Distributional reinforcement learning in prefrontal cortex

Muller, TH; Butler, JL; Veselic, S; Miranda, B; Wallis, JD; Dayan, P; Behrens, TEJ; Kurth-Nelson, Z; Kennerley , SW

doi:10.1038/s41593-023-01535-w

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Distributional reinforcement learning in prefrontal cortex

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Dayan, P
Department of Computational Neuroscience, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Muller, T., Butler, J., Veselic, S., Miranda, B., Wallis, J., Dayan, P., et al. (2024). Distributional reinforcement learning in prefrontal cortex. Nature Neuroscience, 27(3), 403-408. doi:10.1038/s41593-023-01535-w.

Cite as: https://hdl.handle.net/21.11116/0000-000E-34E4-6

Abstract

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.