Depressive symptoms are associated with blunted reward learning in social contexts

PLoS Comput Biol. 2019 Jul 29;15(7):e1007224. doi: 10.1371/journal.pcbi.1007224. eCollection 2019 Jul.

Abstract

Depression is characterized by a marked decrease in social interactions and blunted sensitivity to rewards. Surprisingly, despite the importance of social deficits in depression, non-social aspects have been disproportionally investigated. As a consequence, the cognitive mechanisms underlying atypical decision-making in social contexts in depression are poorly understood. In the present study, we investigate whether deficits in reward processing interact with the social context and how this interaction is affected by self-reported depression and anxiety symptoms in the general population. Two cohorts of subjects (discovery and replication sample: N = 50 each) took part in an experiment involving reward learning in contexts with different levels of social information (absent, partial and complete). Behavioral analyses revealed a specific detrimental effect of depressive symptoms-but not anxiety-on behavioral performance in the presence of social information, i.e. when participants were informed about the choices of another player. Model-based analyses further characterized the computational nature of this deficit as a negative audience effect, rather than a deficit in the way others' choices and rewards are integrated in decision making. To conclude, our results shed light on the cognitive and computational mechanisms underlying the interaction between social cognition, reward learning and decision-making in depressive disorders.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Anxiety / psychology
  • Cohort Studies
  • Computational Biology
  • Computer Simulation
  • Decision Making
  • Depression / psychology*
  • Female
  • Humans
  • Interpersonal Relations*
  • Learning*
  • Linear Models
  • Male
  • Middle Aged
  • Models, Psychological
  • Reinforcement, Psychology
  • Reward*
  • Young Adult

Associated data

  • figshare/10.6084/m9.figshare.8199293
  • figshare/10.6084/m9.figshare.8198837
  • figshare/10.6084/m9.figshare.8199296

Grants and funding

SP is supported by an ATIP-Avenir grant (R16069JS) Collaborative Research in Computational Neuroscience ANR-NSF grant (ANR-16-NEUC-0004), the Programme Emergence(s) de la Ville de Paris, the Fyssen foundation and the Fondation Schlumberger pour l’Education et la Recherche (FSER). LS was supported by a PHD fellowship of the ENS/PSL and the Fondation nationale des sciences politiques. The Institut d’Etude de la Cognition is supported financially by the LabEx IEC (ANR-10-LABX-0087 IEC) and the IDEX PSL* (ANR-10-IDEX-0001-02 PSL*). The funders had no role in the conceptualization, design, data collection, analysis, decision to publish, or preparation of the manuscript.