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The Lure of Beauty: People Select Representations of Statistical Information Largely Based on Attractiveness, Not Comprehensibility

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  • Wolfgang Gaissmaier

    (Department of Psychology, University of Konstanz, Konstanz, Germany
    Centre for the Advanced Study of Collective Behaviour, University of Konstanz, Germany)

  • Kevin E. Tiede

    (Department of Psychology, University of Konstanz, Konstanz, Germany
    Center for Adaptive Rationality, Max Planck Institute for Human Development, Berlin, Germany
    Graduate School of Decision Sciences, University of Konstanz, Germany)

  • Rocio Garcia-Retamero

    (Department of Experimental Psychology, University of Granada, Granada, Spain)

Abstract

Objective People differ in whether they understand graphical or numerical representations of statistical information better. However, assessing these skills is often not feasible when deciding which representation to select or use. This study investigates whether people choose the representation they understand better, whether this choice can improve risk comprehension, and whether results are influenced by participants’ skills (graph literacy and numeracy). Methods In an experiment, 160 participants received information about the benefits and side effects of painkillers using either a numerical or a graphical representation. In the “no choice†condition, the representation was randomly assigned to each participant. In the “choice†condition, participants could select the representation they would like to receive. The study assessed gist and verbatim knowledge (immediate comprehension and recall), accessibility of the information, attractiveness of the representation, as well as graph literacy and numeracy. Results In the “choice†condition, most (62.5%) chose the graphical format, yet there was no difference in graph literacy or numeracy (nor age or gender) between people who chose the graphical or the numerical format. Whereas choice slightly increased verbatim knowledge, it did not improve gist or overall knowledge compared with random assignment. However, participants who chose a representation rated the representation as more attractive, and those who chose graphs rated them as more accessible than those without a choice. Limitations The sample consisted of highly educated undergraduate students with higher graph literacy than the general population. The task was inconsequential for participants in terms of their health. Conclusions When people can choose between representations, they fail to identify what they comprehend better but largely base that choice on how attractive the representation is for them. Highlights People differ systematically in whether they understand graphical or numerical representations of statistical information better. However, assessing these underlying skills to get the right representation to the right people is not feasible in practice. A simple and efficient method to achieve this could be to let people choose among representations themselves. However, our study showed that allowing participants to choose a representation (numerical v. graphical) did not improve overall or gist knowledge compared with determining the representation randomly, even though it did slightly improve verbatim knowledge. Rather, participants largely chose the representation they found more attractive. Most preferred the graphical representation, including those with low graph literacy. It would therefore be important to develop graphical representations that are not only attractive but also comprehensible even for people with low graph literacy.

Suggested Citation

  • Wolfgang Gaissmaier & Kevin E. Tiede & Rocio Garcia-Retamero, 2023. "The Lure of Beauty: People Select Representations of Statistical Information Largely Based on Attractiveness, Not Comprehensibility," Medical Decision Making, , vol. 43(7-8), pages 774-788, October.
  • Handle: RePEc:sae:medema:v:43:y:2023:i:7-8:p:774-788
    DOI: 10.1177/0272989X231201579
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    References listed on IDEAS

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    1. Valerie F. Reyna, 2008. "A Theory of Medical Decision Making and Health: Fuzzy Trace Theory," Medical Decision Making, , vol. 28(6), pages 850-865, November.
    2. Angela Fagerlin & Brian J. Zikmund-Fisher & Peter A. Ubel & Aleksandra Jankovic & Holly A. Derry & Dylan M. Smith, 2007. "Measuring Numeracy without a Math Test: Development of the Subjective Numeracy Scale," Medical Decision Making, , vol. 27(5), pages 672-680, September.
    3. Brian J. Zikmund-Fisher & Dylan M. Smith & Peter A. Ubel & Angela Fagerlin, 2007. "Validation of the Subjective Numeracy Scale: Effects of Low Numeracy on Comprehension of Risk Communications and Utility Elicitations," Medical Decision Making, , vol. 27(5), pages 663-671, September.
    4. Andreas Ortmann & Ralph Hertwig, 2006. "Monetary Incentives: Usually Neither Necessary Nor Sufficient?," CERGE-EI Working Papers wp307, The Center for Economic Research and Graduate Education - Economics Institute, Prague.
    5. Isaac M. Lipkus & Greg Samsa & Barbara K. Rimer, 2001. "General Performance on a Numeracy Scale among Highly Educated Samples," Medical Decision Making, , vol. 21(1), pages 37-44, February.
    6. repec:cup:judgdm:v:9:y:2014:i:5:p:420-432 is not listed on IDEAS
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