Modeling the Depressed Mind: An Interdisciplinary Exploration of Learned Helplessness, Anhedonia, and Sensorimotor Bayesian Decision-Making Processes in Depression

2023-11
Loading...
Thumbnail Image

Persistent link to this item

Statistics
View Statistics

Journal 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

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

Other identifiers

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.