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Cited by 185Language Development and DisordersSyntax, Semantics, Linguistic VariationCategorization, perception, and language

The DOI matches its Crossref record; 205 of the 205 references it cites were found in a registry or an index.

Against stored abstractions: A radical exemplar model of language acquisition

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Ambridge, Ben·2020·First Language
DOI 10.1177/0142723719869731
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Abstract

The goal of this article is to make the case for a radical exemplar account of child language acquisition, under which unwitnessed forms are produced and comprehended by on-the-fly analogy across multiple stored exemplars, weighted by their degree of similarity to the target with regard to the task at hand. Across the domains of (1) word meanings, (2) morphologically inflected words, (3) n-grams, (4) sentence-level constructions and (5) phonetics and phonology, accounts based on independently-represented abstractions (whether formal rules or prototype categories) fail for two reasons. First, it is not possible to posit abstractions that delineate possible and impossible form; e.g. that (1) rule in pool tables and data tables, but rule out chairs, (2) rule in the past-tense forms netted and bet but rule out * setted and * jet, (3) rule in the bigram f+t but rule out (probabilistically) v+t, (4) rule in both John feared Bill and John frightened Bill but rule out * John laughed Bill, (5) rule in Speaker A but rule out Speaker B as the person who produced a particular word (e.g. Sa’urday). Second, for each domain, empirical data provide evidence of exemplar storage that cannot be captured by putative abstractions: e.g. speakers prefer and/or show an advantage for (1) exemplar variation even within word-meaning ‘category boundaries’, (2) novel inflected forms that are similar to existing exemplars, (3) n-grams that have occurred frequently in the input, (4) SVO sentences with he as SUBJECT and it as OBJECT and (5) repeated productions of ‘the same’ word that are phonologically similar or, better still, identical. An exemplar account avoids an intractable lumping-or-splitting dilemma facing abstraction-based accounts and provides a unitary explanation of language acquisition across all domains; one that is consistent with models and empirical findings from the computational modelling and neuroimaging literature.

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Cites

Research works printed but not linked: 16

  1. Baayen R. H., 2016, From semantics to dialectometry, 1
  2. Bidgood A., Cognitive Science
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  13. Skousen R., 2005, arXiv preprint quant-ph/0510146 · arXiv
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  15. Van Noord R., 2015, Computational Linguistics in the Netherlands Journal, 5, 65
  16. Ziegler J., 2018, Poster presented at the 31st Annual Meeting of the CUNY Conference on Human Sentence Processing

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Metadata

Authors
Ambridge, B.
Affiliations
University of Liverpool (GB)
Corresponding author
Ambridge, Ben
ORCID
Ambridge, Ben
Venue
First Language
Publisher
SAGE Publications
Published
2019-09-09
Volume
40
Issue
5-6
Pages
509-559
ISSN
  • 0142-7237 (ISSN-L)
  • 1740-2344
DOI
10.1177/0142723719869731
OpenAlex
W2973169533
Cited by
185
References
205
Citations by year
2026: 13 · 2025: 19 · 2024: 18 · 2023: 44 · 2022: 25 · 2021: 30 · 2020: 34 · 2019: 2
FWCI
41.02
Type
journal article
Language
en
Open access
Licence
Locations
  • First Language (publishedVersion, cc-by)
  • Research Explorer (The University of Manchester) (submittedVersion, other-oa)
  • OSF Preprints (OSF Preprints) (submittedVersion)
Funders
  • H2020 European Research Council (681296: CLASS)
  • Economic and Social Research Council (ES/L008955/1)
Topics
  • Social Sciences › Psychology › Developmental and Educational Psychology › Language Development and Disorders
  • Social Sciences › Arts and Humanities › Language and Linguistics › Syntax, Semantics, Linguistic Variation
  • Social Sciences › Psychology › Experimental and Cognitive Psychology › Categorization, perception, and language
Keywords
exemplar models, language acquisition, child language, analogy, abstraction, word meaning, morphological inflection, n-grams, sentence construction, phonology, phonetics, category boundaries, novel word learning, computational modelling

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