Browsing by Subject "neuroeconomics"

Now showing 1 - 4 of 4
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    Beyond Simple Tests of Value: A neuroeconomic, translational, disease-relevant, and circuit-based approach to resolve the computational complexity of decision making
    (2018-07) Sweis, Brian
    How the brain processes information when making decisions depends on how that information is stored. Distinct neural circuits are capable of storing information in many different ways that are better suited for different situations. The decision-making processes that access those different bits of stored information are not singular and occupy separable neural circuits, each of which can operate in parallel with one another, and each of which can confer different information processing properties based on the neural constraints within which a given computation resides. Such is the framework of recent theories in neuroeconomics, which suggest that decisions are multi-faceted and action-selection processes can arise from fundamentally distinct circuit-specific neural computations. In this thesis, I present a body of work that takes a neuroeconomics approach through a series of experiments that reveal the complexities of multiple, parallel decision-making systems through complex behaviors by moving beyond simple tests of value. In the first half of this thesis, I demonstrate how complex behavioral computations can resolve fundamentally distinct valuation algorithms thought to reside in separable neural circuits. I then translate this approach between human and non-human rodent animal models in order to reveal how multiple, parallel decision-making systems are conserved across species over evolution. In the second half of this thesis, I demonstrate the utility of behavioral economics in disease-relevant and circuit-based studies. If multiple, parallel decision-making processes are thought to be intimately related to the heterogeneous ways in which information can be stored in separable neural circuits, I examine how addiction – a disease which is thought to be a disorder of the neurobiological mechanisms of learning and memory – might alter how stored information is processed in separable decision-making systems uniquely using a mouse model of two different forms of addiction. In doing so, I demonstrate how different forms of addiction give rise to unique, lasting vulnerabilities in fundamentally distinct decision-making computations. These discoveries can aid in resolving neuropsychiatric disease heterogeneity by moving beyond simple tests of value where complex behaviors that are measured can more accurately reflect the neurally distinct computations that underlie those behaviors. Finally, I take a neuromodulation approach and directly alter the strength of synaptic transmission in a circuit-specific manner using optogenetics in mice tested in this neuroeconomic framework. I demonstrate how plasticity alterations in projections between the infralimbic cortex and the nucleus accumbens are capable of giving rise to long-lasting disruptions of self-control related decision processes in a foraging valuation algorithm independent of and separate from a deliberative valuation algorithm measured within the same trial. Furthermore, I developed a novel plasticity measurement tool that is assayed at the neuronal population ensemble level and reveals individual differences in separable decision processes. The second half of the thesis demonstrates a potential biomarker to target as a circuit-computation-specific therapeutic intervention tailored to those types of decision-making dysfunctions. Taken together, I present a body of work in this thesis that demonstrates the utility of moving beyond simple tests of value in order to resolve the computational complexity of decision making.
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    Distributed Encoding Architecture in Prefrontal Cortex during Abstracted and Embodied Decision Making
    (2022-11) Maisson, David
    On one hand, decision-making can be viewed as a process by which individual functions, such as valuation and choice, are abstracted from movement. On the other hand, decision-making can be viewed as an embodied process by which choice and movement are inexorably linked. In either case, neural activity in a range of cortical structures in the primate prefrontal cortex have been implicated in decision-making. I employ both a traditional neuroeconomic paradigm and a novel free-range foraging paradigm to understand the encoding architecture in prefrontal cortices during both abstract and embodied decision-making. First, I show that in an abstracted decision-making paradigm, constituent and higher-level functional computations are not circumscribed to discrete anatomical boundaries. Indeed, the encoding of both feature information and subjective value are distributed across multiple structures. Next, I show that a range of higher-level choice-relevant functions are also computed in a distributed framework that, along the prefrontal medial wall are organized along a ventral-to-dorsal gradient. Last, I show that navigational and foraging task variables in an embodied decision-making paradigm are distributed across prefrontal cortex, organized along a ventral-to-dorsal gradient, and show no evidence of modular functional specialization by neuronal subpopulations. These results strongly support the need for a dramatic shift in the way we view the organization of functional computations in the brain, and thus inform how we might think about targeting interventions for the treatment of neurological and neuropsychiatric disorders.
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    Dopaminergic control of neuroeconomic decision making
    (2023) Kocharian, Adrina
    Dopamine in the nucleus accumbens is an important neural substrate for valuation and decision-making. Dominant theories generally discretize and homogenize decision-making, when it is in fact a continuous process, with evaluation and re-evaluation components that extend beyond simple outcome prediction into consideration of past and future value. Furthermore, individual animals use distinct strategies to achieve their goals, requiring different computational processes. Extensive work has examined mesolimbic dopamine in the context of reward prediction error, but major gaps persist in our understanding of how dopamine tracks imagined past and future rewards to influence decision confidence. Moreover, there is little consideration of strategy-dependent differences in value processing that may shape dopaminergic encoding. In the studies presented in this dissertation we used an economic foraging task in mice, and found that strategy-specific dopamine dynamics reflected decision confidence during evaluation, as well as both past and future counterfactual value during re-evaluation. We found that inhibition of dopamine terminals altered counterfactual processing during re-evaluation. Individually-tailored optogenetic stimulation of mesolimbic dopamine terminals altered decision confidence during evaluation and carried over to counterfactual re-evaluation, in a strategy-specific manner. We provide evidence that mesolimbic dopamine is tightly linked to decision confidence and counterfactual information, through signals that go beyond reward prediction errors to more complex encoding of imagined past and future value.
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    Information Processing in the Orbitofrontal Cortex and the Ventral Striatum in Rats Performing an Economic Decision-Making Task
    (2015-08) Stott, Jeffrey
    The orbitofrontal cortex (OFC) and ventral striatum (vStr) are key brain structures that represent information about value during decision-making tasks. Despite their very different anatomical properties, numerous studies have found similar patterns of value-related signaling in these structures. In particular, both structures are intimately involved in delay-discounting tasks, which involve a tradeoff between reward magnitude and delay to reward. However, the overlapping activity profiles of these brain regions makes it difficult to tease apart their specific contributions to delay-discounting behavior, and to economic decision-making more generally. In order to better understand the contributions of these two regions to value-based choice, we made simultaneous recordings in the OFC and vStr in rats performing a spatial variant of a traditional delay-discounting task. This allowed us to compare OFC and vStr activity directly in the same subjects while they engaged in a prototypical economic decision-making task, and additionally it allowed us to leverage the tools of spatial decoding analysis to measure non-local reward signaling. Chapter 1 provides an introduction to current theories of OFC and vStr function within the decision-making literature, in particular contrasting the concepts of neuroeconomics with the multiple decision-making systems framework. Chapter 2 describes the methods used in this thesis, including the design of the spatial delay-discounting task and the analysis of the neural data. Chapter 3 presents the results of single-unit and Bayesian decoding analyses from this dataset. We found that activity in the OFC and vStr was quite similar at the single-unit level, and inconsistent with the neuroeconomic account of value signaling in a common currency. Instead, when we looked specifically at moments of deliberative decision-making (as emphasized by the multiple systems account), we found important differences between the OFC and vStr. Both the OFC and the vStr showed covert reward signaling during deliberative, vicarious trial-and-error (VTE) behaviors. But vStr signals emerged earlier, before the moment of choice, while covert reward coding in the OFC appeared after the rats had committed to their decision. These analyses were extended to the level of local field potentials (LFPs), recorded from the same dataset. Local field potentials are a useful tool for studying local processing and interactions between brain regions. Chapter 4 describes the LFP results. Important among these was the finding that the vStr led the OFC at the LFP level (again showing temporal precedence), and furthermore, that the vStr was a stronger driver of OFC activity than vice versa, particularly during VTE. The implications of these results, along with those from the single-unit and Bayesian decoding analyses, are discussed in Chapter 5. Emphasis is placed on our emerging understanding of the role of the vStr in flexible behavior, and how the OFC and the vStr might cooperate to influence value-based choice.