Limits...
A quantitative theory of human color choices.

Komarova NL, Jameson KA - PLoS ONE (2013)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Mathematics, University of California Irvine, Irvine, California, USA. komarova@uci.edu

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.

Show MeSH

Related in: MedlinePlus

Optimal category choices in the three experimental conditions, global (a), red (b) and blue (c).The 21 stimuli in each condition are plotted on the a*b* plane (please note the scale difference among the three conditions). The color stimuli are presented by colored dots that approximate the colors of the stimuli. The categories are separated by radial dashed lines.
© Copyright Policy
Related In: Results  -  Collection


getmorefigures.php?uid=PMC3569434&req=5

pone-0055986-g002: Optimal category choices in the three experimental conditions, global (a), red (b) and blue (c).The 21 stimuli in each condition are plotted on the a*b* plane (please note the scale difference among the three conditions). The color stimuli are presented by colored dots that approximate the colors of the stimuli. The categories are separated by radial dashed lines.

Mentions: In the present analyses categories are based on considerations of hue and warm-cool biases (see Figure 2). In the two local conditions, two categories were considered, as shown in Figure 2 by radial dashed lines in the a*b* plane of CIELAB (1976) space [1]. The particular choice of the location of the dividing lines, as well as the numerical value of parameter Wcat, was obtained as part of the optimization problem (see Text S1). In the red condition, the two categories that resulted in the best match of the model with the observed data, empirically correspond to “orange-red” and “burgundy-red” biases. In the blue category, we have “teal-blue” and “purple-blue” biases. In the case of the global condition (Figure 2(a)) we find not two but three different categories termed as “warm”, “cool” and “brown”. As an aside, the emergence of a separate “brown” factor, or category, in these data is not surprising since browns differ from other colored light mixtures in that they exist as “relational colors” that are experienced in the context of a brighter surrounding field, and in CIELAB or CIELUV spaces correspond to “orange” color space coordinates [1], implying that “brown” light mixtures are absent from those models. Similarity judgments on “relational” colors like brown are possible in these data because surrounding contrast was supplied by the gray background used in our triad stimulus configuration [27].


A quantitative theory of human color choices.

Komarova NL, Jameson KA - PLoS ONE (2013)

Optimal category choices in the three experimental conditions, global (a), red (b) and blue (c).The 21 stimuli in each condition are plotted on the a*b* plane (please note the scale difference among the three conditions). The color stimuli are presented by colored dots that approximate the colors of the stimuli. The categories are separated by radial dashed lines.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3569434&req=5

pone-0055986-g002: Optimal category choices in the three experimental conditions, global (a), red (b) and blue (c).The 21 stimuli in each condition are plotted on the a*b* plane (please note the scale difference among the three conditions). The color stimuli are presented by colored dots that approximate the colors of the stimuli. The categories are separated by radial dashed lines.
Mentions: In the present analyses categories are based on considerations of hue and warm-cool biases (see Figure 2). In the two local conditions, two categories were considered, as shown in Figure 2 by radial dashed lines in the a*b* plane of CIELAB (1976) space [1]. The particular choice of the location of the dividing lines, as well as the numerical value of parameter Wcat, was obtained as part of the optimization problem (see Text S1). In the red condition, the two categories that resulted in the best match of the model with the observed data, empirically correspond to “orange-red” and “burgundy-red” biases. In the blue category, we have “teal-blue” and “purple-blue” biases. In the case of the global condition (Figure 2(a)) we find not two but three different categories termed as “warm”, “cool” and “brown”. As an aside, the emergence of a separate “brown” factor, or category, in these data is not surprising since browns differ from other colored light mixtures in that they exist as “relational colors” that are experienced in the context of a brighter surrounding field, and in CIELAB or CIELUV spaces correspond to “orange” color space coordinates [1], implying that “brown” light mixtures are absent from those models. Similarity judgments on “relational” colors like brown are possible in these data because surrounding contrast was supplied by the gray background used in our triad stimulus configuration [27].

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Mathematics, University of California Irvine, Irvine, California, USA. komarova@uci.edu

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.

Show MeSH
Related in: MedlinePlus