Confidence as Bayesian probability: From neural origins to behavior
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Research on confidence spreads across several sub-fields of psychology and neuroscience. Here, we explore how a definition of confidence as Bayesian probability can unify these viewpoints. This computational view entails that there are distinct forms in which confidence is represented and used in the brain, including distributional confidence, pertaining to neural representations of probability distributions, and summary confidence, pertaining to scalar summaries of those distributions. Summary confidence is, normatively, derived or "read out" from distributional confidence. Neural implementations of readout will trade off optimality versus flexibility of routing across brain systems, allowing confidence to serve diverse cognitive functions.
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Propositional confidence can be distinguished from other confidences or uncertainties inherent to perception, cognition, and action, although the latter often inform the former.
“The most important idea, building on the work of Pouget et al. (2016), is that propositional confidence can be distinguished from a myriad of other confidences or uncertainties that are inherent to perception, cognition, and action, although the latter often inform the former (Meyniel et al. 2015).”
From Metacognition and Confidence: A Review and Synthesis (2024) · cites this paper as Meyniel et al. 2015
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Barthelmé, S., & Mamassian, P. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1007704107 · Cited by 82 · see citing works
King, J.-R., & Dehaene, S. (2014) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2013.0204 · Cited by 82 · see citing works
Effects of loss aversion on post-decision wagering: Implications for measures of awareness
Fleming, S. M., & Dolan, R. J. (2009) · Consciousness and Cognition · doi:10.1016/j.concog.2009.11.002 · Cited by 77 · see citing works
One and Done? Optimal Decisions from Very Few Samples
Goodman, N. D., Griffiths, T. L., Tenenbaum, J. B., & Vul, E. (2009) · eScholarship (California Digital Library) · Cited by 68 · see citing works
The Human Brain Encodes Event Frequencies While Forming Subjective Beliefs
d’Acremont, M., Schultz, W., & Bossaerts, P. L. (2013) · Journal of Neuroscience · doi:10.1523/jneurosci.5829-12.2013 · Cited by 67 · see citing works
Perceptuo-motor, cognitive, and description-based decision-making seem equally good
Jarvstad, A., Hahn, U., Rushton, S. K., & Warren, P. A. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1300239110 · Cited by 63 · see citing works
Chunking processes in the learning of event sequences: Electrophysiological indicators
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A Probabilistic Strategy for Understanding Action Selection
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Nonconscious Learning From Crowded Sequences
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Model Uncertainty in Classical Conditioning
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Brain Systems for Probabilistic and Dynamic Prediction: Computational Specificity and Integration
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The Spatial and Temporal Construction of Confidence in the Visual Scene
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Self-citationNeural representation of probabilities for Bayesian inference
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Flagged first, then by citations
Research works printed but not linked: 3
- Kahneman, D. (2013). Thinking, Fast and Slow (New York: Farrar, Straus and Giroux). http://refhub.elsevier.com/S0896-6273(1500828-4/sref57)
- Shea,N., Boldt, A., Bang, D., Yeung, N., Heyes, C., and Frith, C.D. (2014). Suprapersonal cognitive control and metacognition. Trends Cogn. Sci. 18 , 186-193. http://refhub.elsevier.com/S0896-6273(1500828-4/sref103)
- Yeung, N., and Summerfield, C. (2012). Metacognition in human decisionmaking: confidence and error monitoring. Philos. Trans. R. Soc. Lond. B Biol. Sci. 367 , 1310-1321. http://refhub.elsevier.com/S0896-6273(1500828-4/sref123)
Cited by
Sorted by citations
Confidence and certainty: Distinct probabilistic quantities for different goals
Pouget, A., Drugowitsch, J., & Kepecs, A. (2016) · Nature Neuroscience · doi:10.1038/nn.4240 · Cited by 528 · see citing works
Metacognition and Confidence: A Review and Synthesis
Fleming, S. M. (2024) · Annual Review of Psychology · doi:10.1146/annurev-psych-022423-032425 · Cited by 256 · see citing works
“Recently there has been surge of interest in the neuroscience of uncertainty and confidence, leading to a marriage of computational work in cognitive science with human neuroimaging studies and animal models of metacognitive judgments (Pouget et al., 2016, Meyniel et al., 2015).”
InfluentialDistinct encoding of decision confidence in human medial prefrontal cortex
Bang, D., & Fleming, S. M. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1800795115 · Cited by 207 · see citing works
Brain networks for confidence weighting and hierarchical inference during probabilistic learning
Meyniel, F., & Dehaene, S. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1615773114 · Cited by 138 · see citing works
Self-citationMultivoxel neurofeedback selectively modulates confidence without changing perceptual performance
Cortese, A., Amano, K., Koizumi, A., Kawato, M., & Lau, H. (2016) · Nature Communications · doi:10.1038/ncomms13669 · Cited by 157 · see citing works
Comparing Bayesian and non-Bayesian accounts of human confidence reports
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Compulsivity reveals a novel dissociation between action and confidence
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The impact of evidence reliability on sensitivity and bias in decision confidence.
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Behavior- and Modality-General Representation of Confidence in Orbitofrontal Cortex
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Human VMPFC encodes early signatures of confidence in perceptual decisions
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A postdecisional neural marker of confidence predicts information-seeking in decision-making
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Subjective confidence reflects representation of Bayesian probability in cortex
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Balancing new against old information: the role of puzzlement surprise in learning
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Modeling perceptual confidence and the confidence forced-choice paradigm.
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Consensus Goals in the Field of Visual Metacognition
Rahnev, D., Balsdon, T., Charles, L., de Gardelle, V., Denison, R., Desender, K., Faivre, N., Filevich, E., Fleming, S. M., Jehee, J., et al. (2022) · Perspectives on Psychological Science · doi:10.1177/17456916221075615 · Cited by 63 · see citing works
Studying the neural representations of uncertainty
Walker, E. Y., Pohl, S., Denison, R. N., Barack, D. L., Lee, J., Block, N., Ma, W. J., & Meyniel, F. (2023) · Nature Neuroscience · doi:10.1038/s41593-023-01444-y · Cited by 56 · see citing works
Self-citationSapey-Triomphe, L.-A., Weilnhammer, V. A., & Wagemans, J. (2022) · Autism · doi:10.1177/13623613211045026 · Cited by 45 · see citing works
Neurocomputational mechanisms of confidence in self and others
Bang, D., Moran, R., Daw, N. D., & Fleming, S. M. (2022) · Nature Communications · doi:10.1038/s41467-022-31674-w · Cited by 39 · see citing works
Lee, A. L. F., de Gardelle, V., & Mamassian, P. (2021) · Psychonomic Bulletin & Review · doi:10.3758/s13423-020-01869-7 · Cited by 32 · see citing works
Pasquinelli, E., Farina, M., Bedel, A., & Casati, R. (2021) · Mind Brain and Education · doi:10.1111/mbe.12286 · Cited by 29 · see citing works
- Authors
- Meyniel, F., Sigman, M., & Mainen, Z. F.
- Affiliations
- Université Paris-Sud (FR)
- Universidad Torcuato Di Tella (AR)
- Centro Champalimaud (PT)
- Corresponding author
- Meyniel, Florent
- Venue
- Neuron
- Publisher
- Elsevier BV
- Published
- 2015-10
- Volume
- 88
- Issue
- 1
- Pages
- 78-92
- ISSN
- 0896-6273 (ISSN-L)
- OpenAlex
- W1939692617
- Cited by
- 349
- References
- 134
- Citations by year
- 2026: 49 · 2025: 32 · 2024: 31 · 2023: 33 · 2022: 34 · 2021: 38 · 2020: 41 · 2019: 24 · 2018: 21 · 2017: 26 · 2016: 20
- FWCI
- 2.18
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Neuron (publishedVersion)
- PubMed (publishedVersion)
- Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) (submittedVersion, other-oa)
- CONICET Digital (CONICET) (submittedVersion, cc-by-nc-sa)
- Funders
- Simons Foundation
- European Commission (604102)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
- Keywords
- confidence, Bayesian probability, decision-making, cognitive flexibility, normative model
- MeSH terms
- Animals, Bayes Theorem, Brain, Cognition, Decision Making, Humans, Models, Psychological, Probability
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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