| Literature DB >> 29760527 |
Jane X Wang1, Zeb Kurth-Nelson1,2, Dharshan Kumaran1,3, Dhruva Tirumala1, Hubert Soyer1, Joel Z Leibo1, Demis Hassabis1,4, Matthew Botvinick5,6.
Abstract
Over the past 20 years, neuroscience research on reward-based learning has converged on a canonical model, under which the neurotransmitter dopamine 'stamps in' associations between situations, actions and rewards by modulating the strength of synaptic connections between neurons. However, a growing number of recent findings have placed this standard model under strain. We now draw on recent advances in artificial intelligence to introduce a new theory of reward-based learning. Here, the dopamine system trains another part of the brain, the prefrontal cortex, to operate as its own free-standing learning system. This new perspective accommodates the findings that motivated the standard model, but also deals gracefully with a wider range of observations, providing a fresh foundation for future research.Entities:
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Year: 2018 PMID: 29760527 DOI: 10.1038/s41593-018-0147-8
Source DB: PubMed Journal: Nat Neurosci ISSN: 1097-6256 Impact factor: 24.884