ArticleJournal of experimental psychology. General2024
How do humans give confidence? A comprehensive comparison of process models of perceptual metacognition.
Article in Journal of experimental psychology. General, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed.
- Cognitive Control and Metacognition: The Effect of Interference, Working-Memory Load, and Shifting on Confidence in Response Correctness and Partial Error.Brain sciences · 2026Article
- Prior Expectations Bias Confidence Judgments Through Parietal Alpha-Band Modulation.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Stimulus reliability but not boundary distance manipulations violate the folded-X pattern of confidence.Cognition · 2026Article
- dynConfiR: An R package for sequential sampling models of decision confidence.Behavior research methods · 2026Article
- Type-1 and type-2 decisions feature computational noise of similar magnitude.Communications psychology · 2026Article
- Visual confidence accurately tracks increasing internal noise with eccentricity in peripheral vision.bioRxiv : the preprint server for biology · 2026Article
- Response Time as a Proxy for Decision Confidence: Insights From Type-2 ROC Analysis.Open mind : discoveries in cognitive science · 2026Article
- Confidence in naturalistic decision making.Neuroscience of consciousness · 2026Article
- Neural basis of concurrent deliberation toward a choice and confidence judgment.Nature neuroscience · 2026Article
- Using live-action 360-degree video to assess the impact of exposure duration on eyewitness identification accuracy at high confidence in children and adults.Cognitive research: principles and implications · 2025Article
- Using artificial neural networks to reveal the human confidence computation.PLoS computational biology · 2025Article
- Bits of confidence: Metacognition as uncertainty reduction.Psychonomic bulletin & review · 2025Review
- How disconfirmatory evidence shapes confidence in decision-making.Communications psychology · 2025Article
- Computational characterization of metacognitive ability in subjective decision-making.bioRxiv : the preprint server for biology · 2025Article
- Neural basis of concurrent deliberation toward a choice and confidence judgment.bioRxiv : the preprint server for biology · 2025Article
- The confidence-noise confidence-boost (CNCB) model of confidence rating data.PLoS computational biology · 2025Article
- Probing the origins of subjective confidence in source memory decisions in young and older adults: A sequential sampling account.Journal of experimental psychology. General · 2025Article
- A comprehensive assessment of current methods for measuring metacognition.Nature communications · 2025Article
- Information-Theoretic Measures of Metacognition: Bounds and Relation to Group Performance.Open mind : discoveries in cognitive science · 2025Article
- Multifaceted confidence in exploratory choice.PloS one · 2025Article
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2 authors.
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Abstract
Humans have the metacognitive ability to assess the accuracy of their decisions via confidence judgments. Several computational models of confidence have been developed but not enough has been done to compare these models, making it difficult to adjudicate between them. Here, we compare 14 popular models of confidence that make various assumptions, such as confidence being derived from postdecisional evidence, from positive (decision-congruent) evidence, from posterior probability computations, or from a separate decision-making system for metacognitive judgments. We fit all models to three large experiments in which subjects completed a basic perceptual task with confidence ratings. In Experiments 1 and 2, the best-fitting model was the lognormal meta noise (LogN) model, which postulates that confidence is selectively corrupted by signal-dependent noise. However, in Experiment 3, the positive evidence (PE) model provided the best fits. We evaluated a new model combining the two consistently best-performing models-LogN and the weighted evidence and visibility (WEV). The resulting model, which we call logWEV, outperformed its individual counterparts and the PE model across all data sets, offering a better, more generalizable explanation for these data. Parameter and model recovery analyses showed mostly good recoverability but with important exceptions carrying implications for our ability to discriminate between models. Finally, we evaluated each model's ability to explain different patterns in the data, which led to additional insight into their performances. These results comprehensively characterize the relative adequacy of current confidence models to fit data from basic perceptual tasks and highlight the most plausible mechanisms underlying confidence generation. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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