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Related Experiment Video

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Categorical colour geometry.

Lewis D Griffin1, Dimitris Mylonas1

  • 1Computer Science, UCL, London, United Kingdom.

Plos One
|May 11, 2019
PubMed
Summary

Researchers developed a new color metric based on how people name colors, not just how they see them. This "categorical metric" reveals distinct color regions, estimating 27 such areas in the RGB color space.

Area of Science:

  • Color Science
  • Information Geometry
  • Human Perception

Background:

  • Ordinary language users categorize colors using names (e.g., pale green).
  • Previous color metrics relied on stimulus discriminability or color match acceptability.
  • A gap existed in quantifying color perception based on linguistic categorization.

Purpose of the Study:

  • To compute a novel color metric based on linguistic categorization data.
  • To analyze the properties of this new categorical color metric.
  • To estimate the number of distinct color categories within the RGB color space.

Main Methods:

  • Collected color categorization data from 1,000 English speakers via online crowdsourcing.
  • Generated 20,000 unconstrained color names for 600 color stimuli.

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  • Applied Information Geometry to compute a Riemannian metric from the naming data.

    • Main Results:

    • Developed the first color metric derived from linguistic categorization data.
    • The computed categorical metric differs significantly from discriminability-based metrics.
    • Derived natural units of categorical length, area, and volume.
    • Estimated 27 categorically-distinct color regions within the RGB cube.

    Conclusions:

    • The new categorical color metric provides a novel way to understand color space.
    • The findings align with previous estimates of the number of nameable colors.
    • This approach offers insights into the relationship between language, perception, and color representation.