Variance misperception under skewed empirical noise statistics explains overconfidence in the visual periphery
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Abstract
Perceptual confidence typically corresponds to accuracy. However, observers can be overconfident relative to accuracy, termed "subjective inflation." Inflation is stronger in the visual periphery relative to central vision, especially under conditions of peripheral inattention. Previous literature suggests inflation stems from errors in estimating noise (i.e., "variance misperception"). However, despite previous Bayesian hypotheses about metacognitive noise estimation, no work has systematically explored how noise estimation may critically depend on empirical noise statistics, which may differ across the visual field, with central noise distributed symmetrically but peripheral noise positively skewed. Here, we examined central and peripheral vision predictions from five Bayesian-inspired noise-estimation algorithms under varying usage of noise priors, including effects of attention. Models that failed to optimally estimate noise exhibited peripheral inflation, but only models that explicitly used peripheral noise priors-but used them incorrectly-showed increasing peripheral inflation under increasing peripheral inattention. Further, only one model successfully captured previous empirical results, which showed a selective increase in confidence in incorrect responses under performance reductions due to inattention accompanied by no change in confidence in correct responses; this was the model that implemented Bayesian estimation of peripheral noise, but using an (incorrect) symmetric rather than the correct positively skewed peripheral noise prior. Our findings explain peripheral inflation, especially under inattention, and suggest future experiments that might reveal the noise expectations used by the visual metacognitive system.
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People misperceive sensory noise in the periphery of the visual field, leading to an inflation of perceptual confidence relative to perceptual acuity.
“Similarly, in the perceptual domain, Winter & Peters (2022) found that people misperceive sensory noise in the periphery of the visual field, leading to an inflation of perceptual confidence relative to perceptual acuity.”
From Metacognition and Confidence: A Review and Synthesis (2024) · cites this paper as Winter & Peters 2022
The Significance and Discussion sections link misestimation of peripheral visual noise to confidence that exceeds perceptual accuracy, supporting the proposed explanation. But the paper reports simulations of noise-estimation models and frames misperception in people as a hypothesis, not an empirical finding that people misperceive that noise. (LLM, conf=0.86)
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1 of the 113 references couldn’t be matched to a known paper.
Research works printed but not linked: 1
- Maniscalco, B., Castaneda, O. G., Odegaard, B., Morales, J., Rajananda, S., & Peters, M. A. K. (2020). The metaperceptual function: Exploring dissociations between confidence and task performance with type 2 psychometric curves. https://doi.org/10.31234/osf.io/5qrjn · DOI
Cited by
Sorted by citations
Metacognition and Confidence: A Review and Synthesis
Fleming, S. M. (2024) · Annual Review of Psychology · doi:10.1146/annurev-psych-022423-032425 · Cited by 256 · see citing works
“Similarly, in the perceptual domain, Winter & Peters (2022) found that people misperceive sensory noise in the periphery of the visual field, leading to an inflation of perceptual confidence relative to perceptual acuity.”
Dual strategies in human confidence judgments
Bertana, A., Четвериков, А. А., van Bergen, R. S., Ling, S., & Jehee, J. F. M. (2021) · Journal of Vision · doi:10.1167/jov.21.5.21 · Cited by 27 · see citing works
Metacognition and confidence: A review and synthesisPreprint
Fleming, S. M. (2023) · doi:10.31234/osf.io/ge7tz · Cited by 27 · see citing works
Towards characterizing the canonical computations generating phenomenal experience
Peters, M. A. K. (2022) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2022.104903 · Cited by 23 · see citing works
Self-citationIntrospective psychophysics for the study of subjective experience
Peters, M. A. K. (2024) · Cerebral Cortex · doi:10.1093/cercor/bhae455 · Cited by 11 · see citing works
Self-citationAutomatic multisensory integration follows subjective confidence rather than objective performance
Gao, Y., Xue, K., Odegaard, B., & Rahnev, D. (2025) · Communications Psychology · doi:10.1038/s44271-025-00221-w · Cited by 10 · see citing works
Metacognition of perceptual resolution across and around the visual field
Kim, C., & Chong, S. C. (2024) · Cognition · doi:10.1016/j.cognition.2024.105938 · Cited by 9 · see citing works
On why we lack confidence in some signal-detection-based analyses of confidence
Arnold, D. H., Johnston, A., Adie, J. M., & Yarrow, K. (2023) · Consciousness and Cognition · doi:10.1016/j.concog.2023.103532 · Cited by 7 · see citing works
The presence of irrelevant alternatives paradoxically increases confidence in perceptual decisions
Comay, N. A., Bella, G. A. D., Lamberti, P. W., Sigman, M., Solovey, G., & Barttfeld, P. (2023) · Cognition · doi:10.1016/j.cognition.2023.105377 · Cited by 6 · see citing works
Peripheral overconfidence in a scene categorization task
Sharvashidze, N., Toscani, M., & Valsecchi, M. (2025) · Journal of Vision · doi:10.1167/jov.25.10.2 · Cited by 3 · see citing works
Rong, Y., & Peters, M. A. K. (2023) · Open Mind · doi:10.1162/opmi_a_00100 · Cited by 3 · see citing works
Self-citationComay, N. A., Solovey, G., & Barttfeld, P. (2025) · Royal Society Open Science · doi:10.1098/rsos.231805
Confidence-accuracy dissociations in perceptual decision making
Rahnev, D. (2026) · Vision Research · doi:10.1016/j.visres.2026.108831 · Cited by 1 · see citing works
Rong, Y., & Peters, M. A. K. (2023) · doi:10.31234/osf.io/7pga2 · Cited by 1 · see citing works
Self-citationOn why we lack confidence in signal-detection-based analyses of confidencePreprint
Arnold, D. H., Johnston, A., Adie, J. M., & Yarrow, K. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.11.07.515537
Metacognition and consciousness
Peters, M. A. K. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-29258-3.00052-5
Self-citationHow Does Peripheral Vision Feel Rich? A Selective Review
Beatson, A., Metzger, A., & Toscani, M. (2026) · Journal of Eye Movement Research · doi:10.3390/jemr19050104
- Authors
- Winter, C. J., & Peters, M. A. K.
- Affiliations
- University of California, Irvine (US)
- Corresponding author
- Winter, Charles J.
- Peters, Megan A. K.
- ORCID
- Peters, Megan A. K.
- Venue
- Attention, Perception, & Psychophysics
- Published
- 2021-08-23
- Volume
- 84
- Issue
- 1
- Pages
- 161-178
- ISSN
- 1943-3921 (ISSN-L)
- 1943-393X
- OpenAlex
- W3195225214
- Cited by
- 17
- References
- 113
- Citations by year
- 2026: 3 · 2025: 3 · 2024: 2 · 2023: 6 · 2022: 2 · 2021: 1
- FWCI
- 1.45
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Attention Perception & Psychophysics (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Canadian Institute for Advanced Research
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
- Life Sciences › Neuroscience › Cognitive Neuroscience › Face Recognition and Perception
- Keywords
- overconfidence, metacognition, noise estimation, visual periphery, Bayesian model, visual attention
- MeSH terms
- Metacognition, Bayes Theorem, Cognition, Humans, Vision, Ocular, Visual Perception
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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