Learning Perceptual Kernels for Visualization Design

Abstract

Abstract-Visualization design can benefit from careful consideration of perception, as different assignments of visual encoding variables such as color, shape and size affect how viewers interpret data. In this work, we introduce perceptual kernels: distance matrices derived from aggregate perceptual judgments. Perceptual kernels represent perceptual differences between and within visual variables in a reusable form that is directly applicable to visualization evaluation and automated design. We report results from crowdsourced experiments to estimate kernels for color, shape, size and combinations thereof. We analyze kernels estimated using five different judgment types -including Likert ratings among pairs, ordinal triplet comparisons, and manual spatial arrangement -and compare them to existing perceptual models. We derive recommendations for collecting perceptual similarities, and then demonstrate how the resulting kernels can be applied to automate visualization design decisions.

Categorical Colormap Optimization with Visualization Case Studies

“…Ware [48] conducted empirical studies on colormaps in the context of visualization. Borgo et al [5] and Demiralp et al [8] studied the distance between visual objects featuring integrated visual channels (including colors) using crowdsourced experiments.…”

Evaluation in the Crowd. Crowdsourcing and Human-Centered Experiments

“…Related concepts include graphicacy [98] as the ability to understand simple bar charts and diagrams, and visual literacy [26] as the ability to understand signs. Studies on perception of visual variables (e.g., [36]) and how people associate values to visual variables, provide some general understanding of what to "expect" from a normal participant. However, these studies do not tell how to asses a person's visual understanding, or the ability to build up a methodology to correctly understand and interpret the meaning behind a picture.…”

“…Being aware of the problem should encourage study authors to include simple tests in their studies, and to focus on sufficient training. It is also important to deliver very precise task descriptions upfront, in order to discourage less motivated workers [36].…”

A crowdsourcing system for integrated and reproducible evaluation in scientific visualization

“…An additional benefit is that participants of crowdsourced user studies can participate at any given time, and is not limited to the opening hours of the lab. While there has not yet been any work published where crowdsourcing has been utilized for evaluating scientific visualization research, crowdsourcing has been successfully utilized for user studies in information visualization [9,10,16,17,19,24,38,47].…”