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Phonetic categorization ability and vocabulary size contribute to the encoding of difficult second-language phonological contrasts into the lexicon

Datos Bibliográficos

ID21697537
AutoresMiquel Llompart (0000-0002-2002-8778, Friedrich-Alexander-Universität Erlangen-Nürnberg, autor de correspondencia)
Año2021
Volumen24
Número3
Páginas481-496
Fecha de publicación2021-05-01
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaBilingualism Language and Cognition (JOURNAL)
Identificadores de la revistaISSN: 1366-7289 • E-ISSN: 1469-1841
EditorialCambridge University Press (CUP) (PUBLISHER)
DOI10.1017/s1366728920000656
OpenAlexW3105263706
IdiomaEN
Citas recibidas17
Referencias citadas67

This study investigated the contribution of second-language (L2) phonetic categorization abilities and vocabulary size to the phonolexical encoding of challenging non-native phonological contrasts into the L2 lexicon. Two groups of German learners of English differing in L2 proficiency (advanced vs. intermediate) participated in an English lexical decision task including words and nonwords with /ɛ/ and /æ/ (/æ/ does not exist in German), an /ɛ/-/æ/ phonetic categorization task and an English vocabulary test. Results showed that the effects of phonetic categorization and vocabulary size on lexical decision performance were modulated by proficiency: categorization predicted /ɛ/-/æ/ nonword rejection accuracy for intermediate learners, whereas vocabulary did so for advanced learners. This suggests that sufficient phonetic identification ability is key for an accurate phonological representation of difficult L2 phones, but, for learners for whom robust phonetic identification is already in place, their ultimate success is tightly linked to their vocabulary size in the L2

Categorization · Cognition · German · Lexical decision task · Lexicon · Linguistics · Natural language processing · Phonology · Vocabulary · Computer Science · Linguistic Variation and Morphology · Phonetics and Phonology Research · Psychology · Reading and Literacy Development · Artificial Intelligence

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Obras citantes distintas17
Citas por año3,4
Intervalo de citas2021 - 2026 (6)
Velocidad de citacióncurrent
Altamente citadoNo
Tipos de citaNeutras: 17
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