Information Visualization

Papers on this topic

Schoenlein, M. A., Campos, J., Lande, K. J., Lessard, L., & Schloss, K. B. (in press). Unifying Effects of Direct and Relational Associations for Visual Communication. IEEE Transactions on Visualization and Computer Graphics.  PDF

Mukherjee, K., Yin, B., Sherman, B. E., Lessard, L., &  Schloss, K. B., (2022). Context matters: A theory of semantic discriminability for perceptual encoding systems. IEEE Transactions on Visualization and Computer Graphics, 28, 1, 697-706. PDF

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

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

Sibrel, S. C., Rathore, R., Lessard, L., & Schloss, K. B., (2020). The relation between color and spatial structure for interpreting colormap data visualizations. Journal of Vision, 20, 7, 1-20. ArticlePDF SuppMatPDF

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

Kinateder, M. Warren, W. H., & Schloss, K. B. (2019). What color are emergency exit signs? Egress behavior differs from verbal report. Applied Ergonomics, 75, 155-160Link

Flack, S., Ponto, K. Tangen, T., Schloss, K. B. (2019). Lego as language for visual communication. VisComm: Workshop on Visualization for Communication, IEEE VIS 2019. Link

Schloss, K. B., Gramazio, C. C., Silverman, A. T., Parker, M., L., & Wang, A. S. (2019). Mapping color to meaning in colormap data visualizations. IEEE Transactions on Visualization and Computer Graphics, 25, 1, 1-10. ArticlePDF SuppMatPDF

Schloss, K. B. (2018). A color inference framework. In L. MacDonald, C. P. Biggam, & G. V. Paramei (Eds.), Progress in Colour Studies: Cognition, language, and beyond. Amsterdam: John Benjamins.

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

Gramazio, C. C., Laidlaw, D. H., and Schloss, K. B. (2017). Colorgorical: Creating discriminable and preferable color palettes for information visualization. IEEE Transactions on Visualization and Computer Graphics, 23, 1. PDF

Gramazio, C. C., Schloss, K. B., & Laidlaw, D. H., (2014). The relation between visualization size, grouping, and user performance. IEEE Transactions on Visualization and Computer Graphics, 20, 12, 1953-1962. Link

Color Brewer Blue

To interpret information visualizations, people use visual reasoning to determine how visual features map onto concepts. For example, to interpret the colors in weather maps, in neuroimaging, bar graphs, and recycling bin signs, people must determine which colors in the visualization map on different quantities or categories represented in the visualization. People have expectations, or “inferred mappings” for how visual features will map on concepts, and they have an easier time interpreting visualizations that match those expectations. The challenge is understanding what determines people’s inferred mappings. Addressing this challenge will advance knowledge about how visual reasoning works, and will translate to designing effective and efficient information visualizations.

More details coming soon!

Project Types: Current