Abstract

We previously showed that impressions of nine semantic words expressing abstract meanings (like “tranquil”) can be expressed by 12 hues in a paired comparison method; in this study, White, Gray, and Black were added (Exp. 1) to the previous 12 hues. Color impressions were also estimated using a set of 35 paired words by a semantic differential (SD) method (Exp. 2). The data of nine color vision normal (CVN) and seven color vision deficient (CVD) observers (one protanope and six deuteranopes) were analyzed separately by principal component analysis (PCA). In the results of Exp. 1, all hues used as loadings were distributed in a hue-circle shape in the 2D color space of PC axes for both observer groups [however, the four bluish hues (Blue-Green to Violet) tended toward convergence]. One data set of five CVNs and five deuteranopes was analyzed together using PCA because of high concordance. In the word distribution of the CVDs in Exp. 1, because second PC scores tended to be smaller, the categorization of the words was not clear; the points of five word scores were approximately on one line, reflecting that the colors used in the paired comparison were treated in one-dimensional scaling (which correlates to lightness) in the CVDs. In the results of Exp. 2, the word distribution of loadings was similar between the CVNs and CVDs, and the color score distribution had a similar tendency of showing an ellipse-shaped hue circle; it was probably caused by their experience of being associated with color names rather than color appearance (although the radius of the short axis is shorter in the CVDs’ data). The comparison of the word distribution between experiments suggests that two to five semantic word impressions can be stably expressed by hue, but the impression of other words, such as “Magnificent” for the CVNs and “Fine” for the CVDs, cannot. The hue circle is conceptually kept in the SD measurement for all observers; however, it was not kept in the paired comparison for the CVDs. The analysis of one combined data set suggests that the 2D color distribution is not caused by a 3D color system because the lightness scaling is involved in the 2D color distribution.

© 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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References

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2019 (3)

C. Ilhan, M. A. Sekeroglu, S. Doguizi, and P. Yilmazbas, “Contrast sensitivity of patients with congenital color vision deficiency,” Int. Ophthalmol. 39, 797–801 (2019).
[Crossref]

R. Ma, N. Liao, P. Yan, and K. Shinomori, “Influences of lighting time course and background on categorical colour constancy with RGB-LED light sources,” Color Res. Appl. 44, 694–708 (2019).
[Crossref]

F. Samsel, P. Wolfram, A. Bares, T. L. Turton, and R. Bujack, “Colormapping resources and strategies for organized intuitive environmental visualization,” Environ. Earth Sci. 78, 269 (2019).
[Crossref]

2018 (4)

2017 (2)

K. J. Emery, V. J. Volbrecht, D. H. Peterzell, and M. A. Webster, “Variations in normal color vision. VII. Relationships between color naming and hue scaling,” Vis. Res. 141, 66–75 (2017).
[Crossref]

I. Kuriki, R. Lange, Y. Muto, A. M. Brown, K. Fukuda, R. Tokunaga, D. T. Lindsey, K. Uchikawa, and S. Shioiri, “The modern Japanese color lexicon,” J. Vis. 17(3):1, 18 (2017).
[Crossref]

2016 (3)

2014 (2)

B. Nagy, Z. Németh, K. Samu, and G. Ábrahám, “Variability and systematic differences in normal, protan, and deutan color naming,” Front. Psychol. 5, 1416 (2014).
[Crossref]

M. Janáky, J. Borbély, G. Benedek, B. P. Kocsis, and G. Braunitzer, “Achromatic luminance contrast sensitivity in x-linked color-deficient observers: an addition to the debate,” Vis. Neurosci. 31, 99–103 (2014).
[Crossref]

2012 (1)

K. Shinomori and J. S. Werner, “Aging of human short-wave cone pathways,” Proc. Natl. Acad. Sci. U.S.A. 109, 13422–13427 (2012).
[Crossref]

2008 (1)

K. Shinomori and J. S. Werner, “The impulse response of S-cone pathways in detection of increments and decrements,” Vis. Neurosci. 25, 341–347 (2008).
[Crossref]

2006 (2)

K. Shinomori and J. S. Werner, “Impulse response of an S-cone pathway in the aging visual system,” J. Opt. Soc. Am. A 23, 1570–1577 (2006).
[Crossref]

X. P. Gao and J. H. Xin, “Investigation of human’s emotional responses on colors,” Color Res. Appl. 31, 411–417 (2006).
[Crossref]

2004 (1)

L. C. Ou, M. R. Luo, A. Woodcock, and A. Wright, “A study of colour emotion and colour preference. Part II: colour emotions for two-colour combinations,” Color Res. Appl. 29, 292–298 (2004).
[Crossref]

1999 (1)

G. V. Paramei and C. R. Cavonius, “Color spaces of color-normal and color-abnormal observers reconstructed from response times and dissimilarity ratings,” Percept. Psychophys. 61, 1662–1674 (1999).
[Crossref]

1997 (3)

1994 (2)

K. Shinomori, Y. Nakano, and K. Uchikawa, “Influence of the illuminance and spectral composition of surround fields on spatially induced blackness,” J. Opt. Soc. Am. A 11, 2383–2388 (1994).
[Crossref]

B. C. Regan, J. P. Reffin, and J. D. Mollon, “Luminance noise and the rapid determination of discrimination ellipses in colour deficiency,” Vis. Res. 34, 1279–1299 (1994).
[Crossref]

1992 (2)

P. DeMarco, J. Pokorny, and V. C. Smith, “Full-spectrum cone sensitivity functions for X-chromosome-linked anomalous trichromats,” J. Opt. Soc. Am. A 9, 1465–1476 (1992).
[Crossref]

R. N. Shepard and L. A. Cooper, “Representation of colors in the blind, color-blind, and normally sighted,” Psycho. Sci. 3, 97–104 (1992).
[Crossref]

1991 (1)

G. V. Paramei, C. A. Izmailov, and E. N. Sokolov, “Multidimensional scaling of large chromatic differences by normal and color-deficient subjects,” Psychol. Sci. 2, 244–249 (1991).
[Crossref]

1990 (1)

R. M. Boynton and C. X. Olson, “Salience of chromatic basic color terms confirmed by three measures,” Vis. Res. 30, 1311–1317 (1990).
[Crossref]

1987 (1)

K. Uchikawa and R. M. Boynton, “Categorical color perception of Japanese observers: comparison with that of Americans,” Vis. Res. 27, 1825–1833 (1987).
[Crossref]

1981 (1)

S. Kobayashi, “The aim and method of the color image scale,” Color Res. Appl. 6, 93–107 (1981).
[Crossref]

1975 (1)

V. C. Smith and J. Pokorny, “Spectral sensitivity of the foveal cone photopigments between 400 and 500 nm,” Vis. Res. 15, 161–171 (1975).
[Crossref]

1972 (1)

E. R. Heider and D. C. Oliver, “The structure of the color space in naming and memory for two languages,” Cognit. Psychol. 3, 337–354 (1972).
[Crossref]

1969 (1)

J. Hogg, “A principal component analysis of semantic differential judgements of single colors and color pairs,” J. Gen. Psychol. 80, 129–140 (1969).
[Crossref]

1965 (1)

J. L. Horn, “A rationale and test for the number of factors in factor analysis,” Psychometrika 30, 179–185 (1965).
[Crossref]

1952 (1)

C. E. Osgood, “The nature and measurement of meaning,” Psychol. Bull. 49(3), 197–237 (1952).
[Crossref]

Ábrahám, G.

B. Nagy, Z. Németh, K. Samu, and G. Ábrahám, “Variability and systematic differences in normal, protan, and deutan color naming,” Front. Psychol. 5, 1416 (2014).
[Crossref]

Bares, A.

F. Samsel, P. Wolfram, A. Bares, T. L. Turton, and R. Bujack, “Colormapping resources and strategies for organized intuitive environmental visualization,” Environ. Earth Sci. 78, 269 (2019).
[Crossref]

Benedek, G.

M. Janáky, J. Borbély, G. Benedek, B. P. Kocsis, and G. Braunitzer, “Achromatic luminance contrast sensitivity in x-linked color-deficient observers: an addition to the debate,” Vis. Neurosci. 31, 99–103 (2014).
[Crossref]

Bimler, D.

Bonnardel, V.

Borbély, J.

M. Janáky, J. Borbély, G. Benedek, B. P. Kocsis, and G. Braunitzer, “Achromatic luminance contrast sensitivity in x-linked color-deficient observers: an addition to the debate,” Vis. Neurosci. 31, 99–103 (2014).
[Crossref]

Boynton, R. M.

R. M. Boynton and C. X. Olson, “Salience of chromatic basic color terms confirmed by three measures,” Vis. Res. 30, 1311–1317 (1990).
[Crossref]

K. Uchikawa and R. M. Boynton, “Categorical color perception of Japanese observers: comparison with that of Americans,” Vis. Res. 27, 1825–1833 (1987).
[Crossref]

P. K. Kaiser and R. M. Boynton, Human Color Vision, 2nd ed. (Optical Society of America, 1996), p. 557.

Braunitzer, G.

M. Janáky, J. Borbély, G. Benedek, B. P. Kocsis, and G. Braunitzer, “Achromatic luminance contrast sensitivity in x-linked color-deficient observers: an addition to the debate,” Vis. Neurosci. 31, 99–103 (2014).
[Crossref]

Brettel, H.

Brown, A. M.

I. Kuriki, R. Lange, Y. Muto, A. M. Brown, K. Fukuda, R. Tokunaga, D. T. Lindsey, K. Uchikawa, and S. Shioiri, “The modern Japanese color lexicon,” J. Vis. 17(3):1, 18 (2017).
[Crossref]

Bujack, R.

F. Samsel, P. Wolfram, A. Bares, T. L. Turton, and R. Bujack, “Colormapping resources and strategies for organized intuitive environmental visualization,” Environ. Earth Sci. 78, 269 (2019).
[Crossref]

Cavonius, C. R.

G. V. Paramei and C. R. Cavonius, “Color spaces of color-normal and color-abnormal observers reconstructed from response times and dissimilarity ratings,” Percept. Psychophys. 61, 1662–1674 (1999).
[Crossref]

Chihara, T.

T. Chihara and H. Sakaida, SD-Rates and Associations of Twenty Color-Names and Their Colored-Patches (Shiga University, 1990) [in Japanese].

Cooper, L. A.

R. N. Shepard and L. A. Cooper, “Representation of colors in the blind, color-blind, and normally sighted,” Psycho. Sci. 3, 97–104 (1992).
[Crossref]

Corballis, M. C.

A. Saysani, M. C. Corballis, and P. M. Corballis, “The colour of words: how dichromats construct a colour space,” Vis. Cognit. 26, 601–607 (2018).
[Crossref]

Corballis, P. M.

A. Saysani, M. C. Corballis, and P. M. Corballis, “The colour of words: how dichromats construct a colour space,” Vis. Cognit. 26, 601–607 (2018).
[Crossref]

DeMarco, P.

Doguizi, S.

C. Ilhan, M. A. Sekeroglu, S. Doguizi, and P. Yilmazbas, “Contrast sensitivity of patients with congenital color vision deficiency,” Int. Ophthalmol. 39, 797–801 (2019).
[Crossref]

Emery, K. J.

K. J. Emery, V. J. Volbrecht, D. H. Peterzell, and M. A. Webster, “Variations in normal color vision. VII. Relationships between color naming and hue scaling,” Vis. Res. 141, 66–75 (2017).
[Crossref]

Fukuda, K.

I. Kuriki, R. Lange, Y. Muto, A. M. Brown, K. Fukuda, R. Tokunaga, D. T. Lindsey, K. Uchikawa, and S. Shioiri, “The modern Japanese color lexicon,” J. Vis. 17(3):1, 18 (2017).
[Crossref]

Gao, X. P.

X. P. Gao and J. H. Xin, “Investigation of human’s emotional responses on colors,” Color Res. Appl. 31, 411–417 (2006).
[Crossref]

Heider, E. R.

E. R. Heider and D. C. Oliver, “The structure of the color space in naming and memory for two languages,” Cognit. Psychol. 3, 337–354 (1972).
[Crossref]

Hering, E.

E. Hering, Outlines of a Theory of the Light Sense, L. M. Hurvich and D. Jameson, eds. (Harvard University, 1964).

Hogg, J.

J. Hogg, “A principal component analysis of semantic differential judgements of single colors and color pairs,” J. Gen. Psychol. 80, 129–140 (1969).
[Crossref]

Horn, J. L.

J. L. Horn, “A rationale and test for the number of factors in factor analysis,” Psychometrika 30, 179–185 (1965).
[Crossref]

Ilhan, C.

C. Ilhan, M. A. Sekeroglu, S. Doguizi, and P. Yilmazbas, “Contrast sensitivity of patients with congenital color vision deficiency,” Int. Ophthalmol. 39, 797–801 (2019).
[Crossref]

Izmailov, C. A.

G. V. Paramei, C. A. Izmailov, and E. N. Sokolov, “Multidimensional scaling of large chromatic differences by normal and color-deficient subjects,” Psychol. Sci. 2, 244–249 (1991).
[Crossref]

Janáky, M.

M. Janáky, J. Borbély, G. Benedek, B. P. Kocsis, and G. Braunitzer, “Achromatic luminance contrast sensitivity in x-linked color-deficient observers: an addition to the debate,” Vis. Neurosci. 31, 99–103 (2014).
[Crossref]

Kaiser, P. K.

P. K. Kaiser and R. M. Boynton, Human Color Vision, 2nd ed. (Optical Society of America, 1996), p. 557.

Kawamoto, K.

Kobayashi, S.

S. Kobayashi, “The aim and method of the color image scale,” Color Res. Appl. 6, 93–107 (1981).
[Crossref]

S. Kobayashi, Color Image Scale, L. Matsunaga, ed. (Kodansha Int., 1990).

Kocsis, B. P.

M. Janáky, J. Borbély, G. Benedek, B. P. Kocsis, and G. Braunitzer, “Achromatic luminance contrast sensitivity in x-linked color-deficient observers: an addition to the debate,” Vis. Neurosci. 31, 99–103 (2014).
[Crossref]

Komatsu, H.

Kuriki, I.

I. Kuriki, R. Lange, Y. Muto, A. M. Brown, K. Fukuda, R. Tokunaga, D. T. Lindsey, K. Uchikawa, and S. Shioiri, “The modern Japanese color lexicon,” J. Vis. 17(3):1, 18 (2017).
[Crossref]

Lange, R.

I. Kuriki, R. Lange, Y. Muto, A. M. Brown, K. Fukuda, R. Tokunaga, D. T. Lindsey, K. Uchikawa, and S. Shioiri, “The modern Japanese color lexicon,” J. Vis. 17(3):1, 18 (2017).
[Crossref]

Liao, N.

R. Ma, N. Liao, P. Yan, and K. Shinomori, “Influences of lighting time course and background on categorical colour constancy with RGB-LED light sources,” Color Res. Appl. 44, 694–708 (2019).
[Crossref]

Lindsey, D. T.

I. Kuriki, R. Lange, Y. Muto, A. M. Brown, K. Fukuda, R. Tokunaga, D. T. Lindsey, K. Uchikawa, and S. Shioiri, “The modern Japanese color lexicon,” J. Vis. 17(3):1, 18 (2017).
[Crossref]

Luo, M. R.

L. C. Ou, M. R. Luo, A. Woodcock, and A. Wright, “A study of colour emotion and colour preference. Part II: colour emotions for two-colour combinations,” Color Res. Appl. 29, 292–298 (2004).
[Crossref]

Ma, R.

R. Ma, N. Liao, P. Yan, and K. Shinomori, “Influences of lighting time course and background on categorical colour constancy with RGB-LED light sources,” Color Res. Appl. 44, 694–708 (2019).
[Crossref]

R. Ma, K. Kawamoto, and K. Shinomori, “Color constancy of color deficient observers under illuminations defined by individual color discrimination ellipsoids,” J. Opt. Soc. Am. A 33, A283–A299 (2016).
[Crossref]

Mollon, J. D.

H. Brettel, F. Viénot, and J. D. Mollon, “Computerized simulation of color appearance for dichromats,” J. Opt. Soc. Am. A 14, 2647–2655 (1997).
[Crossref]

B. C. Regan, J. P. Reffin, and J. D. Mollon, “Luminance noise and the rapid determination of discrimination ellipses in colour deficiency,” Vis. Res. 34, 1279–1299 (1994).
[Crossref]

Moore, C. C.

A. K. Romney, C. C. Moore, and C. D. Rusch, “Cultural universals: measuring the semantic structure of emotion terms in English and Japanese,” Proc. Natl. Acad. Sci. U.S.A. 94, 5489–5494 (1997).
[Crossref]

Muto, Y.

I. Kuriki, R. Lange, Y. Muto, A. M. Brown, K. Fukuda, R. Tokunaga, D. T. Lindsey, K. Uchikawa, and S. Shioiri, “The modern Japanese color lexicon,” J. Vis. 17(3):1, 18 (2017).
[Crossref]

Nagy, B.

B. Nagy, Z. Németh, K. Samu, and G. Ábrahám, “Variability and systematic differences in normal, protan, and deutan color naming,” Front. Psychol. 5, 1416 (2014).
[Crossref]

Nakano, Y.

Németh, Z.

B. Nagy, Z. Németh, K. Samu, and G. Ábrahám, “Variability and systematic differences in normal, protan, and deutan color naming,” Front. Psychol. 5, 1416 (2014).
[Crossref]

Oliver, D. C.

E. R. Heider and D. C. Oliver, “The structure of the color space in naming and memory for two languages,” Cognit. Psychol. 3, 337–354 (1972).
[Crossref]

Olson, C. X.

R. M. Boynton and C. X. Olson, “Salience of chromatic basic color terms confirmed by three measures,” Vis. Res. 30, 1311–1317 (1990).
[Crossref]

Osgood, C. E.

C. E. Osgood, “The nature and measurement of meaning,” Psychol. Bull. 49(3), 197–237 (1952).
[Crossref]

C. E. Osgood, G. J. Suci, and P. H. Tannenbaum, The Measurement of Meaning (Univeristy of Illinois, 1957).

Ou, L. C.

L. C. Ou, M. R. Luo, A. Woodcock, and A. Wright, “A study of colour emotion and colour preference. Part II: colour emotions for two-colour combinations,” Color Res. Appl. 29, 292–298 (2004).
[Crossref]

Panorgias, A.

Paramei, G. V.

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[Crossref]

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K. J. Emery, V. J. Volbrecht, D. H. Peterzell, and M. A. Webster, “Variations in normal color vision. VII. Relationships between color naming and hue scaling,” Vis. Res. 141, 66–75 (2017).
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[Crossref]

R. Ma, K. Kawamoto, and K. Shinomori, “Color constancy of color deficient observers under illuminations defined by individual color discrimination ellipsoids,” J. Opt. Soc. Am. A 33, A283–A299 (2016).
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[Crossref]

K. Shinomori, B. E. Schefrin, and J. S. Werner, “Spectral mechanisms of spatially induced blackness: data and quantitative model,” J. Opt. Soc. Am. A 14, 372–387 (1997).
[Crossref]

K. Shinomori, A. Panorgias, and J. S. Werner, “Age-related changes in ON and OFF responses to luminance increments and decrements,” J. Opt. Soc. Am. A 35, B26–B34 (2018).
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G. V. Paramei and C. R. Cavonius, “Color spaces of color-normal and color-abnormal observers reconstructed from response times and dissimilarity ratings,” Percept. Psychophys. 61, 1662–1674 (1999).
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A. K. Romney, C. C. Moore, and C. D. Rusch, “Cultural universals: measuring the semantic structure of emotion terms in English and Japanese,” Proc. Natl. Acad. Sci. U.S.A. 94, 5489–5494 (1997).
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[Crossref]

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P. K. Kaiser and R. M. Boynton, Human Color Vision, 2nd ed. (Optical Society of America, 1996), p. 557.

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Figures (18)

Fig. 1.
Fig. 1. Twelve stimulus colors and neutral colors [White (D65), Gray (N4.7), and Black]. (Top panel) Diamonds, squares, and crosses denote lightness ${\rm L}^*$ of protanope, deuteranope, and color vision normal, respectively (see text for details). (Bottom panel) Color chip number and PCCS names are presented near each point. The circle and cross denote chromaticity coordinates of Gray (N4.7) and White (D65), respectively. Red thin-dotted curves and green thin-solid curves are protan and deutan confusion lines for each stimulus color, respectively. Red thick-dotted curves and green thick-solid curves are protan and deutan confusion lines of White in ${77}.{4}\;{{\rm cd}/{\rm m}^2}$ . The outer gray curve denotes the gamut of the monitor. Black ellipse and black solid lines denote the best ellipse fits to 12 hues and short and long axes, respectively. The distribution of 12 stimulus colors consisted of a hue circle in color appearance space with smooth lightness change.
Fig. 2.
Fig. 2. Proportion of variance from the first to eighth PCs in paired comparison data (denoted by circles) and in semantic differential method data (denoted by triangles). Filled and open symbols denote CVN data and CVD data, respectively. First PCs are more dominant in paired comparison data, and proportions of variance are similar between observer groups.
Fig. 3.
Fig. 3. Distribution of stimulus colors as defined by the first and second PC loading values in CVN data (top panel) and in CVD data (bottom panel). Color chip number and names in PCCS are presented near each point. Black ellipses denote the best fit to all data points. Dotted ellipses denote the best fit under the limitations of no central point shift and no rotation of ellipse axes. The color distribution is similar between observer groups, and neutral colors (White, Gray, and Black) can also be fitted by the ellipses.
Fig. 4.
Fig. 4. Modified selection rate as a function of stimulus color for CVN observers (denoted by color chip number and neutral color names) for nine semantic words. Error bars denote ${ \pm }\;{2.26}$ S.E.M. as a 95% confidence interval. Blue curves are fits by the first and second PCs with no offset. The order of the stimulus color was obtained from Fig. 3. Modified section rates change smoothly, and model fits can predict the data reasonably well, except for the three neutral colors (White, Gray, and Black).
Fig. 5.
Fig. 5. Modified selection rate as in Fig. 4, but for CVD observers, and error bars denote ${\pm }\;{2}.{37}$ S.E.M.
Fig. 6.
Fig. 6. Point distribution of semantic words obtained by the first and second PC score values in CVN data (top panel) and in CVD data (bottom panel). Different symbols and colors denote all observers’ score-value points for semantic words of Extreme (gray triangles), Vigorous (blue squares), Visible (open diamonds), Magnificent (red circles), Clean (black triangles), Tranquil (green squares), Fine (yellow diamonds), Deserted (pink asterisks), and Massive (purple circles). Crosses and ellipses in the same colors denote the centroids (means) and areas of point distributions of each word, respectively. In CVN data, the ellipse of Vigorous is the same as the ellipse of Visible. In CVD data, the ellipse of Deserted is the same as the ellipse of Massive. Symbol labels of the observer number and word denote outliers from the area ellipses, and in the CVD data, the orange ellipse denotes the point-distribution area of observer #4, except Clean and Fine. In CVD data, area ellipses were distributed on the first axis, and the influence of the second PC value is much smaller.
Fig. 7.
Fig. 7. Distribution of nine core semantic words (denoted by sky-blue font), two important words (Warm and Soft, denoted by red squares and larger size lack font), and 24 semantic words (denoted by smaller size black font) defined by the first and second PC loading values in CVN data (top panel) and in CVD data (bottom panel). Semantic words are shown near their symbols. The symbol colors for nine core semantic words were the most selected color in the mean of all observers. The dotted lines denote the orthogonal corner obtained by Warm and Soft (see the text for details). The ordinate was flipped to match the direction of the axis to the score-value data in Fig. 6.
Fig. 8.
Fig. 8. Distribution of 12 hues, White, Gray, and Black obtained from the first and second principal component score values in CVN data (top panel) and in CVD data (bottom panel). Error bars denote the confidence interval of 95%. Color chip number and names in PCCS are presented near each point. Black and blue ellipses denote the best fit to all hue points (White, Gray, and Black excluded) in CVN and CVD observers, respectively. Dotted ellipses denote the best fits under the limitations of no central point shift and no rotation of ellipse axes. Small black circles and broken lines denote the directions of Soft and Warm in Fig. 7 defined by the orthogonal corner. The ordinate is flipped to correspond to the loading value data in Fig. 7. Twelve hues are still contained in a hue circle and not compressed to one-dimensional scaling from Blue to Yellow.
Fig. 9.
Fig. 9. Distribution of hues as defined by the first and second PC loading values in CVN and deutan observers. Other details are the same as Fig. 3 except the green line denotes the linear fit in Fig. 10 (see the text for details). The color distribution is approximately in the middle of the two distributions of the CVNs and CVDs shown in Fig. 3.
Fig. 10.
Fig. 10. Distribution of semantic words obtained by the first and second PC score values in CVN and deutan observers. Circles and diamonds denote average of score-value points for one semantic word in CVN and deutan observers, respectively. Different colors denote semantic words of Extreme (Gray), Vigorous (Blue), Visible (White), Magnificent (Red), Clean (Black), Tranquil (Green), Fine (Yellow), Deserted (Pink), and Massive (Purple). Error bars denote the confidence interval of 95%. The gray ellipse denotes the best fit to all word points of CVN data. Green line denotes linear fit to five word points of deutan data, and the parameters of the fit are shown at left bottom. Red and blue ellipses denote categories defined by absolute values greater than 70% of the maximum absolute values. The word distribution was largely different between observer groups, and five words of the deutan observers can be fitted by one line.
Fig. 11.
Fig. 11. Correlation of weighted summation of the first and second PC loading values in ratio of 0.737 to luminance, L- and M-cone stimulations. Circles, squares and diamonds denote the point of luminance, L-cone stimulation, and M-cone stimulation, respectively. Coefficients of determination are presented near each correlation line.
Fig. 12.
Fig. 12. Distribution of nine core semantic words, two important words, and 24 semantic words defined by the first and second PC loading values (top panel) and by the third and fourth PC loading values (bottom panel) in CVN and deutan observers. The dotted lines denote the orthogonal corner obtained by Warm and Soft (top panel only). Other details are the same as Fig. 7, except no axis was flipped.
Fig. 13.
Fig. 13. Distribution of 12 hues, White, Gray, and Black obtained from the first and second PC score values (top panel) and from the third and fourth PC score values (bottom panel) in CVN and deutan observers. Circles and diamonds denote average of score-value points for one color in CVN and deutan observers, respectively. Color chip number and names in PCCS are presented near each point. (Top panel) Error bars denote the confidence interval of 95%. Gray and blue ellipses denote the best fit to all hue points (White, Gray, and Black excluded) in CVN and deutan observers, respectively. Small black circles and broken lines denote the directions of Soft and Warm in the top panel of Fig. 12 defined by the orthogonal corner. (Bottom panel) Black-dotted, red-solid, and blue-thick lines denote connections of score-value points between CVN and deutan data categorized to the first, second, and third categories, respectively. The distribution of hues basically maintains the structure of hue circle in the first and second PC score values, and the third and fourth PCs may have a role in explaining the difference between observer groups.
Fig. 14.
Fig. 14. Comparison between color distribution defined by loading values of color selection in the pair comparison method (denoted by squares; labeled by color chip number and name in PCCS) and color distributions obtained by score values of word grade rating in SD method in five CVN observers (circles; “SD,” number and name) and five deuteranopes (diamonds; “SD-CVD,” number and name) for the first and second PCs. Two color distributions of score values (by the SD method) were separately expanded, rotated, and shifted using the best fit ellipses (shown in Fig. 13) for the best fit to the distribution of the loading values. In the point labeled as “SD-CVD-black” (the lowest point in the space), the value of the second PC axis was halved for presentation. Gray, black, and blue ellipses denote the best fit ellipses to all hue points (White, Gray, and Black excluded) after displacement of points for fit, to points in CVN observers, and to points in deuteranopes, respectively. Some colors were stable in the structure comparison defined by the distances, but others were not.
Fig. 15.
Fig. 15. Distance between loading value points in the pair comparison method and score-value points in the SD method in five CVN observers (denoted by circles) and five deuteranopes (diamonds) after displacement of points for fits as shown in Fig. 14. Dotted lines denote [mean  ${ \pm }\;{0.5}$ SD] calculated from CVN observer data. All data points and horizontal lines of Black, Gray, and White were halved for presentation.
Fig. 16.
Fig. 16. Comparison between semantic-word distribution defined by loading values of word grade rating in the SD method (denoted by squares; labeled by word) and word distributions obtained by score values of color selection in pair comparison method in five CVN observers (circles; “Pa” and word) and five deuteranopes (diamonds; “Pa-CVD” and word) for the first and second PCs. Two word distributions of score values (by paired comparison method) were separately expanded, rotated, and shifted using the best fit ellipses (shown in Fig. 10 for CVN data) for the best fit to the distribution of the loading values (see text for details). Black and gray ellipses denote the best fit to all word points of the word distribution of loadings and scores in CVN observers after displacement of points for fit, respectively. The green line denotes the best fit to score points of Deserted, Massive, Fine, Tranquil, and Clean in deuteranopes after displacement of points for fit. Red dotted lines denote the connection for each semantic word between loading points (square) and score points in CVN observers (circles).
Fig. 17.
Fig. 17. Distance between loading value points in the SD method and score-value points in the paired comparison method in five CVN observers (denoted by circles) and five deuteranopes (diamonds) after displacement of points for fits as shown in Fig. 16. Dotted lines denote [mean  ${ \pm }\;{0}.{5}$ SD] calculated from CVN observer data. Order of semantic word is ascending order of the mean of the two data.
Fig. 18.
Fig. 18. Kendall’s coefficient of concordance $W$ applied to hue selection data from the paired comparison in order of the words in Fig. 10 going counterclockwise from “Extreme” (top panel) and grade-rating data from the SD method in order of color chip number (bottom panel). Symbols denote the data set of all observers ( ${\rm N} = {16}$ ) (denoted by squares), CVN observers ( ${\rm N} = {9}$ ) (circles), CVD observers ( ${\rm N} = {7}$ ) (diamonds), and five CVNs and five deutans ( ${\rm N} = {10}$ ) (triangles). Black, gray, green, and blue horizontal lines denote the limit of ${W}$ for 95% statistical significance for the data sets of ${\rm N} = {16}$ , ${\rm N} = {9}$ , ${\rm N} = {7}$ , and ${\rm N} = {10}$ , respectively.

Tables (1)

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Table 1. Determined Number of Components in PCA by Scree Test and Parallel Analysis

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