Dopamine selectively remediates ‘model-based’ reward learning: a computational approach

摘要

Patients with Parkinson’s disease show impaired reinforcement learning, but the exact nature of this deficit and the role of dopamine remain unclear. Using computational methods, Sharp et al. show that patients with Parkinson’s disease have a specific model-based, i.e. goal-directed, learning deficit that can be completely remediated by dopamine replacement.