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Sixteen patients with Parkinson's disease (PD), 15 older controls (OCs), and 109 younger controls (YCs) were compared in 2 category-learning tasks. Participants attempted to assign colored geometric figures to 1 of 2 categories. In rule-based tasks, category membership was defined by an explicit rule that was easy to verbalize, whereas in information-integration tasks, there was no salient verbal rule and accuracy was maximized only if information from 3 stimulus components was integrated at some predecisional stage. The YCs performed the best on both tasks. The PD patients were highly impaired compared with the OCs, in the rule-based categorization task but were not different from the OCs in the information-integration task. These results support the hypothesis that learning in these 2 tasks is mediated by functionally separate systems.

Many studies suggest that the sustained activation underlying working memory (WM) maintenance is mediated by a distributed network that includes the prefrontal cortex and other structures (e.g., posterior parietal cortex, thalamus, globus pallidus, and the caudate nucleus). A computational model of WM, called FROST (short for FROntal-Striatal-Thalamic), is proposed in which the representation of items and spatial positions is encoded in the lateral prefrontal cortex. During delay intervals, activation in these prefrontal cells is sustained via parallel, prefrontal cortical-thalamic loops. Activation reverberates in these loops because prefrontal cortical excitation of the head of the caudate nucleus leads to disinhibition of the thalamus (via the globus pallidus). FROST successfully accounts for a wide variety of WM data, including single-cell recording data and human behavioral data.

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