Editors' Review and Introduction: Learning Grammatical Structures: Developmental, Cross‐Species, and Computational Approaches. (5th March 2020)

• Record Type: Journal Article
• Authors: ten Cate, Carel; Gervain, Judit; Levelt, Clara C.; Petkov, Christopher I.; Zuidema, Willem
• Is Part Of: Topics in cognitive science. Volume 12:Number 3(2020)
• Page Start: 804
• Page End: 814
• Publication Date: 2020-03-05
• Subjects: Artificial grammar learning -- Development -- Sequence learning -- Language -- Computational models -- Humans -- Infants -- Animals -- Comparative studies; Cognitive science -- Periodicals; Cognitive Science -- Periodicals; 153.05
• DOI: 10.1111/tops.12493
• Languages: English
• ISSNs: 1756-8757

Abstract

Abstract: 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. Abstract : Artificial grammar learning (AGL) is used to study how human adults, infants, animals or machines learn various sorts of rules defined over sounds or visual items. Ten Cate et al. introduce the topic and provide a critical synthesis of this important interdisciplinary area of research. They identify the questions that remain open and the challenges that lie ahead, and argue that the limits of human, animal and machine learning abilities have yet to be found.