A Quantitative Theory of Human Color Choices

Abstract

The system for colorimetry adopted by the Commission Internationale de l’Eclairage (CIE) in 1931, along with its subsequent improvements, represents a family of light mixture models that has served well for many decades for stimulus specification and reproduction when highly controlled color standards are important. Still, with regard to color appearance many perceptual and cognitive factors are known to contribute to color similarity, and, in general, to all cognitive judgments of color. Using experimentally obtained odd-one-out triad similarity judgments from 52 observers, we demonstrate that CIE-based models can explain a good portion (but not all) of the color similarity data. Color difference quantified by CIELAB ΔE explained behavior at levels of 81% (across all colors), 79% (across red colors), and 66% (across blue colors). We show that the unexplained variation cannot be ascribed to inter- or intra-individual variations among the observers, and points to the presence of additional factors shared by the majority of responders. Based on this, we create a quantitative model of a lexicographic semiorder type, which shows how different perceptual and cognitive influences can trade-off when making color similarity judgments. We show that by incorporating additional influences related to categorical and lightness and saturation factors, the model explains more of the triad similarity behavior, namely, 91% (all colors), 90% (reds), and 87% (blues). We conclude that distance in a CIE model is but the first of several layers in a hierarchy of higher-order cognitive influences that shape color triad choices. We further discuss additional mitigating influences outside the scope of CIE modeling, which can be incorporated in this framework, including well-known influences from language, stimulus set effects, and color preference bias. We also discuss universal and cultural aspects of the model as well as non-uniformity of the color space with respect to different cultural biases.

Variations in normal color vision. VI. Factors underlying individual differences in hue scaling and their implications for models of color appearance

“…However, local variations might also occur if observers apply different rules, e.g. in the categorical biases they exhibit, for different subsets of colors (Komarova & Jameson, 2013). …”

“…These could reflect not only early nonlinearities but also “higher-level” processes such as the influence of language or categorical biases that might differentially impact different colors (Komarova & Jameson, 2013). Yet an alternative is that different hues are constrained by different processes, such that orange and red and purple are each intrinsically free to vary.…”

Getting the gist of multiple hues: metric and categorical effects on ensemble perception of hue

“…The experiment also offered a crucial improvement to the control of the color stimuli used in the Demeyere et al paper through the equating of hue difference in just noticeable differences (JNDs). Equating color differences using JNDs was chosen over alternative models of perceptual uniformity, such as CIE spaces, as these have been shown to be limited in the accuracy of their estimation of cognitive color similarity judgments (e.g., [44]). …”

“…This would imply that the multicategory ensemble is encoded as having two categories present, rather than a single mean. Alternatively, another possibility is that the presence of a categorical boundary would disrupt the averaging process, leading to consideration of the hues by their metric relationship (see [44] for models including categorical and metric differences in color similarity judgments). Therefore, we may see a greater tendency toward the mean hue for different-category ensembles, as the categorical relationship between the colors in the ensemble means that color category is no longer diagnostic of ensemble membership.…”

Focal colors across languages are representative members of color categories

“…For short distances at least, Euclidean distance between two colors in CIELAB is roughly proportional to the perceptual dissimilarity of those colors (25) (but also see ref. 26). We generate focal color predictions at the individual speaker level: for each named color category used by each speaker in each language of the WCS, we used each model to predict that speaker's focus data from that speaker's naming data.…”