The DOI matches its Crossref record; 37 of the 37 references it cites were found in a registry or an index.
What Makes a Visualization Memorable?
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.
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Cites
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Cited by
The Persuasive Power of Data Visualization
Pandey, A. V., Manivannan, A., Nov, O., Satterthwaite, M., & Bertini, E. (2014) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2014.2346419 · Cited by 217
How do visual explanations foster end users' appropriate trust in machine learning?
Yang, F., Huang, Z., Scholtz, J., & Arendt, D. L. (2020) · Proceedings of the 25th International Conference on Intelligent User Interfaces · doi:10.1145/3377325.3377480 · Cited by 200
Accessible Visualization via Natural Language Descriptions: A Four-Level Model of Semantic Content
Lundgard, A., & Satyanarayan, A. (2022) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2021.3114770 · Cited by 155
Ethical Dimensions of Visualization Research
Correll, M. (2019) · Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300418 · Cited by 150
Boy, J., Pandey, A. V., Emerson, J., Satterthwaite, M., Nov, O., & Bertini, E. (2017) · Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3025453.3025512 · Cited by 104
Metadata
- Authors
- Borkin, M. A., Vo, A. A., Bylinskii, Z., Isola, P., Sunkavalli, S., Oliva, A., & Pfister, H.
- Venue
- IEEE Transactions on Visualization and Computer Graphics
- Published
- 2013-12
- Volume
- 19
- Issue
- 12
- Pages
- 2306-2315
- ISSN
- 1077-2626 (ISSN-L)
- OpenAlex
- W2054901814
- Cited by
- 666
- References
- 37
- FWCI
- 28.99
- Type
- journal article
- Language
- en
- Open access
- closed
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