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Thirty-Third Annual Meeting of the Society for Research in Psychopathology

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50. Psychotic-like experiences and model-free versus model-based reinforcement learning

Recent work separates reinforcement-learning into two systems: model-based and model-free. The model-free system learns about the value of actions by reflecting on previous reward outcomes, while the model-based system learns by considering both previous outcomes and the likelihood of future outcomes. In individuals with schizophrenia (SZ), studies show that there is a reduced reliance on model-based learning relative to controls. Moreover, recent research indicates that this decreased reliance on model-based decision-making may be modulated by cognitive impairments in SZ such as working memory deficits. However, it is unknown whether this same pattern is shown in individuals who experience psychotic-like experiences (PLEs), but who do not have a clinical diagnosis. We examined reinforcement-learning, working memory, and PLEs in a large university and community sample (n= 183). Results showed that greater working memory capacity was related to greater use of model-based learning (p<0.01). Further, we found a negative relationship between model-based learning and PLEs, with higher PLEs associated with reduced reliance on model-based learning (p=.05). These findings suggest that reduced reliance on model-based learning may exist across the psychosis continuum, which could inform our understanding of the role reinforcement-learning plays in the positive symptoms of psychosis.

Katherine Pereira
Washington University in St. Louis

Adam Culbreth
Washington University in St. Louis

Erin Moran
Washington University in St. Louis

Deanna Barch
Washington University in St. Louis


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