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BackgroundTo assess the impact of risk perceptions on prevention efforts or behavior change, best practices involve conditional risk measures, which ask people to estimate their risk contingent on a course of action (e.g., “if not vaccinated”). PurposeTo determine whether the use of conditional wording—and its drawing of attention to one specific contingency—has an important downside that could lead researchers to overestimate the true relationship between perceptions of risk and intended prevention behavior. MethodsIn an online experiment, US participants from Amazon’s MTurk ( N = 750) were presented with information about an unfamiliar fungal disease and then randomly assigned among 3 conditions. In all conditions, participants were asked to estimate their risk for the disease (i.e., subjective likelihood) and to decide whether they would get vaccinated. In 2 conditional-wording conditions (1 of which involved a delayed decision), participants were asked about their risk if they did not get vaccinated. For an unconditional/benchmark condition, this conditional was not explicitly stated but was still formally applicable because participants had not yet been informed that a vaccine was even available for this disease. ResultsWhen people gave risk estimates to a conditionally worded risk question after making a decision, the observed relationship between perceived risk and prevention decisions was inflated (relative to in the unconditional/benchmark condition). ConclusionsThe use of conditionals in risk questions can lead to overestimates of the impact of perceived risk on prevention decisions but not necessarily to a degree that should call for their omission. HighlightsConditional wording, which is commonly recommended for eliciting risk perceptions, has a potential downside. It can produce overestimates of the true relationship between perceived risk and prevention behavior, as established in the current work. Though concerning, the biasing effect of conditional wording was small—relative to the measurement benefits that conditioning usually provides—and should not deter researchers from conditioning risk perceptions. More research is needed to determine when the biasing impact of conditional wording is strongest. 

Abstract During a global crisis, does the desire for good news also mean an endorsement of an optimistic bias? Five pre‐registered studies, conducted at the start of the COVID pandemic, examined people's lay prescriptions for thinking about uncertainty—specifically whether they thought forecasters should be optimistic, realistic, or pessimistic in how they estimated key likelihoods. Participants gave prescriptions for forecasters with different roles (e.g., self, family member, and public official) and for several key outcomes (e.g., contracting COVID and vaccine development). Overall, prescribed optimism was not the norm. In fact, for negative outcomes that were of high concern, participants generally wanted others to have a pessimistic bias in how they estimated likelihoods. For positive outcomes, people favored more accurate estimation. These patterns held regardless of the assumed forecaster's role. A common justification for advocating for a pessimistic bias in forecasts was to increase others' engagement in protective or preventative behaviors. 

Abstract The phenomenon of ambiguity aversion suggests that people prefer options that offer precisely rather than imprecisely known chances of success. However, past work on people's responses to ambiguity in health treatment contexts found ambiguity seeking rather than aversion. The present work addressed whether such findings reflected a broad tendency for ambiguity seeking in health treatment contexts or whether specific attributions for ambiguity play a substantial role. In three studies, people choose between two treatment options that involved similar underlying probabilities, except that the probabilities for one option involved ambiguity. The attributions offered for the ambiguity played an important role in the results. For example, when the range of probabilities associated with an ambiguous treatment was attributed to the fact that different studies yield different results, participants tended to show ambiguity aversion or indifference. However, when the range was attributed to something that participants could control (e.g., regular application of a cream) or something about which they were overoptimistic (e.g., their immune system function), participants tended to show ambiguity seeking. Health professionals should be mindful of how people will interpret and use information about ambiguity when choosing among treatments. 

Past research on advice-taking has suggested that people are often insensitive to the level of advice independence when combining forecasts from advisors. However, this has primarily been tested for cases in which people receive numeric forecasts. Recent work by Mislavsky and Gaertig (2022) shows that people sometimes employ different strategies when combining verbal versus numeric forecasts about the likelihood of future events. Specifically, likelihood judgments based on two verbal forecasts (e.g., "rather likely") are more often extreme (relative to the forecasts) than are likelihood judgments based on two numeric forecasts (e.g., "70% probability"). The goal of the present research was to investigate whether advice-takers' use of combination strategies can be sensitive to advice independence when differences in independence are highly salient and whether sensitivity to advice independence depends on the format in which advice is given. In two studies, we found that advice-takers became more extreme with their own likelihood estimate when combining forecasts from advisors who use separate evidence, as opposed to the same evidence. We also found that two verbal forecasts generally resulted in more extreme combined likelihood estimates than two numeric forecasts. However, the results did not suggest that sensitivity to advice independence depends on the format of advice. 

Assessing perceived vulnerability to a health threat is essential to understanding how people conceptualize their risk, and to predicting how likely they are to engage in protective behaviors. However, there is limited consensus about which of many measures of perceived vulnerability predict behavior best. We tested whether the ability of different measures to predict protective intentions varies as a function of the type of information people learn about their risk. Online participants (N = 909) read information about a novel respiratory disease before answering measures of perceived vulnerability and vaccination intentions. Type-of-risk information was varied across three between-participant groups. Participants learned either: (1) only information about their comparative standing on the primary risk factors (comparative-only), (2) their comparative standing as well as the base-rate of the disease in the population (+ base-rate), or (3) their comparative standing as well as more specific estimates of their absolute risk (+ absolute-chart). Experiential and affective measures of perceived vulnerability predicted protective intentions well regardless of how participants learned about their risk, while the predictive ability of deliberative numeric and comparative measures varied based on the type of risk information provided. These results broaden the generalizability of key prior findings (i.e., some prior findings about which measures predict best may apply no matter how people learn about their risk), but the results also reveal boundary conditions and critical points of distinction for determining how to best assess perceived vulnerability. 

When making decisions involving risk, people may learn about the risk from descriptions or from experience. The description-experience gap refers to the difference in decision patterns driven by this discrepancy in learning format. Across two experiments, we investigated whether learning from description versus experience differentially affects the direction and the magnitude of a context effect in risky decision making. In Study 1 and 2, a computerized game called the Decisions about Risk Task (DART) was used to measure people’s risk-taking tendencies toward hazard stimuli that exploded probabilistically. The rate at which a context hazard caused harm was manipulated, while the rate at which a focal hazard caused harm was held constant. The format by which this information was learned was also manipulated; it was learned primarily by experience or by description. The results revealed that participants’ behavior toward the focal hazard varied depending on what they had learned about the context hazard. Specifically, there were contrast effects in which participants were more likely to choose a risky behavior toward the focal hazard when the harm rate posed by the context hazard was high rather than low. Critically, these contrast effects were of similar strength irrespective of whether the risk information was learned from experience or description. Participants’ verbal assessments of risk likelihood also showed contrast effects, irrespective of learning format. Although risk information about a context hazard in DART does nothing to affect the objective expected value of risky versus safe behaviors toward focal hazards, it did affect participants’ perceptions and behaviors—regardless of whether the information was learned from description or experience. Our findings suggest that context has a broad-based role in how people assess and make decisions about hazards. 

Past work has suggested that people prescribe optimism—believing it is better to be optimistic, instead of accurate or pessimistic, about uncertain future events. Here, we identified and addressed an important ambiguity about whether those findings reflect an endorsement of biased beliefs—i.e., whether people prescribe likelihood estimates that reflect overoptimism. In three studies, participants (total N = 663 U.S. university students) read scenarios about protagonists facing uncertain events with a desired outcome. Results replicated prescriptions of optimism when using the same solicitations as in past work. However, we found quite different prescriptions when using alternative solicitations that asked about potential bias in likelihood estimations and that did not involve vague terms like “optimistic.” Participants generally prescribed being optimistic, feeling optimistic, and even thinking optimistically about the events, but they did not prescribe overestimating the likelihood of those events.