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Abstractions are good for brains and machines: A commentary on Ambridge (2020)

  • University of Pennsylvania

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

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.

Original languageEnglish
Pages (from-to)631-635
Number of pages5
JournalFirst Language
Volume40
Issue number5-6
DOIs
StatePublished - Oct 1 2020

Keywords

  • Abstraction
  • child language acquisition
  • exemplar account
  • natural language processing
  • neuroscience

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