Experts’ Familiarity Versus Optimality of Visualization Design: How Familiarity Affects Perceived and Objective Task Performance

  • Aritra DasguptaEmail author


Visualization techniques are gaining increasing prominence across domains as a principal medium for sharing and communicating experts’ insights. A heuristic used by domain experts while choosing or designing a visualization is often their prior familiarity with an encoding choice or a visual representation. However, familiar visualizations and encodings within a domain are often in conflict with the optimality of visualization design, in terms of how well they support the data analysis or communication tasks. The use of the familiarity heuristic leads to a perceived cognitive ease in processing familiar representations of information and causes scientists to undermine the effect of visualization best practices. Recent studies have shown that this bias often results in a discrepancy between scientists’ perceived and actual performance quality. It has also been shown that in some cases, participatory design sessions and qualitative and quantitative user studies are able to mitigate the effects of such bias. In this chapter, we discuss the potential causes and effects of familiarity related biases with examples from recent studies and reflect on the associated research questions.


  1. 1.
    Allen TT (2005) Introduction to engineering statistics and six sigma: statistical quality control and design of experiments and systems. Springer, BerlinGoogle Scholar
  2. 2.
    Ashcraft M (2006) CognitionGoogle Scholar
  3. 3.
    Brehmer M, Ng J, Tate K, Munzner T (2016) Matches, mismatches, and methods: multiple-view workflows for energy portfolio analysis. IEEE Trans Visual Comput Graphics 22(1):449–458CrossRefGoogle Scholar
  4. 4.
    Dasgupta A, Poco J, Wei Y, Cook R, Bertini E, Silva CT (2015) Bridging theory with practice: an exploratory study of visualization use and design for climate model comparison. IEEE Trans Visual Comput Graphics 21(9):996–1014CrossRefGoogle Scholar
  5. 5.
    Dasgupta A, Burrows S, Han K, Rasch PJ (2017a) Empirical analysis of the subjective impressions and objective measures of domain scientists’ visual analytic judgments. In: Proceedings of the 2017 CHI conference on human factors in computing systems. ACM, New York, pp 1193–1204Google Scholar
  6. 6.
    Dasgupta A, Lee JY, Wilson R, Lafrance RA, Cramer N, Cook K, Payne S (2017b) Familiarity versus trust: a comparative study of domain scientists’ trust in visual analytics and conventional analysis methods. IEEE Trans Visual Comput Graphics 23(1):271–280CrossRefGoogle Scholar
  7. 7.
    Dasgupta A, Poco J, Rogowitz B, Bertini E, Silva CT (2018) The effect of color scales on climate scientists objective and subjective performance in spatial data analysis tasks. IEEE Trans Visual Comput Graphics (in publication)Google Scholar
  8. 8.
    Davis FD (1989) Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q 13:319–340CrossRefGoogle Scholar
  9. 9.
    Gist ME, Mitchell TR (1992) Self-efficacy: a theoretical analysis of its determinants and malleability. Acad Manage Rev 17(2):183–211CrossRefGoogle Scholar
  10. 10.
    Gleckler PJ, Taylor KE, Doutriaux C (2008) Performance metrics for climate models. J Geophys Res: Atmos 113(D6):D06104CrossRefGoogle Scholar
  11. 11.
    Gulati R (1995) Does familiarity breed trust? the implications of repeated ties for contractual choice in alliances. Acad Manage J 38(1):85–112Google Scholar
  12. 12.
    Pandey AV, Manivannan A, Nov O, Satterthwaite M, Bertini E (2014) The persuasive power of data visualization. IEEE Trans Visual Comput Graphics 20(12):2211–2220CrossRefGoogle Scholar
  13. 13.
    Park CW, Lessig VP (1981) Familiarity and its impact on consumer decision biases and heuristics. J Consum Res 8(2):223–230CrossRefGoogle Scholar
  14. 14.
    Plaisant C (2004) The challenge of information visualization evaluation. In: Proceedings of the working conference on Advanced visual interfaces. ACM, New York, pp 109–116Google Scholar
  15. 15.
    Raskin J (1994) Intuitive equals familiar. Commun ACM 37(9):17–19Google Scholar
  16. 16.
    Samuelson W, Zeckhauser R (1988) Status quo bias in decision making. J Risk Uncertainty 1(1):7–59CrossRefGoogle Scholar
  17. 17.
    Stajkovic AD, Luthans F (1998) Self-efficacy and work-related performance: a meta-analysis. Psychol Bull 124(2):240CrossRefGoogle Scholar
  18. 18.
    Takayama L, Kandogan E (2006) Trust as an underlying factor of system administrator interface choice. In: Extended abstracts on Human factors in computing systems. ACM, New York, pp 1391–1396Google Scholar
  19. 19.
    Tversky A, Kahneman D (1973) Availability: a heuristic for judging frequency and probability. Cogn Psychol 5(2):207–232CrossRefGoogle Scholar
  20. 20.
    Tversky A, Kahneman D (1991) Loss aversion in riskless choice: a reference-dependent model. Q J Econ 106(4):1039–1061CrossRefGoogle Scholar

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Authors and Affiliations

  1. 1.New Jersey Institute of TechnologyNew JerseyUSA

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