@inproceedings{471c9f9f34d347ec945ed4c50a5065b9,
title = "A Bayesian model of knowledge and metacognitive control: Applications to opt-in tasks",
abstract = "In many ecologically situated cognitive tasks, participants engage in self-selection of the particular stimuli they choose to evaluate or test themselves on. This contrasts with a traditional experimental approach in which an experimenter has complete control over the participant's experience. Considering these two situations jointly provides an opportunity to understand why participants opt in to some stimuli or tasks but not to others. We present here a Bayesian model of cognitive and metacognitive processes that uses latent contextual knowledge to model how learners use knowledge to make opt-in decisions. We leverage the model to describe how performance on self-selected stimuli relates to performance on true experimental tasks that deny learners the opportunity for self-selection. We illustrate the utility of the approach with an application to a general-knowledge answering task.",
keywords = "Bayesian cognitive model, metacognitive control, missing not at random, opt-in, wisdom of the crowd",
author = "Bennett, {Stephen T.} and Benjamin, {Aaron S.} and Mark Steyvers",
note = "Publisher Copyright: {\textcopyright} CogSci 2017.; 39th Annual Meeting of the Cognitive Science Society: Computational Foundations of Cognition, CogSci 2017 ; Conference date: 26-07-2017 Through 29-07-2017",
year = "2017",
language = "English (US)",
series = "CogSci 2017 - Proceedings of the 39th Annual Meeting of the Cognitive Science Society: Computational Foundations of Cognition",
publisher = "The Cognitive Science Society",
pages = "1623--1628",
booktitle = "CogSci 2017 - Proceedings of the 39th Annual Meeting of the Cognitive Science Society",
}