Variance misperception explains illusions of confidence in simple perceptual decisions
Open accessAbstract
Confidence in a perceptual decision is a judgment about the quality of the sensory evidence. The quality of the evidence depends not only on its strength ('signal') but critically on its reliability ('noise'), but the separate contribution of these quantities to the formation of confidence judgments has not been investigated before in the context of perceptual decisions. We studied subjective confidence reports in a multi-element perceptual task where evidence strength and reliability could be manipulated independently. Our results reveal a confidence paradox: confidence is higher for stimuli of lower reliability that are associated with a lower accuracy. We show that the subjects' overconfidence in trials with unreliable evidence is caused by a reduced sensitivity to stimulus variability. Our results bridge between the investigation of miss-attributions of confidence in behavioral economics and the domain of simple perceptual decisions amenable to neuroscience research.
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Studies have documented dissociations between confidence and accuracy as a function of attention, variability in perceptual evidence, asymmetries in processing supporting and disconfirming evidence, and response times.
“Within the field of metaperception research, studies have documented dissociations between confidence and accuracy as a function of attention (e.g., Wilimzig et al. 2008), variability in perceptual evidence (e.g., Spence et al. 2016, Zylberberg et al. 2014), asymmetries in the processing of supporting and disconfirming evidence (e.g., Miyoshi & Lau 2020, Zylberberg et al. 2012), and response times”
From Metacognition and Confidence: A Review and Synthesis (2024) · cites this paper as Zylberberg et al. 2014
Confidence judgments are sensitive to the variability in perceptual evidence, although sometimes to a greater or lesser degree than predicted by an ideal observer model.
“A number of studies have presented evidence that confidence judgments are sensitive to the variability in perceptual evidence, although sometimes to a greater or lesser degree than predicted by an ideal observer model (Boldt et al. 2017, Spence et al. 2016, Zylberberg et al. 2014).”
From Metacognition and Confidence: A Review and Synthesis (2024) · cites this paper as Zylberberg et al. 2014
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References 32 · Cited by 97
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- de Gardelle, V., & Summerfield, C. (2011). Robust averaging during perceptual judgment. Proceedings of the National Academy of Sciences of the United States of America, 108(32), 13341-13346.
- Gorea, A., & Sagi, D. (2000). Failure to handle more than one internal representation in visual detection tasks. Proceedings of the National Academy of Sciences of the United States of America, 97(22), 12380-12384.
- Griffin, Dale, & Tversky, Amos (1992). The weighing of evidence and the determinants of confidence. Cognitive Psychology, 24(3), 411-435.
- Hosmer, D. W., & Lemeshow, S. (2000). Applied logistic regression. New York: Wiley.
- Jarvstad, Andreas, Hahn, Ulrike, Rushton, Simon K., & Warren, Paul A. (2013). Perceptuo-motor, cognitive, and description-based decision-making seem equally good. Proceedings of the National Academy of Sciences, 110(40), 16271-16276.
- Juslin, P., & Olsson, H. (1997). Thurstonian and Brunswikian origins of uncertainty in judgment: A sampling model of confidence in sensory discrimination. Psychological Review, 104(2), 344.
- Kepecs, A., & Mainen, Z. F. (2012). A computational framework for the study of confidence in humans and animals. Philosophical Transactions of the Royal Society B: Biological Sciences, 367(1594), 1322-1337.
- Kepecs, A., Uchida, N., Zariwala, H. A., & Mainen, Z. F. (2008). Neural correlates, computation and behavioural impact of decision confidence. Nature, 455(7210), 227-231.
- Körding, Konrad P., & Wolpert, Daniel M. (2006). Bayesian decision theory in sensorimotor control. Trends in Cognitive Sciences, 10(7), 319-326.
- Lichtenstein, Sarah, Fischhoff, Baruch, & Phillips, Lawrence D. (1977). Calibration of probabilities: The state of the art. Springer.
- Nassar, Matthew R., Wilson, Robert C., Heasly, Benjamin., & Gold, Joshua I. (2010). An approximately Bayesian delta-rule model explains the dynamics of belief updating in a changing environment. The Journal of Neuroscience, 30(37), 12366-12378.
- Pitz, Gordon F. (1974). Subjective probability distributions for imperfectly known quantities. In L. W. Gregg (Ed.), Knowledge and cognition. New York: Wiley.
- Rahnev, D., Maniscalco, B., Graves, T., Huang, E., de Lange, F. P., & Lau, H. (2011a). Attention induces conservative subjective biases in visual perception. Nature Neuroscience, 14(12), 1513-1515.
- Rahnev, D. A., Maniscalco, B., Luber, B., Lau, H., & Lisanby, S. H. (2011b). Direct injection of noise to the visual cortex decreases accuracy but increases decision confidence. Journal of Neurophysiology, 107(6), 1556-1563.
- Rehder, Bob, & Hastie, Reid (1996). The moderating influence of variability on belief revision. Psychonomic Bulletin and Review, 3(4), 499-503.
- Slovic, Paul (1972). From Shakespeare to Simon: Speculations -and some evidence -about man's ability to process information. Oregon Research Institute Research Bulletin, 12(2).
- Slovic, Paul, & Lichtenstein, Sarah (1968). Importance of variance preferences in gambling decisions. Journal of Experimental Psychology, 78(4p1), 646.
- Tversky, Amos, & Kahneman, Daniel (1973). Availability: A heuristic for judging frequency and probability. Cognitive Psychology, 5(2), 207-232.
- Tversky, Amos, & Kahneman, Daniel (1974). Judgment under uncertainty: Heuristics and biases. Science, 185(4157), 1124-1131.
- Wald, A., & Wolfowitz, J. (1948). Optimum character of the sequential probability ratio test. The Annals of Mathematical Statistics, 19(3), 326-339.
- Wu, Shih-Wei, Delgado, Mauricio R., & Maloney, Laurence T. (2009). Economic decision-making compared with an equivalent motor task. Proceedings of the National Academy of Sciences, 106(15), 6088-6093.
- Yeung, N., & Summerfield, C. (2012). Metacognition in human decision-making: confidence and error monitoring. Philosophical Transactions of the Royal Society B: Biological Sciences, 367(1594), 1310-1321.
- Authors
- Zylberberg, A., Roelfsema, P. R., & Sigman, M.
- Affiliations
- Netherlands Institute for Neuroscience (NL)
- Consejo Nacional de Investigaciones Científicas y Técnicas (AR)
- Corresponding author
- Zylberberg, Ariel
- Venue
- Consciousness and Cognition
- Publisher
- Elsevier BV
- Published
- 2014-07
- Volume
- 27
- Pages
- 246-253
- ISSN
- 1053-8100 (ISSN-L)
- OpenAlex
- W1987074562
- Cited by
- 97
- References
- 32
- Citations by year
- 2026: 7 · 2025: 6 · 2024: 5 · 2023: 7 · 2022: 8 · 2021: 11 · 2020: 13 · 2019: 6 · 2018: 9 · 2017: 11 · 2016: 8 · 2015: 6
- FWCI
- 4.64
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Consciousness and Cognition (publishedVersion, cc-by)
- PubMed (publishedVersion)
- KNAW Research Portal (The Royal Netherlands Academy of Arts and Sciences) (publishedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- Pure Amsterdam UMC (submittedVersion)
- VU Research Portal (submittedVersion)
- CONICET Digital (CONICET) (submittedVersion, cc-by-nc-sa)
- Funders
- James S. McDonnell Foundation
- Consejo Nacional de Investigaciones Científicas y Técnicas
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Keywords
- confidence judgments, overconfidence, perceptual decision making, stimulus variability, reliability of evidence, signal-to-noise ratio, subjective confidence
- MeSH terms
- Adult, Decision Making, Humans, Illusions, Space Perception, Signal Detection, Psychological
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