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Interactive influences of color attributes on color perception bias

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Abstract

Graphic user interfaces and information visualization use color to represent qualitative or quantitative information. The interaction between adjacent colors leads to perceptual bias, known as simultaneous color contrast, and implicitly distort the understanding of visualized information presentation. To investigate the effect of simultaneous color contrast, we conduct two empirical experiments, in both theoretical and application settings, using a set of random target/proximal combinations of colors in the CIEL*a*b* color space. The perception bias of a target color, induced by its surround, is measured. Linear regression analysis indicates that both a high saturation of the proximal color and a high a*/low b* value of the target color cause a strong simultaneous color contrast (i.e., high perception bias). A moderating effect analysis indicates that a* value/b* value of the target color moderates the influence of the saturation of the proximal color on the perception bias. For example, controlling the saturation of the proximal color, the more reddish/yellowish the target color is, the more alleviated the perceptual bias is.

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Funding

This research is supported by National Natural Science Foundation of China (71702080), Humanities and Social Sciences Fund of Ministry of Education (17YJC630071), and an Australian Government Research Training Program Scholarship.

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Authors and Affiliations

  1. College of Intelligence and Computing, Tianjin University, Tianjin, China

    Huan Yang

  2. School of Management, University of Science and Technology of China, Hefei, China

    Yi-Na Li

  3. Department of Computer Science, The University of Texas at Dallas, Richardson, USA

    Kang Zhang

  4. Faculty of Information Technology, Macau University of Science and Technology, Macau, China

    Kang Zhang

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  1. Huan Yang
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Correspondence to Yi-Na Li.

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Yang, H., Li, YN. & Zhang, K. Interactive influences of color attributes on color perception bias. Vis Comput 36, 925–937 (2020). https://doi.org/10.1007/s00371-019-01706-6

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