Our paper “Affective color scales for colormap data visualizations” received Honorable Mention for Best Paper at IEEE VIS 2025!
Abstract: Research on affective visualization design has shown that color is an especially powerful feature for influencing the emotional connotation of visualizations. Associations between colors and emotions are largely driven by lightness (e.g., lighter colors are associated with positive emotions, whereas darker colors are associated with negative emotions). Designing visualizations to have all light or all dark colors to convey particular emotions may work well for visualizations in which colors represent categories and spatial channels encode data values. However, this approach poses a problem for visualizations that use color to represent spatial patterns in data (e.g., colormap data visualizations) because lightness contrast is needed to reveal fine details in spatial structure. In this study, we found it is possible to design colormaps that have strong lightness contrast to support spatial vision while communicating clear affective connotation. We also found that affective connotation depended not only on the color scales used to construct the colormaps, but also the frequency with which colors appeared in the map, as determined by the underlying dataset (data-dependence hypothesis). These results emphasize the importance of data-aware design, which accounts for not only the design features that encode data (e.g., colors, shapes, textures), but also how those design features are instantiated in a visualization, given the properties of the data.
Reference: Braun, H. C., Mukherjee, K., Gorelik, S. R., & Schloss, K. B (2026). Affective color scales for colormap data visualizations. IEEE Transactions on Visualization and Computer Graphics. Honorable mention for Best Paper at IEEE VIS 2025.
Information visualization is central to how humans communicate. Designers produce visualizations to represent information about the world, and observers construct interpretations based on the visual input as well as their heuristics, biases, prior knowledge, and beliefs. Several layers of processing go into the design and interpretation of visualizations. This review focuses on processes that observers use for interpretation: perceiving visual features and their interrelations, mapping those visual features onto the concepts they represent, and comprehending information about the world based on observations from visualizations. Observers are more effective at interpreting visualizations when the design is well-aligned with the way their perceptual and cognitive systems naturally construct interpretations. By understanding how these systems work, it is possible to design visualizations that play to their strengths and thereby facilitate visual communication.
Dr. Kushin Mukherjee defended his dissertation on Cognitive Abstractions for Visual Communication, and now he is off to a postdoc at Stanford University! Congratulations Kushin, you did outstanding work at UW-Madison and we can’t wait to see where your career takes you!
Congratulations to Zoe Howard for receiving a 2025 Glushko Outstanding Undergraduate Cognitive Scientist Prize from the Department of Psychology at UW-Madison!
Congratulations to Nancy Davis (left) for receiving an Outstanding Undergraduate Research Scholar (OURS) Award from the UW-Madison Department of Psychology!
Dr. Melissa Schoenlein defended her dissertation on Effects of color category structure on learning and generalization of color-concept associations for novel concepts. Now, Melissa is off to start a faculty position in Psychology at High Point University!
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