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People's associations between colors and concepts influence their ability tointerpret the meanings of colors in information visualizations. Previous workhas suggested such effects are limited to concepts that have strong, specificassociations with colors. However, although a concept may not be stronglyassociated with any colors, its mapping can be disambiguated in the context ofother concepts in an encoding system. We articulate this view in semanticdiscriminability theory, a general framework for understanding conditionsdetermining when people can infer meaning from perceptual features. Semanticdiscriminability is the degree to which observers can infer a unique mappingbetween visual features and concepts. Semantic discriminability theory positsthat the capacity for semantic discriminability for a set of concepts isconstrained by the difference between the feature-concept associationdistributions across the concepts in the set. We define formal properties ofthis theory and test its implications in two experiments. The results show thatthe capacity to produce semantically discriminable colors for sets of conceptswas indeed constrained by the statistical distance between color-conceptassociation distributions (Experiment 1). Moreover, people could interpretmeanings of colors in bar graphs insofar as the colors were semanticallydiscriminable, even for concepts previously considered "non-colorable"(Experiment 2). The results suggest that colors are more robust for visualcommunication than previously thought.

Context Matters: A Theory of Semantic Discriminability for Perceptual Encoding Systems