Neural basis of reinforcement learning and decision making.
Neural basis of reinforcement learning and decision making.
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DOI：
10.1146/annurev-neuro-062111-150512
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发表时间：
2012
影响因子：
13.9
通讯作者：
Jung MW
中科院分区：
文献类型：
--
作者：
Lee D;Seo H;Jung MW
强化学习是一种自适应过程，动物利用其先前的经验来改善未来选择的结果。强化学习的计算理论在神经经济学和决策神经科学的新兴领域中发挥着核心作用。在这个框架中，行为是根据它们的价值函数来选择的，价值函数描述了每个行为预期的未来回报。价值函数不仅可以通过奖励和惩罚来调整，还可以通过动物对其当前环境的了解来调整。研究表明，大脑的很大一部分参与了代表和更新价值功能，并使用它们来选择行动。然而，行为任务的性质如何影响强化学习的神经机制仍然不完全清楚。未来的研究应该揭示强化学习的不同计算元素在整个大脑中动态协调的原理。
Reinforcement learning is an adaptive process in which an animal utilizes its previous experience to improve the outcomes of future choices. Computational theories of reinforcement learning play a central role in the newly emerging areas of neuroeconomics and decision neuroscience. In this framework, actions are chosen according to their value functions, which describe how much future reward is expected from each action. Value functions can be adjusted not only through reward and penalty, but also by the animal’s knowledge of its current environment. Studies have revealed that a large proportion of the brain is involved in representing and updating value functions and using them to choose an action. However, how the nature of a behavioral task affects the neural mechanisms of reinforcement learning remains incompletely understood. Future studies should uncover the principles by which different computational elements of reinforcement learning are dynamically coordinated across the entire brain.
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DOI：
10.1523/jneurosci.3793-11.2011
发表时间：
2011-12-07
期刊：
The Journal of neuroscience : the official journal of the Society for Neuroscience
影响因子：
--
作者：
Asaad WF;Eskandar EN
通讯作者：
Eskandar EN
影响因子：
16.2
作者：
Beck JM;Ma WJ;Kiani R;Hanks T;Churchland AK;Roitman J;Shadlen MN;Latham PE;Pouget A
通讯作者：
Pouget A
影响因子：
16.2
作者：
Daw ND;Gershman SJ;Seymour B;Dayan P;Dolan RJ
通讯作者：
Dolan RJ
影响因子：
56.9
作者：
Camille, N;Coricelli, G;Sirigu, A
通讯作者：
Sirigu, A
影响因子：
3.7
作者：
Ding, Long;Gold, Joshua I.
通讯作者：
Gold, Joshua I.