Using Computational Modeling to Capture Schizophrenia-Specific Reinforcement Learning Differences and Their Implications on Patient Classification

Background Psychiatric diagnosis and treatment have historically taken a symptom-based approach, with less attention on identifying underlying symptom-producing mechanisms. Recent efforts have illuminated the extent to which different underlying circuitry can produce phenotypically similar symptomatology (e.g., psychosis in bipolar disorder vs. schizophrenia). Computational modeling makes it possible to identify and mathematically differentiate behaviorally unobservable, specific reinforcement learning differences in patients with schizophrenia versus other disorders, likely owing to a higher reliance on prediction error–driven learning associated with basal ganglia and underreliance on explicit value representations associated with orbitofrontal cortex. Methods We used a well-established probabilistic reinforcement learning task to replicate those findings in individuals with schizophrenia both on (n = 120) and off (n = 44) antipsychotic medications and included a patient comparison group of bipolar patients with psychosis (n = 60) and healthy control subjects (n = 72). Results Using accuracy, there was a main effect of group (F3,279 = 7.87, p Conclusions Both medicated and unmedicated patients showed overreliance on prediction error–driven learning as well as significantly higher noise and value-related memory decay, compared with the healthy control subjects and the patients with bipolar disorder. Additionally, the computational model parameters capturing these processes can significantly improve patient/control classification, potentially providing useful diagnosis insight.