Negative Symptoms and the Failure to Represent the Expected Reward Value of Actions

Context: Negative symptoms are a core feature of schizophrenia, but their pathogenesis remains unclear. Negative symptoms are defined by the absence of normal function. However, there must be a productive mechanism that leads to this absence. Objective: To test a reinforcement learning account suggesting that negative symptoms result from a failure in the representation of the expected value of rewards coupled with preserved loss-avoidance learning. Design: Participants performed a probabilistic reinforcement learning paradigm involving stimulus pairs in which choices resulted in reward or in loss avoidance. Following training, participants indicated their valuation of the stimuli in a transfer test phase. Computational modeling was used to distinguish between alternative accounts of the data. Setting: A tertiary care research outpatient clinic. Patients: In total, 47 clinically stable patients with a diagnosis of schizophrenia or schizoaffective disorder and 28 healthy volunteers participated in the study. Patients were divided into a high-negative symptom group and a low-negative symptom group. Main Outcome Measures: The number of choices leading to reward or loss avoidance, as well as performance in the transfer test phase. Quantitative fits from 3 different models were examined. Results: Patients in the high-negative symptom group demonstrated impaired learning from rewards but intact loss-avoidance learning and failed to distinguish rewarding stimuli from loss-avoiding stimuli in the transfer test phase. Model fits revealed that patients in the high-negative symptom group were better characterized by an "actor-critic" model, learning stimulus-response associations, whereas control subjects and patients in the low-negative symptom group incorporated expected value of their actions ("Q learning") into the selection process. Conclusions: Negative symptoms in schizophrenia are associated with a specific reinforcement learning abnormality: patients with high-negative symptoms do not represent the expected value of rewards when making decisions but learn to avoid punishments through the use of prediction errors. This computational framework offers the potential to understand negative symptoms at a mechanistic level.