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Editors' Review and Introduction: Learning Grammatical Structures: Developmental, Cross-Species, and Computational Approaches

Lookup NU author(s): Professor Christopher Petkov


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© 2020 Cognitive Science Society, Inc. Human languages all have a grammar, that is, rules that determine how symbols in a language can be combined to create complex meaningful expressions. Despite decades of research, the evolutionary, developmental, cognitive, and computational bases of grammatical abilities are still not fully understood. “Artificial Grammar Learning” (AGL) studies provide important insights into how rules and structured sequences are learned, the relevance of these processes to language in humans, and whether the cognitive systems involved are shared with other animals. AGL tasks can be used to study how human adults, infants, animals, or machines learn artificial grammars of various sorts, consisting of rules defined typically over syllables, sounds, or visual items. In this introduction, we distill some lessons from the nine other papers in this special issue, which review the advances made from this growing body of literature. We provide a critical synthesis, identify the questions that remain open, and recognize the challenges that lie ahead. A key observation across the disciplines is that the limits of human, animal, and machine capabilities have yet to be found. Thus, this interdisciplinary area of research firmly rooted in the cognitive sciences has unearthed exciting new questions and venues for research, along the way fostering impactful collaborations between traditionally disconnected disciplines that are breaking scientific ground.

Publication metadata

Author(s): ten Cate C, Gervain J, Levelt CC, Petkov CI, Zuidema W

Publication type: Review

Publication status: Published

Journal: Topics in Cognitive Science

Year: 2020

Volume: 12

Issue: 3

Pages: 804-814

Print publication date: 01/07/2020

Online publication date: 05/03/2020

Acceptance date: 08/01/2020

ISSN (print): 1756-8757

ISSN (electronic): 1756-8765

Publisher: Wiley-Blackwell


DOI: 10.1111/tops.12493