Proceedings of the Annual Meeting of the Cognitive Science Society

People’s assessment of their ability varies in whether it is mea-sured once following a task or sequentially via confidencejudgments recorded throughout. Multiple models have beendeveloped to predict one-off judgments of performance, whichhave often distinguished between peoples’ biases about theirgeneral ability in a domain and their sensitivity to correctness.We propose a rational model of sequential self-assessmentwhich allows us to make predictions about each individualseparately—unlike in the one-off case which looks exclusivelyat the population level—and to identify, in addition to bias andsensitivity, the extent to which individuals’ beliefs are respon-sive to their most recent evidence over the course of a task. Wefit our model to data where participants solve algebraic equa-tions and show that bias, sensitivity, and responsiveness varymeaningfully across participants.