Brain networks for confidence weighting and hierarchical inference during probabilistic learning
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Significance: What has been learned must sometimes be unlearned in a changing world. Yet knowledge updating is difficult since our world is also inherently uncertain. For instance, a heatwave in winter is surprising and ambiguous: does it denote an infrequent fluctuation in normal weather or a profound change? Should I trust my current knowledge, or revise it? We propose that humans possess an accurate sense of confidence that allows them to evaluate the reliability of their knowledge, and use this information to strike the balance between prior knowledge and current evidence. Our functional MRI data suggest that a frontoparietal network implements this confidence-weighted learning algorithm, acting as a statistician that uses probabilistic information to estimate a hierarchical model of the world.
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- Authors
- Meyniel, F., & Dehaene, S.
- Affiliations
- Université Paris-Sud (FR)
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2017-04-24
- Volume
- 114
- Issue
- 19
- Pages
- E3859-E3868
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC5441722
- OpenAlex
- W2608711182
- Cited by
- 138
- References
- 64
- Citations by year
- 2026: 7 · 2025: 16 · 2024: 28 · 2023: 10 · 2022: 11 · 2021: 25 · 2020: 15 · 2019: 15 · 2018: 6 · 2017: 5
- FWCI
- 6.43
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Seventh Framework Programme (604102)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Keywords
- confidence weighting, probabilistic learning, Bayesian inference, hierarchical inference, statistical learning, subjective confidence, right inferior frontal gyrus, likelihood estimation, ideal observer analysis
- MeSH terms
- Adult, Female, Humans, Learning, Magnetic Resonance Imaging, Male, Models, Neurological, Nerve Net, Prefrontal Cortex
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