Literature DB >> 10721236

Strategy selection in causal reasoning: when beliefs and covariation collide.

J A Fugelsang1, V A Thompson.   

Abstract

The present study investigated how people combine covariation information (Cheng & Novick, 1990, 1992) with pre-existing beliefs (White, 1989) when evaluating causal hypotheses. Three experiments, using both within- and between-subjects designs, found that the use of covariation information and beliefs interacted, such that the effects of covariation were larger when people assessed hypotheses about believable than about unbelievable causal candidates. In Experiment 2, this interaction was observed when participants made judgments in stages (e.g., first evaluating covariation information about a causal candidate and then evaluating the believability of a candidate), as well as when the information was presented simultaneously. Experiment 3 demonstrated that this pattern was also reflected in participants' metacognitive judgments: Participants indicated that they weighed covariation information more heavily for believable than unbelievable candidates. Finally, Experiments 1 and 2 demonstrated the presence of individual differences in the use of covariation- and belief-based cues. That is, individuals who tended to base their causality judgments primarily on belief were less likely to make use of covariation information and vice versa. The findings were most consistent with White's (1989) causal power theory, which suggests that covariation information is more likely to be considered relevant to believable than unbelievable causes.

Entities:  

Mesh:

Year:  2000        PMID: 10721236     DOI: 10.1037/h0087327

Source DB:  PubMed          Journal:  Can J Exp Psychol        ISSN: 1196-1961


  14 in total

1.  A dual-process model of belief and evidence interactions in causal reasoning.

Authors:  Jonathan A Fugelsang; Valerie A Thompson
Journal:  Mem Cognit       Date:  2003-07

2.  Likelihood ratios: a simple and flexible statistic for empirical psychologists.

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Journal:  Psychon Bull Rev       Date:  2004-10

Review 3.  A cognitive neuroscience framework for understanding causal reasoning and the law.

Authors:  Jonathan A Fugelsang; Kevin N Dunbar
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2004-11-29       Impact factor: 6.237

4.  The role of age and prior beliefs in contingency judgment.

Authors:  Sharon A Mutter; Laura M Strain; Leslie F Plumlee
Journal:  Mem Cognit       Date:  2007-07

5.  The effects of problem content and scientific background on information search and the assessment and valuation of correlations.

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Journal:  Mem Cognit       Date:  2011-01

6.  Aging and integration of contingency evidence in causal judgment.

Authors:  Sharon A Mutter; Leslie F Plumlee
Journal:  Psychol Aging       Date:  2009-12

7.  Expectations and interpretations during causal learning.

Authors:  Christian C Luhmann; Woo-Kyoung Ahn
Journal:  J Exp Psychol Learn Mem Cogn       Date:  2011-05       Impact factor: 3.051

8.  Reasoning strategies and prior knowledge effects in contingency learning.

Authors:  Gaëtan Béghin; Henry Markovits
Journal:  Mem Cognit       Date:  2022-04-28

9.  Does causal knowledge help us be faster and more frugal in our decisions?

Authors:  Rocio Garcia-Retamero; Annika Wallin; Anja Dieckmann
Journal:  Mem Cognit       Date:  2007-09

10.  Previous knowledge can induce an illusion of causality through actively biasing behavior.

Authors:  Ion Yarritu; Helena Matute
Journal:  Front Psychol       Date:  2015-04-08
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