Drift diffusion model of reward and punishment learning in schizophrenia: Modeling and experimental data

Ahmed A. Moustafa, Szabolcs Kéri, Zsuzsanna Somlai, Tarryn Balsdon, Dorota Frydecka, Blazej Misiak, Corey White

Research output: Contribution to journalArticle

17 Citations (Scopus)


In this study, we tested reward- and punishment learning performance using a probabilistic classification learning task in patients with schizophrenia (. n=. 37) and healthy controls (. n=. 48). We also fit subjects' data using a Drift Diffusion Model (DDM) of simple decisions to investigate which components of the decision process differ between patients and controls. Modeling results show between-group differences in multiple components of the decision process. Specifically, patients had slower motor/encoding time, higher response caution (favoring accuracy over speed), and a deficit in classification learning for punishment, but not reward, trials. The results suggest that patients with schizophrenia adopt a compensatory strategy of favoring accuracy over speed to improve performance, yet still show signs of a deficit in learning based on negative feedback. Our data highlights the importance of applying fitting models (particularly drift diffusion models) to behavioral data. The implications of these findings are discussed relative to theories of schizophrenia and cognitive processing.

Original languageEnglish
Pages (from-to)147-154
Number of pages8
JournalBehavioural Brain Research
Publication statusPublished - Sep 5 2015



  • Decision making
  • Drift diffusion model (DDM)
  • Punishment
  • Reinforcement learning
  • Reward
  • Schizophrenia

ASJC Scopus subject areas

  • Behavioral Neuroscience

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