Science in a Blink: Supporting Ensemble Perception in Scalar Fields

TL;DR

This study demonstrates that viewers can quickly estimate mean and variance in scalar field visualizations, with multi-hue colormaps enhancing perception, indicating that color segmentation aids rapid data interpretation.

Contribution

It reveals that spatial visualizations support ensemble perception of data statistics, highlighting the effectiveness of multi-hue colormaps over monochromatic schemes.

Findings

Participants reliably estimated mean and variance.

Multi-hue and diverging colormaps improved perception.

Color segmentation influences quick data judgments.

Abstract

Visualizations support rapid analysis of scientific datasets, allowing viewers to glean aggregate information (e.g., the mean) within split-seconds. While prior research has explored this ability in conventional charts, it is unclear if spatial visualizations used by computational scientists afford a similar ensemble perception capacity. We investigate people's ability to estimate two summary statistics, mean and variance, from pseudocolor scalar fields. In a crowdsourced experiment, we find that participants can reliably characterize both statistics, although variance discrimination requires a much stronger signal. Multi-hue and diverging colormaps outperformed monochromatic, luminance ramps in aiding this extraction. Analysis of qualitative responses suggests that participants often estimate the distribution of hotspots and valleys as visual proxies for data statistics. These findings suggest that people's summary interpretation of spatial datasets is likely driven by the appearance of discrete color segments, rather than assessments of overall luminance. Implicit color segmentation in quantitative displays could thus prove more useful than previously assumed by facilitating quick, gist-level judgments about color-coded visualizations.