Proceedings of the Annual Meeting of the Cognitive Science Society

Confidence is an important concept in cognitive science, as it integrates seamlessly with our beliefs, goals, and decisions. Humans naturally represent and express degrees of confidence in beliefs and predictions that reflect their accuracy. However, the dynamics of how our underlying beliefs about the world relate to explicitly represented confidence over those beliefs is yet not well understood. In this work, we make progress on this question in the domain of \textit{concept learning}. Specifically, we analyze how confidence and beliefs jointly evolve in the absence of explicit feedback. We evaluate some leading computational accounts of confidence in the present literature, and we find that these accounts do not accurately predict confidence in the context of our task. We advocate for caution in making claims about the generalizability of such accounts across tasks or domains and propose some new model-based measurements for predicting humans' confidence judgments. Of these measurements, we find that ones taking individual-level response patterns into account perform the best. We close by suggesting promising future directions for the study of confidence in concept learning.