We advance a model of human probability judgment and apply it to the design of an extrapolation algorithm. Such an algorithm examines a person's judgment about the likelihood of various statements and is then able to predict the same person's judgments about new statements. The algorithm is tested against judgments produced by thirty undergraduates asked to assign probabilities to statements about mammals.

Establishing reasonable, prior distributions remains a significant obstacle for the construction of probabilistic expert systems. Human assessment of chance is often relied upon for this purpose, but this has the drawback of being inconsistent with axioms of probability. This article advances a method for extracting a coherent distribution of probability from human judgment. The method is based on a psychological model of probabilistic reasoning, followed by a correction phase using linear programming.

The cheater-detection (CD) hypothesis suggests that people who otherwise perform poorly on the Wason selection task perform well when the task is couched in cheater-detection contexts. We report three studies with new selection problems that are similar to the originals but that question the CD hypothesis. The first two studies document a pattern heretofore attributed to CD mechanisms, namely good performance with “regular” rules and inferior performance with “switched” rules, all in problems that lack a cheater-detection context. The final study (...) finds an interaction: not only is good performance elicited on non-CD problems, but poor performance is found in the context of CD problems. Performance on the selection task cannot be predicted based on the presence or absence of cheater-detection contexts, which brings into question the need to invoke a specialised cheater-detection module. (shrink)