Generic Priors Yield Competition Between Independently-Occurring Preventive Causes

Derek Powell, M. Alice Merrick, Hongjing Lu, Keith J. Holyoak

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Recent work on causal learning has investigated the possible role of generic priors in guiding human judgments of causal strength. One proposal has been that people have a preference for causes that are sparse and strong-i.e., few in number and individually strong (Lu et al., 2008). Sparse-and-strong priors predict that competition can be observed between candidate causes of the same polarity (i.e., generative or else preventive) even if they occur independently. For instance, the strength of a moderately strong cause should be underestimated when a strong cause is also present, relative to when a weaker cause is present. In previous work (Powell et al., 2013) we found such competition effects for causal setups involving multiple generative causes. Here we investigate whether analogous competition is found for strength judgments about multiple preventive causes. An experiment revealed that a cue competition effect is indeed observed for preventive causes; moreover, the effect appears to be more persistent (as the number of observations increases) than the corresponding effect observed for generative causes. These findings, which are consistent with predictions of a Bayesian learning model with sparse-and-strong priors, provide further evidence that a preference for parsimony guides inferences about causal strength.

Original languageEnglish (US)
Title of host publicationProceedings of the 36th Annual Meeting of the Cognitive Science Society, CogSci 2014
PublisherThe Cognitive Science Society
Pages2793-2798
Number of pages6
ISBN (Electronic)9780991196708
StatePublished - 2014
Externally publishedYes
Event36th Annual Meeting of the Cognitive Science Society, CogSci 2014 - Quebec City, Canada
Duration: Jul 23 2014Jul 26 2014

Publication series

NameProceedings of the 36th Annual Meeting of the Cognitive Science Society, CogSci 2014

Conference

Conference36th Annual Meeting of the Cognitive Science Society, CogSci 2014
Country/TerritoryCanada
CityQuebec City
Period7/23/147/26/14

Keywords

  • Bayesian modeling
  • causal learning
  • causal strength
  • generic priors
  • parsimony

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Human-Computer Interaction
  • Cognitive Neuroscience

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