Abstractions Are Good for Brains and Machines: A Commentary on Ambridge (2020)

Autor/inn/en	Schuler, Kathryn D.; Kodner, Jordan; Caplan, Spencer
Titel	Abstractions Are Good for Brains and Machines: A Commentary on Ambridge (2020)
Quelle	In: First Language, 40 (2020) 5-6, S.631-635 (5 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Schuler, Kathryn D.) ORCID (Caplan, Spencer)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0142-7237
DOI	10.1177/0142723720906233
Schlagwörter	Language Processing; Language Acquisition; Computational Linguistics; Linguistic Theory; Abstract Reasoning; Artificial Intelligence; Neurosciences; Evidence; Brain Hemisphere Functions; Classification; Models; Children; Child Language; Natural Language Processing; Memory; Psycholinguistics + Suchen Sie Ihr Suchwort? Sprachverarbeitung; Sprachaneignung; Spracherwerb; Linguistics; Computerlinguistik; Linguistische Theorie; Abstraktes Denken; Denken; Künstliche Intelligenz; Neuroscience; Neurowissenschaften; Neurowissenschaft; Evidenz; Classification system; Klassifikation; Klassifikationssystem; Analogiemodell; Child; Kind; Kinder; 'Children''s language'; Kindersprache; Natürliche Sprache; Gedächtnis; Psycholinguistik
Abstract	In 'Against Stored Abstractions,' Ambridge uses neural and computational evidence to make his case against abstract representations. He argues that storing only exemplars is more parsimonious -- why bother with abstraction when exemplar models with on-the-fly calculation can do everything abstracting models can and more -- and implies that his view is well supported by neuroscience and computer science. We argue that there is substantial neural, experimental, and computational evidence to the contrary: while both brains and machines can store exemplars, forming categories and storing abstractions is a fundamental part of what they do. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2022/1/01