Constraining the search space in cross-situational word learning: different models make different predictions

Giovanni Cassani*, Robert Grimm, Steven Gillis, Walter Daelemans

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Abstract

We test the predictions of different computational models of cross-situational word learning that have been proposed in the literature by comparing their behavior to that of young children and adults in the word learning task conducted by Ramscar, Dye, and Klein (2013). Our experimental results show that a Hebbian learner and a model that relies on hypothesis testing fail to account for the behavioral data obtained from both pop- ulations. Ruling out such accounts might help reducing the search space and better focus on the most relevant aspects of the problem, in order to disentangle the mechanisms used dur- ing language acquisition to map words and referents in a highly noisy environment.
Original languageEnglish
Title of host publicationProceedings of the 38th Annual Conference of the Cognitive Science Society
PublisherCognitive Science Society
Pages1152-1157
Number of pages6
Publication statusPublished - Aug 2016
Externally publishedYes

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