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Issue No. 12 - Dec. (2013 vol. 19)
ISSN: 1077-2626
pp: 2306-2315
Michelle A. Borkin , Sch. of Eng. & Appl. Sci., Harvard Univ., Cambridge, MA, USA
Azalea A. Vo , Sch. of Eng. & Appl. Sci., Harvard Univ., Cambridge, MA, USA
Zoya Bylinskii , Comput. Sci. & Artificial Intell. Lab., Massachusetts Inst. of Technol., Cambridge, MA, USA
Phillip Isola , Dept. of Brain & Cognitive Sci., Massachusetts Inst. of Technol., Cambridge, MA, USA
Shashank Sunkavalli , Sch. of Eng. & Appl. Sci., Harvard Univ., Cambridge, MA, USA
Aude Oliva , Comput. Sci. & Artificial Intell. Lab., Massachusetts Inst. of Technol., Cambridge, MA, USA
Hanspeter Pfister , Sch. of Eng. & Appl. Sci., Harvard Univ., Cambridge, MA, USA
ABSTRACT
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.
INDEX TERMS
Data visualization, Taxonomy, Information technology, Encoding,information visualization, Data visualization, Taxonomy, Information technology, Encoding, memorability, Visualization taxonomy
CITATION
Michelle A. Borkin, Azalea A. Vo, Zoya Bylinskii, Phillip Isola, Shashank Sunkavalli, Aude Oliva, Hanspeter Pfister, "What Makes a Visualization Memorable?", IEEE Transactions on Visualization & Computer Graphics, vol. 19, no. , pp. 2306-2315, Dec. 2013, doi:10.1109/TVCG.2013.234
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