The DOI matches its Crossref record; 61 of the 61 references it cites were found in a registry or an index.
The role of causality in judgment under uncertainty
Abstract
Leading accounts of judgment under uncertainty evaluate performance within purely statistical frameworks, holding people to the standards of classical Bayesian (A. Tversky & D. Kahneman, 1974) or frequentist (G. Gigerenzer & U. Hoffrage, 1995) norms. The authors argue that these frameworks have limited ability to explain the success and flexibility of people's real-world judgments and propose an alternative normative framework based on Bayesian inferences over causal models. Deviations from traditional norms of judgment, such as base-rate neglect, may then be explained in terms of a mismatch between the statistics given to people and the causal models they intuitively construct to support probabilistic reasoning. Four experiments show that when a clear mapping can be established from given statistics to the parameters of an intuitive causal model, people are more likely to use the statistics appropriately, and that when the classical and causal Bayesian norms differ in their prescriptions, people's judgments are more consistent with causal Bayesian norms.
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“accuracy is improved in base-rate neglect studies when a causal link is provided between the individual events [55,56], and experiencing outcomes together weakens conjunction fallacies [57]”
From Bayesian Brains without Probabilities (2016) · cites this paper as [56]
Citations
Cites
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Using Bayesian Networks to Analyze Expression Data
Friedman, N., Michal Linial, B., Nachman, I., & Pe’er, D. (2000) · Journal of Computational Biology · doi:10.1089/106652700750050961 · Cited by 3,248
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Cited by
Sorted by citations
How to grow a mind: Statistics, structure, and abstraction
Tenenbaum, J. B., Kemp, C., Griffiths, T. L., & Goodman, N. D. (2011) · Science · doi:10.1126/science.1192788 · Cited by 1,815
Self-citationWhat do we need to build explainable AI systems for the medical domain?
Holzinger, A., Biemann, C., Pattichis, C. S., & Kell, D. B. (2017) · arXiv · doi:10.48550/arxiv.1712.09923 · Cited by 636
Bayesian Brains without Probabilities
Sanborn, A. N., & Chater, N. (2016) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2016.10.003 · Cited by 307
Royce, C. S., Hayes, M. M., & Schwartzstein, R. M. (2019) · Academic Medicine · doi:10.1097/acm.0000000000002518 · Cited by 242
Controlling uncertainty: A review of human behavior in complex dynamic environments.
Osman, M. (2010) · Psychological Bulletin · doi:10.1037/a0017815 · Cited by 266
Metadata
- Authors
- Krynski, T. R., & Tenenbaum, J. B.
- Affiliations
- Massachusetts Institute of Technology (US)
- ORCID
- Tenenbaum, Joshua B.
- Venue
- Journal of Experimental Psychology General
- Published
- 2007
- Volume
- 136
- Issue
- 3
- Pages
- 430-450
- ISSN
- 0096-3445 (ISSN-L)
- 1939-2222
- OpenAlex
- W2000020549
- Cited by
- 183
- References
- 61
- Citations by year
- 2026: 5 · 2025: 6 · 2024: 10 · 2023: 3 · 2022: 8 · 2021: 8 · 2020: 8 · 2019: 20 · 2018: 12 · 2017: 9 · 2016: 9 · 2015: 15 · 2014: 17 · 2013: 9 · 2012: 12
- FWCI
- 7.89
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Experimental Psychology General (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Science Foundation
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
- Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
- Health Sciences › Medicine › Family Practice › Clinical Reasoning and Diagnostic Skills
- Keywords
- judgment under uncertainty, causal models, Bayesian inference, base rate neglect, probabilistic reasoning, statistical reasoning
- MeSH terms
- Bayes Theorem, Breast Neoplasms, Diagnosis, Differential, False Positive Reactions, Female, Humans, Judgment, Mammography, Middle Aged, Probability Learning, Models, Statistical, Causality, Likelihood Functions, Intuition, Uncertainty
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