The Sense of Confidence during Probabilistic Learning: A Normative Account
Open accessAbstract
Learning in a stochastic environment consists of estimating a model from a limited amount of noisy data, and is therefore inherently uncertain. However, many classical models reduce the learning process to the updating of parameter estimates and neglect the fact that learning is also frequently accompanied by a variable "feeling of knowing" or confidence. The characteristics and the origin of these subjective confidence estimates thus remain largely unknown. Here we investigate whether, during learning, humans not only infer a model of their environment, but also derive an accurate sense of confidence from their inferences. In our experiment, humans estimated the transition probabilities between two visual or auditory stimuli in a changing environment, and reported their mean estimate and their confidence in this report. To formalize the link between both kinds of estimate and assess their accuracy in comparison to a normative reference, we derive the optimal inference strategy for our task. Our results indicate that subjects accurately track the likelihood that their inferences are correct. Learning and estimating confidence in what has been learned appear to be two intimately related abilities, suggesting that they arise from a single inference process. We show that human performance matches several properties of the optimal probabilistic inference. In particular, subjective confidence is impacted by environmental uncertainty, both at the first level (uncertainty in stimulus occurrence given the inferred stochastic characteristics) and at the second level (uncertainty due to unexpected changes in these stochastic characteristics). Confidence also increases appropriately with the number of observations within stable periods. Our results support the idea that humans possess a quantitative sense of confidence in their inferences about abstract non-sensory parameters of the environment. This ability cannot be reduced to simple heuristics, it seems instead a core property of the learning process.
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The Neurobiology of Confidence: From Beliefs to NeuronsPreprint
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30th Annual Computational Neuroscience Meeting: CNS*2021–Meeting Abstracts
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- Authors
- Meyniel, F., Schlunegger, D., & Dehaene, S.
- Affiliations
- Inserm (FR)
- Corresponding author
- Meyniel, Florent
- Venue
- PLOS Computational Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2015-06-15
- Volume
- 11
- Issue
- 6
- Pages
- e1004305
- ISSN
- 1553-734X (ISSN-L)
- 1553-7358
- PMC
- PMC4468157
- OpenAlex
- W580437509
- Cited by
- 163
- References
- 49
- Citations by year
- 2026: 13 · 2025: 15 · 2024: 13 · 2023: 16 · 2022: 15 · 2021: 20 · 2020: 13 · 2019: 12 · 2018: 16 · 2017: 14 · 2016: 15 · 2015: 1
- FWCI
- 15.03
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS Computational Biology (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- HAL (submittedVersion, other-oa)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- Funders
- European Commission (604102, 2007–2013, FP7 2007-2013, FP7/2007)
- Institut National de la Santé et de la Recherche Médicale
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Keywords
- feeling of knowing, probabilistic learning, Bayesian inference, normative model, environmental uncertainty, transition probability estimation, sequential sampling, environmental change detection
- MeSH terms
- Adult, Algorithms, Female, Humans, Learning, Male, Models, Neurological, Task Performance and Analysis, Models, Statistical, Computational Biology, Uncertainty, Young Adult
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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