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Word repetition, masked orthographic priming, and language switching: Bilingual studies and BIA+ simulations

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons41928

Lam,  Kevin J.Y.
Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, Netherlands;
International Max Planck Research School for Language Sciences, MPI for Psycholinguistics, Max Planck Society;

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Zitation

Lam, K. J., & Dijkstra, T. (2010). Word repetition, masked orthographic priming, and language switching: Bilingual studies and BIA+ simulations. International Journal of Bilingual Education and Bilingualism, 13, 487-503. doi:10.1080/13670050.2010.488283.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-000F-82AB-7
Zusammenfassung
Daily conversations contain many repetitions of identical and similar word forms. For bilinguals, the words can even come from the same or different languages. How do such repetitions affect the human word recognition system? The Bilingual Interactive Activation Plus (BIA+) model provides a theoretical and computational framework for understanding word recognition and word repetition in bilinguals. The model assumes that both phenomena involve a language non-selective process that is sensitive to the task context. By means of computer simulations, the model can specify both qualitatively and quantitatively how bilingual lexical processing in one language is affected by the other language. Our review discusses how BIA+ handles cross-linguistic repetition and masked orthographic priming data from two key empirical studies. We show that BIA+ can account for repetition priming effects within- and between-languages through the manipulation of resting-level activations of targets and neighbors (words sharing all but one letter with the target). The model also predicts cross-linguistic performance on within- and between-trial orthographic priming without appealing to conscious strategies or task schema competition as an explanation. At the end of the paper, we briefly evaluate the model and indicate future developments.