Explaining risky choices with judgments: Framing, the zero effect, and the contextual relativity of gist.

Reyna, Valerie F.; Brainerd, Charles J.; Chen, Ziyi; Bookbinder, Sarah

doi:10.1037/xlm0001016

Cited by 13 publications

(37 citation statements)

References 30 publications

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“…Thinking about options categorically typically does not represent a misunderstanding about risk but rather a realization that decision options often have categorical consequences despite differences in details. 32 For example, adolescents in a risk-prevention program were encouraged to think about getting HIV/AIDS in categorical terms-it only takes once-despite being taught the objective probabilities (which are low). Outcomes among those encouraged to think categorically were superior to a group provided everything but that categorical approach 7 (see also Box).…”

Section: Categories Numbers and Downsides Of Precisionmentioning

confidence: 99%

“…As with meaning generally, the gist of numbers depends on the context, and rightfully so. 32,47 A 25% chance of rain should not be perceived as the same level of risk as a 25% chance of breast cancer. However, interpreting risk involves more than the severity of potential outcomes, such as whether risks differ qualitatively rather than just quantitatively.…”

Section: Box Example and Implications Of Fuzzy Trace Theory (Ftt) Ftt...mentioning

confidence: 99%

“…In the example above with lives, it is the qualitative contrast between some lives saved for sure and the possibility of no lives saved, not the difference between the utility of 200 lives (weighted by 1) and the utility of 600 lives (weighted by 1/3), that ultimately matters in choosing between these options. 32,48 Similarly, FTT challenges the claim that noncompensatory processing in the decision strategy, known as ''elimination by aspects,'' 49 is inferior to compensatory processing. In elimination by aspects, options that lack important attributes are discarded until a ''best'' option is left.…”

Section: Trading Offmentioning

confidence: 99%

“…In these examples, emotions and decisions fall out of principled predictions based on perceiving the abstract structure of decisions: If the choice is about certain death versus the possibility of life (and the possibility of death), valuing life over death dictates choosing the risky possibility of life over the certain prospect of death. 32 In explaining why patients seek treatment at the end of life or why they enter clinical trials, we are not implicitly advocating extending life at all costs. The relevant gist of many end-of-life decisions is not necessarily life over death but could be spending the final few months of life able versus unable to communicate with family, the latter being technically (literally) alive but not living in a gist sense.…”

Section: Connecting Mental Representations Of Options To Emotionsmentioning

confidence: 99%

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

et al. 2022

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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.

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Section: Categories Numbers and Downsides Of Precisionmentioning

confidence: 99%

Section: Box Example and Implications Of Fuzzy Trace Theory (Ftt) Ftt...mentioning

confidence: 99%

Section: Trading Offmentioning

confidence: 99%

Section: Connecting Mental Representations Of Options To Emotionsmentioning

confidence: 99%

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

et al. 2022

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“…For example, critical tests that eliminate parts of the gamble, such as the zero complement which amounts literally to removing nothing in expectancy-value theories (because any probability multiplied by zero is zero), have revealed that decision makers choose equal-expected-value options about equally often when zero complements are removed (Kühberger & Tanner, 2010). Thus, processing of numerical outcomes and probabilities, when the simpler gist is not available, does not elicit framing effects in risky-choice tasks, ruling out classical (rational choice) and contemporary (prospect theory) expectancy-value theories (see Reyna, Brainerd, et al, 2021). The latter-prospect theory-has also been incorporated into modern dual-process approaches (e.g., De Martino et al, 2006;Kahneman, 2003;Kahneman & Frederick, 2007).…”

Section: How Memory Representations Predict Risky Decision-makingmentioning

confidence: 99%

Viruses, vaccines, and COVID-19: Explaining and improving risky decision-making.

Reyna

Broniatowski

Edelson

2021

Journal of Applied Research in Memory and Cognition

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Risky decision-making lies at the center of the COVID-19 pandemic and will determine future viral outbreaks. Therefore, a critical evaluation of major explanations of such decision-making is of acute practical importance. We review the underlying mechanisms and predictions offered by expectancy-value and dual-process theories. We then highlight how fuzzy-trace theory builds on these approaches and provides further insight into how knowledge, emotions, values, and metacognitive inhibition influence risky decision-making through its unique mental representational architecture (i.e., parallel verbatim and gist representations of information). We discuss how social values relate to decision-making according to fuzzy-trace theory, including how categorical gist representations cue core values. Although gist often supports health-promoting behaviors such as vaccination, social distancing, and mask-wearing, why this is not always the case as with status-quo gist is explained, and suggestions are offered for how to overcome the “battle for the gist” as it plays out in social media.

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The Zero Effect: An Eye‐Tracking Study of Affect and Motivation in Risky Choices

Ludwig,

Jaudas,

Achtziger

2024

Behavioral Decision Making

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Decision makers often prefer safe wins over risky gambles, even if the latter promise higher payoffs than the former. One mechanism that explains this choice pattern is the certainty effect, whereby probabilities of 0 and 1 are interpreted accurately but intermediate probabilities are distorted by diminishing sensitivity. We tested an alternative explanation that was recently proposed, the idea that people would be motivated by avoiding zero outcomes rather than being attracted to sure gains. This zero‐outcome aversion in gain‐domain choices was called the zero effect. By analogy, we proposed that decision makers would approach zero outcomes in the loss domain. Two eye‐tracking experiments investigated visual attention as a key component of the zero effect in the gain domain (Experiment 1) and the loss domain (Experiment 2). Choices were consistent with the zero effect. In the gain domain, gambles were chosen less frequently if they included a zero outcome. In contrast, zero‐outcome gambles were chosen more frequently in the loss domain. Eye movements and pupillometry indicated that zero outcomes in both domains (a) were less frequently fixated than other outcomes and (b) were associated with increased arousal. We concluded that domain‐specific affective responses to zero outcomes aligned with approach/avoidance motivation. These distinct motivations in turn biased information search and choice behavior.

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Explaining risky choices with judgments: Framing, the zero effect, and the contextual relativity of gist.

Cited by 13 publications

References 30 publications

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

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

Viruses, vaccines, and COVID-19: Explaining and improving risky decision-making.

The Zero Effect: An Eye‐Tracking Study of Affect and Motivation in Risky Choices

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