Testing the Value of Salience in Statistical Graphs

• Published in: Electronic Imaging Vol. 33; no. 1; pp. 329-1 - 329-11
• Main Authors: Livingston, Mark A., Matzen, Laura, Brock, Derek, Harrison, Andre, Decker, Jonathan W.
• Format: Journal Article Conference Proceeding
• Language: English
• Published: IS&T 7003 Kilworth Lane, Springfield, VA 22151 USA Society for Imaging Science and Technology 18.01.2021
• Subjects: Charts, Diagrams, And Plots; Cognition; Highlights; Human-Subjects Quantitative Studies; Perception; Salient Cues
• ISSN: 2470-1173; 2470-1173
• DOI: 10.2352/ISSN.2470-1173.2021.1.VDA-329

Expert advice and conventional wisdom say that important information within a statistical graph should be more salient than the other components. If readers are able to find relevant information quickly, in theory, they should perform better on corresponding response tasks. To our knowledge, this premise has not been thoroughly tested. We designed two types of salient cues to draw attention to task-relevant information within statistical graphs. One type primarily relied on text labels and the other on color highlights. The utility of these manipulations was assessed with groups of questions that varied from easy to hard. We found main effects from the use of our salient cues. Error and response time were reduced, and the portion of eye fixations near the key information increased. An interaction between the cues and the difficulty of the questions was also observed. In addition, participants were given a baseline skills test, and we report the corresponding effects. We discuss our experimental design, our results, and implications for future work with salience in statistical graphs.