神经科学
钢筋
前额叶皮质
强化学习
心理学
认知心理学
认知科学
认知
计算机科学
社会心理学
人工智能
作者
Jane X. Wang,Zeb Kurth‐Nelson,Dharshan Kumaran,Dhruva Tirumala,Hubert Soyer,Joel Z. Leibo,Demis Hassabis,Matthew Botvinick
标识
DOI:10.1038/s41593-018-0147-8
摘要
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.
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