A Holey Perspective on Venn Diagrams

Abstract

When interpreting the meanings of visual features in information visualizations, observers have expectations about how visual features map onto concepts (inferred mappings.) In this study, we examined whether aspects of inferred mappings that have been previously identified for colormap data visualizations generalize to a different type of visualization, Venn diagrams. Venn diagrams offer an interesting test case because empirical evidence about the nature of inferred mappings for colormaps suggests that established conventions for Venn diagrams are counterintuitive. Venn diagrams represent classes using overlapping circles and express logical relationships between those classes by shading out regions to encode the concept of non‐existence, or none. We propose that people do not simply expect shading to signify non‐existence, but rather they expect regions that appear as holes to signify non‐existence (the hole hypothesis.) The appearance of a hole depends on perceptual properties in the diagram in relation to its background. Across three experiments, results supported the hole hypothesis, underscoring the importance of configural processing for interpreting the meanings of visual features in information visualizations.

Unifying Effects of Direct and Relational Associations for Visual Communication

“…In testing this possibility, our work makes the following contributions: (1) We broaden the notion of "merit" in assignment inference to include relational associations, and show that both relational and direct associations influence inferred mappings for colormap visualizations. (2) We develop a method for combining multiple (sometimes conflicting) sources of merit for simulating assignment inference, and show that our method effectively predicts inferred mappings for colormap visualizations.…”

“…The opaque-is-more bias is the expectation that regions appearing more opaque represent larger quantities. This bias is only applicable when visualizations appear to vary in opacity [2,35], such as in value-by-alpha maps [33]. When the opaqueis-more bias is activated, it aligns with the dark-is-more bias on light backgrounds but conflicts with the dark-is-more bias on dark backgrounds.…”

“…When the opaqueis-more bias is activated, it aligns with the dark-is-more bias on light backgrounds but conflicts with the dark-is-more bias on dark backgrounds. Under such conflicts, the opaque-is-more bias can cancel or even override the dark-is-more bias, leading observers to infer that lighter colors map to larger quantities [2,35]. When the opaque-is-more bias is non-applicable (i.e., a visualization does not appear to vary in opacity), the dark-is-more bias leads observers to infer that darker colors map to larger quantities on both dark and light backgrounds [2,35].…”