Distributional reinforcement learning in prefrontal cortex.
Distributional reinforcement learning in prefrontal cortex.
复制标题
前额皮质的分布式强化学习。
DOI：
10.1038/s41593-023-01535-w
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发表时间：
2024
影响因子：
25
通讯作者：
Kennerley,StevenW
中科院分区：
文献类型：
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作者：
Muller,TimothyH;Butler,JamesL;Veselic,Sebastijan;Miranda,Bruno;Wallis,JoniD;Dayan,Peter;Behrens,TimothyEJ;Kurth-Nelson,Zeb;Kennerley,StevenW
前额叶皮质对学习和决策至关重要。经典强化学习(RL)理论的核心是学习对潜在回报结果的预期，并解释前额叶皮质中丰富的神经数据。另一方面，分布式RL学习奖励结果的完全分布，并更好地解释多巴胺反应。在本研究中，我们表明，分布的RL也能更好地解释猕猴前扣带回皮质神经元的反应，这表明这是奖赏引导学习的一种常见机制。
The prefrontal cortex is crucial for learning and decision-making. Classic reinforcement learning (RL) theories center on learning the expectation of potential rewarding outcomes and explain a wealth of neural data in the prefrontal cortex. Distributional RL, on the other hand, learns the full distribution of rewarding outcomes and better explains dopamine responses. In the present study, we show that distributional RL also better explains macaque anterior cingulate cortex neuronal responses, suggesting that it is a common mechanism for reward-guided learning.