Relating Introspective Accuracy to Individual Differences in Brain Structure
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Naval Gazing: Simple perceptual tasks, such as detecting the contrast between light and dark bars in a grating, have been a mainstay of psychophysical research for decades. This kind of task makes it possible to obtain both an objective measure of how accurate subjects are and a subjective measure of how confident they are in their judgments. Fleming et al. (p. 1541 ; see the Perspective by Lau and Maniscalco ) have taken this approach one step further by constructing a measure of how accurate subjects are in their confidence judgments. This capacity for introspection, which can be regarded as one facet of metacognition (thinking about thinking), is shown to vary across individuals and to correlate positively with the gray matter volume of the frontopolar cortex (the frontmost region of the brain) and also with white matter in the tracts of the corpus callosum that connect these regions in the left and right hemispheres.
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Individual differences in perceptual metacognitive sensitivity are linked to variation in the structure and function of human anterior prefrontal cortex.
“Individual differences in perceptual metacognitive sensitivity have been similarly linked to variation in the structure and function of human anterior prefrontal cortex (Allen et al. 2017, Baird et al. 2013, Fleming et al. 2010, McCurdy et al. 2013).”
From Metacognition and Confidence: A Review and Synthesis (2024) · cites this paper as Fleming et al. 2010
The results passage (section labeled Abstract; page unavailable) links individual differences in perceptual metacognitive sensitivity to anterior prefrontal gray-matter volume, and the abstract also reports an association with connected white-matter microstructure. The paper discusses a functional role for prefrontal cortex, but its own analysis measured brain structure, not individual variation in prefrontal function. (LLM, conf=0.86)
Experimental — accuracy not yet measuredMetacognitive efficiency shows moderate test-retest reliability, both across different sessions of the same experiment and across different days.
“Metacognitive efficiency shows moderate test-retest reliability, both across different sessions of the same experiment (Ais et al. 2016, Fleming et al. 2010) and across different days (Wright et al. 2012).”
From Metacognition and Confidence: A Review and Synthesis (2024) · cites this paper as Fleming et al. 2010
The passage labeled Abstract (page unavailable) reports split-half test–retest consistency for metacognitive ability measured by Aroc (r = 0.69). It does not say the halves were different sessions or identify Aroc as a measure of metacognitive efficiency. The across-days claim is attributed to Wright et al., not Fleming et al. (LLM, conf=0.84)
Experimental — accuracy not yet measuredFrontopolar cortex plays a role in metacognitive efficiency.
“These findings are intriguing in light of other work emphasizing the role of frontopolar cortex in metacognitive efficiency (Allen et al. 2017, Baird et al. 2013, Fleming et al. 2010, McCurdy et al. 2013, Miyamoto et al. 2018).”
From Metacognition and Confidence: A Review and Synthesis (2024) · cites this paper as Fleming et al. 2010
Possible anatomical substrates of metacognition have been investigated for several cognitive domains (exteroceptive perceptual performance, memory) but not for interoception or homeostasis/allostasis.
“Turning to the neuroanatomy of metacognition, possible anatomical substrates have been investigated for several cognitive domains, in particular (extero)perceptual performance or memory (Shimamura and Squire, 1986; Schnyer et al., 2004; Fleming et al., 2010, 2012, 2014; McCurdy et al., 2013), but not, to our knowledge, for interoception or homeostasis/allostasis.”
From Allostatic Self-efficacy: A Metacognitive Theory of Dyshomeostasis-Induced Fatigue and Depression (2016) · cites this paper as Fleming et al., 2010
Confidence can be measured independently of the choice process and is conceptualized as a second-order metacognitive evaluation.
“In contrast, the field of perceptual decision-making has highlighted the fact that confidence can be measured independently of the choice process itself9,10, where it is conceptualized as reflecting a “second-order” metacognitive evaluation.”
From Confidence in value-based choice (2013) · cites this paper as [9]
Right rostrolateral prefrontal cortex (rRLPFC) is a likely candidate for metacognitive appraisal because it is widely implicated in metacognitive assessments of perceptual decisions.
“Right rostrolateral prefrontal cortex (rRLPFC) is a likely candidate as this region is widely implicated in metacognitive assessments of perceptual decisions9,14,25.”
From Confidence in value-based choice (2013) · cites this paper as [9]
The capacity of confidence to distinguish correct from incorrect knowledge can vary markedly between subjects for a given task.
“research in behavioral economics and cognitive sciences has repeatedly shown that the capacity of confidence to distinguish correct from incorrect knowledge -henceforth referred as the accuracy of confidence -can vary markedly between subjects for a given task.”
From Individual consistency in the accuracy and distribution of confidence judgments (2016) · cites this paper as Fleming, Weil, Nagy, Dolan, & Rees, 2010
This variability in confidence accuracy has been related to individual differences in brain structure and function.
“This variability has been related to individual differences in brain structure and function (Barttfeld et al., 2013;Fleming, Weil, Nagy, Dolan, & Rees, 2010).”
From Individual consistency in the accuracy and distribution of confidence judgments (2016) · cites this paper as Fleming, Weil, Nagy, Dolan, & Rees, 2010
Possible candidates for a meta-cognitive system for monitoring learning progress include frontal systems that encode uncertainty or confidence in humans and monkeys.
“Possible candidates for such a system include frontal systems that encode the uncertainty or confidence in humans and monkeys (Fleming, Weil et al. 2010; Fleming, Huijgen et al. 2012; De Martino, Fleming et al. 2013)”
From Information-seeking, curiosity, and attention: computational and neural mechanisms (2013) · cites this paper as Fleming, Weil et al. 2010
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- Authors
- Fleming, S. M., Weil, R. S., Nagy, Z., Dolan, R. J., & Rees, G.
- Affiliations
- Wellcome Centre for Human Neuroimaging (GB)
- Wellcome Trust Centre for Neuroimaging, University College London, 12 Queen Square, London WC1N 3BG, UK.
- Corresponding author
- Fleming, Stephen M.
- Weil, Rimona S.
- Venue
- Science
- Published
- 2010-09-17
- Volume
- 329
- Issue
- 5998
- Pages
- 1541-1543
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- PMC
- PMC3173849
- OpenAlex
- W2089580004
- Cited by
- 809
- References
- 32
- Citations by year
- 2026: 30 · 2025: 44 · 2024: 49 · 2023: 54 · 2022: 56 · 2021: 57 · 2020: 61 · 2019: 66 · 2018: 56 · 2017: 58 · 2016: 60 · 2015: 46 · 2014: 52 · 2013: 33 · 2012: 41
- FWCI
- 23.28
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Science (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Max Planck Digital Library (submittedVersion, other-oa)
- MPG.PuRe (Max Planck Society) (submittedVersion, other-oa)
- Funders
- Wellcome Trust (078865, 082334)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Philosophy and Theoretical Science
- Keywords
- self-reflection, introspective accuracy, interindividual variability, anterior prefrontal cortex, gray matter volume, white matter microstructure, perceptual decision making, subjective experience, brain structure
- MeSH terms
- Adult, Brain Mapping, Cognition, Corpus Callosum, Decision Making, Humans, Judgment, Male, ROC Curve, Thinking, Visual Perception, Prefrontal Cortex, 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.
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