Does domain matter? Monitoring accuracy across domains

Abstract

Confidence and its accuracy have been most commonly examined in domains such as general knowledge and learning, with less study of other domains, such as applied knowledge and problem solving. Monitoring accuracy in real-world competencies may depend on characteristics of the domain. In this study, we examined whether monitoring accuracy, both calibration (resistance to overconfidence) and resolution (discrimination) indices, are stable within individuals across tasks that represent highly diverse domains. We examined the well-established domain of general knowledge and three understudied applied domains of financial calculation, probability calculation, and the social skill of emotion recognition. In addition, we examined correlations between monitoring accuracy and cognitive abilities (intellectual ability and working memory) and several aggregated judgments regarding each task as a whole (ratings of predicted performance, task difficulty, and effort required) as well as the classic postdictive itemized confidence ratings. We found that resistance to overconfidence (calibration) was significantly positively correlated across tasks, reflecting a confidence trait, but not resolution. We also found that cognitive abilities were more consistently predictive of calibration than of resolution. Aggregated judgments and postdictive confidence were significant predictors of both calibration and resolution, but associations were task specific. Emotion recognition displayed the most unique profile of findings relative to other tasks. We conclude that when considering a wide range of domains, calibration displays domain generality, but resolution may display specificity across tasks.