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Infant statistical-learning ability is related to real-time language processing*

  • JILL LANY (a1), AMBER SHOAIB (a1), ABBIE THOMPSON (a1) and KATHARINE GRAF ESTES (a2)
Abstract
Abstract

Infants are adept at learning statistical regularities in artificial language materials, suggesting that the ability to learn statistical structure may support language development. Indeed, infants who perform better on statistical learning tasks tend to be more advanced in parental reports of infants’ language skills. Work with adults suggests that one way statistical learning ability affects language proficiency is by facilitating real-time language processing. Here we tested whether 15-month-olds’ ability to learn sequential statistical structure in artificial language materials is related to their ability to encode and interpret native-language speech. Specifically, we tested their ability to learn sequential structure among syllables (Experiment 1) and words (Experiment 2), as well as their ability to encode familiar English words in sentences. The results suggest that infants' ability to learn sequential structure among syllables is related to their lexical-processing efficiency, providing continuity with findings from children and adults, though effects were modest.

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Corresponding author
Address for correspondence: Jill Lany, Department of Psychology, Haggar Hall, Notre Dame, IN 46556. e-mail: jlany@nd.edu
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This work was supported by funds from NSF BCS-1352443 and by Notre Dame's Institute for Scholarship in the Liberal Arts to JL.

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Journal of Child Language
  • ISSN: 0305-0009
  • EISSN: 1469-7602
  • URL: /core/journals/journal-of-child-language
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