A model-based quantification of action control deficits in Parkinson's disease

Abstract

Basal ganglia dysfunction in Parkinson's disease (PD) is thought to generate deficits in action control, but the characterization of these deficits have been qualitative rather than quantitative. Patients with PD typically show prolonged response times on tasks that instantiate a conflict between goal-directed processing and automatic response tendencies. In the Simon task, for example, the irrelevant location of the stimulus automatically activates a corresponding lateralized response, generating a potential conflict with goal-directed choices. We applied a new computational model of conflict processing to two sets of behavioral data from the Simon task to quantify the effects of PD and dopaminergic (DA) medication on action control mechanisms. Compared to healthy controls (HC) matched in age gender and education, patients with PD showed a deficit in goal-directed processing, and the magnitude of this deficit positively correlated with cognitive symptoms. Analyses of the time-course of the location-based automatic activation yielded mixed findings. In both datasets, we found that the peak amplitude of the automatic activation was similar between PD and HC, demonstrating a similar degree of response capture. However, PD patients showed a prolonged automatic activation in only one dataset. This discrepancy was resolved by theoretical analyses of conflict resolution in the Simon task. The reduction of interference generated by the automatic activation appears to be driven by a mixture of passive decay and top-down inhibitory control, the contribution of each component being modulated by task demands. Our results suggest that PD selectively impairs the inhibitory control component, a deficit likely remediated by DA medication. This work advances our understanding of action control deficits in PD, and illustrates the benefit of using computational models to quantitatively measure cognitive processes in clinical populations.