Article

Learning to select optimal behavior in new and uncertain situations is a crucial
aspect of living and requires the ability to quickly associate stimuli with
actions that lead to rewarding outcomes. Mathematical models of
reinforcement-based learning to select rewarding actions distinguish between (1)
the formation of stimulus-action-reward associations, such that, at the instant a
specific stimulus is presented, it activates a specific action, based on the
expectation that that particular action will likely incur reward (or avoid
punishment); and (2) the comparison of predicted and actual outcomes to determine
whether the specific stimulus-action association yielded the intended outcome or
needs revision. Animal electrophysiology and human fMRI studies converge on the
notion that dissociable neural circuitries centered on the striatum are
differentially involved in different components of this learning process. The
modulatory role of dopamine (DA) in these respective circuits and component
processes is of particular relevance to the study of reward-based learning in
patients diagnosed with Parkinson's disease (PD). Here we show that the first
component process, learning to predict which actions yield reward (supported by
the anterior putamen and associated motor circuitry) is impaired when PD patients
are taken off their DA medication, whereas DA medication has no systematic
effects on the second processes, outcome evaluation (supported by caudate and
ventral striatum and associated frontal circuitries). However, the effects of DA
medication on these processes depend on dosage, with larger daily doses leading
to a decrease in predictability of stimulus-action-reward relations and increase
in reward-prediction errors.
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Langue : ANGLAIS