Learning to generalise but not segment an artificial language at 17 months predicts children’s language skills 3 years later

Padraic Monaghan*, Seamus Donnelly, Katie Alcock, Amy Bidgood, Kate Cain, Samantha Durrant, Rebecca L.A. Frost, Lana S. Jago, Michelle S. Peter, Julian M. Pine, Heather Turnbull, Caroline F. Rowland

*Corresponding author for this work

Research output: Contribution to journalArticle (journal)peer-review

1 Citation (Scopus)
27 Downloads (Pure)

Abstract

We investigated whether learning an artificial language at 17 months was predictive of children’s natural language vocabulary and grammar skills at 54 months. Children at 17 months listened to an artificial language containing non-adjacent dependencies, and were then tested on their learning to segment and to generalise the structure of the language. At 54 months, children were then tested on a range of standardised natural language tasks that assessed receptive and expressive vocabulary and grammar. A structural equation model demonstrated that learning the artificial language generalisation at 17 months predicted language abilities – a composite of vocabulary and grammar skills – at 54 months, whereas artificial language segmentation at 17 months did not predict language abilities at this age. Artificial language learning tasks – especially those that probe grammar learning – provide a valuable tool for uncovering the mechanisms driving children’s early language development.
Original languageEnglish
Article number101607
Pages (from-to)1-13
Number of pages13
JournalCognitive Psychology
Volume147
Early online date7 Oct 2023
DOIs
Publication statusPublished - 31 Dec 2023

Keywords

  • Artificial language learning
  • Generalisation
  • Grammar
  • Non-adjacent dependencies
  • Segmentation
  • Vocabulary
  • Learning
  • Language
  • Humans
  • Language Development
  • Linguistics
  • Child

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