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

R. D. Samson, M. J. Frank, Jean Marc Fellous

Research output: Contribution to journalReview articlepeer-review

42 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)91-105
Number of pages15
JournalCognitive Neurodynamics
Issue number2
StatePublished - Jun 2010


  • Dopamine
  • Reinforcement learning
  • Reward
  • Temporal difference

ASJC Scopus subject areas

  • Cognitive Neuroscience


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