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Minimization of Boolean complexity in human concept learning

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  • Jacob Feldman

    (Center for Cognitive Science, Rutgers University)

Abstract

One of the unsolved problems in the field of human concept learning concerns the factors that determine the subjective difficulty of concepts: why are some concepts psychologically simple and easy to learn, while others seem difficult, complex or incoherent? This question was much studied in the 1960s1 but was never answered, and more recent characterizations of concepts as prototypes rather than logical rules2,3 leave it unsolved4,5,6. Here I investigate this question in the domain of Boolean concepts (categories defined by logical rules). A series of experiments measured the subjective difficulty of a wide range of logical varieties of concepts (41 mathematically distinct types in six families—a far wider range than has been tested previously). The data reveal a surprisingly simple empirical ‘law’: the subjective difficulty of a concept is directly proportional to its Boolean complexity (the length of the shortest logically equivalent propositional formula)—that is, to its logical incompressibility.

Suggested Citation

  • Jacob Feldman, 2000. "Minimization of Boolean complexity in human concept learning," Nature, Nature, vol. 407(6804), pages 630-633, October.
    • Handle: RePEc:nat:nature:v:407:y:2000:i:6804:d:10.1038_35036586
    DOI: 10.1038/35036586
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    Cited by:

    1. Andrés Rieznik & Lorena Moscovich & Alan Frieiro & Julieta Figini & Rodrigo Catalano & Juan Manuel Garrido & Facundo Álvarez Heduan & Mariano Sigman & Pablo A Gonzalez, 2017. "A massive experiment on choice blindness in political decisions: Confidence, confabulation, and unconscious detection of self-deception," PLOS ONE, Public Library of Science, vol. 12(2), pages 1-16, February.
    2. J. Gerard Wolff, 2019. "Information Compression as a Unifying Principle in Human Learning, Perception, and Cognition," Complexity, Hindawi, vol. 2019, pages 1-38, February.
    3. Mi, Yunlong & Wang, Zongrun & Quan, Pei & Shi, Yong, 2024. "A semi-supervised concept-cognitive computing system for dynamic classification decision making with limited feedback information," European Journal of Operational Research, Elsevier, vol. 315(3), pages 1123-1138.
    4. Theocharis, Zoe & Harvey, Nigel, 2019. "When does more mean worse? Accuracy of judgmental forecasting is nonlinearly related to length of data series," Omega, Elsevier, vol. 87(C), pages 10-19.
    5. Joshua S. Rule & Steven T. Piantadosi & Andrew Cropper & Kevin Ellis & Maxwell Nye & Joshua B. Tenenbaum, 2024. "Symbolic metaprogram search improves learning efficiency and explains rule learning in humans," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    6. J David Smith & Shawn W Ell, 2015. "One Giant Leap for Categorizers: One Small Step for Categorization Theory," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-22, September.

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