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An Audit Tool for Assessing the Visuocognitive Design of Infographics

Published:10 September 2019Publication History

ABSTRACT

Visuocognitive design accommodates the alignment of visualization to human cognitive processes. Established theory suggests that 1) recognition is easier than recall [1], 2) spatial visualizations are less abstract than temporal ones [2], and 3) aesthetics induce cognitive ease [3]. These principles, and others, underpin our new audit tool that focusses on design for cognition.

Theories of form, function and utility have been known for many decades and are well-known in the field of design, but infovisualization is a relatively new field, as are associated fields such as user-experience (UX), user-centered design and information design. Therefore, generally, design schools focus far more (possibly, exclusively) on teaching form, style, function, sustainability and user-experience than on visuocognition. The same emphasis is found in the design industry. This audit tool has been created to provide heuristic evaluations based on a set of visuocognitive design principles and is, therefore, a valuable contribution.

To devise the visuocognitive principles, we conducted a narrative review as a method of approach. The tool is composed of one prerequisite and six principles. ‘Informed Engagement’ is the prerequisite to accurately inform the graphics with ground truth, and to give them substance. The six principles are: 1) clarity, 2) arrangement, 3) cued meaning, 4) intuitive meaning, 5) cognitive fit, and 6) cognitive preference. They are divided into three groups: the first two principles concern appearance, the second two principles concern meaning, and the last two principles concern cognition (Figure 1). The term ‘meaning’ can imply intended meaning by the designer (in a graphic representation), or construed meaning by the user. The novelty of this audit tool is that it fixes ‘meaning’ as the pivotal point between aesthetic visual display and mental cognition, with the aim to align construed meaning with intended meaning and achieve fluent cognition.

References

  1. A. Baddleley, M. Eysenck, and M. Anderson (2015). Memory. Psychology Press.Google ScholarGoogle ScholarCross RefCross Ref
  2. B. Tversky (2000). Some Ways that Maps and Diagrams Communicate. (Ch. Freksa (Eds.): Spatial Cognition II, LNAI 1849, pp. 72-79, 2000). Springer-Verlag Berlin Heidelberg. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. R. Bond (2017). Skype conversation with Jonathan Gay, 1 June 2017.Google ScholarGoogle Scholar
  4. H. Purchase (2018). A Classification of Infographics. In: 10th International Conference on the Theory and Application of Diagrams, Edinburgh, UK, 18-22 June 2018, pp. 210-218.Google ScholarGoogle ScholarCross RefCross Ref
  5. R. Kosara (2010). The Difference Between Infographics and Visualization {Blog}. Available at https://eagereyes.org/blog/2010/the-difference-between-infographics-and-visualizationGoogle ScholarGoogle Scholar
  6. Y. Engelhardt (2002). The Language of Graphics: A framework for the analysis of syntax and meaning in maps, charts and diagrams. PhD Thesis, Amsterdam, University of Amsterdam.Google ScholarGoogle Scholar
  7. J. Fiske (1990). Introduction to Communication Studies (Studies in Culture and Communication). Taylor and Francis.Google ScholarGoogle Scholar
  8. R. Bestley and I. Noble (2001). We Interrupt the Programme. Mamara University, Istanbul.Google ScholarGoogle Scholar
  9. G. Bateson (1972). Steps to an Ecology of Mind: Collected essays in Anthropology, Psychiatry, Evolution, and Epistemology. Jason Aronson Inc.Google ScholarGoogle Scholar
  10. J. Sweller (1988). Cognitive Load During Problem Solving. Cognitive Science, Vol 12, Issue 2, pp:257-285.Google ScholarGoogle Scholar
  11. L. H. Sullivan (1896). Available at https://archive.org/details/tallofficebuildi00sull/page/n9Google ScholarGoogle Scholar
  12. K. Rowland (1973). A History of the Modern Movement: Art Architecture Design. (3rd impression 1980), Chapter 3, Page 40. Ginn and Company Ltd.Google ScholarGoogle Scholar
  13. C. E. Shannon (1948). The Bell System Technical Journal, Vol. 27, pp. 379–423, 623–656, July, October 1948.Google ScholarGoogle ScholarCross RefCross Ref
  14. E. Tufte (1983). The Visual Display of Quantitative Information. Graphics Press, USA. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. P. Chandler and J. Sweller (1992). The Split-Attention Effect as a Factor in the Design of Instruction. British Journal of Educational Psychology.Google ScholarGoogle Scholar
  16. M. Eysenck and M. Brysbaert (2018). Fundamentals of Cognition. Routledge.Google ScholarGoogle ScholarCross RefCross Ref
  17. S. Kosslyn, M. M.N. Alpert, W. L. Thompson, V. Maljkovic, S. B. Weise, C.F. Chabris, S. E. Hamilton, S. L. Rauch, and F. S. Buonanno (1993). Visual Mental Imagery Activates Topographically Organized Visual Cortex: PET Investigations. Harvard University.Google ScholarGoogle Scholar
  18. M. Borkin (2015). Beyond Memorability: Visualization Recognition and Recall. IEEE Transactions on Visualization and Computer Graphics (Proceedings of InfoVis 2015), 22, 1, pp: 519-528.Google ScholarGoogle Scholar
  19. R. Barthes (1964). Rhetoric of the Image {online}. Available from: https://faculty.georgetown.edu/irvinem/theory/Barthes-Rhetoric-of-the-image-ex.pdfGoogle ScholarGoogle Scholar
  20. J. Kennedy (1982). Metaphor in Pictures. Perception, 1982, Vol 11, PP: 589-605.Google ScholarGoogle Scholar
  21. T. Jappy (2013) Introduction to Peircean Visual Semiotics. Bloomsbury.Google ScholarGoogle Scholar
  22. A. Ojha (2015). Visual Metaphor and Cognition. LAP Lambert Academic Publishing.Google ScholarGoogle Scholar
  23. P. A. Nobel and R. M. Shiffrin (2001). Retrieval Processes in Recognition and Cued Recall. Journal of Experimental Psychology. Learning, Memory and Cognition. Mar,27(2) pp:384-413.Google ScholarGoogle ScholarCross RefCross Ref
  24. D. Kahneman (2012). Thinking Fast and Slow. Penguin.Google ScholarGoogle Scholar
  25. K. E. Stanovich (1999) Who Is Rational?: Studies of individual Differences in Reasoning. Psychology Press.Google ScholarGoogle ScholarCross RefCross Ref
  26. C. Ware (2008). Visual Thinking: for Design. Morgan Kauffman. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. D. Kolb (1984). Experiential Learning: Experience as the Source of Learning and Development. Prentice-Hall.Google ScholarGoogle Scholar
  28. H. Witkin and J. Berry (1975). Psychological Differentiation in Cross-Cultural Perspective. ETS, Volume 1975, Issue 1, pp: i-100.Google ScholarGoogle Scholar

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  • Published in

    cover image ACM Other conferences
    ECCE '19: Proceedings of the 31st European Conference on Cognitive Ergonomics
    September 2019
    231 pages
    ISBN:9781450371667
    DOI:10.1145/3335082

    Copyright © 2019 ACM

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    Publication History

    • Published: 10 September 2019

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