The Community for Technology Leaders
RSS Icon
Subscribe
Issue No.12 - Dec. (2013 vol.19)
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. 12, pp. 2306-2315, Dec. 2013, doi:10.1109/TVCG.2013.234
REFERENCES
[1] R. C. Anderson and J. W. Pichert., Recall of previously unrecallable information following a shift in perspective. Journal of Verbal Learning and Verbal Behavior, 17(1) 1-12, 1978.
[2] W. A. Bainbridge,P. Isola,, and A. Oliva., The intrinsic memorability of face images (in press). Journal of Experimental Psychology: General, 2013.
[3] M. Bar and M. Neta., Humans prefer curved visual objects. Psychological science, 17(8) 645-648, 2006.
[4] S. Bateman,R. L. Mandryk,C. Gutwin,A. Genest,D. McDine,, and C. Brooks., Useful junk?: the effects of visual embellishment on comprehension and memorability of charts. In Proceedings of the 28th International Conference on Human Factors in Computing Systems, CHI’ 10, pages 2573-2582. ACM, 2010.
[5] C. Behrens., Infodesignpatterns. http:/www.niceone.org/ infodesignpatterns/index.php5, 2013.
[6] J. Bertin., Semiology of graphics: diagrams, networks, maps. 1983.
[7] A. F. Blackwell and T. Green., Does metaphor increase visual language usability? In Visual Languages, 1999. Proceedings. 1999 IEEE Symposium on, pages 246-253. IEEE, 1999.
[8] R. Borgo,A. Abdul-Rahman,F. Mohamed,P. W. Grant,I. Reppa,L. Floridi,, and M. Chen., An empirical study on using visual embellishments in visualization. Visualization and Computer Graphics, IEEE Transactions on, 18(12) 2759-2768, 2012.
[9] T. F. Brady,T. Konkle,G. A. Alvarez,, and A. Oliva., Visual long-term memory has a massive storage capacity for object details. Proceedings of the National Academy of Sciences, 105(38) 14325-14329, 2008.
[10] A. Cairo., The Functional Art: An Introduction to Information Graphics and Visualization. New Riders, 2013.
[11] W. S. Cleveland and R. McGill., Graphical perception: Theory, experimentation, and application to the development of graphical methods. Journal of the American Statistical Association, 79(387) 531-554, 1984.
[12] Y. Englehardt., The language of graphics: Aframeworkfor the analysis of syntax and meaning in maps, charts and diagrams. PhD thesis, Institute for Logic, Language and Computation, University of Amsterdam, 2002.
[13] S. Few., Benefitting infovis with visual difficulties? Provocation without a cause. Visual Business Intelligence Newsletter, 2011.
[14] S. Few., The chart junk debate: A close examination of recent findings. Visual Business Intelligence Newsletter, 2011.
[15] R. L. Harris., Information graphics: A comprehensive illustrated reference. Oxford University Press, 1999.
[16] J. Heer,M. Bostock,, and V. Ogievetsky., A tour through the visualization zoo. Communications of the ACM, 53(6) 59-67, 2010.
[17] J. Heer and B. Shneiderman., Interactive dynamics for visual analysis. Queue, 10(2)30, 2012.
[18] N. Holmes., Designer's Guide to Creating Charts and Diagrams. Watson-Guptill Publications, 1984.
[19] J. Hullman,E. Adar,, and P. Shah., Benefitting infovis with visual difficulties. Visualization and Computer Graphics, IEEE Transactions on, 17(12) 2213-2222, 2011.
[20] P. Isola,D. Parikh,A. Torralba,, and A. Oliva., Understanding the intrinsic memorability of images. In J. Shawe-Taylor, R. Zemel, P. Bartlett, F. Pereira, and K. Weinberger, editors, Advances in Neural Information Processing Systems (NIPS), volume 24, pages 2429-2437, 2011.
[21] P. Isola,J. Xiao,A. Torralba,, and A. Oliva., What makes an image memorable? In Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on, pages 145-152. IEEE, 2011.
[22] T. Konkle,T. F. Brady,G. A. Alvarez,, and A. Oliva., Conceptual distinctiveness supports detailed visual long-term memory for real-world objects. Journal of experimental psychology. General, 139(3) 558-578, 2010.
[23] T. Konkle,T. F. Brady,G. A. Alvarez,, and A. Oliva., Scene memory is more detailed than you think the role of categories in visual long-term memory. Psychological Science, 21(11) 1551-1556, 2010.
[24] S. M. Kosslyn., Understanding charts and graphs. Applied cognitive psychology, 3(3) 185-225, 1989.
[25] E. Lee., Visualizing blog: A taxonomy of data visualization. http:/www.visualizing.org, 2013.
[26] R. Lengler and M. J. Eppler., Towards a periodic table of visualization methods for management. In lASTED Proceedings of the Conference on Graphics and Visualization in Engineering (GVE 2007), Clearwater, Florida, USA, 2007.
[27] K. Marriott,H. Purchase,M. Wybrow,, and C. Goncu., Memorability of visual features in network diagrams. Visualization and Computer Graphics, IEEE Transactions on, 18(12) 2477-2485, 2012.
[28] W. Mason and S. Suri., Conducting behavioral research on Amazon's Mechanical Turk. Behav Res, 44: 1-23, 2012.
[29] O. Neurath., From hieroglyphics to Isotype: A visual autobiography. Hyphen Press, London, 2010.
[30] S. Pinker., A theory of graph comprehension. Artificial intelligence and the future of testing, pages 73-126, 1990.
[31] R. Reber,N. Schwarz,, and P. Winkielman., Processing fluency and aesthetic pleasure: is beauty in the perceiver's processing experience? Personality and social psychology review, 8(4) 364-382, 2004.
[32] J. C. Roberts., Display models-ways to classify visual representations. International Journal of Computer Integrated Design and Construction, 2(4) 241-250, 2000.
[33] J. F. Rodrigues,A. J. Traina,M. C. F. de Oliveira,, and C. Traina., Reviewing data visualization: an analytical taxonomical study. In Information Visualization, 2006. IV 2006. Tenth International Conference on, pages 713-720. IEEE, 2006.
[34] J. Ross,L. Irani,M. S. Silberman,A. Zaldivar,, and B. Tomlinson., Who are the Crowdworkers? Shifting Demographics in Mechanical Turk. CHI EA, pages 2863-2872, 2010.
[35] B. Shneiderman., The eyes have it: A task by data type taxonomy for information visualizations. In Visual Languages, 1996. Proceedings., IEEE Symposium on, pages 336-343. IEEE, 1996.
[36] [36] M. Tory and T. Moller., Rethinking visualization: A high-level taxonomy. In Information Visualization, 2004. INFOVIS 2004. IEEE Symposium on, pages 151-158. IEEE, 2004.
[37] E. Tufte., Envisioning Information. Cheshire (Conn.), 1990.
[38] E. Tufte., The Visual Display of Quantitative Information. Cheshire (Conn.), 2001.
[39] A. Vande Moere,M. Tomitsch,C. Wimmer,B. Christoph,, and T. Grechenig., Evaluating the effect of style in information visualization. Visualization and Computer Graphics, IEEE Transactions on, 18(12) 2739-2748, 2012.
477 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool