Effects of depression on reward-based decision making and variability of action in probabilistic learning

Abstract

Background and objectivesDepression is characterized by low reward sensitivity in behavioral studies applying signal detection theory. We examined deficits in reward-based decision making in depressed participants during a probabilistic learning task, and used a reinforcement learning model to examine learning parameters during the task. MethodsThirty-six nonclinical undergraduates completed a probabilistic selection task. Participants were divided into depressed and non-depressed groups based on Center for Epidemiologic Studies–Depression (CES-D) cut scores. We then applied a reinforcement learning model to every participant's behavioral data. ResultsDepressed participants showed a reward-based decision making deficit and higher levels of the learning parameter τ, which modulates variability of action selection, as compared to non-depressed participants. Highly variable action selection is more random and characterized by difficulties with selecting a specific course of action. ConclusionThese results suggest that depression is characterized by deficits in reward-based decision making as well as high variability in terms of action selection.