Categorical formation of Mandarin color terms at different luminance levels

Abstract

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.

Categorical color constancy under RGB‐LED light sources

“…It was previously reported that orange and yellow categories were luminance-dependent 51,52 and that the areas of the 2 categories overlapped with that of the brown category in low luminance conditions. 56,57 As the experiment in this study was performed in relatively low luminance conditions compared to the gray background, orange and yellow colors may appear brown. Additionally, the spectral deficiency of approximately 575 nm in the spectral power of the RGB-LED light tended to make yellow and orange surfaces in high Chroma appear dark and desaturated, which contributed to the turning of orange and yellow colors into brown.…”

Basic color categories in Mandarin Chinese revealed by cluster analysis

“…These three excluded categories presented multiple inconsistent terms in studies that claimed 11 Mandarin BCTs. Specifically, both " chéng" (Lu,1997) and " jú" (Lin, Luo, MacDonald, & Tarrant , 2001a;Hsieh & Chen, 2011) were found to be orange BCTs. Both " táo" (Lu,1997) and " fěnhóng" (Lin et al, 2001;Hsieh & Chen, 2011;Gao & Sutrop, 2014) were pink BCTs.…”

“…Specifically, both " chéng" (Lu,1997) and " jú" (Lin, Luo, MacDonald, & Tarrant , 2001a;Hsieh & Chen, 2011) were found to be orange BCTs. Both " táo" (Lu,1997) and " fěnhóng" (Lin et al, 2001;Hsieh & Chen, 2011;Gao & Sutrop, 2014) were pink BCTs. Brown was especially controversial as it could be " hé" (Lu, 1997), " zōng" (Lin, Luo, MacDonald, & Tarrant, 2001b), or a foreign loanword meaning coffee " kāfēi" (Hsieh & Chen, 2011).…”

“…The study by Berlin & Kay (1969) recognized only six BCTs referring to red, yellow, green, blue, black, and white. A few later studies stated the imperfections in Berlin and Kay's survey and presumed that there should be 11 BCTs in Mandarin Chinese, but their surveys resulted in different BCTs within specific categories such as brown (Lu, 1997;Lin, 2001;Hsieh & Chen, 2011). Through the approach of the diachronic linguistic corpus, Wu (2011) quantified eight modern Mandarin BCTs, whereas Gao and Sutrop (2014) identified nine based on the criteria of BCTs proposed earlier (Berlin & Kay, 1969).…”

“…Both " táo" (Lu,1997) and " fěnhóng" (Lin et al, 2001;Hsieh & Chen, 2011;Gao & Sutrop, 2014) were pink BCTs. Brown was especially controversial as it could be " hé" (Lu, 1997), " zōng" (Lin, Luo, MacDonald, & Tarrant, 2001b), or a foreign loanword meaning coffee " kāfēi" (Hsieh & Chen, 2011). Sun & Chen (2018) ascribed the discrepancy to the language characteristic of having multiple synonyms in Mandarin color terms, therefore, they adopted a rather novel method that combined the corpus data and lab-based color-matching task.…”