发现人眼对颜色的分类存在不对称，黄比绿更集中。
Perceptual Asymmetry Between Hue Categories: Evidence from Human Color Categorization
- 用模糊隶属度分析人类颜色分类数据，量化类别范围与边界模糊度。
- 黄色区域紧凑清晰，绿色范围宽广且过渡模糊，差异显著。
- 为颜色建模提供新视角，适合关注感知一致性的人工智能研究者。
人类颜色范畴在感知空间中并非均匀分布，但多数计算颜色模型仍假设固定且均匀的结构。本文通过对COLIBRI模糊颜色模型进行聚焦分析，研究色相范畴间的感知不对称性。基于已有大规模人类颜色分类数据，引入定量指标Wideness（宽度）与Boundary Width（边界宽度），源自α=0.5水平下的模糊隶属函数。分析显示：黄色在色相空间中占据紧凑且边界清晰的区域，而绿色则覆盖更广区间，具有更长的过渡结构。结果表明，感知颜色范畴不仅模糊，且几何组织高度非均匀。这种不对称性说明某些范畴是窄而精确的感知标签，另一些则是宽泛容错的命名区域。研究为语言色彩分类提供了新视角，并拓展了COLIBRI框架在感知基础颜色建模中的可解释性。
原文摘要 · Abstract (English)
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 α = 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.
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