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Abstract

Decision making lies at the very heart of many psychiatric diseases. It is also a
central theoretical concern in a wide variety of fields and has undergone detailed,
in-depth, analyses. We take as an example Major Depressive Disorder (MDD),
applying insights from a Bayesian reinforcement learning framework. We focus
on anhedonia and helplessness. Helplessness—a core element in the conceptual-
izations of MDD that has lead to major advances in its treatment, pharmacolog-
ical and neurobiological understanding—is formalized as a simple prior over the
outcome entropy of actions in uncertain environments. Anhedonia, which is an
equally fundamental aspect of the disease, is related to the effective reward size.
These formulations allow for the design of specific tasks to measure anhedonia
and helplessness behaviorally. We show that these behavioral measures capture
explicit, questionnaire-based cognitions. We also provide evidence that these tasks
may allow classification of subjects into healthy and MDD groups based purely
on a behavioural measure and avoiding any verbal reports.