ERIC Number: EJ1270173
Record Type: Journal
Publication Date: 2020
Pages: 5
Abstractor: As Provided
ISBN: N/A
ISSN: ISSN-0142-7237
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Abstractions Are Good for Brains and Machines: A Commentary on Ambridge (2020)
Schuler, Kathryn D.; Kodner, Jordan; Caplan, Spencer
First Language, v40 n5-6 p631-635 Oct-Dec 2020
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.
Descriptors: 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
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Publication Type: Journal Articles; Reports - Evaluative
Education Level: N/A
Audience: N/A
Language: English
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