Abstractions made of exemplars or ‘You’re all right, and I’ve changed my mind’: Response to commentators

• Published in: First language Vol. 40; no. 5-6; pp. 640 - 659
• Main Author: Ambridge, Ben
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01.10.2020; Sage Publications Ltd
• Subjects: Abstract Reasoning; Computational Linguistics; Deep learning; Definitions; Figurative Language; Language Acquisition; Language Processing; Language Research; Language Usage; Learning transfer; Mathematical models; Morphology (Languages); Networks; Neural networks; Phonetics; Phonology; Syntax; Word order; Abstractions; computational modelling; deep learning; exemplars; neural networks
• ISSN: 0142-7237; 1740-2344
• DOI: 10.1177/0142723720949723

In this response to commentators, I agree with those who suggested that the distinction between exemplar- and abstraction-based accounts is something of a false dichotomy and therefore move to an abstractions-made-of-exemplars account under which (a) we store all the exemplars that we hear (subject to attention, decay, interference, etc.) but (b) in the service of language use, re-represent these exemplars at multiple levels of abstraction, as simulated by computational neural-network models such as BERT, ELMo and GPT-3. Whilst I maintain that traditional linguistic abstractions (e.g. a DETERMINER category; SUBJECT VERB OBJECT word order) are no more than human-readable approximations of the type of abstractions formed by both human and artificial multiple-layer networks, I express hope that the abstractions-made-of-exemplars position can point the way towards a truce in the language acquisition wars: We were all right all along, just focusing on different levels of abstraction.