Four laboratory studies were conducted to test the hypothesis that correct Bayesian reasoning can be predicted by two factors of task complexity — the number of mental steps required to reach the normative solution, and the compatibility between the framing of data presented and the framing of the question posed. The findings show that participants performed better on frequency format questions only when one mental step was required to solve the task and when the data were in a compatible frequency format. By contrast, participants performed more poorly on more complicated tasks which required more mental steps (in a compatible frequency or probability format) or when the data and question formats were incompatible (Studies 1 and 2). Incompatibility between data and question formats was also associated with higher reaction times (Study 2b). Furthermore, on problems that incorporated incompatibility between the data sample size and the target (question) sample size, participants performed better on the probability question than the frequency question, regardless of data format (Study 3). The latter findings highlight the ecological advantage of translating data into probability terms, which are normalized in a range between 0 and 1, and thus can be transferred from one situation to another.

The article examines whether female political candidates are disfavored in terms of persuasiveness potential based on their expertise and trustworthiness. Using a Bayesian argumentation paradigm in which candidates endorse policies, this study shows that male voters regard female candidates as less persuasive than male candidates. A controlled between-subjects experiment among 202 potential voters in the United States suggests that female election candidates are subject to sex biases in two central ways. First, despite agreeing on their trustworthiness and expertise, male voters find highly credible female candidates less persuasive than identical male candidates. Second, female candidates are affected more adversely if they are perceived as lacking in trustworthiness. Male candidates, on the other hand, are affected more negatively if they are perceived as lacking in expertise. Whereas perceived lack of expertise is relatively easy to repair, trustworthiness may be difficult to regain once it is lost. In a political environment in which attack ads are prevalent, this may carry a greater negative impact for female candidates.

Objectives: The aim of this study was to explore whether Bayesian reasoning can be applied to therapeutic questions in a way that is similar to its application in diagnostics.

Methods: A clinically relevant, therapeutic question was formulated in accordance with Bayesian reasoning for the clinical management of patients with newly diagnosed rheumatoid arthritis (RA). Prior probability estimates of response to drug treatment (methotrexate, MTX) were obtained from the literature. As a marker of treatment response, changes in the Health Assessment Questionnaire (HAQ) scores were assessed after three months of treatment. Likelihood ratios for this marker were calculated on the basis of data from a clinical registry, using changes in the Disease Activity Score (DAS) as gold standard. Using Bayes’ theorem, prior probability and likelihood ratios were combined to estimate posterior probabilities of treatment response in individual patients.

Results: On the basis of the literature, the prior probability of response of RA patients to MTX was estimated 45 percent. At 3 months follow-up, this probability increased to 80 percent or decreased to 23 percent, depending on the changes that were observed in Health Assessment Questionnaire scores.

Conclusions: Bayesian reasoning can be applied to therapeutic issues in a way that is conceptually fully compatible with its use in diagnostics. As such, it can be used to bridge the gap between aggregate data and individual patient management.

A new model for the construction of a sonar map in a specular environment has been developed and implemented. In a real world, where most of the object surfaces are specular ones, a sonar sensor surfers from a multipath effect which results in a wrong interpretation of an object's location. To reduce this effect and hence to construct a reliable map of a robot's surroundings, a probabilistic approach based on Bayesian reasoning is adopted to both evaluation of object orientations and estimation of an occupancy probability of a cell by an object. The usefulness of this approach is illustrated with the results produced by our mobile robot equipped with ultrasonic sensors.

The phenomenon of base-rate neglect has elicited much debate. One arena of debate concerns how people make judgments under conditions of uncertainty. Another more controversial arena concerns human rationality. In this target article, we attempt to unpack the perspectives in the literature on both kinds of issues and evaluate their ability to explain existing data and their conceptual coherence. From this evaluation we conclude that the best account of the data should be framed in terms of a dual-process model of judgment, which attributes base-rate neglect to associative judgment strategies that fail to adequately represent the set structure of the problem. Base-rate neglect is reduced when problems are presented in a format that affords accurate representation in terms of nested sets of individuals.

We present a feature-based probabilistic map building algorithm which directly utilizes time and amplitude information of sonar in indoor environments. Utilizing additional amplitude-of-signal (AOS) obtained concurrently with time-of-flight (TOF), the amount of inclination of target can be directly calculated from a single echo, and the number of measurements can be greatly reduced with result similar to dense scanning. A set of target groups (set of hypothesized targets originated from one measurement) is used and refined by each measurement using an extended Kalman filter and Bayesian conditional probability. Experimental results in a real indoor environment are presented to show the validity of our algorithm.