The basic color terms of Russian

Davies, Ian; Corbett, Greville G.

doi:10.1515/ling.1994.32.1.65

Cited by 75 publications

(96 citation statements)

References 8 publications

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“…However, the tables of n i and mr i that have been published for a number of languages (e.g. Davies & Corbett 1994a, 1994bDavies & Corbett & Margalef 1995;Hippisley 2001) are not sufficient for calculating S(i).…”

Section: Discussionmentioning

confidence: 99%

“…Previous studies of colour linguistics used the listing or free-emission task as part of a standard field method for eliciting the basic colour terms in a given language (Davies & Corbett 1994a, 1994bSutrop 2002;Uusküla 2007;Vejdemo et al 2015). In this context, the participants' lists are processed to yield a sali- On its own, a discontinuity in the otherwise-exponential decline of CSI is not an infallible criterion of 'basicness'.…”

Section: Discussionmentioning

confidence: 99%

“…An unequivocal BCT can slip down the rankings if its appearance in lists is reduced by the weakness of the cognitive associations connecting it to other BCTs, while conversely a hyponym or recessive near-synonym of a BCT can be elevated into the 'basic' side of the step if it is strongly linked to its dominant partner (this occurs for 'violet' in the English language results from Smith et al 1995). In addition, the step is not universally obvious or abrupt (Davies & Corbett 1994b); it may continue over more than one interval and leave some terms partway down the steeper incline, requiring a subjective judgement whether they are BCTs or not. Cognitive salience is only one indicator, to be weighed with others.…”

Section: Discussionmentioning

confidence: 99%

“…Both indicators of salience have been published in a number of studies of a variety of languages (e.g. Davies & Corbett 1994a, 1994bDavies & Corbett & Margalef 1995;Hippisley 2001). Urmas Sutrop (2001) combined them in the Cognitive Salience Index, CSI(i) = n i / N / mr i .…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

From Listing Data to Semantic Maps: Cross-Linguistic Commonalities in Cognitive Representation of Colour

Uusküla¹,

Bimler²

2016

FEJF

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When a free-listing task is used to elicit verbal concepts from a given semantic domain, it provides two indicators of the salience of each word for that linguistic community. These are the proportion of the subjects who include a word in their lists, and its average ranking priority position across the lists. The data also contain cues about the cognitive representation of the semantic domain, and in particular about the conceptual closeness among words. Closely associated words tend to prime each other and to appear in the lists in close succession. Clusters of mutually associated terms can be recognised, listed in one another's company, although with different priority for different subjects. We applied this approach to the domain of colour terms, converting lists for fourteen European languages into matrices of inter-term similarity, for analysis with multidimensional scaling (MDS) and hierarchical clustering. Two-dimensional MDS solutions or 'maps' were typically required to reflect two competing criteria by which terms were sequenced. Speakers of each language tended to follow a salience gradient, but also made separate clusters of fully-chromatic concepts -colour terms in strictu sensu -and unsaturated or desaturated concepts defined primarily by lightness rather than by hue. This and other features recurred across the languages despite their geographical and phylogenetic diversity, as cross-cultural universals in colour language, in addition to the well-known regularities governing basic colour terms and the stages of colour-lexicon development.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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From Listing Data to Semantic Maps: Cross-Linguistic Commonalities in Cognitive Representation of Colour

Uusküla¹,

Bimler²

2016

FEJF

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“…For example, yellow denotes only light colors. Similarly, the distinction between the two blue terms in Russian (Davies & Corbett, 1994) and Turkish (Ö zgen & Davies, 1998) is primarily based on lightness. In fact, the only basic color terms in English that have examples across all lightness levels are blue and green (Boynton & Olson, 1987).…”

Section: Experiments 4: Induced Cp Effects On Hue and Lightnessmentioning

confidence: 99%

Acquisition of categorical color perception: A perceptual learning approach to the linguistic relativity hypothesis.

Özgen

Davies

2002

Journal of Experimental Psychology: General

153

142

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Color perception can be categorical: Between-category discriminations are more accurate than equivalent within-category discriminations. The effects could be inherited, learned, or both. The authors provide evidence that supports the possibility of learned categorical perception (CP). Experiment 1 demonstrated that observers' color discrimination is flexible and improves through repeated practice. Experiment 2 demonstrated that category learning simulates effects of "natural" color categories on color discrimination. Experiment 3 investigated the time course of acquired CP. Experiment 4 found that CP effects are acquired through hue-and lightness-based category learning and obtained interesting data on the dimensional perception of color. The data are consistent with the possibility that language may shape color perception and suggest a plausible mechanism for the linguistic relativity hypothesis.This article explores the nature and origin of color categorization by considering how categorical perception (CP; e.g., Harnad, 1987), linguistic relativity (e.g., Whorf, 1940Whorf, /1956, and perceptual learning (e.g., Goldstone, 1998) are related. These three areas of inquiry overlap because, first, differences in CP across languages are one manifestation of linguistic relativity (e.g., Kay & Kempton, 1984), and, second, perceptual learning is a likely route through which language influences color cognition, and more specifically through which CP is induced. Our experiments explored whether adult color discrimination can be improved through training and whether CP is induced by learning novel color categories. We argue that if they can be, then this increases the plausibility that similar mechanisms may be involved during language learning, giving rise to relativistic effects. Before explicating this sketch of our argument further, we briefly review each of the three areas.

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Color naming for the Persian language

Kandi

Tehran

Hassani

et al. 2014

Color Research & Application

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According to Berlin and Kay, there is a set of 11 universal basic color terms: White, Black, Red, Green, Yellow, Blue, Brown, Purple, Pink, Orange, and Gray. To approve this, a world survey (WCS) was begun to gather data on color categorization through the world, although, despite many languages, Persian language has not been studied so far. We report a study of color naming in Iran. A study was carried out in six major cities including: Tehran, Isfahan, Mashhad, Yazd, Rasht, and Shiraz to investigate the color naming for Persian language and also the effect of local culture. The main purpose of this study was to describe the basic color terms of Persian language and to verify Berlin and Kay's theory. In total, 200 subjects were studied from all the six cities. Our results showed that there were the same basic color terms (as Berlin and Kay's theory) in Persian language overall including:“ (White) ”,“ (Black) ”, “ (Red) ”, “ (Green) ”, “ (Yellow) ”, “ (Blue) ”, “ (Brown) ”, “ (Purple) ”, “ (Pink) ”, “ (Orange), ” and “ (Gray) ”. However, this was not valid for all the cities when they were investigated individually. There were 10 basic terms in Mashhad and Shiraz while the other cities had 11 basic terms. Also, the set of the used terms would differ by the city. © 2014 Wiley Periodicals, Inc. Col Res Appl, 40, 352–360, 2015

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The basic color terms of Russian

Cited by 75 publications

References 8 publications

From Listing Data to Semantic Maps: Cross-Linguistic Commonalities in Cognitive Representation of Colour

From Listing Data to Semantic Maps: Cross-Linguistic Commonalities in Cognitive Representation of Colour

Acquisition of categorical color perception: A perceptual learning approach to the linguistic relativity hypothesis.

Color naming for the Persian language

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