When and How Children Use Explanations to Guide Generalizations

Nadya Vasilyeva, Azzurra Ruggeri, Tania Lombrozo

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

3 Scopus citations

Abstract

Explanations often highlight inductively rich relationships that support further generalizations: learning that the knife is sharp because it is for cutting, we correspondingly infer that other things for cutting might also be sharp. When do children appreciate that explanations are good guides to generalization? We report a study in which 108 4- to 7-year-old children evaluated mechanistic, functional, and categorical explanations for the properties of objects, and subsequently generalized those properties to novel objects on the basis of shared mechanisms, functions, or category membership. Older children, but not younger children, were significantly more likely to generalize when the explanation they had received matched the subsequent basis for generalization (e.g., generalizing on the basis of a shared mechanism after hearing a mechanistic explanation). These findings shed light on how explanation and generalization are coordinated over development, as well as the role of explanations in young children's learning.

Original languageEnglish (US)
Title of host publicationProceedings of the 40th Annual Meeting of the Cognitive Science Society, CogSci 2018
PublisherThe Cognitive Science Society
Pages2609-2614
Number of pages6
ISBN (Electronic)9780991196784
StatePublished - 2018
Event40th Annual Meeting of the Cognitive Science Society: Changing Minds, CogSci 2018 - Madison, United States
Duration: Jul 25 2018Jul 28 2018

Publication series

NameProceedings of the 40th Annual Meeting of the Cognitive Science Society, CogSci 2018

Conference

Conference40th Annual Meeting of the Cognitive Science Society: Changing Minds, CogSci 2018
Country/TerritoryUnited States
CityMadison
Period7/25/187/28/18

All Science Journal Classification (ASJC) codes

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

Keywords

  • categorical
  • explanation
  • formal
  • functional
  • generalization
  • inductive inference
  • mechanistic
  • prediction
  • teleological

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