Orthographic neighbourhood effects in parallel distributed processing models

Christopher R. Sears*, Yasushi Hino, Stephen J. Lupker

*この研究の対応する著者

研究成果: Article査読

29 被引用数 (Scopus)

抄録

Recent research in visual word recognition suggests that the speed with which a word is identified is influenced by the reader's knowledge of other, orthographically similar words (Andrews, 1997). In serial-search and activation-based models of word recognition, mental representations of these "orthographic neighbours" of a word are explicitly assumed to play a role in the lexical selection process. Thus, it has been possible to determine the specific predictions that these models make about the effects of orthographic neighbours and to test a number of those predictions empirically. In contrast, the role of orthographic neighbours in parallel distributed processing models (e.g., Plaut, McClelland, Seidenberg, & Patterson, 1996; Seidenberg & McClelland, 1989) is less clear. In this paper, several statistical analyses of error scores from these types of models revealed that low frequency words with large neighbourhoods had lower orthographic, phonological, and cross-entropy error scores than low frequency words with small neighbourhoods; and that low frequency words with higher frequency neighbours had lower error scores than low frequency words without higher frequency neighbours. According to these models then, processing should be more rapid for low frequency words with large neighbourhoods and for low frequency words with higher frequency neighbours.

本文言語English
ページ(範囲)220-229
ページ数10
ジャーナルCanadian Journal of Experimental Psychology
53
3
DOI
出版ステータスPublished - 1999 9
外部発表はい

ASJC Scopus subject areas

  • 実験心理学および認知心理学

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