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Behavioral and Imaging Studies of Infant Artificial Grammar Learning
Author(s) -
Gervain Judit,
la CruzPavía Irene,
Gerken LouAnn
Publication year - 2020
Publication title -
topics in cognitive science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.191
H-Index - 56
eISSN - 1756-8765
pISSN - 1756-8757
DOI - 10.1111/tops.12400
Subject(s) - grammar , language acquisition , linguistics , categorization , computer science , artificial intelligence , emergent grammar , phonotactics , psychology , generative grammar , natural language processing , cognitive science , phonology , philosophy
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.