Impaired expected value computations coupled with overreliance on prediction error learning in schizophrenia

Background While many have emphasized impaired reward prediction error (RPE) signaling in schizophrenia, multiple studies suggest that some decision-making deficits may arise from overreliance on RPE systems together with a compromised ability to represent expected value. Guided by computational frameworks, we formulated and tested two scenarios in which maladaptive representation of expected value should be most evident, thereby delineating conditions that may evoke decision-making impairments in schizophrenia. Methods In a modified reinforcement learning paradigm, 42 medicated people with schizophrenia (PSZ) and 36 healthy volunteers learned to select the most frequently rewarded option in a 75-25 pair: once when presented with more deterministic (90–10) and once when presented with more probabilistic (60–40) pairs. Novel and old combinations of choice options were presented in a subsequent transfer phase. Computational modeling was employed to elucidate contributions from RPE systems (“actor-critic”) and expected value (“Q-leaming”). Results PSZ showed robust performance impairments with increasing value difference between two competing options, which strongly correlated with decreased contributions from expected value-based (“Q-leaming”) learning. Moreover, a subtle yet consistent contextual choice bias for the “probabilistic” 75 option was present in PSZ, which could be accounted for by a context-dependent RPE in the “actor-critic”. Conclusions We provide evidence that decision-making impairments in schizophrenia increase monotonically with demands placed on expected value computations. A contextual choice bias is consistent with overreliance on RPE-based learning, which may signify a deficit secondary to the maladaptive representation of expected value. These results shed new light on conditions under which decisionmaking impairments may arise.