Modeling Causal Learning Using Bayesian Generic Priors on Generative and Preventive Powers
Hongjing Lu Alan Yuille, Mimi Lijieholm, Patrica W. Cheng, and Keith J. Holyoak
We present a Bayesian model of causal learning that incorporates generic priors on distributions of weights representing potential powers to either produce or prevent an effect. These generic priors favor necessary and sufficient
causes. Across three experiments, the model explains the systematic pattern of human judgments observed for questions regarding support for a causal link, for both generative and preventive causes.
causes. Across three experiments, the model explains the systematic pattern of human judgments observed for questions regarding support for a causal link, for both generative and preventive causes.
2006-09-01