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Human Metacognition Across Domains: Insights from Individual Differences and Neuroimaging
Open accessAbstract
Metacognition is the capacity to evaluate and control one’s own cognitive processes. Metacognition operates over a range of cognitive domains, such as perception and memory, but the neurocognitive architecture supporting this ability remains controversial. Is metacognition enabled by a common, domain-general resource that is recruited to evaluate performance on a variety of tasks? Or is metacognition reliant on domain-specific modules? This article reviews recent literature on the domain-generality of human metacognition, drawing on evidence from individual differences and neuroimaging. A meta-analysis of behavioral studies found that perceptual metacognitive ability was correlated across different sensory modalities, but found no correlation between metacognition of perception and memory. However, evidence for domain-generality from behavioral data may suffer from a lack of power to identify correlations across model parameters indexing metacognitive efficiency. Neuroimaging data provide a complementary perspective on the domain-generality of metacognition, revealing co-existence of neural signatures that are common and distinct across tasks. We suggest that such an architecture may be appropriate for “tagging” generic feelings of confidence with domain-specific information, in turn forming the basis for priors about self-ability and modulation of higher-order behavioral control.
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“The generative nature of GenAI-its 'originality' [157]-means that many user workflows will or have shifted from users generating content to evaluating it [149,154], as already documented in programming [156] and manufacturing design [70].”
From The Metacognitive Demands and Opportunities of Generative AI (2024) · cites this paper as [149]
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“We argue that this imposes a distinct metacognitive demand on users that must make these decisions, akin to [149], who make a similar argument for digital storage and memory.”
From The Metacognitive Demands and Opportunities of Generative AI (2024) · cites this paper as [149]
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“Analogously, as we offload more of our cognition to today's GenAI systems, the demand for our metacognition increases [149].”
From The Metacognitive Demands and Opportunities of Generative AI (2024) · cites this paper as [149]
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Metadata
- Authors
- Rouault, M., McWilliams, A., Allen, M. G., & Fleming, S. M.
- Affiliations
- Wellcome Centre for Human Neuroimaging (GB)
- Max Planck Society (DE)
- Corresponding author
- Fleming, Stephen M.
- Venue
- Personality Neuroscience
- Publisher
- Cambridge University Press (CUP)
- Published
- 2018
- Volume
- 1
- Pages
- e17
- ISSN
- 2513-9886 (ISSN-L)
- PMC
- PMC6217996
- OpenAlex
- W2899651677
- Cited by
- 176
- References
- 113
- Citations by year
- 2026: 18 · 2025: 25 · 2024: 24 · 2023: 25 · 2022: 33 · 2021: 25 · 2020: 16 · 2019: 7 · 2018: 3
- FWCI
- 7.59
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by-nc-nd
- Locations
- Personality Neuroscience (publishedVersion, cc-by-nc-nd)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Research Portal (King's College London) (publishedVersion, cc-by-nc-nd)
- HAL (submittedVersion, cc-by-nc-nd)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Funders
- Wellcome Trust (203147/Z/16/Z, 203147, 206648, 203147/Z/16/)
- National Institute for Health and Care Research (ACF-2015-17-019)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory Processes and Influences
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Cognitive Functions and Memory
- Keywords
- metacognition, domain generality, individual differences, confidence, self-assessment, sensory modalities, metacognitive efficiency
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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