Context Matters: A Theory of Semantic Discriminability for Perceptual Encoding Systems
Kushin Mukherjee, Brian Yin, Brianne Sherman, Laurent Lessard, Karen Schloss
View presentation: 2021-10-27T13:00:00Z GMT-0600 Change your timezone on the schedule page
People’s associations between colors and concepts influence their ability to interpret the meanings of colors in information visualizations. Previous work has suggested such effects are limited to concepts that have strong, specific associations with colors. However, although a concept may not be strongly associated with any colors, its mapping can be disambiguated in the context of other concepts in an encoding system. We articulate this view in Semantic Discriminability Theory, a general framework for understanding conditions determining when people can infer meaning from perceptual features. Semantic discriminability is the degree to which observers can infer a unique mapping between visual features and concepts. Semantic Discriminability Theory posits that the capacity for semantic discriminability for a set of concepts is constrained by the difference between the feature-concept association distributions across the concepts in the set. We define formal properties of this theory, and test its implications in two experiments. The results show that the capacity to produce semantically discriminable colors for sets of concepts was indeed constrained by the statistical distance between color-concept association distributions (Experiment 1). Moreover, people could interpret meanings of colors in bar graphs insofar as the colors were semantically discriminable, even for concepts previously deemed “non-colorable” (Experiment 2). The results suggest that colors are more robust for visual communication than previously thought.