Why learning can be hard: Preschooler's causal inferences

Elizabeth Baraff Bonawitz, Laura Schulz

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

1 Scopus citations


Human intelligence has long inspired new benchmarks for research in artificial intelligence. However, recently, research in machine learning and AI has influenced research on children's learning. In particular, Bayesian frameworks capture hallmarks of children's causal reasoning: given causally ambiguous evidence, prior beliefs and data interact. However, we suggest that the rational frameworks that support rapid, accurate causal learning can actually lead children to generate and maintain incorrect beliefs. In this paper we present three studies demonstrating these surprising misunderstandings in children and show how these errors in fact reflect sophisticated inferences.

Original languageEnglish (US)
Title of host publicationNaturally-Inspired Artificial Intelligence - Papers from the AAAI Fall Symposium, Technical Report
Number of pages8
StatePublished - 2008
Externally publishedYes
Event2008 AAAI Fall Symposium - Arlington, VA, United States
Duration: Nov 7 2008Nov 9 2008

Publication series

NameAAAI Fall Symposium - Technical Report


Other2008 AAAI Fall Symposium
Country/TerritoryUnited States
CityArlington, VA

All Science Journal Classification (ASJC) codes

  • Engineering(all)


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