Syntactic harmony arises from a domain-general learning bias

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Syntactic harmony occurs when heads and dependents align within and across different types of phrases in a language. Harmony is a well-known (statistical) typological universal: in most languages, many if not all heads and dependents are consistently ordered (i.e., either head-dependent, or dependent-head). Despite decades of work, from every conceivable theoretical perspective, the origins of syntactic harmony remain opaque. However, recent work using artificial language learning has suggested that harmonic patterns are easier to learn than their non-harmonic counter-parts. Thus at least part of the explanation for this tendency may be linked to learning. Here, we explore whether the mechanism behind the learning bias for syntactic harmony is fundamentally domain-general by instantiating harmony in non-linguistic stimuli. Our findings support the claim that the origins of syntactic harmony lie in a domain-general bias for simplicity acting on linearized, language-specific categories.
Original languageEnglish
Title of host publicationProceedings of the 44th Annual Conference of the Cognitive Science Society
EditorsJennifer Culbertson, Andrew Perfors, Hugh Rabagliati, Veronica Ramenzoni
PublishereScholarship University of California
Pages1519-1525
Publication statusE-pub ahead of print - 17 Jun 2022
Event44th Annual Meeting of the Cognitive Science Society - Toronto, Canada
Duration: 27 Jul 202230 Jul 2022
Conference number: 44
https://cognitivesciencesociety.org/cogsci-2022/

Publication series

NameProceedings of the Annual Conference of the Cognitive Science Society
PublisherCognitive Science Society
Volume44
ISSN (Electronic)1069-7977

Conference

Conference44th Annual Meeting of the Cognitive Science Society
Abbreviated titleCogSci 2022
Country/TerritoryCanada
CityToronto
Period27/07/2230/07/22
Internet address

Keywords

  • language universals
  • syntax
  • cognition
  • learning biases
  • artificial grammar learning

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