Evaluating Causal Hypotheses: The Curious Case of Correlated Cues

Bob Rehder, Zachary Davis

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

Abstract

Although the causal graphical model framework has achieved considerable success accounting for causal learning data, application of that formalism to multi-cause situations assumes that people are insensitive to the statistical properties of the causes themselves. The present experiment tests this assumption by first instructing subjects on a causal model consisting of two independent and generative causes and then requesting them to make data likelihood judgments, that is, to estimate the probability of some data given the model. The correlation between the causes in the data was either positive, zero, or negative. The data was judged as most likely in the positive condition and least likely in the negative condition, a finding that obtained even though all other statistical properties of the data (e.g., causal strengths, outcome density) were controlled. These results pose a problem for current models of causal learning.

Original languageEnglish (US)
Title of host publicationProceedings of the 38th Annual Meeting of the Cognitive Science Society, CogSci 2016
EditorsAnna Papafragou, Daniel Grodner, Daniel Mirman, John C. Trueswell
PublisherThe Cognitive Science Society
Pages1002-1007
Number of pages6
ISBN (Electronic)9780991196739
StatePublished - 2016
Event38th Annual Meeting of the Cognitive Science Society: Recognizing and Representing Events, CogSci 2016 - Philadelphia, United States
Duration: Aug 10 2016Aug 13 2016

Publication series

NameProceedings of the 38th Annual Meeting of the Cognitive Science Society, CogSci 2016

Conference

Conference38th Annual Meeting of the Cognitive Science Society: Recognizing and Representing Events, CogSci 2016
Country/TerritoryUnited States
CityPhiladelphia
Period8/10/168/13/16

ASJC Scopus subject areas

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

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