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. (Contains 5 figures and 2 tables.)