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  1. #1
    Mustafa Umut Sarac's Avatar
    Join Date
    Oct 2006
    İstanbul - Türkiye
    35mm RF

    Russian Chromatic Terms from 30 Muscovites aged 25-45

    krasnyj 1090Y80R
    oranževyj 0090Y40R
    žëltyj 0080Y
    zelënyj 2070G10Y
    sinij 3060R80B
    ‘dark blue’
    goluboj 1060R90B
    ‘light blue’
    rozovyj 0040R,
    ‘pink’ 0040R20B
    fioletovyj 3060R50B
    koriˇcnevyj 6030Y50R,
    ‘brown’ 6030Y50R
    kofejnyj coffee-colored 5030Y30R
    persikovyj peach-colored 0050Y30R
    slonovaja kost’ ivory-colored 0010Y10R
    beževyj beige 020Y30R
    kremovyj (mean) creamy 0020Y50R
    zeltovato-kremovyj yellowish-creamy 0020Y30R
    oranževo-kremovyj orange-creamy, salmon 0020Y50R
    rozovato-kremovyj pinkish-creamy 0020Y70R
    pesoˇcnyj sandy 8.2±4.1 42.4±12.6 18.8±8.9 1030Y20R
    ryžij brownish red, ginger 1080Y50R
    kirpiˇcnyj brick-colored 1080Y70R
    korallovyj (mean) coral 1070Y70R
    oranževo-korallovyj orange coral 1060Y70R
    krasno-korallovyj red coral 1070Y80R
    alyj scarlet 1090Y90R
    malinovyj raspberry 1070R10B
    rubinovyj ruby 2080R
    bordovyj red whine, claret 4060R
    višnevyj cherry 3070R10B
    sirenevyj lilac 2050R50B
    lilovyj mauve 3050R50B
    biruzovyj (mean) turquoise 2050B30G
    sinij-birusovyj dark-blue turquoise 2050B20G
    zelënyj-biruzovyj green turquoise 2050B50G
    morskaja volna (mean) sea wave 3050B40G
    sinyaja morskaja volna dark-blue sea wave 3050B30G
    zelënaja morskaja volna green sea wave 2060B50G
    izumrudnyj emerald-green 2060G
    salatovyj light green, lettuce 0060G30Y
    fistaškovyj pistachio-green 12.1±9.5 50.0±10.4 44.3±19.8 –
    limonnyj lemon-colored 0070G90Y
    tabaˇcnyj tobacco 4040G80Y
    xaki khaki 4040G70Y
    olivkovyj olive 36.6±12.1 43.8±13.2 56.3±15.7
    bolotnyj marsh 5050G70Y
    zasˇcitnyj brown-green, khaki 3050G80Y
    purpurnyj cardinal red 2080R

  2. #2
    Mustafa Umut Sarac's Avatar
    Join Date
    Oct 2006
    İstanbul - Türkiye
    35mm RF
    Thirty native Russian individuals (Muscovites) volunteered for the experiment. They were
    aged between 20 and 45 years, and had no formal training in color. All were screened for
    red-green abnormalities using the Rabkin Pseudoisochromatic Plates. The experiment was
    conducted with each participant individually.
    The color samples of the NCS atlas were presented under standard daylight illumination.
    For each color term, the focal color was estimated. When presented with the color term,
    the individual was requested to indicate in the atlas page the only sample that would best
    represent the term. If in the person’s view, none of the samples adequately represented the
    color name, or the meaning of the name was unknown, no response was recorded.
    The order of presentation of color names was yielded by an experimenter in the
    following steps. First, according to the hue of the NCS atlas page, one of the eight Russian
    chromatic basic color terms was named: krasnyj ‘red,’ oranževyj ‘orange,’ and so
    on. Second, color-term variants with the achromatic modifiers were named. Next, compound
    chromatic terms were presented, comprising two basic color names (or more, if a participant insisted), such as oranževato-krasnyj ‘orangish-red’ or žëlto-zelënyj ‘yellowgreen.’
    (In Russian, the suffix -ato in an adjective indicates lower salience of the denoted
    quality.) Then followed the names comprising basic chromatic and achromatic components,
    such as rozovato-belyj ‘pinkish-white,’ krasnovato-ˇcërnyj ‘reddish-black,’ rozovatoseryj
    ‘pinkish-gray’; and finally, frequent non-basic names, such as malinovyj ‘raspberry,’
    or sirenevyj ‘lilac.’

  3. #3
    Mustafa Umut Sarac's Avatar
    Join Date
    Oct 2006
    İstanbul - Türkiye
    35mm RF
    I want to put each colors in a chart. I need to create the colors in a small square. I need your help , I want to do it with may be GIMP and I need help for this. ıs there anyone who can help me ?


  4. #4
    Mustafa Umut Sarac's Avatar
    Join Date
    Oct 2006
    İstanbul - Türkiye
    35mm RF
    You Only See Colors You Can Name

    While color is a purely visual phenomenon, the way we see color is not only a matter of our visual systems. It is well known that we are faster in telling colors apart that have different names, but do the names determine the colors or the colors the names? Recent work shows that language has a stronger influence than previously thought.

    The Sapir-Whorf Hypothesis

    If and how much language shapes our thought has been the subject of many debates over the years. In the 1930s, Edward Sapir and Benjamin Lee Whorf described a view that language determines our thinking: if we don’t have a word for a concept, we cannot think about it. This was a popular view for a while, but fell out of favor in the 1960s. The pendulum then swung the other way, with researchers believing that there was no connection between language and thought, and that language was a purely abstract construct.

    In the last 20 years or so, a middle ground has started to develop. While it’s clear that language does not entirely determine our thinking, there is certainly an influence. The surprising thing is how deeply seated that influence can be.

    Russian Blues

    In their paper, Russian blues reveal effects of language on color discrimination, Jonathan Winawer, Nathan Witthoft, Michael C. Frank, Lisa Wu, Alex R. Wade, and Lera Boroditsky looked at differences in how native English and Russian speakers distinguish shades of blue.

    It turns out that there is no single word for the English “blue” in Russian. The term siniy describes what most other languages know as dark blue, while goluboy is the name for lighter blues. The question is, does that difference mean that there is a difference in color perception between Russian speakers and speakers of other languages, like English?

    The test Winawer and colleagues came up with is based on the well-known fact that it is easier for us to distinguish colors that have different names. When shown a reference color and two possible matching colors, we’re much faster when presented with, say, blue and orange than just two shades of orange.

    The question is whether that is also true for Russian speakers and their different words for shades of blue. After all, our color names might be based on the same perceptual effects that our color perception uses to distinguish categorically different colors.

    The result was that Russian speakers did indeed have an advantage over English speakers in telling siniy and goluboy apart. The authors of that paper then went on to test whether the reason was really language and not some genetic variation or similar. They had the study participants recite nonsense words (to keep their language centers busy) while performing the study, and found that under this condition, the difference went away.

    It was clearly the language system interfering with a task that was presumably purely visual: distinguishing between different colors. Categories in our thinking may go much deeper than we think.

    The Himba Tribe

    A tribe in northern Namibia, named the Himba, have seemingly unusual names for colors. What the video embedded below (linked here for people reading this in their newsreaders) shows is that those names make it easier for them to see some color differences that most other people would find very difficult, whereas they have trouble telling colors apart that look quite different to most of us.

    What the video unfortunately does not discuss is why they have these names for colors. There is a slight hint when one of the tribesmen describes several things that are “white,” like milk and water. It seems to me that their color names do not only (or primarily) describe hue, but also function of the things whose color they name. This is a very pragmatic way of using language, and is not unlike some languages whose grammatical genders are based not on sex, but on classes of things and animals that are more specific, like large vs. small animals, plants, dead things, etc.


    The impact of language and higher-level concepts on visualization is the key to understanding how visualization actually works. Abstract concepts like color, shape, size, etc. seen in isolation elicit associations and embellishments that influence what we see and how we think about it.

    Caroline Ziemkiewicz’s work on visual and verbal metaphors in tree visualization and the role of gravity in visualization is a case in point. Even seemingly pure and abstract depictions of data are influenced by assumptions about the world and/or the way we think about the structure of the data.

    The beauty of visualization is not only its visual nature and all the complexity it brings with it, but especially the deep connections we’re only discovering as we dive deeper into it.



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