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Download the Paper (245 K, PDF file) - January 1, 2006 Tell a colleague about it. Printing Tips: Select 'print as image' in the Acrobat print dialog if you have trouble printing.

ABSTRACT:
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.

SUGGESTED CITATION:
Hongjing Lu, Alan L. Yuille, Mimi Liljeholm, Patricia W. Cheng, and Keith J. Holyoak, "Modeling Causal Learning Using Bayesian Generic Priors on Generative and Preventive Powers" (January 1, 2006). Department of Statistics, UCLA. Department of Statistics Papers. Paper 2006010105.
http://repositories.cdlib.org/uclastat/papers/2006010105