What Makes a Visualization Memorable?

• Published in: IEEE transactions on visualization and computer graphics 2013-12, Vol.19 (12), p.2306-2315
• Main Authors: Borkin, Michelle A., Vo, Azalea A., Bylinskii, Zoya, Isola, Phillip, Sunkavalli, Shashank, Oliva, Aude, Pfister, Hanspeter
• Format: Article
• Language: English
• Subjects: Artificial Intelligence; Color; Communities; Cues; Data visualization; Density; Encoding; Governments; Graphs; Human; Humans; Image Interpretation, Computer-Assisted - methods; Information technology; information visualization; memorability; Memory - physiology; Pattern Recognition, Visual - physiology; Task Performance and Analysis; Taxonomy; User-Computer Interface; Visual; Visualization; Visualization taxonomy
• ISSN: 1077-2626; 1941-0506
• DOI: 10.1109/TVCG.2013.234

An ongoing debate in the Visualization community concerns the role that visualization types play in data understanding. In human cognition, understanding and memorability are intertwined. As a first step towards being able to ask questions about impact and effectiveness, here we ask: 'What makes a visualization memorable?' We ran the largest scale visualization study to date using 2,070 single-panel visualizations, categorized with visualization type (e.g., bar chart, line graph, etc.), collected from news media sites, government reports, scientific journals, and infographic sources. Each visualization was annotated with additional attributes, including ratings for data-ink ratios and visual densities. Using Amazon's Mechanical Turk, we collected memorability scores for hundreds of these visualizations, and discovered that observers are consistent in which visualizations they find memorable and forgettable. We find intuitive results (e.g., attributes like color and the inclusion of a human recognizable object enhance memorability) and less intuitive results (e.g., common graphs are less memorable than unique visualization types). Altogether our findings suggest that quantifying memorability is a general metric of the utility of information, an essential step towards determining how to design effective visualizations.