Behavioral and Imaging Studies of Infant Artificial Grammar Learning

Judit Gervain, Irene de la Cruz-Pavía, Lou Ann Gerken

Research output: Contribution to journalArticle

4 Scopus citations

Abstract

Artificial grammar learning (AGL) paradigms have proven to be productive and useful to investigate how young infants break into the grammar of their native language(s). The question of when infants first show the ability to learn abstract grammatical rules has been central to theoretical debates about the innate vs. learned nature of grammar. The presence of this ability early in development, that is, before considerable experience with language, has been argued to provide evidence for a biologically endowed ability to acquire language. Artificial grammar learning tasks also allow infant populations to be readily compared with adults and non-human animals. Artificial grammar learning paradigms with infants have been used to investigate a number of linguistic phenomena and learning tasks, from word segmentation to phonotactics and morphosyntax. In this review, we focus on AGL studies testing infants’ ability to learn grammatical/structural properties of language. Specifically, we discuss the results of AGL studies focusing on repetition-based regularities, the categorization of functors, adjacent and non-adjacent dependencies, and word order. We discuss the implications of the results for a general theory of language acquisition, and we outline some of the open questions and challenges.

Original languageEnglish (US)
Pages (from-to)815-827
Number of pages13
JournalTopics in Cognitive Science
Volume12
Issue number3
DOIs
StatePublished - Jul 1 2020

Keywords

  • Adjacent dependencies
  • Functors
  • Infant artificial grammar learning
  • Language acquisition
  • Morphosyntax
  • Non-adjacent dependencies
  • Repetition-based regularities
  • Word order

ASJC Scopus subject areas

  • Experimental and Cognitive Psychology
  • Linguistics and Language
  • Human-Computer Interaction
  • Cognitive Neuroscience
  • Artificial Intelligence

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