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According to the "1-in-X" effect, "1-in-X" ratios (e.g., 1 in 12) trigger a higher subjective probability than do numerically equivalent "N-in-X*N" ratios (e.g., 3 in 36). Here we tested the following: (a) the effect on objective measures, (b) its consequences for decision-making, (c) whether this effect is a form of bias by measuring probability accuracy, and (d) its amplification in people with lower health literacy and numeracy. In parallel-designed experiments, 975 participants from the general adult population participated in 1 of 5 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 (Experiment 1) or a numerical probability/frequency scale with immediate (Experiments 2-3) or delayed presentation (Experiments 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 did 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 did 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. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Many "if p, then q" conditionals have decision-theoretic features, such as antecedents or consequents that relate to the utility functions of various agents. These decision-theoretic features leak into reasoning processes, resulting in various paralogical conclusions. The theory of utility conditionals offers a unified account of the various forms that this phenomenon can take. The theory is built on 2 main components: (1) a representational tool (the utility grid), which summarizes in compact form the decision-theoretic features of a conditional, and (2) a set of folk axioms of decision, which reflect reasoners' beliefs about the way most agents make their decisions. Applying the folk axioms to the utility grid of a conditional allows for the systematic prediction of the paralogical conclusions invited by the utility grid's decision-theoretic features. The theory of utility conditionals significantly extends the scope of current theories of conditional inference and moves reasoning research toward a greater integration with decision-making research.

A successful theory of conditional reasoning requires an account of how reasoners recognize the pragmatic function a conditional statement is meant to perform. Situations in which it is ambiguous whether a conditional statement was meant to add information or to correct a mistake are discussed in this article. This ambiguity has direct consequences on the way reasoners update their beliefs and derive conclusions. In an analysis of ambiguity from the perspective of politeness theory, the authors suggest that any contextual factor that increases the face threat of a correction will encourage reasoners to construe the ambiguous conditional as a correction. This construal will impact their beliefs about the piece of information that is ambiguously corrected, and their beliefs will affect the deductive conclusions they are willing to draw. This nested mediation structure was observed in 2 experiments. The first experiment manipulated the threat level of a correction through the portrayed personality of the person being corrected; the second experiment manipulated the affective distance between the corrector and the corrected.

Consequential conditionals are defined as "if P then Q" statements, where P is an action, and Q a predicted outcome of this action, which is either desirable or undesirable to the agent. Experiment 1 shows that desirable (viz. undesirable) outcomes invite an inference to the truth (viz. falsity) of their antecedent. Experiment 2 shows that the more extreme the outcome is, the stronger the invited inference is. Experiment 3 shows that modus ponens from premises "If A then C, A" can be suppressed with the introduction of a consequential conditional, "If C then Q," where Q is an undesirable outcome. Experiment 4 shows that the more undesirable Q is, the larger the suppression is. The authors discuss how these results can enrich current approaches of conditional inference on the basis of mental models, complementary necessary conditions, and conditional probabilities., (((c) 2004 APA, all rights reserved))