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

Yoshihiko Kunisato, Yasumasa Okamoto, Kazutaka Ueda, Keiichi Onoda, Go Okada, Shinpei Yoshimura, Shin Ichi Suzuki, Kazuyuki Samejima, Shigeto Yamawaki

Research output: Contribution to journalArticle

36 Citations (Scopus)

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 languageEnglish
Pages (from-to)1088-1094
Number of pages7
JournalJournal of Behavior Therapy and Experimental Psychiatry
Volume43
Issue number4
DOIs
Publication statusPublished - 2012 Dec 1

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

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