Enough is enough: Inductive sufficiency guides learners' ratings of informant helpfulness

Patrick Shafto, Hyowon Gweon, Chris Fargen, Laura Schulz

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

4 Scopus citations

Abstract

Much of what we learn, we learn from others. What guides learners' choice of informants? Research suggests that learners resist informants who provide incorrect information or insufficient information for accurate inference. Here we propose that learners’ choices of informants are rationally guided by the extent to which evidence supports accurate inference, rather than the sheer amount of evidence provided. Extending recent research formalizing pedagogical reasoning, we propose a computational model of efficient teaching. We present an experiment on adults testing three different hypotheses about learners’ preferred level of the amount of data. The results suggest that learners care about the inductive sufficiency of evidence, rather than the amount of evidence provided. We conclude by discussing the implications of these findings for cognition and cognitive development.

Original languageEnglish (US)
Title of host publicationBuilding Bridges Across Cognitive Sciences Around the World - Proceedings of the 34th Annual Meeting of the Cognitive Science Society, CogSci 2012
EditorsNaomi Miyake, David Peebles, Richard P. Cooper
PublisherThe Cognitive Science Society
Pages977-982
Number of pages6
ISBN (Electronic)9780976831884
StatePublished - 2012
Externally publishedYes
Event34th Annual Meeting of the Cognitive Science Society: Building Bridges Across Cognitive Sciences Around the World, CogSci 2012 - Sapporo, Japan
Duration: Aug 1 2012Aug 4 2012

Publication series

NameBuilding Bridges Across Cognitive Sciences Around the World - Proceedings of the 34th Annual Meeting of the Cognitive Science Society, CogSci 2012

Conference

Conference34th Annual Meeting of the Cognitive Science Society: Building Bridges Across Cognitive Sciences Around the World, CogSci 2012
Country/TerritoryJapan
CitySapporo
Period8/1/128/4/12

All Science Journal Classification (ASJC) codes

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

Keywords

  • Bayesian model
  • Pedagogical reasoning
  • Trust

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