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The influence of the number of relevant causes on the processing of covariation information in causal reasoning

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Abstract

Research on causal reasoning has focused on the influence of covariation between candidate causes and effects on causal judgments. We suggest that the type of covariation information to which people attend is affected by the task being performed. For this, we manipulated the test questions for the evaluation of contingency information and observed its influence on both contingency learning and subsequent causal selections. When people select one cause related to an effect, they focus on conditional contingencies assuming the absence of alternative causes. When people select two causes related to an effect, they focus on conditional contingencies assuming the presence of alternative causes. We demonstrated this use of contingency information in four experiments.

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Fig. 1
Fig. 2

Notes

  1. There is a discrepancy in viewpoints within the frequency judgment approach about the use of the conditionalizing strategy in the case of multiple causes. Spellman et al. (2001) suggested that reasoners who are evaluating two causes keep track of three covariations: the covariation between each of the causes and the effect and the covariation between the two causes themselves. In contrast, Cheng suggested that once the subjects have reasons to believe that both factors are potentially causal, they assess each cause only in the absence of the other cause.

  2. There was no significant effect of Treatments in each participant’s choice response, p > 0.3.

  3. There were no significant differences in each of the participants’ predictions, either between ΔP A|~B and ΔP A|~C or between ΔP B|C and ΔP C|B within a session; in all cases, p > 0.2.

  4. There were no differences between the prediction data of Experiments 1 and 1a, and their overall data patterns were very similar: As in the collapsed data, a significant main effect for the condition was observed, but not for the treatment or the interaction between condition and treatment (see Appendix for the details).

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Acknowledgments

The authors would like to thank Jeff Laux for helpful comments on the revision of this article. The authors would also like to thank Serge Blok, John Dennis, and Levi Larkey for discussions about these studies, as well as Jacqueline Alcala, Nathan Janak, Leora Orent, and Youngjun Kim for their help in conducting the experiments.

Author information

Authors and Affiliations

  1. Department of Psychology, Ajou University, Suwon, Republic of Korea

    Kyungil Kim

  2. Department of Psychology, University of Texas at Austin, Austin, TX, USA

    Arthur B. Markman

  3. Department of Psychology, Kyungnam University, Changwon, Republic of Korea

    Tae Hoon Kim

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  1. Kyungil Kim
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  2. Arthur B. Markman
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  3. Tae Hoon Kim
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Correspondence to Kyungil Kim.

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Handling editor: Marta Olivetti Belardinelli (Sapienza University of Rome).

Reviewers: Patrice Rusconi (University of Surrey), Simona Sacchi (University of Milano-Bicocca), and a further anonymous reviewer.

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Kim, K., Markman, A.B. & Kim, T.H. The influence of the number of relevant causes on the processing of covariation information in causal reasoning. Cogn Process 17, 399–413 (2016). https://doi.org/10.1007/s10339-016-0770-9

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