Bayesian Updating: A Framework for Understanding Medical Decision Making

Talia RobbinsRutgers University
Pernille HemmerRutgers University
Yubei TangRutgers University

Abstract

Beliefs are a fundamental component of our daily decisions, and as such, beliefs about our health have a huge impact on our health behaviors. Poor medication adherence is a well-documented problem and while it has been extensively researched, it has yet to be addressed using a Bayesian framework. This study aims to use a mixture model to understand belief updating as it affects decision making. Using an established experimental paradigm in categorical perception, we test memory and prediction in order to establish a model that can explain human belief updating. Results indicate that a mixture model provides a good explanation of participant behavior in this paradigm.

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Bayesian Updating: A Framework for Understanding Medical Decision Making (1.0 MB)



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