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Color Semantics for Visual Communication

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Visualization Psychology

Abstract

Visual communication through information visualizations (e.g., graphs, charts, maps, diagrams, and signage) is central to how people share knowledge. In information visualizations, visual features such as color are used to encode concepts represented in the visualization (“encoded mappings”). However, people have expectations about how colors map to concepts (“inferred mappings”), which influence the ability to interpret encoded mappings. Inferred mappings have an effect even when legends explicitly specify the encoded mappings and when encoded concepts lack specific, strongly associated colors. In this chapter, we will discuss factors that contribute to inferred mappings for visualizations of categorical information and visualizations of continuous data. We will then discuss how these different kinds of factors can be united into a single framework of assignment inference. Understanding how people infer meaning from colors will help design information visualizations that facilitate effective and efficient visual communication.

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Notes

  1. 1.

    The abstract concepts had relatively low specificity (close to uniform color–concept association distributions), and the concrete concepts had high specificity (peaky color–concept association distributions), but that correspondence is not always the case (e.g., anger is an abstract concept but has high specificity).

  2. 2.

    Here, when we are discussing semantic discriminability, we are referring to a metric called “semantic contrast.” Unlike semantic distance, which quantifies the semantic discriminability of a color palette as a whole, semantic contrast quantifies the distance between a single color and all other colors in the palette. Computational details of these two metrics can be found at [22].

  3. 3.

    As specified in [37], the opacity variation index is defined as \(log({z}+1)\), where z is the root mean-squared error between each point on the color scale (square markers in Fig. 1.15b and c) and the line between the highest-contrast color and the background (circle markers in Fig. 1.15b and c). Smaller values correspond to greater perceptual evidence for opacity variation.

References

  1. A. N. Bartel, K. J. Lande, J. Roos, and K. B. Schloss. A holey perspective on Venn diagrams. Cognitive Science, 46(1):e13073, 2021.

    Google Scholar 

  2. J. Bertin. Semiology of graphics: diagrams, networks, maps. University of Wisconsin Press, Madison, 1983.

    Google Scholar 

  3. J. Blachowicz. Analog representation beyond mental imagery. The Journal of Philosophy, 94(2):55–84, 1997.

    Article  Google Scholar 

  4. C. A. Brewer. Color use guidelines for mapping and visualization. In A. M. MacEachren and D. R. F. Taylor, editors, Visualization in Modern Cartography, pages 123–148. Elsevier Science Inc., Tarrytown, 1994.

    Chapter  Google Scholar 

  5. R. Burkard, M. Dell’Amico, and S. Martello. Assignment problems: revised reprint. SIAM, 2012.

    Book  MATH  Google Scholar 

  6. M. Christen, D. A. Vitacco, L. Huber, J. Harboe, S. I. Fabrikant, and P. Brugger. Colorful brains: 14 years of display practice in functional neuroimaging. NeuroImage, 73:30–39, 2013.

    Article  Google Scholar 

  7. W. S. Cleveland and R. McGill. Graphical perception and graphical methods for analyzing scientific data. Science, 229(4716):828–833, 1985.

    Article  Google Scholar 

  8. D. J. Cuff. Colour on temperature maps. The Cartographic Journal, 10(1):17–21, 1973.

    Article  Google Scholar 

  9. A. R. Fox. A psychology of visualization or (external) representation? arXiv preprint arXiv:2009.13646, 2020.

    Google Scholar 

  10. R. L. Goldstone, F. Pestilli, and K. Börner. Self-portraits of the brain: cognitive science, data visualization, and communicating brain structure and function. Trends in Cognitive Sciences, 19(8):462–474, 2015.

    Article  Google Scholar 

  11. G. P. Goodwin and P. Johnson-Laird. Reasoning about relations. Psychological Review, 112(2):468, 2005.

    Google Scholar 

  12. M. Hegarty. The cognitive science of visual-spatial displays: Implications for design. Topics in Cognitive Science, 3(3):446–474, 2011.

    Article  Google Scholar 

  13. D. Jonauskaite, A. M. Abdel-Khalek, A. Abu-Akel, A. S. Al-Rasheed, J.-P. Antonietti, Á. G. Ásgeirsson, K. A. Atitsogbe, M. Barma, D. Barratt, V. Bogushevskaya, et al. The sun is no fun without rain: Physical environments affect how we feel about yellow across 55 countries. Journal of Environmental Psychology, 66:101350, 2019.

    Article  Google Scholar 

  14. D. Jonauskaite, J. Wicker, C. Mohr, N. Dael, J. Havelka, M. Papadatou-Pastou, M. Zhang, and D. Oberfeld. A machine learning approach to quantify the specificity of colour–emotion associations and their cultural differences. Royal Society Open Science, 6(9):190741, 2019.

    Google Scholar 

  15. G. Kindlmann, E. Reinhard, and S. Creem. Face-based luminance matching for perceptual colormap generation. In IEEE Visualization, 2002. VIS 2002., pages 299–306. IEEE, 2002.

    Google Scholar 

  16. S. M. Kosslyn. Understanding charts and graphs. Applied Cognitive Psychology, 3(3):185–225, 1989.

    Article  Google Scholar 

  17. H. W. Kuhn. The Hungarian method for the assignment problem. Naval Research Logistics Quarterly, 2(1–2):83–97, 1955.

    Article  MathSciNet  MATH  Google Scholar 

  18. S. Lin, J. Fortuna, C. Kulkarni, M. Stone, and J. Heer. Selecting semantically-resonant colors for data visualization. In Computer Graphics Forum, volume 32, pages 401–410. Wiley Online Library, 2013.

    Google Scholar 

  19. A. Lindner, N. Bonnier, and S. Süsstrunk. What is the color of chocolate?–extracting color values of semantic expressions. In Conference on Colour in Graphics, Imaging, and Vision, volume 2012, pages 355–361. Society for Imaging Science and Technology, 2012.

    Google Scholar 

  20. C. J. Maley. Analog and digital, continuous and discrete. Philosophical Studies, 155(1):117–131, 2011.

    Article  Google Scholar 

  21. M. McGranaghan. Ordering choropleth map symbols: The effect of background. The American Cartographer, 16(4):279–285, 1989.

    Article  Google Scholar 

  22. K. Mukherjee, B. Yin, B. E. Sherman, L. Lessard, and K. B. Schloss. Context matters: A theory of semantic discriminability for perceptual encoding systems, 2021.

    Google Scholar 

  23. J. Munkres. Algorithms for the assignment and transportation problems. Journal of the Society for Industrial and Applied Mathematics, 5(1):32–38, 1957.

    Article  MathSciNet  MATH  Google Scholar 

  24. S. Murthy, R. Hawkins, and T. L. Griffiths. Shades of confusion: Lexical uncertainty modulates ad hoc coordination in an interactive communication task. Cognition, in press.

    Google Scholar 

  25. D. Norman. The Design of Everyday Things: Revised and Expanded Edition. Basic Books (AZ), 2013.

    Google Scholar 

  26. L.-C. Ou, M. R. Luo, A. Woodcock, and A. Wright. A study of colour emotion and colour preference. Part I: Colour emotions for single colours. Color Research & Application, 29(3):232–240, 2004.

    Google Scholar 

  27. S. Palmer. Fundamental aspects of cognitive representation. In E. Roach and B. B. Lloyd, editors, Cognition and Categorization, pages 259–303. Lawrence Erlbaum Associates, Hillsdale, NJ., 1978.

    Google Scholar 

  28. S. Pinker. A theory of graph comprehension. Artificial Intelligence and the Future of Testing, 73:126, 1990.

    Google Scholar 

  29. R. Rathore, Z. Leggon, L. Lessard, and K. B. Schloss. Estimating color-concept associations from image statistics. IEEE Transactions on Visualization and Computer Graphics, 26(1):1226–1235, 2020.

    Google Scholar 

  30. H. Ritchie and M. Roser. Biodiversity. Our World in Data, 2021. https://ourworldindata.org/biodiversity.

  31. H. Ritchie and M. Roser. Clean water and sanitation. Our World in Data, 2021. https://ourworldindata.org/clean-water-sanitation.

  32. H. Ritchie and M. Roser. Farm size. Our World in Data, 2021. https://ourworldindata.org/farm-size.

  33. H. Ritchie, M. Roser, and P. Rosado. CO\({ }_2\) and greenhouse gas emissions. Our World in Data, 2020. https://ourworldindata.org/co2-and-other-greenhouse-gas-emissions.

  34. B. E. Rogowitz, L. A. Treinish, and S. Bryson. How not to lie with visualization. Computers in Physics, 10(3):268–273, 1996.

    Article  Google Scholar 

  35. R. E. Roth, A. W. Woodruff, and Z. F. Johnson. Value-by-alpha maps: An alternative technique to the cartogram. The Cartographic Journal, 47(2):130–140, 2010.

    Article  Google Scholar 

  36. K. B. Schloss. A color inference framework. In . G. V. P. L. MacDonald, C. P. Biggam, editor, Progress in Colour Studies: Cognition, Language, and Beyond. John Benjamins, Amsterdam, 2018.

    Google Scholar 

  37. K. B. Schloss, C. C. Gramazio, A. T. Silverman, M. L. Parker, and A. S. Wang. Mapping color to meaning in colormap data visualizations. IEEE Transactions on Visualization and Computer Graphics, 25(1):810–819, 2019.

    Article  Google Scholar 

  38. K. B. Schloss, Z. Leggon, and L. Lessard. Semantic discriminability for visual communication. IEEE Transactions on Visualization and Computer Graphics, 27(2):1022–1031, 2021.

    Article  Google Scholar 

  39. K. B. Schloss, L. Lessard, C. S. Walmsley, and K. Foley. Color inference in visual communication: the meaning of colors in recycling. Cognitive Research: Principles and Implications, 3(1):5, 2018.

    Google Scholar 

  40. M. A. Schoenlein, L. Campos, J, K. J, Lessard, L, Schloss, and K. B. Unifying effects of direct and relational associations for visual communication. IEEE Transactions on Visualization and Computer Graphics, in press.

    Google Scholar 

  41. M. A. Schoenlein and K. B. Schloss. Colour-concept association formation for novel concepts. Visual Cognition, pages 1–23, 2022.

    Google Scholar 

  42. G. D. Schott. Colored illustrations of the brain: some conceptual and contextual issues. The Neuroscientist, 16(5):508–518, 2010.

    Article  Google Scholar 

  43. V. Setlur and M. C. Stone. A linguistic approach to categorical color assignment for data visualization. IEEE Transactions on Visualization and Computer Graphics, 22(1):698–707, 2016.

    Article  Google Scholar 

  44. P. Shah and P. A. Carpenter. Conceptual limitations in comprehending line graphs. Journal of Experimental Psychology: General, 124(1):43, 1995.

    Google Scholar 

  45. P. Shah and J. Hoeffner. Review of graph comprehension research: Implications for instruction. Educational Psychology Review, 14(1):47–69, 2002.

    Article  Google Scholar 

  46. R. N. Shepard and S. Chipman. Second-order isomorphism of internal representations: Shapes of states. Cognitive Psychology, 1(1):1–17, 1970.

    Article  Google Scholar 

  47. S. C. Sibrel, R. Rathore, L. Lessard, and K. B. Schloss. The relation between color and spatial structure for interpreting colormap data visualizations. Journal of Vision, 20(12):7–7, 2020.

    Article  Google Scholar 

  48. C. Soriano and J. Valenzuela. Emotion and colour across languages: implicit associations in Spanish colour terms. Social Science Information, 48(3):421–445, 2009.

    Article  Google Scholar 

  49. D. S. Y. Tham, P. T. Sowden, A. Grandison, A. Franklin, A. K. W. Lee, M. Ng, J. Park, W. Pang, and J. Zhao. A systematic investigation of conceptual color associations. Journal of Experimental Psychology: General, 149(7):1311, 2020.

    Google Scholar 

  50. B. Tversky. Visualizing thought. Topics in Cognitive Science, pages 499–535, 2011.

    Google Scholar 

  51. B. Tversky, J. B. Morrison, and M. Betrancourt. Animation: can it facilitate? International Journal of Human-Computer Studies, 57(4):247–262, 2002.

    Article  Google Scholar 

  52. C. Ware. Color sequences for univariate maps: Theory, experiments and principles. IEEE Computer Graphics and Applications, 8(5):41–49, 1988.

    Article  Google Scholar 

  53. C. Ware. Information Visualization: Perception for Design. Morgan Kaufmann Publishers Inc., San Francisco, 2nd edition, 2004.

    Google Scholar 

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Author information

Authors and Affiliations

  1. University of Wisconsin–Madison, Madison, WI, USA

    Karen B. Schloss, Melissa A. Schoenlein & Kushin Mukherjee

Authors
  1. Karen B. Schloss
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  2. Melissa A. Schoenlein
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  3. Kushin Mukherjee
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Corresponding author

Correspondence to Karen B. Schloss .

Editor information

Editors and Affiliations

  1. University of Colorado Boulder, Boulder, CO, USA

    Danielle Albers Szafir

  2. Department of Computer Science, King's College London, London, UK

    Rita Borgo

  3. OeRC, Department of Engineering Science, University of Oxford, Oxford, UK

    Min Chen

  4. Department of Public Health, Swansea University, Swansea, UK

    Darren J. Edwards

  5. School of Interactive Arts & Technology, Simon Fraser University, Surrey, BC, Canada

    Brian Fisher

  6. Northeastern University, Boston, USA

    Lace Padilla

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Schloss, K.B., Schoenlein, M.A., Mukherjee, K. (2023). Color Semantics for Visual Communication. In: Albers Szafir, D., Borgo, R., Chen, M., Edwards, D.J., Fisher, B., Padilla, L. (eds) Visualization Psychology. Springer, Cham. https://doi.org/10.1007/978-3-031-34738-2_1

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