Perceptual Asymmetry Between Hue Categories: Evidence from Human Color Categorization

TL;DR

This study reveals perceptual asymmetry in human color categories, showing that some like yellow are narrowly defined while others like green are broader, challenging uniform color models.

Contribution

It extends the COLIBRI fuzzy color model to quantify and analyze perceptual asymmetry between hue categories using large-scale human data.

Findings

Yellow is a compact, sharply constrained hue category.

Green spans a broader, more extended hue region.

Color categories are highly non-uniform and fuzzy in perceptual space.

Abstract

Human color categories are not uniformly distributed in perceptual space, yet most computational color models still assume fixed and evenly structured representations. In this paper, we present a focused analytical extension of the COLIBRI fuzzy color model by investigating perceptual asymmetry between hue categories. Using previously collected large-scale human color categorization data, we introduce quantitative measures of category extent and boundary uncertainty, namely Wideness and Boundary Width, derived from fuzzy membership functions at the {\alpha} = 0.5 level. The analysis reveals a strong imbalance between the two categories: yellow occupies a compact and sharply constrained region of the hue space, whereas green spans a substantially broader interval and exhibits a more extended transition structure. The results show that perceptual color categories are not only fuzzy, but also highly non-uniform in their geometric organization. This asymmetry suggests that some categories behave as narrow, highly specific perceptual labels, while others function as broad, tolerant regions of human color naming. These findings provide a new perspective on linguistic color categorization and extend the interpretability of the COLIBRI framework for perceptually grounded color modeling.