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The crosslinguistic acquisition of sentence structure: Computational modeling and grammaticality judgments from adult and child speakers of English, Japanese, Hindi, Hebrew and K'iche' ☆

Authors
  • Ambridge, Ben1, 2
  • Maitreyee, Ramya1
  • Tatsumi, Tomoko3
  • Doherty, Laura1
  • Zicherman, Shira4
  • Pedro, Pedro Mateo5
  • Bannard, Colin1
  • Samanta, Soumitra1, 2
  • McCauley, Stewart6
  • Arnon, Inbal4
  • Bekman, Dani4
  • Efrati, Amir4
  • Berman, Ruth7
  • Narasimhan, Bhuvana8
  • Sharma, Dipti Misra9
  • Nair, Rukmini Bhaya10
  • Fukumura, Kumiko11
  • Campbell, Seth12
  • Pye, Clifton13
  • Pixabaj, Sindy Fabiola Can5
  • And 2 more
  • 1 University of Liverpool, United Kingdom of Great Britain and Northern Ireland
  • 2 ESRC International Centre for Language and Communicative Development (LuCiD)
  • 3 Kobe University, Japan
  • 4 Hebrew University of Jerusalem, Israel
  • 5 Universidad del Valle de Guatemala, Guatemala
  • 6 University of Iowa, United States of America
  • 7 Tel Aviv University, Israel
  • 8 University of Colorado Boulder, United States of America
  • 9 International Institute of Information Technology, Hyderabad, India
  • 10 Indian Institute of Technology Delhi, India
  • 11 University of Stirling, United Kingdom of Great Britain and Northern Ireland
  • 12 University of Calgary, Canada
  • 13 University of Kansas, United States of America
Type
Published Article
Journal
Cognition
Publisher
Elsevier
Publication Date
Sep 01, 2020
Volume
202
Identifiers
DOI: 10.1016/j.cognition.2020.104310
PMID: 32623135
PMCID: PMC7397526
Source
PubMed Central
Keywords
License
Unknown

Abstract

This preregistered study tested three theoretical proposals for how children form productive yet restricted linguistic generalizations, avoiding errors such as *The clown laughed the man , across three age groups (5–6 years, 9–10 years, adults) and five languages (English, Japanese, Hindi, Hebrew and K'iche'). Participants rated, on a five-point scale, correct and ungrammatical sentences describing events of causation (e.g., *Someone laughed the man; Someone made the man laugh ; Someone broke the truck ; ?Someone made the truck break ). The verb-semantics hypothesis predicts that, for all languages, by-verb differences in acceptability ratings will be predicted by the extent to which the causing and caused event (e.g., amusing and laughing) merge conceptually into a single event (as rated by separate groups of adult participants). The entrenchment and preemption hypotheses predict, for all languages, that by-verb differences in acceptability ratings will be predicted by, respectively, the verb's relative overall frequency, and frequency in nearly-synonymous constructions (e.g., X made Y laugh for *Someone laughed the man ). Analysis using mixed effects models revealed that entrenchment/preemption effects (which could not be distinguished due to collinearity) were observed for all age groups and all languages except K'iche', which suffered from a thin corpus and showed only preemption sporadically. All languages showed effects of event-merge semantics, except K'iche' which showed only effects of supplementary semantic predictors. We end by presenting a computational model which successfully simulates this pattern of results in a single discriminative-learning mechanism, achieving by-verb correlations of around r = 0.75 with human judgment data.

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