Behavioral and Imaging Studies of Infant Artificial Grammar Learning.
Adjacent dependencies
Functors
Infant artificial grammar learning
Language acquisition
Morphosyntax
Non-adjacent dependencies
Repetition-based regularities
Word order
Journal
Topics in cognitive science
ISSN: 1756-8765
Titre abrégé: Top Cogn Sci
Pays: United States
ID NLM: 101506764
Informations de publication
Date de publication:
07 2020
07 2020
Historique:
received:
17
01
2018
revised:
14
07
2018
accepted:
07
08
2018
pubmed:
17
12
2018
medline:
25
5
2021
entrez:
17
12
2018
Statut:
ppublish
Résumé
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.
Types de publication
Journal Article
Research Support, U.S. Gov't, Non-P.H.S.
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
815-827Subventions
Organisme : NSF
ID : 1724842
Pays : International
Organisme : French Government's Investissement d'Avenir LABEX EFL
ID : ANR-10-LABX-0083
Pays : International
Organisme : Human Frontiers Science Program Young Investigator Grant
ID : RGY-0073-2014
Pays : International
Informations de copyright
© 2018 Cognitive Science Society, Inc.
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