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|Title:||The effect of causal strength on the use of causal and similarity-based information in feature inference|
|Citation:||Proceedings of the 9th Conference of the Australasian Society for Cognitive Science, Sydney Sept 30 - Oct 02, 2009 / W. Christensen, E. Schier, and J. Sutton (eds.):325-333|
|Publisher:||Macquarie Centre for Cognitive Science|
|Conference Name:||Conference of the Australasian Society for Cognitive Science (9th : 2009 : Sydney, Australia)|
|Department:||Faculty of Health Sciences|
|Rachel G. Stephens, Daniel J. Navarro, John C. Dunn, Michael D. Lee|
|Abstract:||Category-based feature generalisations are affected by similarity relationships between objects and by knowledge of causal relationships between features. However, there is disagreement between recent studies about whether people will simultaneously consider both relationships. To help resolve this discrepancy, the current study addresses an important difference between past experimental designs: the strength of causal relationships between features. Participants were trained on a set of four different kinds of artificial alien animals (with a known perceptual similarity structure), and were taught about three novel features. Participants were taught that either: 1) there were no relationships between the three features; 2) the features shared weak causal relationships; or 3) the features shared strong causal relationships. After training, all participants then made predictions about the features of the four kinds of animals. As expected, it was found that the strength of the causal relationships influenced the degree to which participants' feature predictions were affected by causal and similarity considerations. Three probabilistic graphical models were fit to the participants' predictions, in a preliminary effort to predict participant responses.|
|Keywords:||feature inference; causal information; similarity; graphical models|
|Rights:||Copyright status unknown|
|Appears in Collections:||Psychology publications|
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