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Explaining quantitative variation in the rate of Optional Infinitive errors across languages

A comparison of Mosaic and the Variational Learning Model

Bibliographic Data

ID20367129
AuthorsDaniel Freudenthal (0000-0003-3915-9484, University of Liverpool, corresponding author), Julian M Pine (0000-0002-7077-9713, University of Liverpool), Julian Pine, Fernand Gobet (0000-0002-9317-6886, Brunel University of London)
Year2010
Volume37
Issue3
Pages643-669
Publication date2010-06-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueJournal of Child Language (JOURNAL)
Journal identifiersISSN: 0305-0009 • E-ISSN: 1469-7602
PublisherCambridge University Press (CUP) (PUBLISHER)
DOI10.1017/s0305000909990523
PMID20334719
OpenAlexW2109934734
LanguageEN
Citations received29
References cited27

In this study, we use corpus analysis and computational modelling techniques to compare two recent accounts of the OI stage: Legate & Yang's (2007) Variational Learning Model and Freudenthal, Pine & Gobet's (2006) Model of Syntax Acquisition in Children. We first assess the extent to which each of these accounts can explain the level of OI errors across five different languages (English, Dutch, German, French and Spanish). We then differentiate between the two accounts by testing their predictions about the relation between children's OI errors and the distribution of infinitival verb forms in the input language. We conclude that, although both accounts fit the cross-linguistic patterning of OI errors reasonably well, only MOSAIC is able to explain why verbs that occur more frequently as infinitives than as finite verb forms in the input also occur more frequently as OI errors than as correct finite verb forms in the children's output

German · Infinitive · Language acquisition · Linguistics · Mosaic · Natural language processing · Physics · Syntax · Verb · Computer Science · History · Language Development and Disorders · Mathematics · Philosophy · Phonetics and Phonology Research · Psychology · Reading and Literacy Development · Artificial Intelligence

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Unique citing works29
Citations per year1,81
Citation span2010 - 2024 (15)
Citation velocityrecent
Highly citedNo
Citation typesNeutral: 24

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