Computational models inform clinical science and assessment: An application to category learning in striatal-damaged patients

摘要: In this article we develop a new model of classification that is intermediate between the static, single strategy decision-bound models and dynamic trial by multiple systems model, dCOVIS. The referred to as sCOVIS assumes hypothesis-testing procedural-based subsystems are active on each trial, but parameters govern behavior system fixed (static) within block trials. To determine clinical utility it was applied nonlinear information-integration data from patients with Parkinson’s (PD) Huntington’s disease (HD). one application, suggest locus HD patients’ deficits in their increased reliance strategies, whereas PD deficit application sub-optimal strategies. second weight associated subsystem shown account for significant amount variance longitudinal cognitive decline non-demented above beyond predicted accuracy alone. Together, rate index 72% total sample patients. Interestingly, Wisconsin Card Sort task added no additional predictive power