Modeling the Depressed Mind: An Interdisciplinary Exploration of Learned Helplessness, Anhedonia, and Sensorimotor Bayesian Decision-Making Processes in Depression
2023-11
Loading...
View/Download File
Persistent link to this item
Statistics
View StatisticsJournal Title
Journal ISSN
Volume Title
Title
Modeling the Depressed Mind: An Interdisciplinary Exploration of Learned Helplessness, Anhedonia, and Sensorimotor Bayesian Decision-Making Processes in Depression
Authors
Published Date
2023-11
Publisher
Type
Thesis or Dissertation
Abstract
Depression, a multifaceted mental health condition, presents a diverse array of behavioral and computational irregularities. In this dissertation, I employed an interdisciplinary approach, examining and bridging traditional animal and human models of depression in conjunction with computational and Bayesian approaches. These explorations span the domains of reward processing, anhedonia, and sensorimotor decision-making within the context of depression. In Chapter 1, I revisited the well-established learned helplessness model and investigated its potential associations with anhedonia—a defining feature of depression. I devised an aversive tone-based task inspired by the original learned helplessness paradigm, manipulating the perception of control. Our findings contest traditional beliefs, suggesting that helplessness is the inherent state and control is acquired. Moreover, our results suggest that it is not perceived control but stress that stands out as the primary driver in eliciting state anhedonia. In Chapter 2, our exploration of anhedonia continues, this time focusing on its behavioral manifestation. Using a Signal Detection Theory task, we probed the potential influences of learned helplessness on reward-driven behaviors. Although perceived control showed no impact over reward responsiveness, a curious disconnect emerged: while self-reported pleasure scales registered changes in state anhedonia and anxiety, these nuances failed to echo in the behavioral markers of reward responsiveness. In Chapter 3, I pivot from reward processing to the domain of sensory processing and decision-making in depression. Using a visuomotor coin-catching task, rooted in the Bayesian Decision Theory framework, we examined how depression might modulate the use of prior and likelihood information when making perceptual decisions under uncertainty. Our findings suggest that depression does not uniformly alter sensory integration, hinting that those with depression may still adeptly navigate sensorimotor tasks. Taken together, these studies present an in-depth exploration of the depressed mind and shed light on the intricacies of anhedonia, sensorimotor decision-making, and broader depression models and mechanisms.
Description
University of Minnesota Ph.D. dissertation. November 2023. Major: Psychology. Advisor: Iris Vilares. 1 computer file (PDF); x, 197 pages.
Related to
Replaces
License
Collections
Series/Report Number
Funding information
Isbn identifier
Doi identifier
Previously Published Citation
Suggested citation
Song, Xin. (2023). Modeling the Depressed Mind: An Interdisciplinary Exploration of Learned Helplessness, Anhedonia, and Sensorimotor Bayesian Decision-Making Processes in Depression. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/260673.
Content distributed via the University Digital Conservancy may be subject to additional license and use restrictions applied by the depositor. By using these files, users agree to the Terms of Use. Materials in the UDC may contain content that is disturbing and/or harmful. For more information, please see our statement on harmful content in digital repositories.