Learning that numbers are the same, while learning that they are different

Jon A. Willits, Michael N. Jones, David Landy

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

Abstract

It has been suggested that the way that number words are used may play an important role in the development of number concepts. However, little is currently known about the overall ways in which number words are used in child-directed speech. To address this, we performed an analysis of how number words are used in the CHILDES database. We looked at four statistics: 1) lexical frequency, 2) contextual diversity, 3) word co-occurrence, and 4) distributional similarity, to see if these distributional statistics suggest why some aspects of number acquisition are easy and others are hard, and if these statistics are informative about specific debates in number acquisition. We found that that are many important differences in how small and large number words are used (such as differences in frequency, co-occurrence patterns, and distributional similarity), differences that may play an role in shaping hypotheses about children's acquisition of number concepts.

Original languageEnglish (US)
Title of host publicationProceedings of the 38th Annual Meeting of the Cognitive Science Society, CogSci 2016
EditorsAnna Papafragou, Daniel Grodner, Daniel Mirman, John C. Trueswell
PublisherThe Cognitive Science Society
Pages1799-1804
Number of pages6
ISBN (Electronic)9780991196739
StatePublished - 2016
Externally publishedYes
Event38th Annual Meeting of the Cognitive Science Society: Recognizing and Representing Events, CogSci 2016 - Philadelphia, United States
Duration: Aug 10 2016Aug 13 2016

Publication series

NameProceedings of the 38th Annual Meeting of the Cognitive Science Society, CogSci 2016

Conference

Conference38th Annual Meeting of the Cognitive Science Society: Recognizing and Representing Events, CogSci 2016
Country/TerritoryUnited States
CityPhiladelphia
Period8/10/168/13/16

Keywords

  • concept acquisition
  • corpus analyses
  • language acquisition
  • number representation
  • statistical learning

ASJC Scopus subject areas

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

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