1-in-X" bias: "1-in-X" format causes overestimation of health-related risks

According to the "1-in-X" effect, "1-in-X" ratios (e.g., 1 in 12) trigger a higher subjective probability than numerically equivalent "N-in-X*N" ratios (e.g., 3 in 36). Here we tested: (i) the effect on objective measures, (ii) its consequences for decision-making, (iii) whether this effect is a form of bias by measuring probability accuracy, and (iv) its amplification in people with lower health literacy and numeracy. In parallel-designed experiments, 975 participants from the general adult population participated in one of five experiments following a 2(format: "1-in-X" or "N-in-X*N") × 4(scenarios) mixed design. Participants assessed the risk of contracting a disease on either a verbal probability scale (Exp. 1), or a numerical probability/frequency scale with immediate (Exp. 2-3) or delayed presentation (Exp. 4-5). Participants also made a health-related decision and completed a health literacy and numeracy scale. The "1-in-X" ratios yielded higher probability perceptions than the "N-in-X*N" ratios and affected relevant decisions. Critically, the "1-in-X" ratios led to a larger objective overestimation of numerical probabilities than the "N-in-X*N" ratios. People with lower levels of health literacy and numeracy were not more sensitive to the bias. Health professionals should use "1-in-X" ratios with great caution when communicating to patients, because they overestimate health risks

Communicating likelihood and managing face: can we say it is probable when we know it to be certain?

Different functions can be performed by probability phrases such as: �it is probable/possible/likely that x.� Mainly, speakers may communicate a vague judgment about the likelihood of event x, or they may wish the recipient to focus on reasons for the occurrence of event x. We argue that there is another communicative function which has yet to be documented, namely, the facemanagement function. Such function consists in mitigating threat to the addressee when x is a criticism or an imposition. Data show that the phrases �possibly� and �probably� are indeed understood differently (have different membership functions) depending on whether they modify neutral or face-threatening contents. We consider the potential misunderstandings and judgmental mistakes that may arise from ambiguity about which function of verbal uncertainty is being performed

Processing scalar inferences in face-threatening contexts

Depending on politeness considerations, the quantifier ‘some’ can receive a broad interpretation (some and possibly all) or a narrow interpretation (some but not all). Face-threatening statements such as ‘some people hated your speech ’ encourage the broad interpretation that everyone hated the speech. Because previous research showed that broad interpretations are normally faster and easier, politeness should be easy to process, since it would encourage what is normally the easier interpretation of the statement. Using response time measures and a cognitive load manipulation, this research shows that just the opposite is true: Face threatening contexts encourage the broad interpretation of ‘some ’ while making it longer and more difficult to reach. This result raises difficulties for current cognitive theories of pragmatic inferences

How Potential BLFs Can Help to Decide under Incomplete Knowledge

International audienceIn a Bipolar Leveled Framework (BLF) [7], the comparison of two candidates is done on the basis of the decision principles and inhibitions which are validated given the available knowledge-bases associated with each candidate. This article defines a refinement of the rules for comparing candidates by using the potential-BLFs which can be built according to what could additionally be learned about the candidates. We also propose a strategy for selecting the knowledge to acquire in order to better discriminate between candidates