Social Prediction Error Signals During the Trust Game in Patients With Schizophrenia: A Functional Magnetic Resonance...

Schizophrenia (SZ) is characterized by marked deficits in social cognition; however, the computational and neural mechanisms underlying social reinforcement learning remain poorly understood. We investigated dynamic social learning in patients with SZ (n = 29) and healthy control (HC) participants (n = 45) during an iterative Trust Game performed under functional magnetic resonance imaging.

Participant behavior was modeled using a Rescorla-Wagner framework to estimate the learning rate (α) and decision precision (inverse temperature, β), alongside trial-by-trial prediction errors (PEs) for neural analyses.

Behaviorally, patients exhibited a marked impairment in utilizing positive social feedback, reflected in reduced win-stay behavior, significantly lower learning rates, and diminished model fit. Critically, patient behavior was best explained by a null model, indicating a failure to consistently integrate feedback into decision making. At the computational level, reduced decision precision (lower β) was significantly associated with positive symptom severity, suggesting a link between stochastic choice behavior and psychosis. Neuronally, while HC participants showed robust PE-related activation in canonical reward and visual processing regions, patients exhibited significantly attenuated PE signaling, particularly within the left lateral occipital cortex and fusiform gyrus.

These findings indicate a fundamental disruption in precision-weighted social belief updating in SZ, characterized by stochastic decision making and degraded neural encoding of PEs. This supports predictive coding accounts of psychosis, in which impaired precision assignment to sensory feedback undermines adaptive learning and contributes to positive symptoms.