Explanations and Causal Judgments are Differentially Sensitive to Covariation and Mechanism Information

Nadya Vasilyeva, Tania Lombrozo

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

4 Scopus citations

Abstract

We report four experiments demonstrating that judgments of explanatory goodness are sensitive both to covariation evidence and to mechanism information. Compared to judgments of causal strength, explanatory judgments tend to be more sensitive to mechanism and less sensitive to covariation. Judgments of understanding tracked covariation least closely. We discuss implications of our findings for theories of explanation, understanding and causal attribution.

Original languageEnglish (US)
Title of host publicationProceedings of the 37th Annual Meeting of the Cognitive Science Society, CogSci 2015
EditorsDavid C. Noelle, Rick Dale, Anne Warlaumont, Jeff Yoshimi, Teenie Matlock, Carolyn D. Jennings, Paul P. Maglio
PublisherThe Cognitive Science Society
Pages2475-2480
Number of pages6
ISBN (Electronic)9780991196722
StatePublished - 2015
Externally publishedYes
Event37th Annual Meeting of the Cognitive Science Society: Mind, Technology, and Society, CogSci 2015 - Pasadena, United States
Duration: Jul 23 2015Jul 25 2015

Publication series

NameProceedings of the 37th Annual Meeting of the Cognitive Science Society, CogSci 2015

Conference

Conference37th Annual Meeting of the Cognitive Science Society: Mind, Technology, and Society, CogSci 2015
Country/TerritoryUnited States
CityPasadena
Period7/23/157/25/15

All Science Journal Classification (ASJC) codes

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

Keywords

  • causal strength
  • covariation
  • explanation
  • mechanism
  • understanding

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