Nonword pronunciation and models of word recognition.

Nonword pronunciation is a form of generalization behavior that has been at the center of debates about models of word recognition, the role of rules in explaining behavior, and the adequacy of the parallel distributed processing approach. An experiment yielded data concerning the pronunciation of...

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Published in:Journal of Experimental Psychology. Human Perception & Performance Vol. 20; pp. 1177 - 1197
Main Authors: Seidenberg, Mark S., Plaut, David C., Petersen, Alan S.
Format: Article
Published: American Psychological Association December 1994
Subjects:
Online Access:View this record in EBSCOhost
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      dt: December 1994
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      pub: American Psychological Association
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        10.1037/0096-1523.20.6.1177
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        atl: Nonword pronunciation and models of word recognition.
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          Seidenberg, Mark S.
          Plaut, David C.
          Petersen, Alan S.
      su:
        English language pronunciation
        Word recognition
      sug:
        subj:
          English language pronunciation
          Word recognition
      ab: Nonword pronunciation is a form of generalization behavior that has been at the center of debates about models of word recognition, the role of rules in explaining behavior, and the adequacy of the parallel distributed processing approach. An experiment yielded data concerning the pronunciation of a large corpus of nonwords. The data were then used to assess 2 models of naming: a model developed by D. C. Plaut and J. L. McClelland (1993), which is similar to the one described by M. S. Seidenberg and J. L. McClelland (1989) but uses improved orthographic and phonological representations, and the grapheme–phoneme correspondence rules of M. Coltheart, B. Curtis, P. Atkins, and M. Haller's (1993) dual-route model. Both models generate plausible nonword pronunciations and match subjects' responses accurately. The dual-route model does so by using rules that generate correct output for most words but mispronounce a significant number of exceptions. The parallel distributed processing model does so by finding a set of weights that allow it to generate correct output for both “rule-governed” items and exceptions. Some ways in which the two approaches differ and other issues facing them are also discussed. Reprinted by permission of the publisher.
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    language: English
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