UC Berkeley

Computational models of cognitive processes have provided deep insights into specific mechanisms of human learning and decision-making. A natural corollary of research into the typical functioning of such mechanisms is to investigate how mental disorders might cause impairment of the underlying algorithmic processes. This line of research, frequently referred to as computational psychiatry, seeks to contribute to more personalized diagnoses and treatments of mental disorders by characterizing behavioral and computational profiles of psychopathology. In the current dissertation we focus on the role of latent dimensions of internalizing psychopathology in computational processes of learning and decision-making to provide insight into dissociable dimensional phenotypes. Chapter 1 of the dissertation provides a primer to computational characterizations of learning and decision-making, including recent and foundational research results in computational psychiatry which motivate the subsequent original research questions. In Chapter 2, we present an investigation of effortful decision-making in reward pursuit and threat avoidance. We use a detailed dimensional profile of depressive and anxious symptoms to characterize unique mechanistic impairments across frequently comorbid symptoms, with individual symptoms of depression relating to specific differences in effortful reward pursuit processes, while a dimensional characterization of anxiety relates to multiple mechanistic differences in effortful threat avoidance. In Chapter 3, we extend the space of psychopathological inquiry to include subclinical levels of mania in an investigation of decision-making under ambiguity in reward pursuit, loss avoidance, and threat avoidance paradigms. Here, individual differences in loss avoidance decisions under ambiguity show opposing effects of anhedonic depression and physiological anxiety in risk sensitivity, with depression showing increased risk sensitivity and anxiety showing reduced nonlinear risk valuation. Ambiguous decisions in threat avoidance reveal a significant dissociation between anhedonic depression and hypomania, with higher levels of hypomania associated with significantly less risk sensitivity in threat avoidance than anhedonic depression. We also find suggestive, although not concrete, evidence that sensitivity to threat magnitude may relate to comorbid variance between anxiety and depression, and we recommend additional work in this area of investigation. In Chapter 4, we present a detailed investigation of dimensional characterizations of anxiety in reinforcement learning impairment, using an experimental paradigm which differentially manipulates the relative load of working memory versus learning systems across task. Here we find that physiological, but not cognitive, anxiety is related to significant learning impairments which are contributed to both by slower learning processes and by increased rate of working memory decay. These results may have implications for a wide variety of extant reinforcement learning modeling studies in computational psychiatry by emphasizing the importance of an algorithmic account of supporting working memory processes in such investigations. Chapter 5 provides a discussion of the results in the framework of dissociating unique profiles of dimensions of internalizing psychopathology. As additional insights into the relationships of latent dimensions of psychopathology with algorithms of cognition are gained, the field as a whole is enabled to approach the idea of replicable computational phenotypes of psychopathology. The aim of this dissertation is not to operationalize such phenotypes at present, but rather to contribute to the growing body of knowledge within the field to enable and inspire more detailed investigations and replications of the findings contained herein.