An online colour naming experiment in Russian using Munsell colour samples

Russian colour naming was explored in a web-based psycholinguistic experiment. The purpose was threefold: to examine (i) CIELAB coordinates of centroids for 12 Russian basic colour terms (BCTs), including two Russian terms for ‘blue’, sinij ‘dark blue’ and goluboj ‘light blue’, and compare these with coordinates for the 11 English BCTs obtained in earlier studies; (ii) frequent non-BCTs and (iii) gender differences in colour naming. Native Russian speakers participated in the experiment using an unconstrained colour-naming method. Each participant named 20 colours, selected from 600 colours densely sampling the Munsell Color Solid. Colour names and response times of typing onset were registered. Several deviations between centroids of the Russian and English BCTs were found. The two Russian ‘blues’, as expected, divided the BLUE area along the lightness dimension; their centroids deviated from a centroid of English blue. Further minor departures were found between centroids of Russian and English counterparts of ‘brown’ and ‘red’. The Russian colour inventory confirmed the linguistic refinement of the PURPLE area, with high frequencies of non-BCTs. In addition, Russian speakers revealed elaborated naming strategies and use of a rich inventory of non-BCTs. Elicitation frequencies of the 12 BCTs were comparable for both genders; however, linguistic segmentation of colour space, employing a synthetic observer, revealed gender differences in naming colours, with more refined naming of the “warm” colours from females. We conclude that, along with universal perceptual factors, that govern categorical partition of colour space, Russian speakers’ colour naming reflects language-specific factors, supporting the weak relativity hypothesis

Objects as culture-specific referents of color terms in Russian

The present study is an extension of our analysis of Russian basic color terms (BCTs) elicited in a web-based psycholinguistic experiment. Color samples (N = 600) were approximately uniformly distributed in the Munsell color solid. An unconstrained color-naming method was employed. Native Russian speakers (N = 713; 333 males) participated in the study. Among 1422 elicited unique color words, 698 terms (49%) were derived from object names. Here we explore object-derived non-BCTs, focusing on broad classes of names referred to objects, categories within these, and the inventory of color terms, as well as their frequency, patterns of derivation, and derivational productivity. Six classes of object referents were identified: flora, fauna, inanimate nature, food and beverages, man-made objects, body and bodily products. In detail, 20 most frequent object-derived terms are reported. These are accompanied by analysis of gender differences and representation of the terms' denotata on the Munsell Mercator projection. In addition, Russian object-derived color terms are related to those in English; discussed are differences between the 2 languages in the color term classes, inventories and incidences. We conclude that Russian object-derived color terms follow the generic metonymy pattern, that is, signifying color of objects in the speakers' natural environment. The inventory is also language-specific, reflecting social practices, preferences and views entrenched in the traditional Russian culture. Furthermore, recent extensive development of the inventory signals 2 novel phenomena: marked globalization influence, surfacing as abundant transliteration of English referent loanwords, and noticeable sociolectal diversification that manifests itself by novel evocative color terms, particularly in marketing and advertisement

An online color naming experiment in Russian using Munsell color samples

Russian color naming was explored in a web-based experiment. The purpose was 3-fold: to examine (1) CIELAB coordinates of centroids for 12 Russian basic color terms (BCTs), including 2 Russian terms for “blue”, sinij “dark blue”, and goluboj “light blue”, and compare these with coordinates for the 11 English BCTs obtained in earlier studies; (2) frequent nonBCTs; and (3) gender differences in color naming. Native Russian speakers participated in the experiment using an unconstrained color-naming method. Each participant named 20 colors, selected from 600 colors densely sampling the Munsell Color Solid. Color names and response times of typing onset were registered. Several deviations between centroids of the Russian and English BCTs were found. The 2 “Russian blues”, as expected, divided the BLUE area along the lightness dimension; their centroids deviated from a centroid of English blue. Further minor departures were found between centroids of Russian and English counterparts of “brown” and “red”. The Russian color inventory confirmed the linguistic refinement of the PURPLE area, with high frequencies of nonBCTs. In addition, Russian speakers revealed elaborated naming strategies and use of a rich inventory of nonBCTs. Elicitation frequencies of the 12 BCTs were comparable for both genders; however, linguistic segmentation of color space, employing a synthetic observer, revealed gender differences in naming colors, with more refined naming of the “warm” colors from females. We conclude that, along with universal perceptual factors, that govern categorical partition of color space, Russian speakers’ color naming reflects language-specific factors, supporting the weak relativity hypothesis

Color naming in Italian language

The present study investigated Italian basic color terms (BCTs). It is an extension of our previous work that explored Italian basic color categories (BCCs) using a constrained color-naming method, with 11 Italian BCTs allowed, including blu for naming the BLUE area. Since a latter outcome indicated a categorization bias, here monolexemic color-naming method was employed, enabling also use of azzurro, deeply entrenched Italian term that designates light blue. In Experiment 1, colors (N=367), sampling the Munsell Mercator projection, were presented on a CRT; color names and reaction times of vocalization onset were recorded. Naming consistency and consensus were estimated. Consistency was obtained for 12 CTs, including the two blue terms; consensus was found for 11 CTs, excluding rosso ‘red’. For each consensus category, color with the shortest RT was considered focal. In Experiment 2, consensus stimuli (N=72) were presented; on each trial, observers indicated the focal color (“best example”) in an array of colors comprising a consensus category. For each of the 12 Italian CCs, centroid was calculated and focal color (two measures) estimated. Compared to English color terms, two outcomes are specific to Italian color naming: (i) naming of the RED-PURPLE area is highly refined, with consistent use of emergent non-BCTs; (ii) azzurro and blu both perform as BCTs dividing the BLUE area along the lightness dimension. The findings are considered in the framework of the weak relativity hypothesis. Historico-linguistic, environmental and pragmatic communication factors are discussed that conceivably have driven the extension of the BCT inventory in Italian

Introduction to cosmetology: Color seasons and palettes

This is a step-by-step cosmetology handbook with instructions and techniques employed by professional makeup artists to transform a woman from average to gorgeous

Intergenerational differences in Russian color naming in the globalized era: linguistic analysis

The present study is an apparent-time analysis of color terms in Russian native speakers (N = 1927), whose age varied between 16 and 98 years. Stratified sampling was employed with the following age groups: 16–19, 20–29, and so on, with the oldest group of 70 years and over. Color names were elicited in a web-based psycholinguistic experiment (http://colournaming.com). Participants labeled color samples (N = 606) using an unconstrained color-naming method. Color vocabulary of each age group was estimated using multiple linguistic measures: diversity index; frequency of occurrences of 12 Russian basic color terms (BCTs) and of most frequent non-BCTs; color-naming pattern. Our findings show intergenerational differences in Russian color-term vocabulary, color-naming patterns, and object referents. The CT diversity (measured by the Margalef index) progressively increments with speakers’ juniority; the lexical refinement is manifested by the increasing variety of BCT modifiers and growing use of non-BCTs, both traditional and novel. Furthermore, the most frequent Russian non-BCTs sirenevyj “lilac”, salatovyj “lettuce‐colored”, and birûzovyj “turquoise” appear to be the emerging BCTs. The greatest diversity and richness of CT inventory is observed in Russian speakers aged 20–59 years, i.e., those who constitute the active workforce and are enthusiastic consumers. In comparison, speakers of 60 and over manifest less diverse color inventory and greater prevalence of (modified) BCTs. The two youngest groups (16–29 years) are linguistic innovators: their color vocabulary includes abundant recent loanwords, predominantly from English and, not infrequently, CTs as nouns rather than adjectives. Moreover, Generation Z (16–19 years) tend to offer highly specific or idiosyncratic color descriptors that serve expressive rather than informative function. The apprehended dynamics of color naming in apparent time reflects intergenerational differences as such, but even more so dramatic changes of sociocultural reality in the post-Soviet era, whereby Russian speakers, in particular under 60 years, were/are greatly impacted by globalization of trade: new market product arrivals resulted in adoption of novel and elaboration of traditional CTs for efficient communication about perceived colo

Augmenting basic colour terms in english

In an unconstrained colour naming experiment conducted over the web, 330 participants named 600 colour samples in English. The 30 most frequent monolexemic colour terms were analyzed with regards to frequency, consensus among genders, response times, consistency of use, denotative volume in the Munsell and OSA colour spaces and inter-experimental agreement. Each of these measures served for ranking colour term salience; rankings were then combined to give a composite index of basicness. The results support the extension of English inventory from the 11 basic colour terms of Berlin and Kay to 13 terms by the addition of lilac and turquoise

Color palettes for Stata graphics: an update

This paper is an update to Jann (2018). It contains a comprehensive discussion of the -colorpalette- command, including various changes and additions that have been made to the software since its first publication. Command -colorpalette- provides colors for use in Stata graphics. In addition to Stata's default colors, -colorpalette- supports a variety of named colors, a selection of palettes that have been proposed by users, numerous collections of palettes and colormaps from sources such as ColorBrewer, Carto, D3.js, or Matplotlib, as well as color generators in different color spaces. The command also provides features such as color interpolation or color vision deficiency simulation

Color & facial analysis...personalized eyewear

This literature review will provide guidelines involving the application of color and facial analysis for the purpose of personalized eyewear dispensing. The concept of color analysis and its various classifications will be explained to provide an understanding of how different color groups affect an individual\u27s appearance. The influence that frame selection has on facial feature proportions and overall facial contour will be presented in a manual format. The combination of these concepts may then be used to provide the patient with the most appropriate selection of eyewear