Predicting Individual Vocabulary Learning: The Importance of Approximating Toddlers' Linguistic Environment.
Using network representations of the lexicon has expanded our understanding of vocabulary growth processes and vocabulary structure during early development. These models of vocabulary development have used multiple types of sources to create lexical representations. More recently, Weber and Colunga...
| Publicado en: | Canadian Journal of Experimental Psychology / Revue Canadienne de Psychologie Expérimentale Vol. 79; no. 1; pp. 28 - 41 |
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| Autores principales: | , |
| Formato: | Artículo |
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Canadian Psychological Association
Mar2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=183568349&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 183568349 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 11961961 CJX jtl: Canadian Journal of Experimental Psychology / Revue Canadienne de Psychologie Expérimentale issn: 11961961 maglogo: N pubinfo: dt: Mar2025 vid: 79 iid: 1 pid: 98 pub: Canadian Psychological Association artinfo: ui: 183568349 10.1037/cep0000364 ppf: 28 ppct: 13 formats: fmt: – @attributes: type: T – @attributes: type: P size: 2.6MB tig: atl: Predicting Individual Vocabulary Learning: The Importance of Approximating Toddlers' Linguistic Environment. aug: au: Weber, Jennifer M. Colunga, Eliana affil: Department of Psychology and Neuroscience, University of Colorado Boulder su: Linguistics Vocabulary Semantics Language acquisition Children Prediction models Research funding sug: subj: Linguistics Vocabulary Semantics Language acquisition Children Prediction models Research funding keyword: apprentissage des langues emboîtements de mots language acquisition réseaux sémantiques semantic networks word embeddings apprentissage des langues emboîtements de mots language acquisition réseaux sémantiques semantic networks word embeddings ab: Using network representations of the lexicon has expanded our understanding of vocabulary growth processes and vocabulary structure during early development. These models of vocabulary development have used multiple types of sources to create lexical representations. More recently, Weber and Colunga (2022) demonstrated that predictions of early vocabulary norms can be improved by using network representations based on a corpus incorporating language a young child might typically hear. The present work goes a step further by evaluating the accuracy of network representations for predicting individual children's word learning that are based on embeddings that are readily available or embeddings gathered from the same child language corpus. We predicted the specific words that individual children add to their vocabulary over time, using a longitudinal data set of 86 monolingual English-speaking toddler's changing vocabulary from18 to 30 months of age. The toddler-based network predicted word learning more accurately than the off-the-shelf network. Further, there was an advantage for prediction methods that took into account the individual child's particular network structure rather than overall network connectivity. These results highlight the importance of tailoring representational and processing choices to the population of interest. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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