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Volume 577 Issue 7792, 30 January 2020

Mapping the future

Reward prediction errors are part of the brain’s learning process. They account for the difference between the expected and the actual outcome of an event. The neurotransmitter dopamine is intimately involved in this form of learning — dopamine neurons tend to show increased activity when the outcome of an event is better than expected, but their activity decreases when the outcome is worse than predicted. As a result, learning has been viewed as the brain assessing the likely mean outcome as the basis for predicting the outcome of future events. In this issue, Will Dabney and his colleagues suggest that the picture is more complex. Taking inspiration from distributional reinforcement learning in artificial intelligence, the researchers analysed neuronal recordings from the mouse midbrain and found that instead of the brain representing the future as a single mean, it uses a probability distribution, effectively considering multiple possible future rewards simultaneously.

Cover image: DeepMind Technologies Ltd

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