Semantic Discriminability for Visual Communication

To interpret information visualizations, observers must determine how visual features map onto concepts. First and foremost, this ability depends on perceptual discriminability; be able to see the difference between different colors for those communicate meanings. However, visualizations also semantic discriminability, degree which can infer a unique mapping concepts, based concepts alone (i.e., without help from verbal cues such as legends or labels). Previous evidence suggested that were better at interpreting encoding systems maximized discriminability (maximizing association strength assigned while minimizing unassigned concepts), compared system only color-concept strength. increasing resulted in increased distance, so it is unclear factor was responsible improved performance. In present study, we conducted two experiments tested independent effects of distance bar graph data visualizations. Perceptual large enough ensure more than just noticeably different. We found performance, variation when these factors uncorrelated, responses dominated by distance. These results have implications navigating trade-offs color palette design optimization communication.