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Cross-Situational Statistical Word Learning in Late Language Emergence: An Online Study.

  • Published In: Journal of Speech, Language & Hearing Research, 2025, v. 68, n. 4. P. 1966 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Schoen Simmons, Elizabeth; Cayward, Olivia; Paula, Rhea 3 of 3

Abstract

Purpose: Cross-situational statistical learning is one mechanism by which typically developing toddlers map words to referents. Yet, this type of statistical learning has been found less efficient in children with developmental language disorder (DLD). The purpose of this article is to evaluate cross-situational statistical learning in very young children with language delay, late talkers (LTs), compared to typically talking toddlers. We predict that LTs will show inefficiency in cross-situational statistical word learning similar to older children with DLD. Method: LT (n = 15, 18-34 months) and typical talker (TT; n = 15, 18-35 months) groups matched on chronological age and sex completed a cross-situational statistical learning task in which they were trained on six novel word--referent pairs and then tested on these word--referent associations. The experiment was completed on the participant's home computer, and gaze was recorded for the duration of the experiment. Mixed-effects models were used to evaluate group differences in time spent looking at labeled referents as a measure of learning. Results: The LT group spent an equal proportion of time looking at the named targets and the unnamed distractors when tested, suggesting minimal learning had occurred. The TT group, in contrast, spent a significantly greater proportion of time looking at the targets when labeled, indicating more established word--referent links. Conclusions: These findings suggest that LTs, like older children with DLD, are less efficient at leveraging cross-situational statistical learning opportunities that may, in addition to other factors, contribute to their slow expressive vocabulary development. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Journal of Speech, Language & Hearing Research. 2025/04, Vol. 68, Issue 4, p1966
  • Document Type:Article
  • Subject Area:Computer Science
  • Publication Date:2025
  • ISSN:1092-4388
  • DOI:10.1044/2025_JSLHR-24-00670
  • Accession Number:184380024
  • Copyright Statement:Copyright of Journal of Speech, Language & Hearing Research is the property of American Speech-Language-Hearing Association and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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