The computational neurobiology of learning and reward

Nathaniel D. Daw, Kenji Doya

Research output: Contribution to journalArticle

Abstract

Following the suggestion that midbrain dopaminergic neurons encode a signal, known as a 'reward prediction error', used by artificial intelligence algorithms for learning to choose advantageous actions, the study of the neural substrates for reward-based learning has been strongly influenced by computational theories. In recent work, such theories have been increasingly integrated into experimental design and analysis. Such hybrid approaches have offered detailed new insights into the function of a number of brain areas, especially the cortex and basal ganglia. In part this is because these approaches enable the study of neural correlates of subjective factors (such as a participant's beliefs about the reward to be received for performing some action) that the computational theories purport to quantify.

Original languageEnglish (US)
Pages (from-to)199-204
Number of pages6
JournalCurrent Opinion in Neurobiology
Volume16
Issue number2
DOIs
StatePublished - Apr 2006

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Neurobiology
Reward
Learning
Artificial Intelligence
Dopaminergic Neurons
Mesencephalon
Basal Ganglia
Research Design
Brain

ASJC Scopus subject areas

  • Neuroscience(all)

Cite this

The computational neurobiology of learning and reward. / Daw, Nathaniel D.; Doya, Kenji.

In: Current Opinion in Neurobiology, Vol. 16, No. 2, 04.2006, p. 199-204.

Research output: Contribution to journalArticle

Daw, Nathaniel D. ; Doya, Kenji. / The computational neurobiology of learning and reward. In: Current Opinion in Neurobiology. 2006 ; Vol. 16, No. 2. pp. 199-204.
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