Learning the form of causal relationships using hierarchical bayesian models

Christopher G. Lucas, Thomas L. Griffiths

Research output: Contribution to journalArticlepeer-review

57 Scopus citations


People learn quickly when reasoning about causal relationships, making inferences from limited data and avoiding spurious inferences. Efficient learning depends on abstract knowledge, which is often domain or context specific, and much of it must be learned. While such knowledge effects are well documented, little is known about exactly how we acquire knowledge that constrains learning. This work focuses on knowledge of the functional form of causal relationships; there are many kinds of relationships that can apply between causes and their effects, and knowledge of the form such a relationship takes is important in order to quickly identify the real causes of an observed effect. We developed a hierarchical Bayesian model of the acquisition of knowledge of the functional form of causal relationships and tested it in five experimental studies, considering disjunctive and conjunctive relationships, failure rates, and cross-domain effects. The Bayesian model accurately predicted human judgments and outperformed several alternative models.

Original languageEnglish (US)
Pages (from-to)113-147
Number of pages35
JournalCognitive science
Issue number1
StatePublished - Jan 2010
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Experimental and Cognitive Psychology
  • Artificial Intelligence
  • Cognitive Neuroscience


  • Bayesian models
  • Bayesian networks
  • Causal reasoning
  • Computer simulation
  • Hierarchical models
  • Human experimentation
  • Rational inference
  • Structure learning


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