May 26th, 2022
Arizona State University
Modeling and leveraging how intuitive theories shape belief revision
Much of the richness of human thought is supported by people’s intuitive theories—mental frameworks capturing the perceived causal and logical structure of the world. Because intuitive theories are such powerful tools for making sense of the world, when these theories are mistaken there can be serious consequences. In this talk, I’ll present my work exploring how better understanding of people’s intuitive theories can be leveraged to develop more persuasive and effective educational interventions in such domains as vaccination decisions and the understanding of climate change. This work combines Bayesian cognitive modeling, machine learning, and behavioral experiments to understand people’s intuitive theories and belief revision. Computational models support quantitative predictions, help to render people’s thinking intelligible, and guide development of interventions in various domains. At the same time, these models provide a foundation for richer understandings of intuitive theories and belief revision more broadly.