Cognitive Neurodynamics

, Volume 4, Issue 2, pp 91–105 | Cite as

Computational models of reinforcement learning: the role of dopamine as a reward signal

  • R. D. Samson
  • M. J. Frank
  • Jean-Marc Fellous


Reinforcement learning is ubiquitous. Unlike other forms of learning, it involves the processing of fast yet content-poor feedback information to correct assumptions about the nature of a task or of a set of stimuli. This feedback information is often delivered as generic rewards or punishments, and has little to do with the stimulus features to be learned. How can such low-content feedback lead to such an efficient learning paradigm? Through a review of existing neuro-computational models of reinforcement learning, we suggest that the efficiency of this type of learning resides in the dynamic and synergistic cooperation of brain systems that use different levels of computations. The implementation of reward signals at the synaptic, cellular, network and system levels give the organism the necessary robustness, adaptability and processing speed required for evolutionary and behavioral success.


Reinforcement learning Dopamine Reward Temporal difference 



The authors wish to thank Dr. Ian Fasel, Nathan Insel and Minryung Song for useful comments on the manuscript. R.D.S. was supported by the Canadian Institute of Health Research SIB 171357.


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Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  • R. D. Samson
    • 1
  • M. J. Frank
    • 2
  • Jean-Marc Fellous
    • 3
  1. 1.Evelyn F. McKnight Brain Institute and Neural Systems, Memory and AgingUniversity of ArizonaTucsonUSA
  2. 2.Department of Cognitive and Linguistic Sciences and Department of Psychology, Brown Institute for Brain ScienceBrown UniversityProvidenceUSA
  3. 3.Department of Psychology and Applied MathematicsUniversity of ArizonaTucsonUSA

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