Explaining to Others Prompts Children to Favor Inductively Rich Properties

Caren M. Walker, Tania Lombrozo, Cristine H. Legare, Alison Gopnik

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

Abstract

Three experiments test the hypothesis that engaging in explanation prompts children to favor inductively rich properties when generalizing to novel cases. In Experiment 1, preschoolers prompted to explain during a causal learning task were more likely to override a tendency to generalize according to perceptual similarity and instead extend an internal feature to an object that shared a causal property. In Experiment 2, we replicated this effect of explanation in a case of label extension. Experiment 3 demonstrated that explanation improves memory for internal features and labels, but impairs memory for superficial features. We conclude that explaining can influence learning by prompting children to favor inductively rich properties over surface similarity.

Original languageEnglish (US)
Title of host publicationCooperative Minds
Subtitle of host publicationSocial Interaction and Group Dynamics - Proceedings of the 35th Annual Meeting of the Cognitive Science Society, CogSci 2013
EditorsMarkus Knauff, Natalie Sebanz, Michael Pauen, Ipke Wachsmuth
PublisherThe Cognitive Science Society
Pages1558-1563
Number of pages6
ISBN (Electronic)9780976831891
StatePublished - 2013
Externally publishedYes
Event35th Annual Meeting of the Cognitive Science Society - Cooperative Minds: Social Interaction and Group Dynamics, CogSci 2013 - Berlin, Germany
Duration: Jul 31 2013Aug 3 2013

Publication series

NameCooperative Minds: Social Interaction and Group Dynamics - Proceedings of the 35th Annual Meeting of the Cognitive Science Society, CogSci 2013

Conference

Conference35th Annual Meeting of the Cognitive Science Society - Cooperative Minds: Social Interaction and Group Dynamics, CogSci 2013
Country/TerritoryGermany
CityBerlin
Period7/31/138/3/13

All Science Journal Classification (ASJC) codes

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

Keywords

  • Explanation
  • category labels
  • causal learning
  • inductive inference
  • non-obvious properties

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