A neurocomputational model of dopamine and prefrontal-striatal interactions during multicue category learning by Parkinson patients

Ahmed A. Moustafa, Mark A. Gluck

    Research output: Contribution to journalArticlepeer-review

    44 Citations (Scopus)

    Abstract

    Most existing models of dopamine and learning in Parkinson disease (PD) focus on simulating the role of basal ganglia dopamine in reinforcement learning. Much data argue, however, for a critical role for prefrontal cortex (PFC) dopamine in stimulus selection in attentional learning. Here, we present a new computational model that simulates performance in multicue category learning, such as the "weather prediction" task. The model addresses how PD and dopamine medications affect stimulus selection processes, which mediate reinforcement learning. In this model, PFC dopamine is key for attentional learning, whereas basal ganglia dopamine, consistent with other models, is key for reinforcement and motor learning. The model assumes that competitive dynamics among PFC neurons is the neural mechanism underlying stimulus selection with limited attentional resources, whereas competitive dynamics among striatal neurons is the neural mechanism underlying action selection. According to our model, PD is associated with decreased phasic and tonic dopamine levels in both PFC and basal ganglia. We assume that dopamine medications increase dopamine levels in both the basal ganglia and PFC, which, in turn, increase tonic dopamine levels but decrease the magnitude of phasic dopamine signaling in these brain structures. Increase of tonic dopamine levels in the simulated PFC enhances attentional shifting performance. The model provides a mechanistic account for several phenomena, including (a) medicated PD patients are more impaired at multicue probabilistic category learning than unmedicated patients and (b) medicated PD patients opt out of reversal when there are alternative and redundant cue dimensions.
    Original languageEnglish
    Pages (from-to)151-167
    Number of pages17
    JournalJournal of Cognitive Neuroscience
    Volume23
    Issue number1
    DOIs
    Publication statusPublished - 2011

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