Linda is not a bearded lady: configural weighting and adding as the cause of extension errors
Abstract
This article explores the configural weighted average (CWA) hypothesis suggesting that extension biases, like conjunction and disjunction errors, occur because people estimate compound probabilities by taking a CWA of the constituent probabilities. The hypothesis suggests a process consistent with well-known cognitive constraints, which nonetheless achieves high robustness and bounded rationality in noisy real-life environments. Predictions by the CWA hypothesis are that in error-free data, conjunction and disjunction errors should be the rule rather than the exception when pairs of statements are randomly sampled from an environment, the rate of extension errors should increase when noise in data is decreased, and that adding a likely component should increase the probability of a conjunction. Four experiments generally verify the predictions by the hypothesis, demonstrating that extension errors are frequent also when tasks are selected according to representative design.
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In these cases, participants are left to combine non-normalized sampled probability estimates for each hypothesis manually, and perhaps choose just one or combine them inappropriately.
“participants are left to combine the non-normalized sampled probability estimates for each hypothesis 'manually', and perhaps choose just one of the probabilities or perhaps combine them inappropriately [52][53][54].”
From Bayesian Brains without Probabilities (2016) · cites this paper as [54]
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Lindskog, M. (2014) · Quarterly Journal of Experimental Psychology · doi:10.1080/17470218.2014.959534 · Cited by 3 · see citing works
Maguire, P., Moser, P., Maguire, R., & Keane, M. T. (2018) · Frontiers in Psychology · doi:10.3389/fpsyg.2018.01011 · Cited by 3 · see citing works
Overextension in verb conjunctions.
Jönsson, M. L. (2015) · Journal of Experimental Psychology Learning Memory and Cognition · doi:10.1037/xlm0000131 · Cited by 2 · see citing works
Andersson, P., & Nilsson, H. (2015) · Journal of Behavioral Decision Making · doi:10.1002/bdm.1851 · Cited by 2 · see citing works
Self-citationAbout Linda Again: How Narratives and Group Reasoning Can Influence Conjunction Fallacy
Donati, C., Guazzini, A., Gronchi, G., & Smorti, A. (2019) · Future Internet · doi:10.3390/fi11100210 · Cited by 2 · see citing works
Novel Data Science Approach for Emotion Analytics: from Machine Learning to Quantum Cognition
Rika, H., Itzhak, A., & Bertha, A. (2023) · Soft Computing Research Society eBooks · doi:10.52458/978-81-955020-5-9-68 · Cited by 2 · see citing works
Models of human probability judgment errors
Huang, J. A., & Busemeyer, J. R. (2025) · Journal of Mathematical Psychology · doi:10.1016/j.jmp.2025.102906 · Cited by 3 · see citing works
An evaluation of the role of inductive confirmation in relation to the conjunction fallacy
Fisk, J. E., Marshall, D. A., Rogers, P., & Stock, R. (2023) · Journal of Cognitive Psychology · doi:10.1080/20445911.2023.2182181 · Cited by 1 · see citing works
Nahinsky, I. D. (2017) · The American Journal of Psychology · doi:10.5406/amerjpsyc.130.2.0201 · Cited by 1 · see citing works
Une nouvelle approche expérimentale pour tester les modèles quantiques de l’erreur de conjonction
Duchêne, S., Boyer‐Kassem, T., & Guerci, É. (2017) · Revue économique · doi:10.3917/reco.pr3.0096 · Cited by 1 · see citing works
An account of subjective probability judgment for joint events: Conjunctive and disjunctive
Fisk, J. E., Marshall, D. A., Rogers, P., & Stock, R. (2019) · Scandinavian Journal of Psychology · doi:10.1111/sjop.12560 · Cited by 1 · see citing works
Review: Models of Human Probability Judgment ErrorsPreprint
Huang, J. A. (2023) · doi:10.31234/osf.io/xbwng · Cited by 1 · see citing works
Noise in Reasoning as a Cause of the Conjunction Fallacy.
Howe, R., & Costello, F. (2016) · AICS
Olsson, H. (2013) · doi:10.1093/obo/9780199828340-0133
Are People Naive Probability Theorists: An Examination of the Probability Theory + Variation Model
Fisher, C. R., & Wolfe, C. R. (2013) · PsycEXTRA Dataset · doi:10.1037/e513702014-074
What Can the Conjunction Fallacy Tell Us about Human Reasoning?
Tentori, K. (2021) · Oxford University Press eBooks · doi:10.1093/oso/9780198862536.003.0022
Probabilistic Reasoning with Transformer Networks: An Application to Predicate JudgmentPreprint
Bhatia, S. (2022) · doi:10.31234/osf.io/s3d74
Nahinsky, I. D. (2023) · The American Journal of Psychology · doi:10.5406/19398298.136.3.02
Liu, X., Bröder, A., & Singmann, H. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4880396
Coherence and the Conjunction Fallacy
Snell, A., Burt, M., & Zuckerman, M. (2025) · Personality and Social Psychology Bulletin · doi:10.1177/01461672251335810
Liu, X., Bröder, A., & Singmann, H. (2025) · Cognition · doi:10.1016/j.cognition.2025.106125
Random variation and systematic biases in probability estimationPreprint
Costello, F., & Howe, R. (2020) · OSF Preprints (OSF Preprints)
- Authors
- Nilsson, H., Winman, A., Juslin, P., & Hansson, G.
- Affiliations
- Uppsala University (SE)
- Venue
- Journal of Experimental Psychology General
- Published
- 2009
- Volume
- 138
- Issue
- 4
- Pages
- 517-534
- ISSN
- 0096-3445 (ISSN-L)
- 1939-2222
- OpenAlex
- W2015527652
- Cited by
- 64
- References
- 86
- Citations by year
- 2025: 4 · 2024: 2 · 2023: 6 · 2022: 2 · 2021: 5 · 2020: 3 · 2019: 4 · 2018: 2 · 2017: 5 · 2016: 6 · 2015: 7 · 2014: 5 · 2013: 5 · 2012: 2
- FWCI
- 1.42
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Experimental Psychology General (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Swedish Research Council
- Topics
- Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Categorization, perception, and language
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
- Keywords
- conjunction errors, Linda problem, bounded rationality, noisy data, representative design, probabilistic reasoning
- MeSH terms
- Adult, Cognition, Female, Humans, Judgment, Male, Probability, Psychological Theory, Social Behavior, Students, Task Performance and Analysis, 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.
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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