What's next? Disentangling availability from representativeness using binary decision tasks

Braga, João Niza; Ferreira, Mário B.; Sherman, Steven J.; Mata, André; Jacinto, Sofia; Ferreira, Marina Silva

doi:10.1016/j.jesp.2018.03.006

Cited by 10 publications

(8 citation statements)

References 28 publications

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“…The availability, affect, and representativeness heuristics are conceptually distinct and represent distinct mechanisms (i.e., reported amount of negative affect for the affect heuristic, perceived similarity for the representativeness heuristic, and availability in memory for the availability heuristic; Finucane et al, 2000;Tversky & Kahneman, 1973, 1974. However, few studies have examined whether the conceptual distinctions translate to empirical distinctions (but see Braga et al, 2018;Efendić, 2021;Keller et al, 2006). Disentangling the heuristics empirically could help identify which heuristic drives judgments and decisions more than others.…”

Section: Heuristics and Decision-makingmentioning

confidence: 99%

“…Such procedures can sacrifice generalizability to the broader population for experimental control. Consequently, some research on heuristics in real‐world health decision‐making (rather than hypothetical scenarios) has instead measured people's responses to heuristic cues (i.e., survey items that assess endorsement of concepts related to heuristics, such as the amount of affect or the ease of retrieving relevant instances from memory) and correlated them with risk judgments (e.g., Gerend, Aiken, & West, 2004; Gerend, Aiken, West, & Erchull, 2004; Greening et al., 1996; McDowell et al., 2013, but see counterexamples Braga et al., 2018; Hertwig et al., 2005; Pachur et al., 2012).…”

Section: Introductionmentioning

confidence: 99%

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Linking cognitive and affective heuristic cues to interpersonal risk perceptions and behavior

et al. 2023

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People often use cognitive and affective heuristics when judging the likelihood of a health outcome and making health decisions. However, little research has examined how heuristics shape risk perceptions and behavior among people who make decisions on behalf of another person. We examined associations between heuristic cues and caregivers’ perceptions of their child's asthma risk, the frequency of caregivers’ asthma management behaviors, and child health outcomes. We used Ipsos KnowledgePanel to recruit 814 U.S. adult caregivers of children with asthma of the age <18 years. Participants completed a survey at baseline (T1) and 3 months later (T2). Caregivers who, at T1, reported greater negative affect about their child's asthma (affect heuristic cue), greater ease of imagining their child experiencing asthma symptoms (availability heuristic cue), and greater perceived similarity between their child and a child who has ever experienced asthma symptoms (representativeness heuristic cue) reported statistically significantly (p < 0.05) higher interpersonal perceived risk of their child having an exacerbation or uncontrolled asthma at T1. They also indicated at T2 that their child had poorer asthma control and more frequent exacerbations. Greater T1 negative affect was associated with more frequent T2 actions to reduce inflammation, manage triggers, and manage symptoms, and with poorer T2 child health outcomes. Heuristic cues are likely important for interpersonal—not just personal—risk perceptions. However, the interrelationship between caregivers’ ratings of heuristic cues (in particular, negative affect) and risk judgments may signify a struggle with managing their child's asthma and need for extra support from health care providers or systems.

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Section: Heuristics and Decision-makingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Linking cognitive and affective heuristic cues to interpersonal risk perceptions and behavior

et al. 2023

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“…The gambler's fallacy starts to show up in elementary school, and peaks among college students (Bogartz, 1965 ; Chiesi & Primi, 2009 ; Derks & Paclisanu, 1967 ; Estes, 1962 ; Craig & Meyers, 1963 ). People also take longer to predict reversal than repetition, and are more likely to predict repetition than reversal under time constraints or cognitive load (Braga, Ferreira, Sherman, Mata, Jacinto, & Ferreira, 2018 ; Diener & Thompson, 1985 ; Militana, Wolfson, & Cleaveland, 2010 ; Tyszka, Markiewicz, Kubińska, Gawryluk, & Zielonka, 2017 ).…”

Section: Theoretical and Empirical Context For The Present Researchmentioning

confidence: 99%

“…Instead of clearly specifying identical base rates for each generator, experimenters either rely on participants’ prior beliefs (e.g., that a fair coin has a stationary .50 base rate, or that a basketball player performs at an unspecified, perhaps “typical,” rate), or they provide base rate information that may be interpreted as stationary for random devices, but shifting for intentional actors. For example, Braga and colleagues ( 2018 ) compared predictions for coin flips to those for athletic performances. The authors provided explicit information that the coin was fair (stationary .50 base rate), and that flipping the coin was a random process.…”

Section: Theoretical and Empirical Context For The Present Researchmentioning

confidence: 99%

Predicting Outcomes in a Sequence of Binary Events: Belief Updating and Gambler's Fallacy Reasoning

Rao

Hastie

2023

Cognitive Science

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Beliefs like the Gambler's Fallacy and the Hot Hand have interested cognitive scientists, economists, and philosophers for centuries. We propose that these judgment patterns arise from the observer's mental models of the sequence-generating mechanism, moderated by the strength of belief in an a priori base rate. In six behavioral experiments, participants observed one of three mechanisms generating sequences of eight binary events: a random mechanical device, an intentional goal-directed actor, and a financial market. We systematically manipulated participants' beliefs about the base rate probabilities at which different outcomes were generated by each mechanism. Participants judged 18 sequences of outcomes produced by a mechanism with either an unknown base rate, a specified distribution of three equiprobable base rates, or a precise, fixed base rate. Six target sequences ended in streaks of between two and seven identical outcomes. The most common predictions for subsequent events were best described as pragmatic belief updating, expressed as an increasingly strong expectation that a streak of identical signals would repeat as the length of that streak increased. The exception to this pattern was for sequences generated by a random mechanical device with a fixed base rate of .50. Under this specific condition, participants exhibited a bias toward reversal of streaks, and this bias was larger when participants were asked to make a dichotomous choice versus a numerical probability rating. We

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“…The gambler's fallacy starts to show up in elementary school, and peaks among college students (Bogartz, 1965;Chiesi & Primi, 2009;Derks & Paclisanu, 1967;Estes, 1962;Craig & Meyers, 1963). People also take longer to predict reversal than repetition, and are more likely to predict repetition than reversal under time constraint or cognitive load (Braga, Ferreira, Sherman, Mata, Jacinto, & Ferreira, 2018;Diener & Thompson, 1985;Militana, Wolfson, & Cleaveland, 2010;Tyszka, Markiewicz, Kubińska, Gawryluk, & Zielonka, 2017).…”

Section: Theoretical Accounts Of Prediction Behaviormentioning

confidence: 99%

Predicting Outcomes in a Sequence of Binary Events: Belief Updating and Gambler’s Fallacy Reasoning

Rao

Hastie

2020

SSRN Journal

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What's next? Disentangling availability from representativeness using binary decision tasks

Cited by 10 publications

References 28 publications

Linking cognitive and affective heuristic cues to interpersonal risk perceptions and behavior

Linking cognitive and affective heuristic cues to interpersonal risk perceptions and behavior

Predicting Outcomes in a Sequence of Binary Events: Belief Updating and Gambler's Fallacy Reasoning

Predicting Outcomes in a Sequence of Binary Events: Belief Updating and Gambler’s Fallacy Reasoning

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