The impact of phonological neighborhood density on typical and atypical emerging lexicons
Datos Bibliográficos
| ID | 20366751 |
|---|---|
| Autores | Stephanie F Stokes (0000-0002-1200-8064, University of Canterbury, autor de correspondencia) |
| Año | 2014 |
| Volumen | 41 |
| Número | 3 |
| Páginas | 634-657 |
| Fecha de publicación | 2014-05-01 |
| Peer Reviewed | Sí |
| Open Access | Sí |
| Tipo | ARTICLE |
| Revista | Journal of Child Language (JOURNAL) |
| Identificadores de la revista | ISSN: 0305-0009 • E-ISSN: 1469-7602 |
| Editorial | Cambridge University Press (CUP) (PUBLISHER) |
| DOI | 10.1017/s030500091300010x |
| PMID | 23651703 |
| OpenAlex | W1966127427 |
| Idioma | EN |
| Citas recibidas | 11 |
| Referencias citadas | 26 |
According to the Extended Statistical Learning account (ExSL; Stokes, Kern & dos Santos, 2012) late talkers (LTs) continue to use neighborhood density (ND) as a cue for word learning when their peers no longer use a density learning mechanism. In the current article, LTs expressive ( active ) lexicon ND values differed from those of their age-matched, but not language-matched, TD peers, a finding that provided support for the ExSL account. Stokes (2010) claimed that LTs had difficulty abstracting sparse words, but not dense, from the ambient language. If true, then LTs' receptive ( passive ), as well as active lexicons should be comprised of words of high ND. However, in the current research only active lexicons were of high ND. LTs' expressive lexicons may be small not because of an abstraction deficit, but because they are unable to develop sufficiently strong phonological representations to support word production
Abstraction · Language acquisition · Lexicon · Linguistics · Natural language processing · Phonology · Statistical Learning · Vocabulary · Word learning · Computer Science · Language Development and Disorders · Neurobiology of Language and Bilingualism · Phonetics and Phonology Research · Psychology · Artificial Intelligence
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| Obras citantes distintas | 11 |
|---|---|
| Citas por año | 1,22 |
| Intervalo de citas | 2017 - 2024 (8) |
| Velocidad de citación | recent |
| Altamente citado | No |
| Tipos de cita | Neutras: 11 |