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Curr Opin Neurobiol. 2018 Apr;49:1-7. doi: 10.1016/j.conb.2017.10.006. Epub 2017 Oct 31.

Model-based predictions for dopamine.

Author information

1
Princeton Neuroscience Institute & Department of Psychology, Princeton University, Princeton, NJ 08540, United States. Electronic address: alangdon@princeton.edu.
2
Princeton Neuroscience Institute & Department of Psychology, Princeton University, Princeton, NJ 08540, United States; National Institute on Drug Abuse, Baltimore, MD 21224, United States; School of Psychology, University of New South Wales, Australia.
3
National Institute on Drug Abuse, Baltimore, MD 21224, United States.
4
Princeton Neuroscience Institute & Department of Psychology, Princeton University, Princeton, NJ 08540, United States.

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.

PMID:
29096115
PMCID:
PMC6034703
DOI:
10.1016/j.conb.2017.10.006
[Indexed for MEDLINE]
Free PMC Article

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