Striatal contributions to category learning: quantitative modeling of simple linear and complex nonlinear rule learning in patients with Parkinson's disease.

Abstract

The contribution of the striatum to category learning was examined by having patients with Parkinson's disease (PD) and matched controls solve categorization problems in which the optimal rule was linear or nonlinear using the perceptual categorization task. Traditional accuracy-based analyses, as well as quantitative model-based analyses were performed. Unlike accuracy-based analyses, the model-based analyses allow one to quantify and separate the effects of categorization rule learning from variability in the trial-by-trial application of the participant's rule. When the categorization rule was linear, PD patients showed no accuracy, categorization rule learning, or rule application variability deficits. Categorization accuracy for the PD patients was associated with their performance on a test believed to be sensitive to frontal lobe functioning. In contrast, when the categorization rule was nonlinear, the PD patients showed accuracy, categorization rule learning, and rule application variability deficits. Furthermore, categorization accuracy was not associated with performance on the test of frontal lobe functioning. Implications for neuropsychological theories of categorization learning are discussed.