Children with hearing loss can use subject–verb agreement to predict during spoken language processing

•School-aged children with hearing loss (HL) predict using syntactic cues during spoken language comprehension.•Children with and without HL use plural agreement ‘are’ to predict upcoming words.•Children with HL are slower syntactic predictors than their normal hearing peers.•Children with and witho...

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Published in:Journal of experimental child psychology Vol. 226; p. 105545
Main Authors: Davies, Benjamin, Holt, Rebecca, Demuth, Katherine
Format: Journal Article
Language:English
Published: United States Elsevier Inc 01.02.2023
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ISSN:0022-0965, 1096-0457, 1096-0457
Online Access:Get full text
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Summary:•School-aged children with hearing loss (HL) predict using syntactic cues during spoken language comprehension.•Children with and without HL use plural agreement ‘are’ to predict upcoming words.•Children with HL are slower syntactic predictors than their normal hearing peers.•Children with and without HL do not use singular agreement ‘is’ to predict.•Questions are raised about children’s morphosyntactic representations. Rapid processing of spoken language is aided by the ability to predict upcoming words using both semantic and syntactic cues. However, although children with hearing loss (HL) can predict upcoming words using semantic associations, little is known about their ability to predict using syntactic dependencies such as subject–verb (SV) agreement. This study examined whether school-aged children with hearing aids and/or cochlear implants can use SV agreement to predict upcoming nouns when processing spoken language. Although they did demonstrate prediction with plural SV agreement, they did so more slowly than their normal hearing (NH) peers. This may be due to weaker grammatical representations given that function words and grammatical inflections typically have lower perceptual salience. Thus, a better understanding of morphosyntactic representations in children with HL, and their ability to use these for prediction, sheds much-needed light on the online language processing challenges and abilities of this population.
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ISSN:0022-0965
1096-0457
1096-0457
DOI:10.1016/j.jecp.2022.105545