Congratulations to PI Karen Schloss for being elected to the VSS Board of Directors! Karen was elected to this position by the VSS membership and will begin her four-year term in May 2026. Karen has attended every VSS for the past 20 years and is driven to support the continued success of the society and its members. Responsibilities of the Board include scheduling the Annual Meeting, implementing and monitoring VSS policies and budget, fundraising, and other VSS-related activities.
Congratulations to Aiyana Mangloña for receiving a Sophomore Research Fellowship! This fellowship provides research training and support to undergraduate students, and the opportunity to undertake their own research project in collaboration with UW–Madison faculty. This award will support Aiyana’s research project focusing on the overarching use of design guidelines for data visualizations.
Congratulations to Ashwini Kumble (third from the right) and Sophia Wang (third from the left) for receiving an Outstanding Undergraduate Research Scholar (OURS) Award. This award recognizes outstanding undergraduate Psychology majors for their contribution to research in our department. We thank Ashwini and Sophia for their outstanding work in our lab!
Ashwini Kumble (left) and Sophia Wang (right) presented their undergraduate honors theses at the 2026 Psychology STaRS (Student Thesis and Research Showcase), which focused on the exciting research they have done in our lab. Congratulations Ashwini and Sophia!
Ashwini’s thesis title: Remove, Replace, Refine: A Computational Approach to Improving Color Palettes for Data Visualizations
Sophia’s thesis title: Color-Texture Associations in Tactile Representations of Visual Art
Students from the Schloss Visual Reasoning Lab presented their work at the 29th annual Undergraduate Symposium! The annual Undergraduate Research Symposium showcases undergraduate research, scholarly work, community-based projects, art, and creativity from all areas of study at UW–Madison.
Aiyana (left) and Aayush (right) presented their poster One size does not fit all: Challenging the use of overarching guidelines in data visualization. These students conducted their research as part of the Undergraduate Research Scholars program at UW-Madison.
Congratulations to PI Karen Schloss for receiving an Excellence in Honors Thesis Advising Award! The L&S Honors Program at UW-Madison solicits student nominations of faculty members who have had a special impact as a senior Honors thesis advisor. Honors Program staff members review these nominations and select the strongest nominee in each of L&S’s broad disciplinary areas to receive an Excellence in Honors Thesis Advising Award.
Congratulations to Anna Chinni for receiving a 2025 Walsh Graduate Student Support Initiative (GSSI) award, supported by the McPherson Eye Research Institute! This award provides support to UW-Madison graduate students training in a McPherson ERI Member’s laboratory. PI Karen Schloss nominated Anna for this award.
When interpreting data visualizations, people have expectations of how colors should map onto quantities. These expectations are constructed from multiple biases, including the dark-is-more bias (darker colors represent larger quantities) and the opaque-is-more bias (regions appearing more opaque represent larger quantities), among others. The extent to which any one bias influences interpretations of data visualizations depends on the degree to which that bias is applicable for a given visualization (applicability principle) and its relative weight in combination with other biases (combination principle). However, basic questions remain concerning the perceptual conditions necessary to activate such biases so they become applicable. For example, in previous studies of the opaque-is-more bias, the test stimuli appeared to vary in opacity because they were created by interpolating between a “base” color and a background color, which was lighter or darker than the base color. As such, opacity variation was confounded with large lightness variation. From prior work, it is unknown whether the opaque-is-more bias can be activated without substantial lightness variation. Here, we varied opacity by varying colormap saturation relative to the background while reducing lightness contrast (holding L* in CIELAB constant). We found that the opaque-is-more bias can indeed be activated without substantial lightness variation. In the process, we also found evidence for a new, “saturated-is-more bias,” leading to expectations that regions greater in saturation map to larger magnitudes. These findings extend knowledge of how people infer meaning from visual features and can translate to inform design of effective information visualizations.
Reference: Schoenlein, M. A., Sidibe, M., & Schloss, K. B. (2026). Understanding the opaque-is-more bias and saturated-is-more bias for colormap data visualizations. Attention, Perception, & Psychophysics. PDF
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. PDF
Studies of visual semantics for information visualization aim to understand observers’ expectations about the meaning of visual features (e.g., color, texture) because visualizations that align with those expectations are easier to interpret. Previous work on visual semantics focused primarily on color, with the implicit assumption that color semantics is unaffected by changes in the size of the visualization (given sufficient perceptual discriminability across sizes). Changing size from small scale (e.g., small figures in a paper) to large scale (e.g., large figures in a slide presentation) is straightforward for visualizations that have solid colored regions, but can be more complicated for visualizations with heterogeneous textures because there are multiple ways to scale textures—zooming or repeating texture elements. Previous work suggested that original textures were more perceptually similar to repeat-scaled rather than zoom-scaled textures. Here, we found that texture semantics was preserved after both types of enlargement, suggesting that texture semantics is robust to scaling, at least for geometric textures in which elements are visible at all scales.
Reference: Howard, Z. S. & Schloss, K. B. (2025). Texture semantics is robust to scaling. 2025 IEEE Visualization and Visual Analytics (VIS). PDF
