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Adaptive learning under expected and unexpected uncertainty
Open accessAbstract
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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“Normatively, learning rates should vary as a function of uncertainty.7–9”
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Metadata
- Authors
- Soltani, A., & Izquierdo, A.
- Affiliations
- Dartmouth College (US)
- University of California, Los Angeles (US)
- Corresponding author
- Soltani, Alireza
- Izquierdo, Alicia
- Venue
- Nature reviews. Neuroscience
- Published
- 2019-05-30
- Volume
- 20
- Issue
- 10
- Pages
- 635-644
- ISSN
- 1471-003X (ISSN-L)
- 1471-0048
- PMC
- PMC6752962
- OpenAlex
- W2946961766
- Cited by
- 286
- References
- 118
- Citations by year
- 2026: 31 · 2025: 63 · 2024: 51 · 2023: 42 · 2022: 29 · 2021: 36 · 2020: 26 · 2019: 8
- FWCI
- 3.10
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Nature reviews. Neuroscience (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- National Institute on Drug Abuse (R01DA047870)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
- Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
- Keywords
- adaptive learning, uncertainty estimation, decision-making, computational model, neural mechanisms, outcome variability, reinforcement learning
- MeSH terms
- Adaptation, Biological, Animals, Brain, Choice Behavior, Decision Making, Humans, Learning, Nerve Net, Uncertainty
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