Non-Bayesian Inference: Causal Structure Trumps Correlation
Article first published online: 26 JUN 2012
Copyright © 2012 Cognitive Science Society, Inc.
Volume 36, Issue 7, pages 1178–1203, September/October 2012
How to Cite
Bes, B., Sloman, S., Lucas, C. G. and Raufaste, É. (2012), Non-Bayesian Inference: Causal Structure Trumps Correlation. Cognitive Science, 36: 1178–1203. doi: 10.1111/j.1551-6709.2012.01262.x
- Issue published online: 5 SEP 2012
- Article first published online: 26 JUN 2012
- Received 21 December 2010; received in revised form 29 February 2012; accepted 29 February 2012
- Probability judgment;
- Causal explanations;
- Bayesian model
The study tests the hypothesis that conditional probability judgments can be influenced by causal links between the target event and the evidence even when the statistical relations among variables are held constant. Three experiments varied the causal structure relating three variables and found that (a) the target event was perceived as more probable when it was linked to evidence by a causal chain than when both variables shared a common cause; (b) predictive chains in which evidence is a cause of the hypothesis gave rise to higher judgments than diagnostic chains in which evidence is an effect of the hypothesis; and (c) direct chains gave rise to higher judgments than indirect chains. A Bayesian learning model was applied to our data but failed to explain them. An explanation-based hypothesis stating that statistical information will affect judgments only to the extent that it changes beliefs about causal structure is consistent with the results.