Centroids of color categories compared by two methods

Boynton, Robert M.; MacLaury, Robert E.; Uchikawa, Keiji

doi:10.1002/col.5080140105

Cited by 26 publications

(21 citation statements)

References 7 publications

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“…It rivaled the performance of landmark colors on all behavioral measures in the Japanese data, even though the color appearance light blue is generally not considered salient enough to earn rank as a basic color category. 11,15,16 Other composite colors named using monolexemic terms (such as orange and purple) showed the same pattern of results as the landmark colors, or sometimes better results. Despite this, Boynton's psychophysical results have been regarded as evidence supporting a panhuman shared opponent-process neural substrate.…”

Section: Saliencementioning

confidence: 56%

“…10,11,15,16 Whereas Heider-Rosch used stimulus samples from the Munsell Book of Color, Boynton methodically assessed cognitive salience of the 424 samples from the OSA space, a color-ordered system created by the Optical Society of America. 17 Boynton used several different behavioral measures in his studies, including monolexemic naming consistency, response time, and consensus or majority choice.…”

Section: Saliencementioning

confidence: 99%

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Differences in color naming and color salience in Vietnamese and English

Jameson

Alvarado

2003

Color Research & Application

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Section: Saliencementioning

confidence: 56%

Section: Saliencementioning

confidence: 99%

Differences in color naming and color salience in Vietnamese and English

Jameson

Alvarado

2003

Color Research & Application

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“…The second source of evidence is based on work by Boynton, MacLaury, and Uchikawa (14) in which Munsell color chips were directly matched to OSA color tiles. Comparing matched OSA and Munsell colors, the same divergence becomes apparent.…”

Section: Obtaining Linear Estimates For the Munsell Color Spacementioning

confidence: 99%

A quantitative model for transforming reflectance spectra into the Munsell color space using cone sensitivity functions and opponent process weights

D’Andrade

Romney

2003

Proc. Natl. Acad. Sci. U.S.A.

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This article presents a computational model of the process through which the human visual system transforms reflectance spectra into perceptions of color. Using physical reflectance spectra data and standard human cone sensitivity functions we describe the transformations necessary for predicting the location of colors in the Munsell color space. These transformations include quantitative estimates of the opponent process weights needed to transform cone activations into Munsell color space coordinates. Using these opponent process weights, the Munsell position of specific colors can be predicted from their physical spectra with a mean correlation of 0.989. C lassical opponent process theory usually is modeled as a linear combination of cone signals, with different color perceptions characterized by different combinations of the activation or inhibition of the long, medium, and short cones. Finding the appropriate weights to assign to the three cones has long been a problem within this theory (1-3). This article presents a quantitative model for the transformation of reflectance spectra into the perceptually based Munsell color space by using regression analysis to estimate opponent process weights. Four sets of data are required for this task: (i) reflectance spectra of Munsell color chips, (ii) the spectral radiant power distribution for D65 illumination, (iii) quantitative estimates of the three human cone sensitivity functions, and (iv) Cartesian coordinates for the Munsell color space.

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“…10,11,39 The observers are all native Mandarin speakers who use traditional complex Chinese characters. The boundary between categories and each focal color, 14 or centroid color, 26,37 is the most representative exemplar within a color category and is demarcated and compared with those in studies of similar experimental design. The dependent variables are sorting items and response time (RT), which are submitted to serve indexes of central tendency (mode) and task difficulty, respectively.…”

Section: Introductionmentioning

confidence: 99%

Categorical formation of Mandarin color terms at different luminance levels

Hsieh

Chen

2011

Color Research & Application

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This study presents the categorical formation of a set of Mandarin color terms on the International Commission on Illumination (CIE) 1931 chromaticity diagram across six luminance levels. This article conducted a study that employed 44 native Mandarin speakers to perform a force-choice sorting task. The Mandarin color terms for sorting were determined by a free-recall pretest and are consistent with basic color terms proposed by Berlin and Kay. The square-sampled stimuli were generated by evenly sweeping the x-y diagram of 5, 10, 25, 50, 100, and 170 cd/m 2 planes. The categorical sorting results and response time (RT) measurements suggest that: (1) the concepts of green, blue, purple, and gray stably exist at most luminance levels. The voting RT for the green, blue, and purple categories is particularly short. (2) Red, orange, yellow, and pink are highly luminancedependent; these can be identified without difficulty only at some restricted luminance levels. (3) The chromaticity areas designated as orange, partial yellow, red, and pink are recognized as brown when the luminance level decreases. (4) Brown and gray serve as representations of two distinct tints in the low saturation condition. (5) The location of boundaries between blue and green are remarkably different than those in a similar study that employed Japanese speakers.

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Centroids of color categories compared by two methods

Cited by 26 publications

References 7 publications

Differences in color naming and color salience in Vietnamese and English

Differences in color naming and color salience in Vietnamese and English

A quantitative model for transforming reflectance spectra into the Munsell color space using cone sensitivity functions and opponent process weights

Categorical formation of Mandarin color terms at different luminance levels

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