The outcome of a decision is often uncertain, and outcomes can vary over repeated decisions. Whether decision outcomes should substantially affect behaviour and learning depends on whether they are representative of a typically experienced range of outcomes or signal a change in the reward environment. Successful learning and decision-making therefore require the ability to estimate expected uncertainty (related to the variability of outcomes) and unexpected uncertainty (related to the variability of the environment). Understanding the bases and effects of these two types of uncertainty and the interactions between them — at the computational and the neural level — is crucial for understanding adaptive learning. Here, we examine computational models and experimental findings to distil computational principles and neural mechanisms for adaptive learning under uncertainty.
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The authors thank D. Lee, P. Rudebeck and A. Wikenheiser for helpful feedback. The authors acknowledge support from the US National Institutes of Health Grant R01DA047870 (A.S. and A.I.), a University of California–Los Angeles (UCLA) Division of Life Sciences Recruitment and Retention Fund (A.I.) and a UCLA Academic Senate Grant (A.I.).
Nature Reviews Neuroscience thanks S. B. Floresco and K. Preuschoff, and the other anonymous reviewer(s), for their contribution to the peer review of this work.
The authors declare no competing interests.
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Soltani, A., Izquierdo, A. Adaptive learning under expected and unexpected uncertainty. Nat Rev Neurosci 20, 635–644 (2019). https://doi.org/10.1038/s41583-019-0180-y
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