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Cited by 1,069Categorization, perception, and languageBayesian Modeling and Causal InferenceDecision-Making and Behavioral Economics

The adaptive nature of human categorization.

Anderson, John R.·1991·Psychological Review
DOI 10.1037/0033-295x.98.3.409
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Abstract

A rational model of human categorization behavior is presented that assumes that categorization reflects the derivation of optimal estimates of the probability of unseen features of objects. A Bayesian analysis is performed of what optimal estimations would be if categories formed a disjoint partitioning of the object space and if features were independently displayed within a category. This Bayesian analysis is placed within an incremental categorization algorithm. The resulting rational model accounts for effects of central tendency of categories, effects of specific instances, learning of linearly nonseparable categories, effects of category labels, extraction of basic level categories, base-rate effects, probability matching in categorization, and trial-by-trial learning functions. Al-though the rational model considers just I level of categorization, it is shown how predictions can be enhanced by considering higher and lower levels. Considering prediction at the lower, individual level allows integration of this rational analysis of categorization with the earlier rational analysis of memory (Anderson & Milson, 1989). Anderson (1990) presented a rational analysis ot 6 human cog-nition. The term rational derives from similar "rational-man" analyses in economics. Rational analyses in other fields are sometimes called adaptationist analyses. Basically, they are ef-forts to explain the behavior in some domain on the assump-tion that the behavior is optimized with respect to some criteria of adaptive importance. This article begins with a general char-acterization ofhow one develops a rational theory of a particu-lar cognitive phenomenon. Then I present the basic theory of categorization developed in Anderson (1990) and review the applications from that book. Since the writing of the book, the theory has been greatly extended and applied to many new phenomena. Most of this article describes these new develop-ments and applications. A Rational Analysis Several theorists have promoted the idea that psychologists might understand human behavior by assuming it is adapted to the environment (e.g., Brunswik, 1956; Campbell, 1974; Gib-

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  • Incoming visual information is compared to previously stored knowledge about a finite set of possible diagnoses in a Bayesian-like procedure that revises the endoscopist's confidence toward those diagnoses.

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    From Experimental evidence of effective human–AI collaboration in medical decision-making (2022) · cites this paper as [26]

  • The endoscopist diagnosis can be modeled as a psychological categorization process.

    “We model the endoscopist diagnosis as a psychological categorization process25,26.”

    From Experimental evidence of effective human–AI collaboration in medical decision-making (2022) · cites this paper as [26]

  • Explicitly representing and calculating probabilities in complex domains such as vision and language is impossible for any physical system, including brains.

    “such calculations are too complex for any physical system, including brains, to perform in even moderately complex domains [4,[28][29][30].”

    From Bayesian Brains without Probabilities (2016) · cites this paper as [4]

  • Bayesian models of cognition have primarily flourished as explanations of the complex dependency of behavior on the environment in domains including intuitive physics, causal learning, perception, motor control, and language.

    “Bayesian models of cognition have primarily flourished as explanations of the complex dependency of behavior on the environment in domains including intuitive physics, causal learning, perception, motor control, and language [1-10]”

    From Bayesian Brains without Probabilities (2016) · cites this paper as [4]

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Authors
Anderson, J. R.
Corresponding author
Anderson, John R.
ORCID
Anderson, John R.
Venue
Psychological Review
Publisher
American Psychological Association (APA)
Published
1991-07
Volume
98
Issue
3
Pages
409-429
ISSN
  • 0033-295X (ISSN-L)
  • 1939-1471
DOI
10.1037/0033-295x.98.3.409
OpenAlex
W2154164802
Cited by
1,069
References
53
Citations by year
2026: 15 · 2025: 28 · 2024: 21 · 2023: 39 · 2022: 74 · 2021: 42 · 2020: 36 · 2019: 38 · 2018: 32 · 2017: 30 · 2016: 43 · 2015: 45 · 2014: 63 · 2013: 43 · 2012: 44
FWCI
18.11
Type
journal article
Language
en
Open access
closed
Locations
Psychological Review (publishedVersion)
Topics
  • Social Sciences › Psychology › Experimental and Cognitive Psychology › Categorization, perception, and language
  • Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
  • Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
Keywords
human categorization, rational analysis, Bayesian estimation, basic-level categorization, probability matching, category learning, category labels, trial-by-trial learning

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