Journal
NATURE
Volume 577, Issue 7792, Pages 671-+Publisher
NATURE PORTFOLIO
DOI: 10.1038/s41586-019-1924-6
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Funding
- NIGMS NIH HHS [T32 GM007753] Funding Source: Medline
- NINDS NIH HHS [R01 NS116753, R01 NS108740] Funding Source: Medline
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Since its introduction, the reward prediction error theory of dopamine has explained a wealth of empirical phenomena, providing a unifying framework for understanding the representation of reward and value in the brain(1-3). According to the now canonical theory, reward predictions are represented as a single scalar quantity, which supports learning about the expectation, or mean, of stochastic outcomes. Here we propose an account of dopamine-based reinforcement learning inspired by recent artificial intelligence research on distributional reinforcement learning(4-6). We hypothesized that the brain represents possible future rewards not as a single mean, but instead as a probability distribution, effectively representing multiple future outcomes simultaneously and in parallel. This idea implies a set of empirical predictions, which we tested using single-unit recordings from mouse ventral tegmental area. Our findings provide strong evidence for a neural realization of distributional reinforcement learning.
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