Computational Contributions to Understanding Impaired Motivation and Effort in Psychosis

Deanna Barch
Washington University in St. Louis

Individuals with various forms of psychosis such as schizophrenia often show impairments in motivation and goal directed behavior, as well as impairments in reward processing. These motivational impairments are a major contributor to functional disability, and as such as a ripe target for intervention. However, in order to intervene effectively, we must identify the psychological and/or neural sources of such impairments. Modern cognitive and affective neuroscience techniques have greatly aided in this endeavor, helping to illustrate roles for both the striatum and frontal parietal networks in different aspects of reinforcement learning and motivated behavior. However many tasks designed to measure such aspects of cognition and behavior are determined by many factors, making it difficult to isolate the source of impairment. Applying computational approaches to both behavior and brain imaging that allow one to dissociate mechanisms is a highly valuable method that allows us to make finer grained interpretations about mechanisms that may better aid treatment development. Here we illustrate this approach using reinforcement learning and effort based decision making in psychosis as an example

Presentation (PowerPoint File)

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