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Differences in color categorization manifested by males and females: a quantitative World Color Survey study

Author

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  • Nicole A. Fider

    (University of California, Irvine)

  • Natalia L. Komarova

    (University of California, Irvine)

Abstract

Gender-related differences in human color preferences, color perception, and color lexicon have been reported in the literature over several decades. This work focuses on the way the two genders categorize color stimuli. Using the cross-cultural data from the World Color Survey (WCS) and rigorous mathematical methodology, a function is constructed, which measures the differences in color categorization systems manifested by men and women. A significant number of cases are identified, where men and women exhibit markedly disparate behavior. Interestingly, of the regions in the Munsell color array, the green-blue (“grue”) region appears to be associated with the largest group of categorization differences, with females revealing a more differentiated color categorization pattern compared to males. More precisely, in those cases, females tend to use separate green and/or blue categories, while males predominantly use the grue category. In general, the cases singled out by our method warrant a closer study, as they may indicate a transitional categorization scheme.

Suggested Citation

  • Nicole A. Fider & Natalia L. Komarova, 2019. "Differences in color categorization manifested by males and females: a quantitative World Color Survey study," Palgrave Communications, Palgrave Macmillan, vol. 5(1), pages 1-10, December.
    • Handle: RePEc:pal:palcom:v:5:y:2019:i:1:d:10.1057_s41599-019-0341-7
    DOI: 10.1057/s41599-019-0341-7
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    Cited by:

    1. Tianyi Zhang, 2024. "Gender Bias and Emotion Reflected Upon The Use of Basic Colorterms: A case Study of Little Women and David Copperfield," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 8(7), pages 1688-1703, July.
    2. Joe, Kirbi & Gooyabadi, Maryam, 2021. "A Bayesian nonparametric mixture model for studying universal patterns in color naming," Applied Mathematics and Computation, Elsevier, vol. 395(C).

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