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 journalArticlepeer-review

    42 Citations (Scopus)

    Abstract

    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
    Volume291
    DOIs
    Publication statusPublished - 2015

    Keywords

    • Drift Diffusion Model
    • decision making
    • punishment
    • reinforcement learning
    • schizophrenia

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