Orthographic neighborhood effects in parallel distributed processing models.
A study was conducted to evaluate orthographic neighborhood effects in parallel distributed processing models. Data were drawn from a set of stimuli that contained 2,073 words. Findings reveal that low frequency words with small neighborhoods had higher phonological, orthographic, and cross-entrop...
| Publicado en: | Canadian Journal of Experimental Psychology Vol. 53; no. 3; pp. 220 - 231 |
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| Autores principales: | , , |
| Formato: | Artículo |
| Publicado: |
Canadian Psychological Association
September 1999
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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=512875160&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 512875160 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 11961961 CJX jtl: Canadian Journal of Experimental Psychology issn: 11961961 maglogo: N pubinfo: dt: September 1999 vid: 53 iid: 3 pid: 98 pub: Canadian Psychological Association artinfo: ui: 512875160 10.1037/h0087311 ppf: 220 ppct: 11 formats: tig: atl: Orthographic neighborhood effects in parallel distributed processing models. aug: au: Sears, Christopher R. Hino, Yasushi Lupker, Stephen J. su: Word recognition Similarity (Psychology) English orthography & spelling sug: subj: Word recognition Similarity (Psychology) English orthography & spelling keyword: Similarity (Perception) ab: A study was conducted to evaluate orthographic neighborhood effects in parallel distributed processing models. Data were drawn from a set of stimuli that contained 2,073 words. Findings reveal that low frequency words with small neighborhoods had higher phonological, orthographic, and cross-entropy error scores than did low frequency words with large neighborhoods. It is also revealed that low frequency words without higher frequency neighbors had higher error scores than did low frequency words with higher frequency neighbors. It is concluded, as the result of an analysis of these models, that processing should be more rapid for low frequency words with higher frequency neighbors and for low frequency words with large neighborhoods. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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