Identifying cognitive remediation change through computational modelling--effects on reinforcement learning in schizophrenia.

OBJECTIVE: Converging research suggests that individuals with schizophrenia show a marked impairment in reinforcement learning, particularly in tasks requiring flexibility and adaptation. The problem has been associated with dopamine reward systems. This study explores, for the first time, the characteristics of this impairment and how it is affected by a behavioral intervention-cognitive remediation.
METHOD: Using computational modelling, 3 reinforcement learning parameters based on the Wisconsin Card Sorting Test (WCST) trial-by-trial performance were estimated: R (reward sensitivity), P (punishment sensitivity), and D (choice consistency). In Study 1 the parameters were compared between a group of individuals with schizophrenia (n = 100) and a healthy control group (n = 50). In Study 2 the effect of cognitive remediation therapy (CRT) on these parameters was assessed in 2 groups of individuals with schizophrenia, one receiving CRT (n = 37) and the other receiving treatment as usual (TAU, n = 34).
RESULTS: In Study 1 individuals with schizophrenia showed impairment in the R and P parameters compared with healthy controls. Study 2 demonstrated that sensitivity to negative feedback (P) and reward (R) improved in the CRT group after therapy compared with the TAU group. R and P parameter change correlated with WCST outputs. Improvements in R and P after CRT were associated with working memory gains and reduction of negative symptoms, respectively.
CONCLUSION: Schizophrenia reinforcement learning difficulties negatively influence performance in shift learning tasks. CRT can improve sensitivity to reward and punishment. Identifying parameters that show change may be useful in experimental medicine studies to identify cognitive domains susceptible to improvement.