Understanding color semantics for visual communication

Visual communication is fundamental to how humans share information, from weather patterns, to disease prevalence, to their latest scientific discoveries. When people attempt to interpret information visualizations, such as graphs, maps, diagrams, and signage, they are faced with the task of mapping perceptual features onto meanings.  Sometimes, visualization designs include legends, labels, or accompanying verbal descriptions to help determine the meaning of colors (color semantics). However, people have expectations about how colors will map to concepts (called inferred mappings), and they find it more difficult to interpret visualizations that violate those expectations. Traditionally, studies on inferred mappings distinguished factors relevant for visualizations of categorical vs. continuous information. In this talk, I will discuss recent work that unites these two domains within a single framework of assignment inference. Assignment inference is the process by which people infer mappings between perceptual features and concepts represented in encoding systems. I will begin by presenting evidence that observers infer globally optimal assignments by maximizing the “merit,” or “goodness,” of assignments between colors and concepts. I will then discuss factors that contribute to merit in assignment inference and explain how we can model the combination of multiple (sometimes competing) sources of merit to predict human judgments. This work has increased our understanding of people’s expectations about color semantics, which can be used to make visual communication more effective and efficient.

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