Editors' Review and Introduction : Learning Grammatical Structures: Developmental, Cross-Species, and Computational Approaches
© 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.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2020 |
|---|---|
| Erschienen: |
2020 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:12 |
|---|---|
| Enthalten in: |
Topics in cognitive science - 12(2020), 3 vom: 19. Juli, Seite 804-814 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Ten Cate, Carel [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
Animals |
|---|
| Anmerkungen: |
Date Completed 24.05.2021 Date Revised 10.04.2024 published: Print-Electronic Citation Status MEDLINE |
|---|
| doi: |
10.1111/tops.12493 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM307255808 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM307255808 | ||
| 003 | DE-627 | ||
| 005 | 20240410232052.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231225s2020 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1111/tops.12493 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1371.xml |
| 035 | |a (DE-627)NLM307255808 | ||
| 035 | |a (NLM)32134565 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Ten Cate, Carel |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Editors' Review and Introduction |b Learning Grammatical Structures: Developmental, Cross-Species, and Computational Approaches |
| 264 | 1 | |c 2020 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Completed 24.05.2021 | ||
| 500 | |a Date Revised 10.04.2024 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a © 2020 Cognitive Science Society, Inc. | ||
| 520 | |a 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 | ||
| 650 | 4 | |a Editorial | |
| 650 | 4 | |a Introductory Journal Article | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 4 | |a Animals | |
| 650 | 4 | |a Artificial grammar learning | |
| 650 | 4 | |a Comparative studies | |
| 650 | 4 | |a Computational models | |
| 650 | 4 | |a Development | |
| 650 | 4 | |a Humans | |
| 650 | 4 | |a Infants | |
| 650 | 4 | |a Language | |
| 650 | 4 | |a Sequence learning | |
| 700 | 1 | |a Gervain, Judit |e verfasserin |4 aut | |
| 700 | 1 | |a Levelt, Clara C |e verfasserin |4 aut | |
| 700 | 1 | |a Petkov, Christopher I |e verfasserin |4 aut | |
| 700 | 1 | |a Zuidema, Willem |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Topics in cognitive science |d 2009 |g 12(2020), 3 vom: 19. Juli, Seite 804-814 |w (DE-627)NLM192619160 |x 1756-8765 |7 nnns |
| 773 | 1 | 8 | |g volume:12 |g year:2020 |g number:3 |g day:19 |g month:07 |g pages:804-814 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1111/tops.12493 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 12 |j 2020 |e 3 |b 19 |c 07 |h 804-814 | ||