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Publication Detail
Do Children Use Multi-Word Information in Real-Time Sentence Comprehension?
  • Publication Type:
    Journal article
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  • Authors:
    Abu-Zhaya R, Arnon I, Borovsky A
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  • Publication date:
  • Pagination:
  • Journal:
    Cognitive Science
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  • Country:
    United States
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  • Keywords:
    Child, Child, Preschool, Comprehension, Cues, Female, Humans, Language, Linguistics, Psychomotor Performance, Eye-tracking, Language processing, Multi-word units, Prediction, Visual world paradigm
Meaning in language emerges from multiple words, and children are sensitive to multi-word frequency from infancy. While children successfully use cues from single words to generate linguistic predictions, it is less clear whether and how they use multi-word sequences to guide real-time language processing and whether they form predictions on the basis of multi-word information or pairwise associations. We address these questions in two visual-world eye-tracking experiments with 5- to 8-year-old children. In Experiment 1, we asked whether children generate more robust predictions for the sentence-final object of highly frequent sequences (e.g., "Throw the ball"), compared to less frequent sequences (e.g., "Throw the book"). We further examined if gaze patterns reflect event knowledge or phrasal frequency by comparing the processing of phrases that have the same event structure but differ in multi-word content (e.g., "Brush your teeth" vs. "Brush her teeth"). In the second study, we employed a training paradigm to ask if children are capable of generating predictio.ns from novel multi-word associations while controlling for the overall frequency of the sequences. While the results of Experiment 1 suggested that children primarily relied on event associations to generate real-time predictions, those of Experiment 2 showed that the same children were able to use recurring novel multi-word sequences to generate real-time linguistic predictions. Together, these findings suggest that children can draw on multi-word information to generate linguistic predictions, in a context-dependent fashion, and highlight the need to account for the influence of multi-word sequences in models of language processing.
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