Literature DB >> 14622051

From covariation to causation: a test of the assumption of causal power.

Marc J Buehner1, Patricia W Cheng, Deborah Clifford.   

Abstract

How humans infer causation from covariation has been the subject of a vigorous debate, most recently between the computational causal power account (P. W. Cheng, 1997) and associative learning theorists (e.g., K. Lober & D. R. Shanks, 2000). Whereas most researchers in the subject area agree that causal power as computed by the power PC theory offers a normative account of the inductive process. Lober and Shanks, among others, have questioned the empirical validity of the theory. This article offers a full report and additional analyses of the original study featured in Lober and Shanks's critique (M. J. Buehner & P. W. Cheng, 1997) and reports tests of Lober and Shanks's and other explanations of the pattern of causal judgments. Deviations from normativity, including the outcome-density bias, were found to be misperceptions of the input or other artifacts of the experimental procedures rather than inherent to the process of causal induction. ((c) 2003 APA, all rights reserved)

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Year:  2003        PMID: 14622051     DOI: 10.1037/0278-7393.29.6.1119

Source DB:  PubMed          Journal:  J Exp Psychol Learn Mem Cogn        ISSN: 0278-7393            Impact factor:   3.051


  38 in total

1.  Models of covariation-based causal judgment: a review and synthesis.

Authors:  José C Perales; David R Shanks
Journal:  Psychon Bull Rev       Date:  2007-08

Review 2.  Comparing associative, statistical, and inferential reasoning accounts of human contingency learning.

Authors:  Oskar Pineño; Ralph R Miller
Journal:  Q J Exp Psychol (Hove)       Date:  2007-03       Impact factor: 2.143

3.  BUCKLE: a model of unobserved cause learning.

Authors:  Christian C Luhmann; Woo-Kyoung Ahn
Journal:  Psychol Rev       Date:  2007-07       Impact factor: 8.934

4.  The meaning and computation of causal power: comment on Cheng (1997) and Novick and Cheng (2004).

Authors:  Christian C Luhmann; Woo-Kyoung Ahn
Journal:  Psychol Rev       Date:  2005-07       Impact factor: 8.934

5.  Contrasting cue-density effects in causal and prediction judgments.

Authors:  Miguel A Vadillo; Serban C Musca; Fernando Blanco; Helena Matute
Journal:  Psychon Bull Rev       Date:  2011-02

6.  Interactive effects of the probability of the cue and the probability of the outcome on the overestimation of null contingency.

Authors:  Fernando Blanco; Helena Matute; Miguel A Vadillo
Journal:  Learn Behav       Date:  2013-12       Impact factor: 1.986

7.  A psychological approach to learning causal networks.

Authors:  Manaf Zargoush; Farrokh Alemi; Vinzenzo Esposito Vinzi; Jee Vang; Raya Kheirbek
Journal:  Health Care Manag Sci       Date:  2013-09-19

Review 8.  Contiguity and covariation in human causal inference.

Authors:  Marc J Buehner
Journal:  Learn Behav       Date:  2005-05       Impact factor: 1.986

9.  Causal and predictive-value judgments, but not predictions, are based on cue-outcome contingency.

Authors:  Miguel A Vadillo; Ralph R Miller; Helena Matute
Journal:  Learn Behav       Date:  2005-05       Impact factor: 1.986

10.  Structural awareness mitigates the effect of delay in human causal learning.

Authors:  W James Greville; Adam A Cassar; Mark K Johansen; Marc J Buehner
Journal:  Mem Cognit       Date:  2013-08
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