Computational Models Inform Clinical Science and Assessment: An Application to Category Learning in Striatal-Damaged Patients.

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