Examining the Acquisition of Phonological Word Forms with Computational Experiments.
It has been hypothesized that known words in the lexicon strengthen newly formed representations of novel words, resulting in words with dense neighborhoods being learned more quickly than words with sparse neighborhoods. Tests of this hypothesis in a connectionist network showed that words with den...
| Publicado en: | Language & Speech Vol. 56; no. 4; pp. 493 - 528 |
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| Autores principales: | , |
| Formato: | algorithm equations & formulas research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
Dec2013
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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=ccm&AN=104166629&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104166629 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00238309 3YY jtl: Language & Speech issn: 00238309 maglogo: Y pubinfo: dt: Dec2013 vid: 56 iid: 4 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 104166629 92580794 92580794 10.1177/0023830912460513 104166629 ppf: 493 ppct: 35 formats: fmt: @attributes: type: P tig: atl: Examining the Acquisition of Phonological Word Forms with Computational Experiments. aug: au: Vitevitch, Michael S Storkel, Holly L sug: subj: Vocabulary Education Learning Methods Language Processing Methods Phonetics Computer Simulation Utilization Human Funding Source Experimental Studies Kansas Kansas Hypothesis Recognition (Psychology) Word Lists Analysis of Variance Time Factors Neural Networks (Computer) Utilization Algorithms Utilization Theory Validation ab: It has been hypothesized that known words in the lexicon strengthen newly formed representations of novel words, resulting in words with dense neighborhoods being learned more quickly than words with sparse neighborhoods. Tests of this hypothesis in a connectionist network showed that words with dense neighborhoods were learned better than words with sparse neighborhoods when the network was exposed to the words all at once (Experiment 1), or gradually over time, like human word-learners (Experiment 2). This pattern was also observed despite variation in the availability of processing resources in the networks (Experiment 3). A learning advantage for words with sparse neighborhoods was observed only when the network was initially exposed to words with sparse neighborhoods and exposed to dense neighborhoods later in training (Experiment 4). The benefits of computational experiments for increasing our understanding of language processes and for the treatment of language processing disorders are discussed. pubtype: Academic Journal doctype: algorithm equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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