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学位論文

Computational modeling of behavior under uncertainty: Commonalities and differences between anxiety and depression

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https://escholarship.org/uc/item/561265zh
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引用

Gagne, C. (2019). Computational modeling of behavior under uncertainty: Commonalities and differences between anxiety and depression. PhD Thesis, University of California, Berkeley, CA, USA.


引用: https://hdl.handle.net/21.11116/0000-0006-FE1F-B
要旨
Individuals who are prone to experiencing high levels of anxiety and depression often exhibit dysfunctional behavior. For example, anxious individuals often avoid situations that have even the slightest chance of a highly negative outcome (e.g. a plane crash), and depressed individuals often show a reduced pursuit of activities that most people find enjoyable. Progress can be made in understanding dysfunctional behavior by using formal, mathematical frameworks of decision making, which break down behavior into its computational components, and in which we can start to pinpoint the specific abnormalities associated with anxiety and depression. Chapter 1.2 and 1.3 review prior literature, highlighting some of the computations that seem to be altered, such as the overestimation for the probability that rare, extremely negative events will occur. Chapter 2 and Chapter 3 empirically examine behavior in situations that require individuals to accurately estimate the probability that an outcome will (or will not) occur as a result of their actions. In a task where individuals have to estimate action-outcome probabilities by trial-and-error (Chapter 2), individuals with high overall levels of anxiety and depression show a reduced ability to align the rate at which they learn to the rate of change in the environment (i.e. the level of volatility). In a task where individuals have to choose between options that have known (risky) versus unknown (ambiguous) probabilities (Chapter 3), individuals who have high levels of physiological anxiety tend to avoid the ambiguous options more than other individuals, as information is removed about those probabilities. On the other hand, individuals who are prone to experiencing mania are more likely to make the opposite choice, seeking ambiguity, when the outcomes are rewarding. Chapter 4 examines possible sources for dysfunctional beliefs, as opposed to behaviors. In a hypothetical vocational setting where individuals estimate their rank relative to others, individuals with high levels of anhedonia-related symptoms show initial beliefs that are more negative relative to the beliefs of others. Individuals with high levels of anxiety, on the other hand, show negatively biased updating of those beliefs in response to unbiased information. Chapter 5 summarizes the empirical findings and discusses more broadly how anxiety and depression seem to impact behavior (and its underlying computations) in uncertain situations.