PT  - JOURNAL ARTICLE
AU  - Wang, Jane X.
AU  - Kurth-Nelson, Zeb
AU  - Kumaran, Dharshan
AU  - Tirumala, Dhruva
AU  - Soyer, Hubert
AU  - Leibo, Joel Z.
AU  - Hassabis, Demis
AU  - Botvinick, Matthew
TI  - Prefrontal Cortex as a Meta-Reinforcement Learning System
AID  - 10.1101/295964
DP  - 2018 Jan 01
TA  - bioRxiv
PG  - 295964
4099  - http://biorxiv.org/content/early/2018/04/06/295964.short
4100  - http://biorxiv.org/content/early/2018/04/06/295964.full
AB  - Over the past twenty 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. In the present work, we 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.