Children consider prior knowledge and the cost of information both in learning from and teaching others

Hyowon Gweon, Patrick Shafto, Laura E. Schulz

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

14 Scopus citations

Abstract

Children are sensitive to whether informants provide sufficient information for accurate learning (Gweon et al., 2011). Do children think that informants should always provide as much information as possible? Here we show that children consider other's prior knowledge and the cost of information to decide how much information is appropriate. We showed children toys that had 20 identical buttons, three of which played music. Given a choice between an informant who demonstrated all 20 buttons (exhaustive informant) or just the three that played music (selective informant), children preferred the exhaustive informant only when the learner was naïve about how many buttons worked and could be mislead by a selective demonstration (Experiment 1). Given an opportunity to teach themselves, children were more likely to provide exhaustive information when the learner did not know how many buttons worked on the toy (Experiment 2). These results suggest that young children consider others' prior knowledge to balance the cost and the benefit of information in learning from others and in teaching others.

Original languageEnglish (US)
Title of host publicationProceedings of the 36th Annual Meeting of the Cognitive Science Society, CogSci 2014
PublisherThe Cognitive Science Society
Pages565-570
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

All Science Journal Classification (ASJC) codes

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

Keywords

  • cognitive development
  • inductive inference
  • pedagogy
  • pragmatics
  • social learning

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