  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 considered "non-colorable"
(Experiment 2). The results suggest that colors are more robust for visual
communication than previously thought.
