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On the Prospects for a Science of Visualization

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Handbook of Human Centric Visualization

Abstract

This paper explores the extent to which a scientific framework for visualization might be possible. It presents several potential parts of a framework, illustrated by application to the visualization of correlation in scatterplots. The first is an extended-vision thesis, which posits that a viewer and visualization system can be usefully considered as a single system that perceives structure in a dataset, much like “basic” vision perceives structure in the world. This characterization is then used to suggest approaches to evaluation that take advantage of techniques used in vision science. Next, an optimal-reduction thesis is presented, which posits that an optimal visualization enables the given task to be reduced to the most suitable operations in the extended system. A systematic comparison of alternative designs is then proposed, guided by what is known about perceptual mechanisms. It is shown that these elements can be extended in various ways—some even overlapping with parts of vision science. As such, a science of some kind appears possible for at least some parts of visualization. It would remain distinct from design practice, but could nevertheless assist with the design of visualizations that better engage human perception and cognition.

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Notes

  1. 1.

    Historically, much of this work focused on graphic displays, which convey information using the geometric and radiometric properties of an image (as opposed to simple text alone). Meanwhile, more modern work focuses on visual displays, which rely on the extensive use of visual intelligence for their interpretation. For purposes here, graphic displays and visual displays are considered much the same, with the former term emphasizing the means, and the latter the ends.

  2. 2.

    A single framework for all aspects of visualization (e.g., usability) is problematic, owing to the heterogeneous nature of the components (e.g., perceptual vs. motor mechanisms) and the possible lack of specificity in the tasks for which it might be used [32]. Discussion here focuses on a more restricted set of issues, viz., the extent to which visualization can enable a human to perceive some well-defined structure in a dataset. This abstracts away from details of particular tasks, and so increases the chances of a systematic framework for at least some parts of visualization. Vision science uses a similar approach, focusing on well-defined functions rather than on ways that vision might help carry out some poorly-defined task [21].

  3. 3.

    In some cases (e.g., cartography), the “machine” component may be distributed over a several different devices—and perhaps even the occasional human—with only the result contained in the display (e.g., a map). From a functional point of view, such a configuration is still considered a single visualization system. The performance of such “off-line” systems differs from “on-line” systems only in one respect: because the time scales of the two components are quite different, the effectiveness of an off-line display can usually be assessed in terms of the speed, accuracy, and effort exerted by the human component alone. (Depending on the situation, however, it may be necessary to take into account such things as the cost of producing the display.)

  4. 4.

    Although inaccuracies are usually caused by mechanisms in the human viewer, they can also be due to the machine component—e.g., insufficient sampling, or a bias in an algorithm. According to the extended-vision thesis, the source is irrelevant: the measure of interest is based on the performance of the entire system. For the most part, discussion here focuses on the human viewer, since this is typically the largest source of inaccuracy. But if need be, the accuracy of the machine component could also be evaluated. Similar considerations apply to other measures of performance.

  5. 5.

    This assumes that the viewer can interpret the visual (or possibly other) representations used as a correlation. Developing such a “return route” to enable the inverse mapping may be an important part of training. Note that some of the performance limitations could occur at this stage, rather than the formation of the visual result proper.

  6. 6.

    Other issues exist, such as those concerned with visualization as a technology [32]. However, these are not directly relevant to concerns about the nature of a scientific framework, and so are not discussed here.

References

  1. W.S. Cleveland. Visualizing Data. Summit, NJ: Hobart Press, 1993.

    Google Scholar 

  2. A.M. MacEachren. How Maps Work: Representation, Visualization, and Design. New York: Guilford Press. pp. 51–149, 1995.

    Google Scholar 

  3. M. Massironi. The Psychology of Graphic Images: Seeing Drawing, Communicating. Matwah NJ: Erlbaum, 2002.

    Google Scholar 

  4. H. Wainer. Graphic Discovery. Princeton: University Press, 2005.

    MATH  Google Scholar 

  5. S.K. Card, J.D. Mackinlay, and B. Shneiderman. Information visualization. In S.K. Card, J.D. Mackinlay, and B. Shneiderman (Eds.) Readings in Information Visualization: Using Vision to Think. San Francisco: Morgan Kaufman. pp. 1–34, 1999.

    Google Scholar 

  6. T.S. Kuhn. The Structure of Scientific Revolutions, 2nd ed. Chicago: University of Chicago Press, 1970.

    Google Scholar 

  7. I. Lakatos. The Methodology of Scientific Research Programmes: Philosophical Papers, Volume 1. Cambridge: Cambridge University Press, 1978.

    Book  Google Scholar 

  8. C. Ware. Information Visualization: Perception for Design, 2nd ed. San Francisco: Morgan Kaufman, 2004.

    Google Scholar 

  9. C. Ware. Visual Thinking for Design. San Francisco: Morgan Kaufman, 2008.

    Google Scholar 

  10. S.C. Few. Information Dashboard Design: The Effective Visual Communication of Data. Sebastopol, CA: O’Reilly Media, Inc., 2006.

    Google Scholar 

  11. R.A. Rensink. The dynamic representation of scenes. Visual Cognition, 7: 17–42, 2000.

    Article  Google Scholar 

  12. A.D. Milner and M.A. Goodale. The Visual Brain in Action. Oxford: Oxford University Press, 1995.

    Google Scholar 

  13. C.A. Brewer. Designing Better Maps: A Guide for GIS Users. Redlands CA: ESRI Press, 2005.

    Google Scholar 

  14. N.B. Robbins. Creating More Effective Graphs. Hoboken, NJ: John Wiley & Sons, 2004.

    Book  Google Scholar 

  15. S.M. Kosslyn. Graph Design for the Eye and Mind. Oxford: Oxford University Press, 2006.

    Book  Google Scholar 

  16. R.A. Rensink. The management of visual attention in graphic displays. In C. Roda (Ed.), Human Attention in Digital Environments. Cambridge: University Press. pp. 63–92, 2010.

    Google Scholar 

  17. C.D. Wickens and J.S. McCarley. Applied Attention Theory. Boca Raton, FL: CRC Press, 2008.

    Google Scholar 

  18. J. Bertin. Semiology of Graphics: Diagrams, Networks, Maps. Madison WI: University of Wisconsin Press, 1983.

    Google Scholar 

  19. J.H. Larkin an H.A. Simon. Why a diagram is (sometimes) worth ten thousand words. Cognitive Science, 11: 65–99, 1987.

    Article  Google Scholar 

  20. J.D. Foley, A. van Dam, S.K. Feiner, and J.F. Hughes. Introduction to Computer Graphics. Reading MA: Addison-Wesley, 1993.

    Google Scholar 

  21. D. Marr. Vision: A Computational Investigation into the Human Representation and Processing of Visual Information. San Francisco: W.H. Freeman, 1982.

    Google Scholar 

  22. J. Zhang and D. Norman. Representations in distributed cognitive tasks. Cognitive Science, 18: 87–122, 1994.

    Article  Google Scholar 

  23. V. Di Lollo, J.T. Enns, and R.A. Rensink. Competition for consciousness among visual events: The psychophysics of reentrant visual processes. Journal of Experimental Psychology: General, 129: 481–507, 2000.

    Article  Google Scholar 

  24. A.K. Mackworth. Vision research strategy: Black magic, metaphors, mechanisms, miniworlds, and maps. In A.R. Hanson, E.M. Riseman (Eds.), Computer Vision Systems. New York: Academic Press. pp. 53–60, 1978,

    Google Scholar 

  25. U. Neisser. Cognition and Reality. San Francisco: W.H. Freeman. pp. 20–24, 1976.

    Google Scholar 

  26. J. Li, J.-B. Martens, and J.J. van Wijk. Judging correlation from scatterplots and parallel coordinate plots. Information Visualization, 9: 13–30, 2010.

    Article  Google Scholar 

  27. R.M. Baecker, J. Grudin, W. Buxton, and S. Greenberg. Readings in Human-Computer Interaction: Toward the Year 2000. San Francisco: Morgan Kaufman, 1995.

    Google Scholar 

  28. Carpendale. Evaluating information visualizations. In A. Kerren et al. (Eds.) Information Visualization: Human-Centered Issues and Perspectives. LNCS 4950. Berlin: Springer. pp. 19–45, 2008.

    Chapter  Google Scholar 

  29. J.-D. Fekete, J.J. van Wijk, J.T. Stasko, and C. North. The value of information visualization. In A, Kerren et al. (Eds). Information Visualization: Human-Centered Issues and Perspectives. LNCS 4950. Berlin: Springer. pp. 1–18, 2008.

    Chapter  Google Scholar 

  30. S.E. Palmer. Vision Science: Photons to Phenomenology. Cambridge MA: MIT Press, 1999.

    Google Scholar 

  31. R.L. Harris. Information Graphics: A Comprehensive Illustrated Reference. Oxford: Oxford University Press, 1999.

    Google Scholar 

  32. J.J. van Wijk. The value of visualization. Proceedings IEEE Visualization 2005, pp. 79–86, 2005.

    Google Scholar 

  33. P. Bobko, and R. Kerren. The perception of Pearson product moment correlations from bivariate scatterplots. Personnel Psychology, 32: 313–325, 1979.

    Article  Google Scholar 

  34. I. Pollack. Identification of visual correlational scatterplots. J. Experimental Psychology, 59: 351–360, 1960.

    Article  Google Scholar 

  35. M.E. Doherty, R.B. Anderson, A.M. Angott, and D.S. Klopfer. The perception of scatterplots. Perception & Psychophysics, 69: 1261–1272, 2007.

    Article  Google Scholar 

  36. W. Ellermeier, and G. Faulhammer. Empirical evaluation of axioms fundamental to Stevens’s ratio-scaling approach: I. Loudness production. Perception & Psychophysics, 62: 1505–1511, 2000.

    Article  Google Scholar 

  37. D. Laming. The Measurement of Sensation. Oxford: Oxford University Press, 1997.

    Book  Google Scholar 

  38. R.A. Rensink, and G. Baldridge. The perception of correlation in scatterplots. Computer Graphics Forum, 29: 1203–1210, 2010.

    Article  Google Scholar 

  39. W.S. Cleveland, P. Diaconis, and R. McGill. Variables on scatterplots look more highly correlated when scales are increased. Science, 216: 1138–1141, 1982.

    Article  Google Scholar 

  40. S. Coren, L.M. Ward, and J.T. Enns. Sensation and Perception, 5th ed. New York: Harcourt Brace. pp. 15–49, 1999.

    Google Scholar 

  41. H.E. Ross. On the possible relations between discriminability and apparent magnitude. British J. Mathematical and Statistical Psychology, 50: 187–203, 1997.

    Article  MATH  Google Scholar 

  42. R.A. Rensink. Rapid Perception of Correlation in Scatterplots. Journal of Vision, 11. [Vision Sciences Society, Naples, FL, USA. May 2011.]

    Google Scholar 

  43. S.C. Chong, and A. Treisman. Representation of statistical properties. Vision Research, 43: 393–404, 2003.

    Article  Google Scholar 

  44. S. Thorpe, D. Fize, and C. Marlot. Speed of processing in the human vision system. Nature, 381: 520–522, 1996.

    Article  Google Scholar 

  45. Z.A. Melzak. Bypasses: A Simple Approach to Complexity. NY: John Wiley & Sons, 1983.

    MATH  Google Scholar 

  46. S. Baase. Computer Algorithms: Introduction to Design and Analysis, 2nd ed. Reading MA: Addison-Wesley, 1988.

    Google Scholar 

  47. R. Amar, J. Eagan, and J. Stasko. Low-level components of analytic activity in information visualization. In Proc IEEE Symposium on Information Visualization 2005, pp. 111–117, 2005.

    Google Scholar 

  48. D.J. Kasik. Strategies for consistent image partitioning. IEEE Multimedia, 11: 32–41, 2004.

    Article  Google Scholar 

  49. R.A. Rensink and G. Provan. The analysis of resource-limited vision systems. Proceedings of the Thirteenth Annual Conference of the Cognitive Science Society, pp. 311–316. Chicago IL, USA, 1991.

    Google Scholar 

  50. M.R. Garey, and D.S. Johnson. Computers and Intractability: A Guide to the Theory of NP-Completeness. San Francisco: Freeman, 1979

    MATH  Google Scholar 

  51. H.A. Simon. The Sciences of the Artifical, 3rd ed. Cambridge MA: MIT Press. pp. 111–138, 1996.

    Google Scholar 

  52. W.D. Gray. Integrated Models of Cognitive Systems. New York: Oxford University Press, 2007.

    Book  Google Scholar 

  53. S.C. Few. Show Me the Numbers: Designing Tables and Graphs to Enlighten. Oakland, CA: Analytics Press. pp. 92–130, 2004.

    Google Scholar 

  54. E.R. Tufte. Visual Explanations. Cheshire CT: Graphics Press, 1997.

    MATH  Google Scholar 

  55. J.D. Mackinlay. Automating the design of graphical presentations of relational information. ACM Transactions on Graphics, 5: 110–141, 1986.

    Article  Google Scholar 

  56. R.A. Rensink. Invariance Of Correlation Perception. Journal of Vision, 12. [Vision Sciences Society, Naples, FL, USA. May 2012.].

    Google Scholar 

  57. P. Martin, and P. Bateson. Measuring Behaviour: An Introductory Guide, 2nd ed. Cambridge: University Press, 1993.

    Book  Google Scholar 

  58. J.J. Garrett. The Elements of User Experience: User-Centered Design for the Web. Berkeley CA: Peachpit Press, 2002.

    Google Scholar 

  59. T. Munzner. A nested model for visualization design and validation. IEEE Transactions on Visualization and Computer Graphics, 15: 921–924, 2009.

    Article  Google Scholar 

  60. M. Sedlmair, M. Meyer, and T. Munzner. Design study methodology: Reflections from the trenches and the stacks. IEEE Information Visualization Conference 2012, Seattle, WA, USA.

    Google Scholar 

  61. W.B. Paley. Interface and mind. it – Information Technology, 51: 131–141, 2009.

    Article  Google Scholar 

  62. R. Arnheim. Visual Thinking. Berkeley: University of California Press, 1972.

    Google Scholar 

  63. J. Heer, and B. Shneiderman. Interactive dynamics for visual analysis: A taxonomy of tools that support the fluent and fleixble use of visualizations. ACM Queue, 10: 30:30–30:55. 2012.

    Google Scholar 

  64. R.A. Rensink. Internal vs. external information in visual perception. Proceedings of the Second International Symposium on Smart Graphics, Hawthorne, NY, USA. pp. 63–70, 2002.

    Google Scholar 

  65. B. Shneiderman. The eyes have it: A task by data type taxonomy for information visualizations. Proceedings of the 1996 IEEE Symposium on Visual Languages. pp. 336–343, 1996.

    Google Scholar 

  66. M. Tory, and T. Möller. Rethinking visualization: A high-level taxonomy. IEEE Symposium on Information Visualization. pp. 151–158, 2004.

    Google Scholar 

  67. S. Conway Morris. Life’s Solution: Inevitable Humans in a Lonely Universe. Cambridge: Cambridge University Press, 2004.

    Google Scholar 

  68. G. McGhee. Convergent Evolution: Limited Forms Most Beautiful. Cambridge MA: MIT Press, 2011.

    Google Scholar 

  69. N. Cross. Designerly ways of knowing: Design discipline versus design science. Design Issues, 17: 49–55, 2001.

    Article  Google Scholar 

  70. D. Grant. Design methodology and design methods, Design Methods and Theories, 13:1, 1979.

    MathSciNet  Google Scholar 

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Acknowledgements

Many thanks to Stephen Few, Dave Kasik, Minjung Kim, Tamara Munzner, Vicki Lemieux, Michael Sedlmair, Ben Shneiderman, and Jack van Wijk for their comments on earlier versions of this paper. Thanks also to Tamara Munzner for feedback on the idea of the operations inventory, and to Jack van Wijk for interesting discussions about the limits of a possible science of visualization. Support for this work and several of the studies mentioned was provided by the Natural Sciences and Engineering Research Council of Canada (NSERC), and The Boeing Company.

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  1. Departments of Computer Science and Psychology, University of British Columbia, 2366 Main Mall, Vancouver, BC, V6T 1Z4, Canada

    Ronald A. Rensink

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Correspondence to Ronald A. Rensink .

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    Weidong Huang

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Rensink, R.A. (2014). On the Prospects for a Science of Visualization. In: Huang, W. (eds) Handbook of Human Centric Visualization. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-7485-2_6

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