Signatures of a Statistical Computation in the Human Sense of Confidence
Open accessAbstract
Human confidence judgments are thought to originate from metacognitive processes that provide a subjective assessment about one's beliefs. Alternatively, confidence is framed in mathematics as an objective statistical quantity: the probability that a chosen hypothesis is correct. Despite similar terminology, it remains unclear whether the subjective feeling of confidence is related to the objective, statistical computation of confidence. To address this, we collected confidence reports from humans performing perceptual and knowledge-based psychometric decision tasks. We observed two counterintuitive patterns relating confidence to choice and evidence: apparent overconfidence in choices based on uninformative evidence, and decreasing confidence with increasing evidence strength for erroneous choices. We show that these patterns lawfully arise from statistical confidence, and therefore occur even for perfectly calibrated confidence measures. Furthermore, statistical confidence quantitatively accounted for human confidence in our tasks without necessitating heuristic operations. Accordingly, we suggest that the human feeling of confidence originates from a mental computation of statistical confidence.
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Recent studies attempted to test the BCH but, because of their experimental designs, are unable to meaningfully distinguish the Bayesian model from any other model of confidence.
“Recent studies have attempted to test the BCH [19, 20] but, because of their experimental designs, are unable to meaningfully distinguish the Bayesian model from any other model of confidence.”
From Comparing Bayesian and non-Bayesian accounts of human confidence reports (2018) · cites this paper as [20]
The Discussion reports that the study compared human confidence with predictions from a statistical confidence model, supporting its identification as an attempt to test Bayesian confidence. It also notes that other algorithms can approximate Bayes-optimal predictions, but does not say the experimental design was unable to meaningfully distinguish the Bayesian model from any other confidence model. The cited passages have section labels but no page numbers. (LLM, conf=0.85)
Experimental — accuracy not yet measuredSubjects were not instructed to use the full range of confidence reports, as that might have biased them away from reporting what felt most natural.
“Subjects were not instructed to use the full range of confidence reports [20], as that might have biased them away from reporting what felt most natural.”
From Comparing Bayesian and non-Bayesian accounts of human confidence reports (2018) · cites this paper as [20]
The Discussion (page unavailable) says subjects selected how confident they felt on a 1-to-5 scale, with the aim of capturing that feeling directly. Neither that passage nor the Results passages on the confidence-report tasks say subjects were not instructed to use the full range or give the claimed reason for avoiding such an instruction. (LLM, conf=0.76)
Experimental — accuracy not yet measuredThe folded X pattern is seen in both human and animal confidence data.
“This pattern is seen in both human and animal confidence data (Sanders et al. 2016)”
From Metacognition and Confidence: A Review and Synthesis (2024) · cites this paper as Sanders et al. 2016
The Results section on auditory perceptual decisions reports the folded-X pattern in human data: confidence rose with evidence discriminability on correct trials and fell on errors. The paper's reported experiments used human subjects; the passages located do not establish that the pattern is also seen in animal confidence data. Page numbers are unavailable in our copy. (LLM, conf=0.86)
Experimental — accuracy not yet measuredSanders et al. [20] reported that confidence has a "statistical" nature.
“Sanders et al. [20] reported that confidence has a “statistical” nature.”
From Comparing Bayesian and non-Bayesian accounts of human confidence reports (2018) · cites this paper as [20]
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- Authors
- Sanders, J. I., Hangya, B., & Kepecs, A.
- Affiliations
- Cold Spring Harbor Laboratory (US)
- Venue
- Neuron
- Publisher
- Elsevier BV
- Published
- 2016-05
- Volume
- 90
- Issue
- 3
- Pages
- 499-506
- ISSN
- 0896-6273 (ISSN-L)
- PMC
- PMC5350614
- OpenAlex
- W2345995343
- Cited by
- 253
- References
- 11
- Citations by year
- 2026: 13 · 2025: 30 · 2024: 24 · 2023: 32 · 2022: 29 · 2021: 25 · 2020: 33 · 2019: 21 · 2018: 23 · 2017: 19 · 2016: 4
- FWCI
- 19.37
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Neuron (publishedVersion)
- PubMed (publishedVersion)
- Repository of the Academy's Library (Library of the Hungarian Academy of Sciences) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Cold Spring Harbor Laboratory Institutional Repository (Cold Spring Harbor Laboratory) (acceptedVersion)
- Funders
- National Institute of Mental Health (R01MH097061)
- Topics
- Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory Processes and Influences
- Keywords
- confidence judgments, metacognition, statistical confidence, overconfidence, calibration, quality of evidence, perceptual decision making, knowledge-based decision making, metacognitive confidence
- MeSH terms
- Metacognition, Choice Behavior, Decision Making, Humans, Judgment, Predictive Value of Tests, Sensation
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