Decision-Making and Computational Psychiatry: An Explanatory and Pragmatic Perspective

Martin Paulus
University of California, San Diego (UCSD)

Differentiating whether an action leads to an outcome by chance or by an underlying statistical regularity that signals environmental change profoundly affects adaptive behavior. Prior studies have shown that anxious individuals may not appropriately differentiate between these situations. Three experimental results will be presented. First, anxious subjects’ exaggerated response to uncertainty leads to a sub-optimal decision strategy that makes it difficult for these individuals to determine whether an action is associated with an outcome by chance or by some statistical regularity. Second, using a PD control model, individuals reporting high levels of fear weigh current error less and also under weigh the rate of change of error leading to overcorrecting oscillations around the goal. Third, anxious individuals show slower updating of models used in perceptual processing, but not those used in decision-making. Together, these findings have important implications for developing new behavioral intervention strategies utilizing learning models.

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