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How Do People Process Different Representations of Statistical Information? Insights into Cognitive Effort, Representational Inconsistencies, and Individual Differences

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  • Kevin E. Tiede

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

  • Wolfgang Gaissmaier

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

Abstract

Background Graphical representation formats (e.g., icon arrays) have been shown to lead to better understanding of the benefits and risks of treatments compared to numbers. We investigate the cognitive processes underlying the effects of format on understanding: how much cognitive effort is required to process numerical and graphical representations, how people process inconsistent representations, and how numeracy and graph literacy affect information processing. Methods In a preregistered between-participants experiment, 665 participants answered questions about the relative frequencies of benefits and side effects of 6 medications. First, we manipulated whether the medical information was represented numerically, graphically (as icon arrays), or inconsistently (numerically for 3 medications and graphically for the other 3). Second, to examine cognitive effort, we manipulated whether there was time pressure or not. In an additional intervention condition, participants translated graphical information into numerical information before answering questions. We also assessed numeracy and graph literacy. Results Processing icon arrays was more strongly affected by time pressure than processing numbers, suggesting that graphical formats required more cognitive effort. Understanding was lower when information was represented inconsistently (v. consistently) but not if there was a preceding intervention. Decisions based on inconsistent representations were biased toward graphically represented options. People with higher numeracy processed quantitative information more efficiently than people with lower numeracy did. Graph literacy was not related to processing efficiency. Limitations Our study was conducted with a nonpatient sample, and the medical information was hypothetical. Conclusions Although graphical (v. numerical) formats have previously been found to lead to better understanding, they may require more cognitive effort. Therefore, the goal of risk communication may play an important role when choosing how to communicate medical information. Highlights This article investigates the cognitive processes underlying the effects of representation format on the understanding of statistical information and individual differences therein. Processing icon arrays required more cognitive effort than processing numbers did. When information was represented inconsistently (i.e., partly numerically and partly graphically), understanding was lower than with consistent representation, and decisions were biased toward the graphically represented options. People with higher numeracy processed quantitative information more efficiently than people with lower numeracy did.

Suggested Citation

  • Kevin E. Tiede & Wolfgang Gaissmaier, 2023. "How Do People Process Different Representations of Statistical Information? Insights into Cognitive Effort, Representational Inconsistencies, and Individual Differences," Medical Decision Making, , vol. 43(7-8), pages 803-820, October.
  • Handle: RePEc:sae:medema:v:43:y:2023:i:7-8:p:803-820
    DOI: 10.1177/0272989X231202505
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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. Deb Feldman-Stewart & Nancy Kocovski & Beth A. McConnell & Michael D. Brundage & William J. Mackillop, 2000. "Perception of Quantitative Information for Treatment Decisions," Medical Decision Making, , vol. 20(2), pages 228-238, April.
    3. 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.
    4. repec:cup:judgdm:v:9:y:2014:i:1:p:15-34 is not listed on IDEAS
    5. Rachel F. Eyler & Sara Cordes & Benjamin R. Szymanski & Liana Fraenkel, 2017. "Utilization of Continuous “Spinners†to Communicate Risk," Medical Decision Making, , vol. 37(6), pages 725-729, August.
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