Neural Coding of Uncertainty and Probability
Open accessAbstract
Organisms must act in the face of sensory, motor, and reward uncertainty stemming from a pandemonium of stochasticity and missing information. In many tasks, organisms can make better decisions if they have at their disposal a representation of the uncertainty associated with task-relevant variables. We formalize this problem using Bayesian decision theory and review recent behavioral and neural evidence that the brain may use knowledge of uncertainty, confidence, and probability.
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Bayesian decision theory provides a general and often quantitatively accurate account of perceptual decisions in a wide variety of tasks.
“Bayesian decision theory provides a general and often quantitatively accurate account of perceptual decisions in a wide variety of tasks [16–18].”
From Comparing Bayesian and non-Bayesian accounts of human confidence reports (2018) · cites this paper as [17]
The Theory of Probabilistic Computation section presents Bayesian decision theory as a general framework for decisions, and Behavioral Evidence for Probabilistic Computation describes near-optimal behavior across several perceptual tasks. The passages support its breadth, but do not establish the claim that it provides an often quantitatively accurate account of perceptual decisions. Page numbers are unavailable in our copy. (LLM, conf=0.78)
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- Authors
- Ma, W. J., & Jazayeri, M.
- Affiliations
- New York University (US)
- McGovern Institute for Brain Research (US)
- Corresponding author
- Ma, Wei Ji
- Venue
- Annual Review of Neuroscience
- Publisher
- Annual Reviews
- Published
- 2014-07-08
- Volume
- 37
- Issue
- 1
- Pages
- 205-220
- ISSN
- 0147-006X (ISSN-L)
- 1545-4126
- OpenAlex
- W2157745749
- Cited by
- 290
- References
- 128
- Citations by year
- 2026: 15 · 2025: 28 · 2024: 24 · 2023: 23 · 2022: 24 · 2021: 33 · 2020: 36 · 2019: 22 · 2018: 29 · 2017: 21 · 2016: 25 · 2015: 9 · 2014: 1
- FWCI
- 1.81
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Annual Review of Neuroscience (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Keywords
- probability representation, Bayesian decision theory, confidence, decision making under uncertainty, sensory uncertainty, reward uncertainty, Bayesian brain
- MeSH terms
- Animals, Bayes Theorem, Brain, Decision Making, Humans, Models, Neurological, Neurons, Probability, Uncertainty
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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