Reinforcement learning profiles and negative symptoms across chronic and clinical high-risk phases of psychotic illness.

Abstract

Negative symptoms are prominent in individuals with schizophrenia (SZ) and youth at clinical high-risk for psychosis (CHR). In SZ, negative symptoms are linked to reinforcement learning (RL) dysfunction; however, previous research suggests implicit RL remains intact. It is unknown whether implicit RL is preserved in the CHR phase where negative symptom mechanisms are unclear, knowledge of which may assist in developing early identification and prevention methods. Participants from two studies completed an implicit RL task: Study 1 included 53 SZ individuals and 54 healthy controls (HC); Study 2 included 26 CHR youth and 23 HCs. Bias trajectories reflecting implicit RL were compared between groups and correlations with negative symptoms were examined. Cluster analysis investigated RL profiles across the combined samples. Implicit RL was comparable between HC and their corresponding SZ and CHR groups. However, cluster analysis was able to parse performance heterogeneity across diagnostic boundaries into two distinct RL profiles: a Positive/Early Learning cluster (65% of participants) with positive bias scores increasing from the first to second task block, and a Negative/Late Learning cluster (35% of participants) with negative bias scores increasing from the second to third block. Clusters did not differ in the proportion of CHR vs. SZ cases; however, the Negative/Late Learning cluster had more severe negative symptoms. Although implicit RL is intact in CHR similar to SZ, distinct implicit RL phenotypic profiles with elevated negative symptoms were identified trans-phasically, suggesting distinct reward-processing mechanisms can contribute to negative symptoms independent of phases of illness.