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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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Self-citationConfidence and certainty: Distinct probabilistic quantities for different goals
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“Similarly, in a probabilistic learning task, subjective confidence in the learned estimates followed the optimal Bayesian confidence levels in a tighter parallel than a whole list of ‘‘cues’’ taken together, suggesting that the representation from which confidence is read out was particularly rich and most likely prob…”
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Metadata
- 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
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