Typical versus atypical unpacking and superadditive probability judgment
Abstract
Probability judgments for packed descriptions of events (e.g., the probability that a businessman does business with a European country) are compared with judgments for unpacked descriptions of the same events (e.g., the probability that a businessman does business with England, France, or some other European country). The prediction that unpacking can decrease probability judgments, derived from the hypothesis that category descriptions are interpreted narrowly in terms of typical instances, is contrasted to the prediction of support theory that unpacking will generally increase judged probabilities (A. Tversky & D. J. Koehler, 1994). The authors varied the typicality of unpacked instances and found no effect of unpacking with typical instances (additivity) and a negative effect with atypical instances (superadditivity). Support theory cannot account for these findings in its current formulation.
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In the unpacking effect, participants judge a packed description (e.g., 'being a lawyer') to be less probable than an unpacked description (e.g., 'being a tax, corporate, patent, or other type of lawyer').
“In the unpacking effect, participants judge, say, 'being a lawyer', to be less probable than the 'unpacked' description 'being a tax, corporate, patent, or other type of lawyer' [44][45][46].”
From Bayesian Brains without Probabilities (2016) · cites this paper as [46]
If the unpacked hypotheses are low probability, the sampler is guided away from the peaks of probability, so the unpacked event is judged less probable than the packed event.
“if the unpacked hypotheses are low probability instead of high, for example trying to assess whether a person with a background in social activism becomes either 'a lawyer' or 'a tax, corporate, patent, or other type of lawyer' then the probability of the unpacked event is judged less than that of the packed event [46] the sampler is guided away from the peaks of probability”
From Bayesian Brains without Probabilities (2016) · cites this paper as [46]
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- Authors
- Sloman, S., Rottenstreich, Y., Wisniewski, E., Hadjichristidis, C., & Fox, C. R.
- Affiliations
- Brown University (US)
- University of Chicago (US)
- University of North Carolina at Greensboro (US)
- University of Plymouth (GB)
- University of California, Los Angeles (US)
- Venue
- Journal of Experimental Psychology Learning Memory and Cognition
- Published
- 2004
- Volume
- 30
- Issue
- 3
- Pages
- 573-582
- ISSN
- 0278-7393 (ISSN-L)
- 1939-1285
- OpenAlex
- W2100056117
- Cited by
- 102
- References
- 31
- Citations by year
- 2026: 1 · 2025: 4 · 2024: 3 · 2023: 7 · 2022: 5 · 2021: 9 · 2020: 6 · 2019: 6 · 2018: 7 · 2017: 6 · 2016: 4 · 2015: 4 · 2014: 2 · 2013: 3 · 2012: 3
- FWCI
- 2.90
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Experimental Psychology Learning Memory and Cognition (publishedVersion)
- PubMed (publishedVersion)
- Iris (University of Trento) (submittedVersion)
- 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
- probability judgment, unpacking, superadditivity, typicality, support theory, additivity
- MeSH terms
- Humans, Judgment, Models, Psychological, Probability, Psychological Theory
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