IDEAS home Printed from https://ideas.repec.org/a/sae/medema/v42y2022i6p741-754.html
   My bibliography  Save this article

Supporting Health and Medical Decision Making: Findings and Insights from Fuzzy-Trace Theory

Author

Listed:
  • Valerie F. Reyna

    (Human Neuroscience Institute and Center for Behavioral Economics and Decision Research, Cornell University, Ithaca, NY, USA)

  • Sarah Edelson

    (Human Neuroscience Institute and Center for Behavioral Economics and Decision Research, Cornell University, Ithaca, NY, USA)

  • Bridget Hayes

    (Human Neuroscience Institute and Center for Behavioral Economics and Decision Research, Cornell University, Ithaca, NY, USA)

  • David Garavito

    (Human Neuroscience Institute and Center for Behavioral Economics and Decision Research, Cornell University, Ithaca, NY, USA)

Abstract

Theory—understanding mental processes that drive decisions—is important to help patients and providers make decisions that reflect medical advances and personal values. Building on a 2008 review, we summarize current tenets of fuzzy-trace theory (FTT) in light of new evidence that provides insight regarding mental representations of options and how such representations connect to values and evoke emotions. We discuss implications for communicating risks, preventing risky behaviors, discouraging misinformation, and choosing appropriate treatments. Findings suggest that simple, fuzzy but meaningful gist representations of information often determine decisions. Within minutes of conversing with their doctor, reading a health-related web post, or processing other health information, patients rely on gist memories of that information rather than verbatim details. This fuzzy-processing preference explains puzzles and paradoxes in how patients (and sometimes providers) think about probabilities (e.g., “50-50†chance), outcomes of treatment (e.g., with antibiotics), experiences of pain, end-of-life decisions, memories for medication instructions, symptoms of concussion, and transmission of viruses (e.g., in AIDS and COVID-19). As examples, participation in clinical trials or seeking treatments with low probabilities of success (e.g., with antibiotics or at the end of life) may indicate a defensibly different categorical gist perspective on risk as opposed to simply misunderstanding probabilities or failing to make prescribed tradeoffs. Thus, FTT explains why people avoid precise tradeoffs despite computing them. Facilitating gist representations of information offers an alternative approach that goes beyond providing uninterpreted “neutral†facts versus persuading or shifting the balance between fast versus slow thinking (or emotion vs. cognition). In contrast to either taking mental shortcuts or deliberating about details, gist processing facilitates application of advanced knowledge and deeply held values to choices. Highlights Fuzzy-trace theory (FTT) supports practical approaches to improving health and medicine. FTT differs in important respects from other theories of decision making, which has implications for how to help patients, providers, and health communicators. Gist mental representations emphasize categorical distinctions, reflect understanding in context, and help cue values relevant to health and patient care. Understanding the science behind theory is crucial for evidence-based medicine.

Suggested Citation

  • Valerie F. Reyna & Sarah Edelson & Bridget Hayes & David Garavito, 2022. "Supporting Health and Medical Decision Making: Findings and Insights from Fuzzy-Trace Theory," Medical Decision Making, , vol. 42(6), pages 741-754, August.
  • Handle: RePEc:sae:medema:v:42:y:2022:i:6:p:741-754
    DOI: 10.1177/0272989X221105473
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/0272989X221105473
    Download Restriction: no

    File URL: https://libkey.io/10.1177/0272989X221105473?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:medema:v:42:y:2022:i:6:p:741-754. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.