{"pk":29411,"title":"A rational model of sequential self-assessment","subtitle":null,"abstract":"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.","language":"eng","license":{"name":"","short_name":"","text":null,"url":""},"keywords":[{"word":"Bayesian modeling; Monte Carlo methods; parti-cle filter; self-assessment; metacognition"}],"section":"Events, Actions, and Sequencing","is_remote":true,"remote_url":"https://escholarship.org/uc/item/4mc5q6gx","frozenauthors":[{"first_name":"Rachel","middle_name":"A.","last_name":"Jansen","name_suffix":"","institution":"University of California, Berkeley","department":""},{"first_name":"Anna","middle_name":"N.","last_name":"Rafferty","name_suffix":"","institution":"Carleton College","department":""},{"first_name":"Thomas","middle_name":"L.","last_name":"Griffiths","name_suffix":"","institution":"Princeton University","department":""}],"date_submitted":null,"date_accepted":null,"date_published":"2020-01-01T18:00:00Z","render_galley":null,"galleys":[{"label":"PDF","type":"pdf","path":"https://journalpub.escholarship.org/cognitivesciencesociety/article/29411/galley/19271/download/"}]}