Selective Reinforcement Learning Deficits in Schizophrenia Support Predictions from Computational Models of Striatal-Cortical Dysfunction

Abstract

Rewards and punishments may make distinct contributions to learning via separate striatal-cortical pathways. We investigated whether fronto-striatal dysfunction in schizophrenia (SZ) is characterized by selective impairment in either reward- (Go) or punishment-driven (NoGo) learning. We administered two versions of a probabilistic selection task to 40 schizophrenia patients and 31 control subjects, using difficult to verbalize stimuli (experiment 1) and nameable objects (experiment 2). In an acquisition phase, participants learned to choose between three different stimulus pairs (AB, CD, EF) presented in random order, based on probabilistic feedback (80%, 70%, 60%). We used analyses of variance (ANOVAs) to assess the effects of group and reinforcement probability on two measures of contingency learning. To characterize the preference of subjects for choosing the most rewarded stimulus and avoiding the most punished stimulus, we subsequently tested participants with novel pairs of stimuli involving either A or B, providing no feedback. Control subjects demonstrated superior performance during the first 40 acquisition trials in each of the 80% and 70% conditions versus the 60% condition; patients showed similarly impaired (<60%) performance in all three conditions. In novel test pairs, patients showed decreased preference for the most rewarded stimulus (A; t = 2.674; p = .01). Patients were unimpaired at avoiding the most negative stimulus (B; t = .737). The results of these experiments provide additional evidence for the presence of deficits in reinforcement learning in SZ, suggesting that reward-driven learning may be more profoundly impaired than punishment-driven learning.