No abstract

Two robust phenomena in research on confidence in one's general knowledge are the overconfidence phenomenon and the hard-easy effect. In this article, the authors propose that the hard-easy effect has been interpreted with insufficient attention to the scale-end effects, the linear dependency, and the regression effects in data and that the continued adherence to the idea of a "cognitive overconfidence bias" is mediated by selective attention to particular data sets. A quantitative review of studies with 2-alternative general knowledge items demonstrates that, contrary to widespread belief, there is (a) very little support for a cognitive-processing bias in these data; (b) a difference between representative and selected item samples that is not reducible to the difference in difficulty; and (c) near elimination of the hard-easy effect when there is control for scale-end effects and linear dependency.

The perspective of the naïve intuitive statistician is outlined and applied to explain overconfidence when people produce intuitive confidence intervals and why this format leads to more overconfidence than other formally equivalent formats. The naïve sampling model implies that people accurately describe the sample information they have but are naïve in the sense that they uncritically take sample properties as estimates of population properties. A review demonstrates that the naïve sampling model accounts for the robust and important findings in previous research as well as provides novel predictions that are confirmed, including a way to minimize the overconfidence with interval production. The authors discuss the naïve sampling model as a representative of models inspired by the naïve intuitive statistician.

This article explores the configural weighted average (CWA) hypothesis suggesting that extension biases, like conjunction and disjunction errors, occur because people estimate compound probabilities by taking a CWA of the constituent probabilities. The hypothesis suggests a process consistent with well-known cognitive constraints, which nonetheless achieves high robustness and bounded rationality in noisy real-life environments. Predictions by the CWA hypothesis are that in error-free data, conjunction and disjunction errors should be the rule rather than the exception when pairs of statements are randomly sampled from an environment, the rate of extension errors should increase when noise in data is decreased, and that adding a likely component should increase the probability of a conjunction. Four experiments generally verify the predictions by the hypothesis, demonstrating that extension errors are frequent also when tasks are selected according to representative design.

In this article, the authors compare 3 generic models of the cognitive processes in a categorization task. The cue abstraction model implies abstraction in training of explicit cue-criterion relations that are mentally integrated to form a judgment, the lexicographic heuristic uses only the most valid cue, and the exemplar-based model relies on retrieval of exemplars. The results from 2 experiments showed that, in lieu of the lexicographic heuristic, most participants spontaneously integrate cues. In contrast to single-system views, exemplar memory appeared to dominate when the feedback was poor, but when the feedback was rich enough to allow the participants to discern the task structure, it was exploited for abstraction of explicit cue-criterion relations.