September 2015
Volume 15, Issue 12
Vision Sciences Society Annual Meeting Abstract  |   September 2015
Which color means more? An investigation of color-quantity mapping in data visualization
Author Affiliations
  • Karen Schloss
    Department of Cognitive, Linguistic, and Psychological Sciences, Brown University
  • Connor Gramazio
    Department of Computer Science, Brown University
  • Charlotte Walmsley
    Department of Biology, Brown University
Journal of Vision September 2015, Vol.15, 1317. doi:
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      Karen Schloss, Connor Gramazio, Charlotte Walmsley; Which color means more? An investigation of color-quantity mapping in data visualization. Journal of Vision 2015;15(12):1317. doi:

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      © ARVO (1962-2015); The Authors (2016-present)

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Colormaps representing quantities are a standard tool of data visualization (e.g., in correlation matrices, brain activation diagrams, and topographic maps). We investigated people’s implicit intuitions about how the extremes of color scales (e.g., dark/light) map onto represented quantities (e.g., large/small, high/low), and whether they are modulated by contrast with the background. Participants were shown fictitious data matrices in which columns represented time, rows represented alien species, and cell color represented how often each species was spotted during each time window. Each participant was shown one colormap and indicated whether there were more animals early or late. Early/late differences were clearly present, but no legend indicated how to interpret the colors. We expected higher-contrast would correspond to larger quantities. In Experiment 1 we tested dark-red/light-orange and dark-blue/light-cyan colormaps on black and white backgrounds. Almost everyone (89%-95%) inferred that darker colors represented larger quantities. Surprisingly, the effect was not modulated by contrast with the background, perhaps because all the colors were relatively high-contrast. In Experiment 2, we used the same colormaps but the background colors were extensions of the scale endpoints (e.g., darker blue or lighter cyan for the dark-blue/light-cyan colormap) so that one endpoint was always low-contrast. We also tested a gray-scale colormap on white and black backgrounds. Against light backgrounds, 90%-98% showed the dark-is-more bias. Against dark backgrounds, significantly fewer participants showed the same bias (53%-74%), but the pattern did not reverse. Therefore, participants have a strong dark-is-more bias, which is diluted, but not reversed, when dark colors are low-contrast. In a systematic survey of colormaps in published visualizations (e.g., in Nature Neuroscience) we found that many violate the dark-is-more bias. Using empirically validated natural intuitions for color-concept associations will help make complex datasets easier to understand.

Meeting abstract presented at VSS 2015


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