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{ "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/" } ] }