Bayes and Blickets: Effects of Knowledge on Causal Induction in Children and Adults. Griffiths, T. L., Sobel, D. M., Tenenbaum, J. B., & Gopnik, A. Cognitive Science, 35(8):1407–1455, November, 2011. ![Bayes and Blickets: Effects of Knowledge on Causal Induction in Children and Adults [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex People are adept at inferring novel causal relations, even from only a few observations. Prior knowledge about the probability of encountering causal relations of various types and the nature of the mechanisms relating causes and effects plays a crucial role in these inferences. We test a formal account of how this knowledge can be used and acquired, based on analyzing causal induction as Bayesian inference. Five studies explored the predictions of this account with adults and 4-year-olds, using tasks in which participants learned about the causal properties of a set of objects. The studies varied the two factors that our Bayesian approach predicted should be relevant to causal induction: the prior probability with which causal relations exist, and the assumption of a deterministic or a probabilistic relation between cause and effect. Adults’ judgments (Experiments 1, 2, and 4) were in close correspondence with the quantitative predictions of the model, and children’s judgments (Experiments 3 and 5) agreed qualitatively with this account.
@article{griffiths_bayes_2011,
title = {Bayes and {Blickets}: {Effects} of {Knowledge} on {Causal} {Induction} in {Children} and {Adults}},
volume = {35},
copyright = {Copyright © 2011 Cognitive Science Society, Inc.},
issn = {1551-6709},
shorttitle = {Bayes and {Blickets}},
url = {http://onlinelibrary.wiley.com/doi/10.1111/j.1551-6709.2011.01203.x/abstract},
doi = {10.1111/j.1551-6709.2011.01203.x},
abstract = {People are adept at inferring novel causal relations, even from only a few observations. Prior knowledge about the probability of encountering causal relations of various types and the nature of the mechanisms relating causes and effects plays a crucial role in these inferences. We test a formal account of how this knowledge can be used and acquired, based on analyzing causal induction as Bayesian inference. Five studies explored the predictions of this account with adults and 4-year-olds, using tasks in which participants learned about the causal properties of a set of objects. The studies varied the two factors that our Bayesian approach predicted should be relevant to causal induction: the prior probability with which causal relations exist, and the assumption of a deterministic or a probabilistic relation between cause and effect. Adults’ judgments (Experiments 1, 2, and 4) were in close correspondence with the quantitative predictions of the model, and children’s judgments (Experiments 3 and 5) agreed qualitatively with this account.},
language = {en},
number = {8},
urldate = {2016-06-21},
journal = {Cognitive Science},
author = {Griffiths, Thomas L. and Sobel, David M. and Tenenbaum, Joshua B. and Gopnik, Alison},
month = nov,
year = {2011},
keywords = {Bayesian inference, Causal induction, Cognitive development, Knowledge effects},
pages = {1407--1455},
}
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