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Reinforcement learning

Anterior cingulate learns reward distribution

Nature Neuroscience volume 27, pages 391–392 (2024)Cite this article

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Muller et al. demonstrate that reward signals recorded from the frontal cortex of nonhuman primates exhibit a population-based scheme for learning probability distributions over reward values. This study provides evidence that neural signals outside of the midbrain reflect the principles of distributional reinforcement-learning theory.

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Fig. 1: Distributional RL models learn the information required to make decisions under changing demands.

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Authors and Affiliations

  1. Department of Neurobiology, University of Pittsburgh, Pittsburgh, PA, USA

    Tao Hong & William R. Stauffer

  2. Program in Neural Computation, Carnegie Mellon University, Pittsburgh, PA, USA

    Tao Hong

  3. Center for the Neural Basis of Cognition, University of Pittsburgh and Carnegie Mellon University, Pittsburgh, PA, USA

    Tao Hong & William R. Stauffer

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  1. Tao Hong
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  2. William R. Stauffer
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Correspondence to William R. Stauffer.

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The authors declare no competing interests.

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Hong, T., Stauffer, W.R. Anterior cingulate learns reward distribution. Nat Neurosci 27, 391–392 (2024). https://doi.org/10.1038/s41593-024-01571-0

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