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arxiv: 2605.09339 · v1 · submitted 2026-05-10 · 💻 cs.CV · cs.AI

Perceptual Asymmetry Between Hue Categories: Evidence from Human Color Categorization

Elnara Kadyrgali , Nuray Toganas , Muragul Muratbekova , Pakizar Shamoi This is my paper

Pith reviewed 2026-05-12 03:53 UTC · model grok-4.3

classification 💻 cs.CV cs.AI
keywords color categorizationperceptual asymmetryhue categoriesfuzzy membershiphuman color namingcategory extentboundary uncertaintyyellow and green
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The pith

Yellow occupies a compact sharp region in hue space while green spans a much broader interval with extended transitions.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper examines whether human color categories are evenly structured in perceptual space or display asymmetries in their extent and boundary sharpness. It applies two new quantitative measures derived from fuzzy membership functions to large-scale human color naming data. The analysis finds yellow tightly constrained and green much wider with fuzzier edges. A reader would care because this challenges the common assumption of uniform category geometry in computational models of color. If the finding holds, it means some color terms act as precise labels while others tolerate wide variation in human perception.

Core claim

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.

What carries the argument

Wideness and Boundary Width measures obtained from the α = 0.5 level sets of fuzzy membership functions, which quantify category extent and transition uncertainty across hue space.

If this is right

  • Perceptual color categories are highly non-uniform in their geometric organization rather than evenly distributed.
  • Some categories serve as narrow and specific perceptual labels while others act as broad and tolerant naming regions.
  • Computational color models that assume fixed and uniform representations will miss important structure in human data.
  • Linguistic color categorization reflects these geometric differences between categories.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The asymmetry may arise from differences in how the visual system samples different regions of the spectrum.
  • Similar measures applied to other hue pairs or to data from additional languages could reveal whether the pattern is general.
  • Color interfaces in vision systems or design tools could improve accuracy by assigning different tolerance levels to different categories.
  • The non-uniformity might help explain why some color terms appear more stable across cultures than others.

Load-bearing premise

That the α = 0.5 level of the fuzzy membership functions directly and accurately reflects the true extents and boundaries of human perceptual color categories.

What would settle it

A new set of human psychophysical experiments that directly measure the perceptual range and transition zones for yellow and green hues and then compare those measurements to the wideness and boundary width values computed from the existing data.

Figures

Figures reproduced from arXiv: 2605.09339 by Elnara Kadyrgali, Muragul Muratbekova, Nuray Toganas, Pakizar Shamoi.

Figure 1
Figure 1. Figure 1: Overview of the COLIBRI Fuzzy Color Modeling Framework [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Fuzzy representation of hue categories with [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Hue spectrum with category boundaries defined by the [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 2 minor

Summary. The paper extends the COLIBRI fuzzy color model by applying it to large-scale human color naming data. It defines two quantitative metrics—Wideness and Boundary Width—extracted from the fuzzy membership functions at the fixed α = 0.5 contour and reports a clear asymmetry: the yellow category occupies a compact, sharply delimited region of hue space while the green category spans a substantially wider interval with a more gradual transition zone. The central claim is that this demonstrates non-uniform geometric organization of perceptual color categories.

Significance. If the asymmetry is shown to be robust to the modeling choices, the result would usefully extend the interpretability of the COLIBRI framework and supply concrete evidence against the common assumption of uniform category structure in computational color models. The use of previously collected large-scale human data is a positive feature; however, the absence of any sensitivity or validation checks on the α = 0.5 threshold substantially reduces the immediate impact.

major comments (1)
  1. [§3] §3 (definition of Wideness and Boundary Width): the metrics are computed exclusively at the α = 0.5 level of the COLIBRI fuzzy memberships without any sensitivity analysis across other contour values or comparison to independent perceptual measures such as discrimination thresholds or naming-consistency data on the same stimuli. Because the reported yellow–green imbalance is defined by these quantities, the central claim that the asymmetry reflects human perceptual organization rather than a modeling artifact rests on an untested assumption.
minor comments (2)
  1. [Abstract] The abstract states the observed imbalance but supplies no sample sizes, statistical tests, or uncertainty estimates; these details should be added so readers can immediately gauge the strength of the reported difference.
  2. [§3] Notation for the fuzzy membership functions and the precise formulas for Wideness and Boundary Width should be written out explicitly (ideally with an equation) rather than described only in prose.

Simulated Author's Rebuttal

1 responses · 1 unresolved

We thank the referee for the constructive feedback. We address the major comment on the α = 0.5 threshold below and will incorporate additional analysis in revision.

read point-by-point responses

  1. Referee: [§3] §3 (definition of Wideness and Boundary Width): the metrics are computed exclusively at the α = 0.5 level of the COLIBRI fuzzy memberships without any sensitivity analysis across other contour values or comparison to independent perceptual measures such as discrimination thresholds or naming-consistency data on the same stimuli. Because the reported yellow–green imbalance is defined by these quantities, the central claim that the asymmetry reflects human perceptual organization rather than a modeling artifact rests on an untested assumption.

    Authors: We selected α = 0.5 because it is the conventional half-membership threshold in fuzzy set theory and directly corresponds to the core region of each category in the COLIBRI model. This choice provides a consistent, interpretable definition of category extent and boundary across hues. We agree that robustness checks are needed. In the revised version we will add a sensitivity analysis computing Wideness and Boundary Width over α ∈ [0.4, 0.6] and demonstrate that the yellow–green asymmetry remains stable. Direct comparison to discrimination thresholds or naming-consistency data on the identical stimuli is not possible with the existing dataset; such validation would require new psychophysical experiments, which we will explicitly list as future work. revision: partial

standing simulated objections not resolved
  • Comparison to independent perceptual measures (discrimination thresholds or naming-consistency data) on the same stimuli, which would require new data collection beyond the scope of the current study.

Circularity Check

0 steps flagged

Minor self-citation to COLIBRI model; central metrics derived from external data without reduction to fitted parameters

full rationale

The paper computes Wideness and Boundary Width directly from the α=0.5 contours of fuzzy membership functions obtained by applying the prior COLIBRI model to independent large-scale human color naming data. No equation or step within the manuscript defines a quantity in terms of itself, renames a fitted parameter as a prediction, or imports a uniqueness result solely via self-citation that then forces the asymmetry result. The reported yellow-green imbalance therefore remains a straightforward geometric summary of the model outputs on external data rather than a self-referential construction. Self-citation to the COLIBRI framework exists but is not load-bearing for the central claim.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the assumption that fuzzy membership functions at a fixed alpha level faithfully encode perceptual extent and uncertainty, plus the representativeness of the prior human dataset; no new free parameters or invented entities are introduced in the abstract.

axioms (1)
  • domain assumption Fuzzy membership functions at alpha=0.5 level accurately represent human perceptual category boundaries and extents.
    Invoked when defining Wideness and Boundary Width from the COLIBRI model.

pith-pipeline@v0.9.0 · 5499 in / 1205 out tokens · 41082 ms · 2026-05-12T03:53:08.767743+00:00 · methodology

discussion (0)

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