A rational model of sequential self-assessment

Rachel A. Jansen, Thomas L. Griffiths

Research output: Contribution to conferencePaperpeer-review

1 Scopus citations

Abstract

People's assessment of their ability varies in whether it is measured once following a task or sequentially via confidence judgments recorded throughout. Multiple models have been developed to predict one-off judgments of performance, which have often distinguished between peoples' biases about their general ability in a domain and their sensitivity to correctness. We propose a rational model of sequential self-assessment which allows us to make predictions about each individual separately-unlike in the one-off case which looks exclusively at the population level-and to identify, in addition to bias and sensitivity, the extent to which individuals' beliefs are responsive to their most recent evidence over the course of a task. We fit our model to data where participants solve algebraic equations and show that bias, sensitivity, and responsiveness vary meaningfully across participants.

Original languageEnglish (US)
Pages397-403
Number of pages7
StatePublished - 2020
Event42nd Annual Meeting of the Cognitive Science Society: Developing a Mind: Learning in Humans, Animals, and Machines, CogSci 2020 - Virtual, Online
Duration: Jul 29 2020Aug 1 2020

Conference

Conference42nd Annual Meeting of the Cognitive Science Society: Developing a Mind: Learning in Humans, Animals, and Machines, CogSci 2020
CityVirtual, Online
Period7/29/208/1/20

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Science Applications
  • Human-Computer Interaction
  • Cognitive Neuroscience

Keywords

  • Bayesian modeling
  • metacognition
  • Monte Carlo methods
  • particle filter
  • self-assessment

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