Modeling Causal Learning Using Bayesian Generic Priors on Generative and Preventive Powers

@inproceedings{Sun2006ModelingCL,
  title={Modeling Causal Learning Using Bayesian Generic Priors on Generative and Preventive Powers},
  author={Ron Sun and Naomi Miyake},
  year={2006}
}
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. 

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References

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Showing 1-10 of 19 references

Structure learning, parameter estimation and causal assumptions

  • M. Liljeholm
  • Ph.D. dissertation, UCLA Department of Psychology…
  • 2006
Highly Influential
4 Excerpts

System of logic, Vol

  • J. S. Mill
  • 1. London: John Parker.
  • 1843
Highly Influential
3 Excerpts

Causal judgments in the trial-by-trial presentation

  • M. Y. Wang, X. L. Fu
  • Acta Psychologica Sinica,
  • 2005
1 Excerpt

Judging covariation and causation

  • D. R. Shanks
  • D. Koehler & N. Harvey (Eds.), Blackwell handbook…
  • 2004
1 Excerpt

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