Phasic dopamine responses are thought to encode a prediction-error signal consistent with model-free reinforcement learning theories. However, a number of recent findings highlight the influence of model-based computations on dopamine responses, and suggest that dopamine prediction errors reflect more dimensions of an expected outcome than scalar reward value. Here, we review a selection of these recent results and discuss the implications and complications of model-based predictions for computational theories of dopamine and learning.
This page contains links to original data from experiments run at the Princeton Neuroscience Institute. These data are available to others for educational purposes. If they are used in publications, please cite the source of the data by indicating the published reference and the address of this website