Model-based predictions for dopamine

Publication date: April 2018
Source:Current Opinion in Neurobiology, Volume 49
Author(s): Angela J Langdon, Melissa J Sharpe, Geoffrey Schoenbaum, Yael Niv
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.



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