Evidence of error-driven cross-situational word learning

Chris Grimmick, Todd Gureckis, George Kachergis

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

Abstract

One powerful way children can learn word meanings is via cross-situational learning, the ability to discern consistent word-referent mappings from a series of ambiguous scenes and utterances. Various computational accounts of word learning have been proposed, with mechanisms ranging from storing and testing a single hypothesized referent for each word, to tracking multiple graded associations and selectively strengthening some of them. Nearly all word learning models assume storage of some feasible word-referent mappings from each situation, resulting in a degree of learning proportional to the number of co-occurrences. While these accumulative models would generally predict that incorrect co-occurrences would slow learning, recent empirical work suggests these accounts are incomplete: paradoxically, giving learners incorrect mappings early in training was found to boost performance (Fitneva & Christiansen, 2015). We test this finding's generality in a new experiment with more items, consider system- and item-level explanations, and find that a model with error-driven learning best accounts for this benefit of initially-inaccurate pairings.

Original languageEnglish (US)
Title of host publicationProceedings of the 41st Annual Meeting of the Cognitive Science Society
Subtitle of host publicationCreativity + Cognition + Computation, CogSci 2019
PublisherThe Cognitive Science Society
Pages373-379
Number of pages7
ISBN (Electronic)0991196775, 9780991196777
StatePublished - 2019
Event41st Annual Meeting of the Cognitive Science Society: Creativity + Cognition + Computation, CogSci 2019 - Montreal, Canada
Duration: Jul 24 2019Jul 27 2019

Publication series

NameProceedings of the 41st Annual Meeting of the Cognitive Science Society: Creativity + Cognition + Computation, CogSci 2019

Conference

Conference41st Annual Meeting of the Cognitive Science Society: Creativity + Cognition + Computation, CogSci 2019
Country/TerritoryCanada
CityMontreal
Period7/24/197/27/19

Keywords

  • cross-situational word learning
  • error-driven associative learning model
  • word learning

ASJC Scopus subject areas

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

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