کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
910456 | 917463 | 2012 | 7 صفحه PDF | دانلود رایگان |
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.
► Depressed subjects showed lower reward-based decision making than non-depressed subjects.
► Depression did not affect on the punishment based decision making.
► Depressed subjects showed higher learning parameter τ than non-depressed subjects.
Journal: Journal of Behavior Therapy and Experimental Psychiatry - Volume 43, Issue 4, December 2012, Pages 1088–1094