Informal versus Formal Judgment of Statistical Models: The Case of Normality Assumptions

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Sequential learning models for the Wisconsin Card Sort Task

R Code for Bishara et al. (2010). Download all 4 files, and read the Instructions first. Reference: Bishara, A. J., Kruschke, J. K., Stout, J. C., Bechara, A., McCabe, D. P., & Busemeyer, J. R. (2010). Sequential learning models for the Wisconsin Card Sort Task: Assessing processes in substance dependent individuals. Journal of Mathematical Psychology, 54, 5-13. article: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2872109

Propensity for risk taking and trait impulsivity in the Iowa Gambling Task

The Iowa Gambling Task (IGT) is sensitive to decision-making impairments in several clinical groups with frontal impairment. However the complexity of the IGT, particularly in terms of its learning requirements, makes it difficult to know whether disadvantageous (risky) selections in this task reflect deliberate risk taking or a failure to recognise risk. To determine whether propensity for risk taking contributes to IGT performance, we correlated IGT selections with a measure of propensity for risk taking from the Balloon Analogue Risk Task (BART), taking into account potential moderating effects of IGT learning requirements, and trait impulsivity, which is associated with learning difficulties. We found that IGT and BART performance were related, but only in the later stages of the IGT, and only in participants with low trait impulsivity. This finding suggests that IGT performance may reflect different underlying processes in individuals with low and high trait impulsivity. In individuals with low trait impulsivity, it appears that risky selections in the IGT reflect in part, propensity for risk seeking, but only after the development of explicit knowledge of IGT risks after a period of learning. Crown Copyright (C) 2010 Published by Elsevier Ltd. All rights reserved.Y

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

Cognitive mechanisms underlying risky decision-making in chronic cannabis users

Chronic cannabis users are known to be impaired on a test of decision-making, the Iowa Gambling Task (IGT). Computational models of the psychological processes underlying this impairment have the potential to provide a rich description of the psychological characteristics of poor performers within particular clinical groups. We used two computational models of IGT performance, the Expectancy Valence Learning model (EVL) and the Prospect Valence Learning model (PVL), to assess motivational, memory, and response processes in 17 chronic cannabis abusers and 15 control participants. Model comparison and simulation methods revealed that the PVL model explained the observed data better than the EVL model. Results indicated that cannabis abusers tended to be under-influenced by loss magnitude, treating each loss as a constant and minor negative outcome regardless of the size of the loss. In addition, they were more influenced by gains, and made decisions that were less consistent with their expectancies relative to non-using controls. (C) 2009 Elsevier Inc. All rights reserved.Y