Model-based predictions for dopamine

Citation:

Langdon, A. J., Sharpe, M. J., Schoenbaum, G., & Niv, Y. (2018). Model-based predictions for dopamine. Current Opinion in Neurobiology , 49, 1–7.

Date Published:

apr

ISSN:

09594388

Abstract:

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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DOI:

10.1016/j.conb.2017.10.006
Last updated on 12/11/2019