IDEAS home Printed from https://ideas.repec.org/a/nat/nature/v407y2000i6804d10.1038_35036586.html
   My bibliography  Save this article

Minimization of Boolean complexity in human concept learning

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/35036586
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1038/35036586?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. 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.
    3. 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.
    4. J. Gerard Wolff, 2019. "Information Compression as a Unifying Principle in Human Learning, Perception, and Cognition," Complexity, Hindawi, vol. 2019, pages 1-38, February.
    5. 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.
    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.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:nature:v:407:y:2000:i:6804:d:10.1038_35036586. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.