psychopharmacology 235 (2): 459–66. https://doi.org/10.1007/s00213-017-4754-y.
Studerus, Erich, Alex Gamma, Michael Kometer, and Franz X Vollenweider. 2012.
“Prediction of Psilocybin Response in Healthy Volunteers.” PloS One 7 (2): e30800.
https://doi.org/10.1371/journal.pone.0030800.
Studerus, Erich, Alex Gamma, and Franz X Vollenweider. 2010. “Psychometric
Evaluation of the Altered States of Consciousness Rating Scale (OAV).” PloS One
5 (8): e12412. https://doi.org/10.1371/journal.pone.0012412.
Studerus, Erich, Michael Kometer, Felix Hasler, and Franz X Vollenweider. 2010.
“Acute, Subacute and Long-Term Subjective Effects of Psilocybin in Healthy Hu-
mans: A Pooled Analysis of Experimental Studies.” Journal of Psychopharmacology
25 (11): 1434–52. https://doi.org/10.1177/0269881110382466.
Suler, J R. 1980. “Primary Process Thinking and Creativity.” Psychological Bulletin.
https://doi.org/10.1037/0033-2909.88.1.144.
Supèr, Hans, August Romeo, and Matthias Keil. 2010. “Feed-Forward Segmentation
of Figure-Ground and Assignment of Border-Ownership.” Edited by Vladimir
Brezina. PLoS ONE 5 (5): e10705. https://doi.org/10.1371/journal.pone.0010705.
Swanson, Link Ray. 2016. “The Predictive Processing Paradigm Has Roots in Kant.”
Frontiers in Systems Neuroscience 10 (October). https://doi.org/10.3389/fnsys.20
16.00079.
219
———. 2018. “Unifying Theories of Psychedelic Drug Effects.” Frontiers in Pharma-
cology 9 (172). https://doi.org/10.3389/fphar.2018.00172.
Sweat, Noah W, Larry W Bates, and Peter S Hendricks. 2016. “The Associations of
Naturalistic Classic Psychedelic Use, Mystical Experience, and Creative Problem
Solving.” Journal of Psychoactive Drugs 48 (5): 344–50. https://doi.org/10.1080/
02791072.2016.1234090.
Tadin, Duje, Jejoong Kim, Mikisha L Doop, Crystal Gibson, Joseph S Lappin,
Randolph Blake, and Sohee Park. 2006. “Weakened Center-Surround Interactions
in Visual Motion Processing in Schizophrenia.” The Journal of Neuroscience:
The Official Journal of the Society for Neuroscience 26 (44): 11403–12. https:
//doi.org/10.1523/jneurosci.2592-06.2006.
Tagliazucchi, Enzo, Robin L Carhart-Harris, Robert Leech, David Nutt, and Dante
R Chialvo. 2014. “Enhanced Repertoire of Brain Dynamical States During
the Psychedelic Experience.” Human Brain Mapping 35 (11): 5442–56. https:
//doi.org/10.1002/hbm.22562.
Tagliazucchi, Enzo, Leor Roseman, Mendel Kaelen, Csaba Orban, Suresh Muthuku-
maraswamy, Kevin Murphy, Helmut Laufs, et al. 2016. “Increased Global Func-
tional Connectivity Correlates with LSD-Induced Ego Dissolution.” Current Biology:
CB, April. https://doi.org/10.1016/j.cub.2016.02.010.
Tenenbaum, Joshua B, Charles Kemp, Thomas L Griffiths, and Noah D Goodman.
2011. “How to Grow a Mind: Statistics, Structure, and Abstraction.” Science 331
(6022): 1279–85. https://doi.org/10.1126/science.1192788.
Terhune, Devin B, David P Luke, Mendel Kaelen, Mark Bolstridge, Amanda Feilding,
David Nutt, Robin L Carhart-Harris, and Jamie Ward. 2016. “A Placebo-
Controlled Investigation of Synaesthesia-Like Experiences Under LSD.” Neuropsy-
chologia, April. https://doi.org/10.1016/j.neuropsychologia.2016.04.005.
Thagard, Paul. 2009. “Why Cognitive Science Needs Philosophy and Vice Versa.”
Topics in Cognitive Science 1 (2): 237–54. https://doi.org/10.1111/j.1756-
8765.2009.01016.x.
Thaler, L., A. C. Schütz, M. A. Goodale, and K. R. Gegenfurtner. 2013. “What Is
the Best Fixation Target? The Effect of Target Shape on Stability of Fixational
Eye Movements.” Vision Research 76 (January): 31–42. https://doi.org/10.1016/j.
visres.2012.10.012.
Tibber, Marc S, Elaine J Anderson, Tracy Bobin, Elena Antonova, Alice Seabright,
Bernice Wright, Patricia Carlin, Sukhwinder S Shergill, and Steven C Dakin.
2013. “Visual Surround Suppression in Schizophrenia.” Frontiers in Psychology 4
(February): 88. https://doi.org/10.3389/fpsyg.2013.00088.
Timmermann, Christopher, Hannes Kettner, Chris Letheby, Leor Roseman, Fernando
Rosas, and Robin L. Carhart-Harris. 2021. “Psychedelics Alter Metaphysical
Beliefs,” June. https://doi.org/10.31234/osf.io/f6sjk.
Titchener, Edward Bradford. 1905. Experimental Psychology: A Manual of Laboratory
Practice. Vol. 2. Macmillan. https://doi.org/10.2307/2177896.
Todorović, Dejan. 2020. “What Are Visual Illusions?” Perception 49 (11): 1128–99.
https://doi.org/10.1177/0301006620962279.
Tononi, Giulio. 2004. “An Information Integration Theory of Consciousness.” BMC
220
Neuroscience 5 (November): 42. https://doi.org/10.1002/9780470751466.ch23.
———. 2008. “Consciousness as Integrated Information: A Provisional Manifesto.”
The Biological Bulletin 215 (3): 216–42. https://doi.org/10.2307/25470707.
Tononi, Giulio, and G M Edelman. 1998. “Consciousness and Complexity.” Science
282 (5395): 1846–51. https://doi.org/10.1126/science.282.5395.1846.
Tononi, Giulio, and Christof Koch. 2008. “The Neural Correlates of Consciousness:
An Update.” Annals of the New York Academy of Sciences 1124 (March): 239–61.
https://doi.org/10.1196/annals.1440.004.
Tupper, Kenneth W, Evan Wood, Richard Yensen, and Matthew W Johnson. 2015.
“Psychedelic Medicine: A Re-Emerging Therapeutic Paradigm.” CMAJ: Canadian
Medical Association Journal = Journal de l’Association Medicale Canadienne 187
(14): 1054–59. https://doi.org/10.1503/cmaj.141124.
Turton, S, D J Nutt, and Robin L Carhart-Harris. 2014. “A Qualitative Report on
the Subjective Experience of Intravenous Psilocybin Administered in an FMRI
Environment.” Current Drug Abuse Reviews 7 (2): 117–27. https://doi.org/10.217
4/1874473708666150107120930.
Uddin, Lucina Q. 2021. “Cognitive and Behavioural Flexibility: Neural Mechanisms
and Clinical Considerations.” Nature Reviews Neuroscience 22 (3): 167–79. https:
//doi.org/10.1038/s41583-021-00428-w.
Vallat, Raphael. 2018. “Pingouin: Statistics in Python.” Journal of Open Source
Software 3 (31): 1026. https://doi.org/10.21105/joss.01026.
Valle, Marta, Ana Elda Maqueda, Mireia Rabella, Aina Rodríguez-Pujadas, Rosa
Maria Antonijoan, Sergio Romero, Joan Francesc Alonso, et al. 2016. “Inhibition
of Alpha Oscillations Through serotonin-2A Receptor Activation Underlies the
Visual Effects of Ayahuasca in Humans.” European Neuropsychopharmacology: The
Journal of the European College of Neuropsychopharmacology 26 (7): 1161–75.
https://doi.org/10.1016/j.euroneuro.2016.03.012.
Vanegas, M. Isabel, Annabelle Blangero, and Simon P. Kelly. 2015. “Electrophysiolog-
ical Indices of Surround Suppression in Humans.” Journal of Neurophysiology 113
(4): 1100–1109. https://doi.org/10.1152/jn.00774.2014.
Verrill, A. E. 1914. “A Recent Case of Mushroom Intoxication.” Science 40 (1029):
408–10. http://www.jstor.org/stable/1639976.
Vigliocco, Gabriella, David P Vinson, Markus F Damian, and Willem Levelt. 2002.
“Semantic Distance Effects on Object and Action Naming.” Cognition 85 (3):
B61–69. https://doi.org/10.1016/s0010-0277(02)00107-5.
Viol, A, Fernanda Palhano-Fontes, Heloisa Onias, Draulio B de Araujo, and G M
Viswanathan. 2016. “Shannon Entropy of Brain Functional Complex Networks
Under the Influence of the Psychedelic Ayahuasca,” November. https://doi.org/10
.1038/s41598-017-06854-0.
Vollenweider, Franz X, Philipp A Csomor, Bernhard Knappe, Mark A Geyer, and
Boris B Quednow. 2007. “The Effects of the Preferential 5-Ht2a Agonist Psilo-
cybin on Prepulse Inhibition of Startle in Healthy Human Volunteers Depend
on Interstimulus Interval.” Neuropsychopharmacology: Official Publication of
the American College of Neuropsychopharmacology 32 (9): 1876–87. https:
//doi.org/10.1038/sj.npp.1301324.
221
Vollenweider, Franz X, and Katrin H Preller. 2020. “Psychedelic Drugs: Neurobiology
and Potential for Treatment of Psychiatric Disorders” 21 (11): 611–24. https:
//doi.org/10.1038/s41583-020-0367-2.
Vollenweider, Franz X, and John W. Smallridge. 2022. “Classic Psychedelic Drugs:
Update on Biological Mechanisms.” Pharmacopsychiatry 55 (03): 121–38. https:
//doi.org/10.1055/a-1721-2914.
Vollenweider, Franz X, M F Vollenweider-Scherpenhuyzen, A Bäbler, H Vogel, and
D Hell. 1998. “Psilocybin Induces Schizophrenia-Like Psychosis in Humans via a
Serotonin-2 Agonist Action.” Neuroreport 9 (17): 3897–3902. https://doi.org/10.1
097/00001756-199812010-00024.
Wackermann, Jiří, Marc Wittmann, Felix Hasler, and Franz X Vollenweider. 2008.
“Effects of Varied Doses of Psilocybin on Time Interval Reproduction in Human
Subjects.” Neuroscience Letters 435 (1): 51–55. https://doi.org/10.1016/j.neulet.2
008.02.006.
Waldman, Ayelet. 2017. A Really Good Day: How Microdosing Made a Mega
Difference in My Mood, My Marriage, and My Life. Knopf Doubleday Publishing
Group.
Wallace, Anthony F. C. 1959. “Cultural Determinants of Response to Hallucinatory
Experience.” Archives of General Psychiatry 1 (1): 58. https://doi.org/10.1001/ar
chpsyc.1959.03590010074009.
Walshe, R. Calen, and Wilson S. Geisler. 2020. “Detection of Occluding Targets in
Natural Backgrounds.” Journal of Vision 20 (13): 14. https://doi.org/10.1167/jov.
20.13.14.
Walter, W. Grey, R. Cooper, V. J. Aldridge, W. C. Mccallum, and A. L. Winter. 1964.
“Contingent Negative Variation : An Electric Sign of Sensori-Motor Association
and Expectancy in the Human Brain.” Nature 203 (4943): 380–84. https://doi.or
g/10.1038/203380a0.
Wang, Ningyuan, Heather A. Kreft, and Andrew J. Oxenham. 2015. “Loudness
Context Effects in Normal-Hearing Listeners and Cochlear-Implant Users.” Journal
of the Association for Research in Otolaryngology 16 (4): 535–45. https://doi.org/
10.1007/s10162-015-0523-y.
Ward, Jamie. 2013. “Synesthesia.” Annual Review of Psychology 64: 49–75. https:
//doi.org/10.1146/annurev-psych-113011-143840.
Watts, R, C Day, J Krzanowski, D Nutt - Journal of . . . , and 2017. 2017. “Patients’
Accounts of Increased ‘Connectedness’ and ‘Acceptance’ After Psilocybin for
Treatment-Resistant Depression.” Journals.sagepub.com.
Weasel, Pope. 1994. “Trans-High Market Quotations, October 1994.” High Times.,
October, 82–83. https://archive.hightimes.com/article/1994/10/01/trans-high-
market-quotations.
Webb, Ben S, Neel T Dhruv, Samuel G Solomon, Chris Tailby, and Peter Lennie.
2005. “Early and Late Mechanisms of Surround Suppression in Striate Cortex of
Macaque.” The Journal of Neuroscience: The Official Journal of the Society for
Neuroscience 25 (50): 11666–75. https://doi.org/10.1523/jneurosci.3414-05.2005.
WHO. 1958. “ATARACTIC and Hallucinogenic Drugs in Psychiatry; Report of a
Study Group.” World Health Organization Technical Report Series 58: 1–72.
222
Wiese, Wanja, and Thomas Metzinger. 2017. “Vanilla PP for Philosophers: A Primer
on Predictive Processing.” In Philosophy and Predictive Processing. MIND Group,
Frankfurt am Main.
Wiessner, Isabel, Marcelo Falchi, Lucas Oliveira Maia, Dimitri Daldegan-Bueno,
Fernanda Palhano-Fontes, Natasha L Mason, Johannes G Ramaekers, et al. 2022.
“LSD and Creativity: Increased Novelty and Symbolic Thinking, Decreased Utility
and Convergent Thinking.” Journal of Psychopharmacology 36 (3): 348–59. https:
//doi.org/10.1177/02698811211069113.
Wiessner, Isabel, Marcelo Falchi, Fernanda Palhano-Fontes, Lucas Oliveira Maia,
Amanda Feilding, Sidarta Ribeiro, Natalia Bezerra Mota, Draulio B. Araujo, and
Luis Fernando Tofoli. 2021. “Low-Dose LSD and the Stream of Thought: Increased
Discontinuity of Mind, Deep Thoughts and Abstract Flow.” Psychopharmacology
239 (6): 1721–33. https://doi.org/10.1007/s00213-021-06006-3.
Wießner, Isabel, Rodolfo Olivieri, Marcelo Falchi, Fernanda Palhano-Fontes, Lucas
Oliveira Maia, Amanda Feilding, Draulio B. Araujo, Sidarta Ribeiro, and Luís Fer-
nando Tófoli. 2022. “LSD, Afterglow and Hangover: Increased Episodic Memory
and Verbal Fluency, Decreased Cognitive Flexibility.” European Neuropsychophar-
macology 58 (May): 7–19. https://doi.org/10.1016/j.euroneuro.2022.01.114.
Winkelman, Michael J. 2017. “The Mechanisms of Psychedelic Visionary Experiences:
Hypotheses from Evolutionary Psychology.” Frontiers in Neuroscience 11: 539.
https://doi.org/10.3389/fnins.2017.00539.
Wittmann, Marc, Olivia Carter, Felix Hasler, B Rael Cahn, Ulrike Grimberg, Philipp
Spring, Daniel Hell, Hans Flohr, and Franz X Vollenweider. 2007. “Effects of
Psilocybin on Time Perception and Temporal Control of Behaviour in Humans.”
Journal of Psychopharmacology 21 (1): 50–64.
Wong, Sam. 2017. “Leading the High Life.” New Scientist 234 (3130): 22–23.
https://doi.org/10.1016/s0262-4079(17)31161-2.
Woolley, D W, and E Shaw. 1954. “A BIOCHEMICAL AND PHARMACOLOGICAL
SUGGESTION ABOUT CERTAIN MENTAL DISORDERS.” Proceedings of the
National Academy of Sciences of the United States of America 40 (4): 228–31.
https://doi.org/10.1073/pnas.40.4.228.
Xing, J., and D. J. Heeger. 2000. “Center-Surround Interactions in Foveal and
Peripheral Vision.” Vision Research 40 (22): 3065–72. https://doi.org/10.1016/s0
042-6989(00)00152-8.
———. 2001. “Measurement and Modeling of Center-Surround Suppression and
Enhancement.” Vision Research 41 (5): 571–83. https://doi.org/10.1016/s0042-
6989(00)00270-4.
Yaden, David B., and Roland R. Griffiths. 2020. “The Subjective Effects of
Psychedelics Are Necessary for Their Enduring Therapeutic Effects.” ACS Phar-
macology & Translational Science 4 (2): 568–72. https://doi.org/10.1021/acsptsci
.0c00194.
Yang, Eunice, Duje Tadin, Davis M. Glasser, Sang Wook Hong, Randolph Blake,
and Sohee Park. 2012. “Visual Context Processing in Schizophrenia.” Clinical
Psychological Science 1 (1): 5–15. https://doi.org/10.1177/2167702612464618.
Yon, Daniel, and Chris D. Frith. 2021. “Precision and the Bayesian Brain.” Current
223
Biology 31 (17): R1026–32. https://doi.org/10.1016/j.cub.2021.07.044.
Yoon, Jong H., Richard J. Maddock, Ariel Rokem, Michael A. Silver, Michael J. Minzen-
berg, J. Daniel Ragland, and Cameron S. Carter. 2010. “GABA Concentration Is
Reduced in Visual Cortex in Schizophrenia and Correlates with Orientation-Specific
Surround Suppression.” The Journal of Neuroscience: The Official Journal of the
Society for Neuroscience 30 (10): 3777–81. https://doi.org/10.1523/jneurosci.6158-
09.2010.
Yoon, Jong H., A. S. Rokem, M. A. Silver, M. J. Minzenberg, S. Ursu, J. D. Ragland,
and C. S. Carter. 2009. “Diminished Orientation-Specific Surround Suppression
of Visual Processing in Schizophrenia.” Schizophrenia Bulletin 35 (6): 1078–84.
https://doi.org/10.1093/schbul/sbp064.
Yu, Cong, Stanley A. Klein, and Dennis M. Levi. 2001. “Surround Modulation of
Perceived Contrast and the Role of Brightness Induction.” Journal of Vision 1 (1):
3. https://doi.org/10.1167/1.1.3.
Yu, Hao, Fei Xu, Xiangmei Hu, Yanni Tu, Qiuyu Zhang, Zheng Ye, and Tianmiao Hua.
2022. “Mechanisms of Surround Suppression Effect on the Contrast Sensitivity of
V1 Neurons in Cats.” Edited by J. Michael Wyss. Neural Plasticity 2022 (March):
1–16. https://doi.org/10.1155/2022/5677655.
Zhou, Chunhong, and Bartlett W. Mel. 2008. “Cue Combination and Color Edge
Detection in Natural Scenes.” Journal of Vision 8 (4): 4. https://doi.org/10.1167/
8.4.4.
Ziemba, Corey M., Jeremy Freeman, J. Anthony Movshon, and Eero P. Simoncelli.
2016. “Selectivity and Tolerance for Visual Texture in Macaque V2.” Proceedings
of the National Academy of Sciences 113 (22). https://doi.org/10.1073/pnas.15108
47113.
Zinberg, Norman Earl. 1984. Drug, Set, and Setting : The Basis for Controlled
Intoxicant Use. New Haven [Conn.]: Yale University Press.
Zizo. 2013. “How Psilocybin Works: Addition by Subtraction - Psychedelic Frontier.”
http://psychedelicfrontier.com/how-psilocybin-works-addition-by-subtraction/.
Zöllner, Friedrich. 1860. “Ueber Eine Neue Art von Pseudoskopie Und Ihre Beziehun-
gen Zu Den von Plateau Und Oppel Beschriebenen Bewegungsphänomenen.” An-
nalen Der Physik 186 (7): 500–523. https://doi.org/10.1002/andp.18601860712.
224