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Impaired expected value computations in schizophrenia are associated with a reduced ability to integrate reward probability and magnitude of recent outcomes

ABSTRACT Background Motivational deficits in people with schizophrenia (PSZ) are associated with an inability to integrate the magnitude and probability of previous outcomes. The mechanisms that underlie probability-magnitude integration deficits, however, are poorly understood. We hypothesized that increased reliance on “value-less” stimulus-response associations, in lieu of expected value (EV)-based learning, could drive probability-magnitude integration deficits in PSZ with motivational deficits. Methods Healthy volunteers (n= 38) and PSZ (n=49) completed a reinforcement learning paradigm consisting of four stimulus pairs. Reward magnitude (3/2/1/0 points) and probability (90%/80%/20%/10%) together determined each stimulus’ EV. Following a learning phase, new and familiar stimulus pairings were presented. Participants were asked to select stimuli with the highest reward value. Results PSZ with high motivational deficits made increasingly less optimal choices as the difference in reward value (probability*magnitude) between two competing stimuli increased. Using a previously-validated computational hybrid model, PSZ relied less on EV (“Q-learning”) and more on stimulus-response learning (“actor-critic”), which correlated with SANS motivational deficit severity. PSZ specifically failed to represent reward magnitude, consistent with model demonstrations showing that response tendencies in the actor-critic were preferentially driven by reward probability. Conclusions Probability-magnitude deficits in PSZ with motivational deficits arise from underutilization of EV in favor of reliance on value-less stimulus-response associations. Consistent with previous work and confirmed by our computational hybrid framework, probability-magnitude integration deficits were driven specifically by a failure to represent reward magnitude. This work reconfirms the importance of decreased Q-learning/increased actor-critic-type learning as an explanatory framework for a range of EV deficits in PSZ.