Year: 2023
Type: article
Abstract: Confidence is an important concept in cognitive science, as it integrates seamlessly with our beliefs, goals, and decisions. However, the dynamics of how beliefs about hypotheses (represented by a Bayesian posterior probability distribution) give rise to explicitly reported confidence judgments about predictions (which are typically measured on various numerical scales) in the human mind is not well understood. In this work, we report findings on this question from a concept learning and confidence judgment experiment. We compare how leading theories in the present literature stands against our data and propose some new model-based measurements and future directions for predicting humans' confidence judgments.
DOI: 10.48448/bgdw-5703
Authors: Cognitive Science Society 2023, Chen, Tony, Mills, Tracey, Tenenbaum, Josh, Zhang, Cedegao E.
Institution: Massachusetts Institute of Technology
Language: English
Subfield: Experimental and Cognitive Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W6948309968