Computational study demonstrates risk-sensitive decision mechanisms in algorithmic models, indicating formal computational foundations for anxiety and rumination.
Risk occupies a central role in both the theory and practice of decision-making. Although it is deeply implicated in many conditions involving dysfunctional behavior and thought, modern theoretical approaches to understanding and mitigating risk, in either one-shot or sequential settings, have yet to permeate fully the fields of neural reinforcement learning and computational psychiatry. Here we use one prominent approach, called conditional value-at-risk (CVaR), to examine optimal risk-sensitive choice and a form of optimal, risk-sensitive offline planning. We relate the former to both a justified form of the gambler’s fallacy and extremely risk-avoidant behavior resembling that observed in anxiety disorders. We relate the latter to worry and rumination.
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Gagne et al. (2021) studied this question.
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