Читать книгу The Science of Reading - Группа авторов - Страница 45

Spelling‐sound consistency also affects nonword pronunciation. Glushko (1979) found that naming was slower for nonwords such as mave, which is inconsistent because of have, an atypically pronounced neighbor, compared to nonwords such as nust, whose neighbors are pronounced alike. The effect of word neighbors on nonword pronunciation presents a particularly strong challenge to the dual‐route approach, which holds that nonwords are pronounced by nonlexical rules, independent of word knowledge.

Coltheart et al. (2001) discussed Glushko’s study in detail, suggesting that consistency effects for words and nonwords could arise from conflicts between the two routes, as in the case of exception words. Simulating such effects in the DRC model requires changing parameters to increase the activation of words in the lexical network to the point where an exception word such as have could influence both gave and mave. Coltheart et al. (2001) noted that this requirement motivated the “cascaded” property of the model, setting the timing of activity in the two routes to allow such conflicts to occur.

This discussion is odd because the authors did not report any simulations of word or nonword consistency effects employing the proposed mechanism. Their account of the consistency effect in the Jared study was that it was due to the inclusion of exception words, not conflicts between the routes. No simulations of nonword consistency effects were reported. Zevin and Seidenberg (2006) examined nonword consistency effects reported in three representative studies: Glushko (1979), Andrews and Scarratt (1998), and Treiman, Kessler, and Bick (2003). Whereas the DRC model did not reproduce any of these effects, they were correctly generated by a model based on Harm and Seidenberg (1999).

In summary, the DRC model presented in Coltheart et al. (2001) does not produce consistency effects for words or nonwords. The authors did not test their proposal that the effects can be obtained by changing lexical activation parameters. In experiments with the model, we have observed that increasing the level of activation in the lexical system creates undesirable side effects, including errors in pronouncing nonwords, especially lexicalizations, and exaggerated regularity effects for higher and lower frequency words.