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Статті в журналах з теми "Color categorization"

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Автор: Grafiati

Опубліковано: 14 березня 2023

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1

Siuda-Krzywicka, Katarzyna, Christoph Witzel, Emma Chabani, Myriam Taga, Cécile Coste, Noëlla Cools, Sophie Ferrieux, Laurent Cohen, Tal Seidel Malkinson, and Paolo Bartolomeo. "Color Categorization Independent of Color Naming." Cell Reports 28, no. 10 (September 2019): 2471–79. http://dx.doi.org/10.1016/j.celrep.2019.08.003.

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2

Dedrick, Don. "Color, Color Terms, Categorization, Cognition, Culture: An Afterword." Journal of Cognition and Culture 5, no. 3-4 (2005): 487–95. http://dx.doi.org/10.1163/156853705774648545.

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Анотація:

AbstractRecent work on color naming challenges the idea that there are shared perceptually salient colors or color categories that are "hardwired" into homo sapiens and provide the basis for one of the most famous cross-cultural claims of all time, Brent Berlin and Paul Kay's claim that there is a small number of "basic" color terms (eleven), and that some subset of these terms is present in every human language (Berlin & Kay, 1969; see Kay and Maffi, 1999; Kay and Reiger, 2003; and Kay 2005 for updates).

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3

van den Broek, E. L., Th E. Schouten, and P. M. F. Kisters. "Modeling human color categorization." Pattern Recognition Letters 29, no. 8 (June 2008): 1136–44. http://dx.doi.org/10.1016/j.patrec.2007.09.006.

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4

Maule, John, and Anna Franklin. "Color categorization in infants." Current Opinion in Behavioral Sciences 30 (December 2019): 163–68. http://dx.doi.org/10.1016/j.cobeha.2019.08.005.

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5

Chang, Audrey LY, Hannah M. Selwyn, Daniel Garside, Joshua Fuller-Deets, Shriya M. Awasthi, and Bevil R. Conway. "Color categorization in macaques." Journal of Vision 22, no. 14 (December 5, 2022): 3979. http://dx.doi.org/10.1167/jov.22.14.3979.

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6

BONNARDEL, VALÉRIE. "Color naming and categorization in inherited color vision deficiencies." Visual Neuroscience 23, no. 3-4 (May 2006): 637–43. http://dx.doi.org/10.1017/s0952523806233558.

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Анотація:

Dichromatic subjects can name colors accurately, even though they cannot discriminate among red-green hues (Jameson & Hurvich, 1978). This result is attributed to a normative language system that dichromatic observers developed by learning subtle visual cues to compensate for their impoverished color system. The present study used multidimensional scaling techniques to compare color categorization spaces of color-vision deficient (CVD) subjects to those of normal trichromat (NT) subjects, and consensus analysis estimated the normative effect of language on categorization. Subjects sorted 140 Munsell color samples in three different ways: a free sorting task (unlimited number of categories), a constrained sorting task (number of categories limited to eight), and a constrained naming task (limited to eight basic color terms). CVD color categories were comparable to those of NT subjects. For both CVD and NT subjects, a common color categorization space derived from the three tasks was well described by a three-dimensional model, with the first two dimensions corresponding to reddish-greenish and yellowish-bluish axes. However, the third axis, which was associated with an achromatic dimension in NTs, was not identified in the CVD model. Individual differences multidimensional scaling failed to reveal group differences in the sorting tasks. In contrast, the personal color naming spaces of CVD subjects exhibited a relative compression of the yellowish-bluish dimension that is inconsistent with the typical deutan-type color spaces derived from more direct measures of perceptual color judgments. As expected, the highest consensus among CVDs (77%) and NTs (82%) occurred in the naming task. The categorization behaviors studied in this experiment seemed to rely more on learning factors, and may reveal little about CVD perceptual representation of colors.

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7

Ross, Peter W. "Trichromacy and the neural basis of color discrimination." Behavioral and Brain Sciences 20, no. 2 (June 1997): 206–7. http://dx.doi.org/10.1017/s0140525x97451427.

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Анотація:

I take issue with Saunders & van Brakel's claim that neural processes play no interesting role in determining color categorizations. I distinguish an aspect of color categorization, namely, color discrimination, from other aspects. The law of trichromacy describes conditions under which physical properties cannot be discriminated in terms of color. Trichromacy is explained by properties of neural processes.

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8

Shinomori, K., R. Yokota, and S. Nakauchi. "Color naming and color categorization by dichromats." Journal of Vision 7, no. 15 (March 28, 2010): 106. http://dx.doi.org/10.1167/7.15.106.

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9

Correia, José Pedro, and Radek Ocelák. "Towards More Realistic Modeling of Linguistic Color Categorization." Open Philosophy 2, no. 1 (August 12, 2019): 160–89. http://dx.doi.org/10.1515/opphil-2019-0013.

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Анотація:

AbstractThe ways in which languages have come to divide the visible spectrum with their color terminology, in both their variety and the apparent universal tendencies, are still largely unexplained. Building on recent work in modeling color perception and categorization, as well as the theory of signaling games, we incrementally construct a color categorization model which combines perceptual characteristics of individual agents, game-theoretic signaling interaction of these agents, and the probability of observing particular colors as an environmental constraint. We also propose a method of transparent evaluation against the data gathered in the World Color Survey. The results show that the model’s predictive power is comparable to the current state of the art. Additionally, we argue that the model we suggest is superior in terms of motivation of the principles involved, and that its explanatory relevance with respect to color categorization in languages is therefore higher. Our results suggest that the universal tendencies of color categorization cannot be explained solely in terms of the shape of the color space induced by our perceptual apparatus. We believe that only by taking the heterogeneity of the phenomenon seriously can we acquire a deeper understanding of why color categorization takes the forms we observe across languages.

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10

Goldstone, Robert L. "Effects of Categorization on Color Perception." Psychological Science 6, no. 5 (September 1995): 298–304. http://dx.doi.org/10.1111/j.1467-9280.1995.tb00514.x.

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Subjects were shown simple objects and were asked to reproduce the colors of the objects Even though the objects remained on the screen while subjects reproduced the colors and the objects' shapes were irrelevant to the subjects' task, subjects' color perceptions were influenced by the shape category of an object For example, objects that belonged to categories with redder objects were judged to be more red than identically colored objects belonging to another category Further experiments showed that the object categories that subjects use, rather than being fixed, depend on the objects to which subjects are exposed

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11

Skelton, Alice E., Gemma Catchpole, Joshua T. Abbott, Jenny M. Bosten, and Anna Franklin. "Biological origins of color categorization." Proceedings of the National Academy of Sciences 114, no. 21 (May 8, 2017): 5545–50. http://dx.doi.org/10.1073/pnas.1612881114.

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The biological basis of the commonality in color lexicons across languages has been hotly debated for decades. Prior evidence that infants categorize color could provide support for the hypothesis that color categorization systems are not purely constructed by communication and culture. Here, we investigate the relationship between infants’ categorization of color and the commonality across color lexicons, and the potential biological origin of infant color categories. We systematically mapped infants’ categorical recognition memory for hue onto a stimulus array used previously to document the color lexicons of 110 nonindustrialized languages. Following familiarization to a given hue, infants’ response to a novel hue indicated that their recognition memory parses the hue continuum into red, yellow, green, blue, and purple categories. Infants’ categorical distinctions aligned with common distinctions in color lexicons and are organized around hues that are commonly central to lexical categories across languages. The boundaries between infants’ categorical distinctions also aligned, relative to the adaptation point, with the cardinal axes that describe the early stages of color representation in retinogeniculate pathways, indicating that infant color categorization may be partly organized by biological mechanisms of color vision. The findings suggest that color categorization in language and thought is partially biologically constrained and have implications for broader debate on how biology, culture, and communication interact in human cognition.

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12

Huette, Stephanie, and Bob McMurray. "Continuous dynamics of color categorization." Psychonomic Bulletin & Review 17, no. 3 (June 2010): 348–54. http://dx.doi.org/10.3758/pbr.17.3.348.

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13

Milojevic, Z., R. Ennis, and K. Gegenfurtner. "Color categorization of natural objects." Journal of Vision 14, no. 10 (August 22, 2014): 464. http://dx.doi.org/10.1167/14.10.464.

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14

Chu, Wei-Ta, Chih-Hao Chen, and Han-Nung Hsu. "Color CENTRIST: Embedding color information in scene categorization." Journal of Visual Communication and Image Representation 25, no. 5 (July 2014): 840–54. http://dx.doi.org/10.1016/j.jvcir.2014.01.013.

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15

Jameson, Kimberly. "Culture and Cognition: What is Universal about the Representation of Color Experience?" Journal of Cognition and Culture 5, no. 3-4 (2005): 293–348. http://dx.doi.org/10.1163/156853705774648527.

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Анотація:

AbstractExisting research in color naming and categorization primarily reflects two opposing views: A Cultural Relativist view that posits color perception is greatly shaped by culturally specific language associations and perceptual learning, and a Universalist view that emphasizes panhuman shared color processing as the basis for color naming similarities within and across cultures. Recent empirical evidence finds color processing differs both within and across cultures. This divergent color processing raises new questions about the sources of previously observed cultural coherence and cross-cultural universality. The present article evaluates the relevance of individual variation on the mainstream model of color naming. It also presents an alternate view that specifies how color naming and categorization is shaped by both panhuman cognitive universals and socio-cultural evolutionary processes. This alternative view, expressed, in part, using an Interpoint Distance Model of color categorization, is compatible with new empirical results showing divergent color processing within and across cultures. It suggests that universalities in color naming and categorization may naturally arise across cultures because color language and color categories primarily reflect culturally modal linguistic mappings, and categories are shaped by universal cognitive constructs and culturally salient features of color. Thus, a shared cultural representation of color based on widely shared cognitive dimensions may be the proper foundation for universalities of color naming and categorization. Across cultures this form of representation may result from convergent responses to similar pressures on color lexicon evolution.

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16

Fagot, Joël, Julie Goldstein, Jules Davidoff, and Alan Pickering. "Cross-species differences in color categorization." Psychonomic Bulletin & Review 13, no. 2 (April 2006): 275–80. http://dx.doi.org/10.3758/bf03193843.

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17

HANARI, Takashi, and Shin'ya TAKAHASHI. "Categorization of Color Preference style(2)." Proceedings of the Annual Convention of the Japanese Psychological Association 75 (2011): 1AM125. http://dx.doi.org/10.4992/pacjpa.75.0_1am125.

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18

Malinowska, Monika, and Maciej Haman. "Subfocal Color Categorization and Naming: The Role of Exposure to Language and Professional Experience." Polish Psychological Bulletin 40, no. 4 (January 1, 2009): 170–75. http://dx.doi.org/10.2478/s10059-009-0012-4.

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Анотація:

Subfocal Color Categorization and Naming: The Role of Exposure to Language and Professional ExperienceThe current state of the debate on the linguistic factors in color perception and categorization is reviewed. Developmental and learning studies were hitherto almost ignored in this debate. A simple experiment is reported in which 20 Academy of Fine Arts, Faculty of Painting students' performance in color discrimination and naming tasks was compared to the performance of 20 Technical University students. Subfocal colors (different hues of red and blue) were used. While there was no difference in overall discrimination ability, AFA students had a much richer and specialized color vocabulary. Both groups also applied different strategies of discrimination and naming. However, naming system in neither group was coherent. This suggests that naming played primarily the role of markers for control processes rather than names for categories. It is concluded that up-to-date debate is too simplified and a complex model of interrelations between perceptual categorization and naming framed in the developmental context is needed rather than the search for a simple answer "language", "environment", or "perceptual universals".

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19

POKORNY, JOEL, MARGARET LUTZE, DINGCAI CAO, and ANDREW J. ZELE. "The color of night: Surface color categorization by color defective observers under dim illuminations." Visual Neuroscience 25, no. 3 (May 2008): 475–80. http://dx.doi.org/10.1017/s0952523808080486.

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People with normal trichromatic color vision experience variegated hue percepts under dim illuminations where only rod photoreceptors mediate vision. Here, hue perceptions were determined for persons with congenital color vision deficiencies over a wide range of light levels, including very low light levels where rods alone mediate vision. Deuteranomalous trichromats, deuteranopes and protanopes served as observers. The appearances of 24 paper color samples from the OSA Uniform Color Scales were gauged under successively dimmer illuminations from 10 to 0.0003 Lux (1.0 to −3.5 log Lux). Triads of samples were chosen representing each of eight basic color categories; “red,” “pink,” “orange,” “yellow,” “green,” “blue,” “purple,” and “gray.” Samples within each triad varied in lightness. Observers sorted samples into groups that they could categorize with specific color names. Above −0.5 log Lux, the dichromatic and anomalous trichromatic observers sorted the samples into the original representative color groups, with some exceptions. At light levels where rods alone mediate vision, the color names assigned by the deuteranomalous trichromats were similar to the color names used by color normals; higher scotopic reflectance samples were classified as blue-green-grey and lower reflectance samples as red-orange. Color names reported by the dichromats at the dimmest light levels had extensive overlap in their sample scotopic lightness distributions. Dichromats did not assign scotopic color names based on the sample scotopic lightness, as did deuteranomalous trichromats and colour-normals. We reasoned that the reduction in color gamut that a dichromat experiences at photopic light levels leads to a limited association of rod color perception with objects differing in scotopic reflectance.

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20

STEINGRIMSSON, RAGNAR. "EVOLUTIONARY GAME THEORETICAL MODEL OF THE EVOLUTION OF THE CONCEPT OF HUE, A HUE STRUCTURE, AND COLOR CATEGORIZATION IN NOVICE AND STABLE LEARNERS." Advances in Complex Systems 15, no. 03n04 (May 2012): 1150018. http://dx.doi.org/10.1142/s0219525911500184.

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Evolutionary game theory is used to form a finite partition of a continuous hue circle in which perceptually similar hues are each represented by an icon chip and the circle by a finite but game dynamically determined number of icon chips. On the basis of such icon chip structures, a color categorization for both an individual learner and a population of learners is then evolved. These results remove limitations of some particular previous color categorization simulation work which assumed a fixed number of color stimuli and a maximal number of predefined color categories. These simulations are extended to demonstrate that learners need neither to share the same icon chip structures, nor do these structures have to be fully developed for a population of learners to produce a stable color categorization system. Additionally, when a naïve learner is introduced into a population with a stable color categorization, the game dynamics result in the learner's adopting the existing categorization. All results are shown to hold while the underlying icon chip structures evolve continuously in response to novel stimuli. The usefulness of the approach as well as some of the potential implications of the results for human learning of color categories are discussed.

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21

Garaus, Marion, and Georgios Halkias. "One color fits all: product category color norms and (a)typical package colors." Review of Managerial Science 14, no. 5 (January 3, 2019): 1077–99. http://dx.doi.org/10.1007/s11846-018-0325-9.

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Abstract Despite the growing amount of research on different aspects of product package design, there is lack of empirical evidence with regard to how package color perceptions may influence consumer preferences. Based on categorization theory, the present paper explores responses to package colors that conform or do not conform to product category color norms. Results of two experiments show that atypical package colors implicate negative consequences to the brand. Findings indicate that perceived package color atypicality increases consumers’ skepticism and, contrary to expectations, decreases interest. These affective reactions negatively influence consumers’ product attitude which subsequently translates into lower purchase intention. The results provide important insights for theory and practice.

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22

NARENS, LOUIS, KIMBERLY A. JAMESON, NATALIA L. KOMAROVA, and SEAN TAUBER. "LANGUAGE, CATEGORIZATION, AND CONVENTION." Advances in Complex Systems 15, no. 03n04 (May 2012): 1150022. http://dx.doi.org/10.1142/s0219525911500226.

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Linguistic meaning is a convention. This article investigates how such conventions can arise for color categories in populations of simulated "agents". The method uses concepts from evolutionary game theory: A language game where agents assign names to color patches and is played repeatedly by members of a population. The evolutionary dynamics employed make minimal assumptions about agents' perceptions and learning processes. Through various simulations it is shown that under different kinds of reasonable conditions involving outcomes of individual games, the evolutionary dynamics push populations to stationary equilibria, which can be interpreted as achieving shared population meaning systems. Optimal population agreement for meaning is characterized through a mathematical formula, and the simulations presented reveal that for a wide variety of situations, optimality is achieved.

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23

jameson, kimberly a. "sharing perceptually grounded categories in uniform and nonuniform populations." Behavioral and Brain Sciences 28, no. 4 (August 2005): 501–2. http://dx.doi.org/10.1017/s0140525x05350084.

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Анотація:

steels & belpaeme's (s&b) procedure does not model much of the important variation that occurs across human color categorizers. human perceptual variation and its corollary consequences impact real-world color categorization. because of this, investigators with the primary aim of understanding color categorization and naming across cultures should exercise some caution extending these findings to explain how different human societies lexicalize color appearance space.

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24

Kay, Paul, and Brent Berlin. "Science ≠ imperialism: There are nontrivial constraints on color naming." Behavioral and Brain Sciences 20, no. 2 (June 1997): 196–201. http://dx.doi.org/10.1017/s0140525x97001420.

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Анотація:

Saunders & van Brakel's claim that Berlin and Kay (1969) assumed a language/vision correlation in the area of color categorization and disguised this assumption as a finding is shown to be false. The methodology of the World Color Survey, now nearing completion, is discussed and the possibility of an additional language/vision correlation in color categorization is suggested.

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25

Kay, Paul, and Brent Berlin. "Science ≠ imperialism: There are nontrivial constraints on color naming." Behavioral and Brain Sciences 20, no. 2 (June 1997): 196–201. http://dx.doi.org/10.1017/s0140525x97391420.

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Анотація:

Saunders & van Brakel's claim that Berlin and Kay (1969) assumed a language/vision correlation in the area of color categorization and disguised this assumption as a finding is shown to be false. The methodology of the World Color Survey, now nearing completion, is discussed and the possibility of an additional language/vision correlation in color categorization is suggested.

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26

Borzunov, G. I., A. V. Firsov, A. N. Novikov, and V. V. Ivanov. "Categorization of Images Based on Color Contrasts." Proceedings of Higher Education Institutions. Textile Industry Technology, no. 4 (2021): 164–67. http://dx.doi.org/10.47367/0021-3497_2021_4_164.

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27

Favorskaya, Margarita Nikolaevna, and Alexander Viktorovich Proskurin. "Scene Categorization Based on Extended Color Descriptors." SPIIRAS Proceedings 3, no. 40 (June 15, 2015): 203. http://dx.doi.org/10.15622/sp.40.13.

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28

Li, Doujie, Zhongyan Fan, and Wallace K. S. Tang. "Domain learning naming game for color categorization." PLOS ONE 12, no. 11 (November 14, 2017): e0188164. http://dx.doi.org/10.1371/journal.pone.0188164.

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29

Egré, Paul, Vincent de Gardelle, and David Ripley. "Vagueness and Order Effects in Color Categorization." Journal of Logic, Language and Information 22, no. 4 (December 2013): 391–420. http://dx.doi.org/10.1007/s10849-013-9183-7.

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30

Okajima, K., A. R. Robertson, and G. H. Fielder. "A quantitative network model for color categorization." Color Research & Application 27, no. 4 (June 17, 2002): 225–32. http://dx.doi.org/10.1002/col.10060.

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31

Ocelák, Radek. "“Categorical Perception” and Linguistic Categorization of Color." Review of Philosophy and Psychology 7, no. 1 (March 7, 2015): 55–70. http://dx.doi.org/10.1007/s13164-015-0237-4.

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32

Matthen, Mohan. "Color nominalism, pluralistic realism, and color science." Behavioral and Brain Sciences 26, no. 1 (February 2003): 39–40. http://dx.doi.org/10.1017/s0140525x03410017.

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Анотація:

AbstractByrne & Hilbert are right that it might be an objective fact that a particular tomato is unique red, but wrong that it cannot simultaneously be yellowish-red (not only objectively, but from somebody else's point of view). Sensory categorization varies among organisms, slightly among conspecifics, and sharply across taxa. There is no question of truth or falsity concerning choice of categories, only of utility and disutility. The appropriate framework for color categories is Nominalism and Pluralistic Realism.

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33

Lücking, Andy, and Alexander Mehler. "A Model of Complexity Levels of Meaning Constitution in Simulation Models of Language Evolution." International Journal of Signs and Semiotic Systems 1, no. 1 (January 2011): 18–38. http://dx.doi.org/10.4018/ijsss.2011010102.

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Currently, some simulative accounts exist within dynamic or evolutionary frameworks that are concerned with the development of linguistic categories within a population of language users. Although these studies mostly emphasize that their models are abstract, the paradigm categorization domain is preferably that of colors. In this paper, the authors argue that color adjectives are special predicates in both linguistic and metaphysical terms: semantically, they are intersective predicates, metaphysically, color properties can be empirically reduced onto purely physical properties. The restriction of categorization simulations to the color paradigm systematically leads to ignoring two ubiquitous features of natural language predicates, namely relativity and context-dependency. Therefore, the models for simulation models of linguistic categories are not able to capture the formation of categories like perspective-dependent predicates ‘left’ and ‘right’, subsective predicates like ‘small’ and ‘big’, or predicates that make reference to abstract objects like ‘I prefer this kind of situation’. The authors develop a three-dimensional grid of ascending complexity that is partitioned according to the semiotic triangle. They also develop a conceptual model in the form of a decision grid by means of which the complexity level of simulation models of linguistic categorization can be assessed in linguistic terms.

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34

Jraissati, Yasmina, and Igor Douven. "Does optimal partitioning of color space account for universal color categorization?" PLOS ONE 12, no. 6 (June 1, 2017): e0178083. http://dx.doi.org/10.1371/journal.pone.0178083.

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35

Komarova, Natalia L., and Kimberly A. Jameson. "Population heterogeneity and color stimulus heterogeneity in agent-based color categorization." Journal of Theoretical Biology 253, no. 4 (August 2008): 680–700. http://dx.doi.org/10.1016/j.jtbi.2008.03.030.

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36

Gibson, Edward, Richard Futrell, Julian Jara-Ettinger, Kyle Mahowald, Leon Bergen, Sivalogeswaran Ratnasingam, Mitchell Gibson, Steven T. Piantadosi, and Bevil R. Conway. "Color naming across languages reflects color use." Proceedings of the National Academy of Sciences 114, no. 40 (September 18, 2017): 10785–90. http://dx.doi.org/10.1073/pnas.1619666114.

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Анотація:

What determines how languages categorize colors? We analyzed results of the World Color Survey (WCS) of 110 languages to show that despite gross differences across languages, communication of chromatic chips is always better for warm colors (yellows/reds) than cool colors (blues/greens). We present an analysis of color statistics in a large databank of natural images curated by human observers for salient objects and show that objects tend to have warm rather than cool colors. These results suggest that the cross-linguistic similarity in color-naming efficiency reflects colors of universal usefulness and provide an account of a principle (color use) that governs how color categories come about. We show that potential methodological issues with the WCS do not corrupt information-theoretic analyses, by collecting original data using two extreme versions of the color-naming task, in three groups: the Tsimane', a remote Amazonian hunter-gatherer isolate; Bolivian-Spanish speakers; and English speakers. These data also enabled us to test another prediction of the color-usefulness hypothesis: that differences in color categorization between languages are caused by differences in overall usefulness of color to a culture. In support, we found that color naming among Tsimane' had relatively low communicative efficiency, and the Tsimane' were less likely to use color terms when describing familiar objects. Color-naming among Tsimane' was boosted when naming artificially colored objects compared with natural objects, suggesting that industrialization promotes color usefulness.

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37

Shamoi, Pakizar, Atsushi Inoue, and Hiroharu Kawanaka. "FHSI: Toward More Human-Consistent Color Representation." Journal of Advanced Computational Intelligence and Intelligent Informatics 20, no. 3 (May 19, 2016): 393–401. http://dx.doi.org/10.20965/jaciii.2016.p0393.

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In this paper, we propose a novel approach toward the development of a perceptual color space, FHSI, which stands for “Fuzzy HSI," because it is based on the fuzzification of the well-known HSI color space. FHSI represents a set of fuzzy colors obtained by partitioning the gamut of feasible colors in the HSI model corresponding to standardized linguistic tags. In fact, color categorization was performed on the basis of personal judgments of humans collected by way of an online survey. This approach helps to significantly enhance color matching and similarity searches by producing more intuitive and human-consistent output for users. The introduced method has potential for use in various color image applications involving query processing, for example, in the coordination of online apparel shopping.

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38

Jraissati, Yasmina. "On Color Categorization: Why Do We Name Seven Colors in the Rainbow?" Philosophy Compass 9, no. 6 (June 2014): 382–91. http://dx.doi.org/10.1111/phc3.12131.

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39

Alvarado, Nancy, and Kimberly Jameson. "The Use of Modifying Terms in the Naming and Categorization of Color Appearances in Vietnamese and English." Journal of Cognition and Culture 2, no. 1 (2002): 53–80. http://dx.doi.org/10.1163/156853702753693307.

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AbstractCross-cultural studies of color naming show that basic terms are universally the most frequently used to name colors. However, such basic color terms are always used in the context of larger linguistic systems when specific properties of color experience are described. To investigate naturalistic naming behaviors, we examined the use of modifiers in English and Vietnamese color naming using an unconstrained naming task (Jameson & Alvarado, in press). Monolingual and bilingual subjects named a representative set of 110 color stimuli sampled from a commonly used color-order stimulus space. Results revealed greater reliance upon polylexemic naming among monolingual Vietnamese speakers and greater use of monolexemic basic hue terms and secondary terms (object glosses) among monolingual English speakers. Systematic differences across these language groups imply that widely used monolexemic naming methods may differentially impact color-naming findings in cross-cultural investigations of color cognition.

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40

MacLaury, Robert E. "Domain-specificity in folk biology and color categorization: Modularity versus global process." Behavioral and Brain Sciences 21, no. 4 (August 1998): 582–83. http://dx.doi.org/10.1017/s0140525x98361270.

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Universal ranks in folk biological taxonomy probably apply to taxonomies of cultural artifacts. We cannot call folk biological cognition domain-specific and modular. Color categorization may manifest unique organization, which would result from known neurology and the nature of color as an attribute. But folk biology does not adduce equivalent evidence. A global process of increasing differentiation similarly affects folk taxonomy, color categorization, and other practices germane to Atran's anthropology of science; this is beclouded by claims of specificity and modularity.

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41

Lindsey, Delwin T. "Lexical and non-lexical color categorization and the universality of color understanding." Journal of Vision 19, no. 15 (December 1, 2019): 3. http://dx.doi.org/10.1167/19.15.3.

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42

Dufort, P. A., and C. J. Lumsden. "Color categorization and color constancy in a neural network model of V4." Biological Cybernetics 65, no. 4 (August 1991): 293–303. http://dx.doi.org/10.1007/bf00206226.

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43

Wuerger, Sophie, Kaida Xiao, Dimitris Mylonas, Qingmei Huang, Dimosthenis Karatzas, Emily Hird, and Galina Paramei. "Blue–green color categorization in Mandarin–English speakers." Journal of the Optical Society of America A 29, no. 2 (January 17, 2012): A102. http://dx.doi.org/10.1364/josaa.29.00a102.

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44

Christensen, J., and W. Miller. "The effects of color categorization on shadow perception." Journal of Vision 10, no. 7 (August 6, 2010): 446. http://dx.doi.org/10.1167/10.7.446.

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45

Komarova, Natalia L., Kimberly A. Jameson, and Louis Narens. "Evolutionary models of color categorization based on discrimination." Journal of Mathematical Psychology 51, no. 6 (December 2007): 359–82. http://dx.doi.org/10.1016/j.jmp.2007.06.001.

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46

Chen, Yuyilan, Yuqian Dai, Li Li, Chenqu Ma, and Xiaogang Liu. "A Graph-Based Representation Method for Fashion Color." Applied Sciences 12, no. 13 (July 3, 2022): 6742. http://dx.doi.org/10.3390/app12136742.

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Fashion color research takes the color information of fashion apparel as the major focus for further studies, such as style categorization or trend prediction. However, the colors in apparel are treated as isolated elements from each other, disregarding the fact that not only the attributes of each color itself but also the collocation relationship of the colors in apparel are important color factors. To provide a more comprehensive abstraction of the information from the fashion colors as well as emulating the human cognition of fashion colors, in this paper, we are the first to propose a knowledge graph-based representation method that captures not only the individual colors but also abstracts the spatial relation of all the colors that appear in a single piece of fashion apparel. This method provides the fundamental definition of the abstraction of the relation of colors, a detailed method to construct the color graph, as well as the practical matrix-based management and the visualization of the constructed graphs. The case studies for color data extraction and extended usage demonstrate the effectiveness of our method with comprehensive color data representation and effective information extraction.

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47

Dubois, Danièle. "Cultural beliefs as nontrivial constraints on categorization: Evidence from colors and odors." Behavioral and Brain Sciences 20, no. 2 (June 1997): 188. http://dx.doi.org/10.1017/s0140525x97321426.

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The following provides further arguments for the nonuniversality of color as an autonomous dimension. Research on odors suggests that there are cultural constraints on the abstraction of dimensions for objects. Color vision analysis leads to an overemphasis on the role of perceptual processes in categorization. The study of odors points to human activities as a more important principle of categorization that drives the perceptual processing and suggests a reconsideration of vision itself.

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48

Hayashi, Reiko. "Categorization in talk." Pragmatics. Quarterly Publication of the International Pragmatics Association (IPrA) 26, no. 2 (June 1, 2016): 197–219. http://dx.doi.org/10.1075/prag.26.2.02hay.

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This article seeks to advance the usage-based discourse research that investigates meanings and processes of meaning construction in interaction by elaboratingan empirically grounded interdisciplinary model. The paradigmatic and sequential analysis employed here brings together linguistic discourse analysis with an ethnomethodological perspective, and presents an innovative take on category organization in talk,explaininghow to capture knowledge resources such as asymmetrical category contrast pairs in talk. In analyzing in detail the speaker’s taxonomy constructionin a sample conversation, the papersystematicallyexploresthefollowing two topicsrelated to the speaker: what category characteristic he is orientingto as a resource for his present talkandwhat socialmeaning the speaker’s taxonomizing isconsistentlycommunicatingin the flow of talk. The proposed model captures a color binary–used to categorize people–of the ‘colored’versus the ‘white’, entailed in theexpression ‘a so-called yellowcolored people’,and reveals that the category pair is used as an organizational device in the speaker’s argument.The paper claims that taxonomy analysis in sequence is useful to examine the selected words in relation to their semiotic resources.

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49

Ruba, Ashley L., Christopher A. Thorstenson, and Betty M. Repacholi. "Red Enhances the Processing of Anger Facial Configurations as a Function of Target Gender." Social Cognition 39, no. 3 (June 2021): 396–407. http://dx.doi.org/10.1521/soco.2021.39.3.396.

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Various contextual factors, such as color, modify how emotions are perceived on the face. In particular, the color red enhances categorization of anger on faces. Yet, an open question remains as to whether red facilitates anger categorization uniformly or whether this effect is specific to targets with characteristics already highly associated with anger. The current work examines whether the color red facilitates anger categorization and whether this effect varies as a function of target gender. We found that red facilitates the processing of anger for male faces (Experiment 1) but not for female faces (Experiment 2), likely due to stronger implicit associations between red with anger for male faces (Experiment 3). The findings suggest that cues to emotion (e.g., red cueing anger) are most salient when the meaning of the signal (e.g., threat) matches observer's implicit notions about the target's characteristics (e.g., capability of doing harm; males).

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50

Imai, C., S. Tajima, K. Aihara, and H. Suzuki. "Hybrid coding of colors: How can we unify color discrimination, categorization and memory?" Journal of Vision 11, no. 11 (September 23, 2011): 383. http://dx.doi.org/10.1167/11.11.383.

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