Abstract
Background and objectives: Depression 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. Methods: Thirty-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. Results: Depressed 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. Conclusion: These results suggest that depression is characterized by deficits in reward-based decision making as well as high variability in terms of action selection.
Original language | English |
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Pages (from-to) | 1088-1094 |
Number of pages | 7 |
Journal | Journal of Behavior Therapy and Experimental Psychiatry |
Volume | 43 |
Issue number | 4 |
DOIs | |
Publication status | Published - 2012 Dec |
Keywords
- Decision making
- Depression
- Probabilistic learning
- Reinforcement learning
- Reward sensitivity
- Variability of action selection
ASJC Scopus subject areas
- Experimental and Cognitive Psychology
- Arts and Humanities (miscellaneous)
- Clinical Psychology
- Psychiatry and Mental health