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Interplay of bigram frequency and orthographic neighborhood statistics in language membership decision*

Published online by Cambridge University Press:  02 July 2015

YULIA OGANIAN*
Affiliation:
Department of Education and Psychology, Freie Universitaet Berlin, Germany Bernstein Center for Computational Neuroscience, Humboldt-Universität zu Berlin, Germany
MARKUS CONRAD
Affiliation:
Department of Education and Psychology, Freie Universitaet Berlin, Germany Universidad de La Laguna, Tenerife, Spain
ARASH ARYANI
Affiliation:
Department of Education and Psychology, Freie Universitaet Berlin, Germany
HAUKE R. HEEKEREN
Affiliation:
Department of Education and Psychology, Freie Universitaet Berlin, Germany Bernstein Center for Computational Neuroscience, Humboldt-Universität zu Berlin, Germany
KATHARINA SPALEK
Affiliation:
Institut für deutsche Sprache und Linguistik, Humboldt Universitaet zu Berlin, Germany
*
Address for correspondence: Yulia Oganian, Freie Universitaet Berlin, Department of Education and Psychology, Habelschwerter Allee 45, JK25/215, 14195 Berlin, Germanyyulia.oganian@fu-berlin.de

Abstract

Language-specific orthography (i.e., letters or bigrams that exist in only one language) is known to facilitate language membership recognition. Yet the contribution of continuous sublexical and lexical statistics to language membership decisions during visual word processing is unknown. Here, we used pseudo-words to investigate whether continuous sublexical and lexical statistics bias explicit language decisions (Experiment 1) and language attribution during naming (Experiment 2). We also asked whether continuous statistics would have an effect in the presence of orthographic markers. Language attribution in both experiments was influenced by lexical neighborhood size differences between languages, even in presence of orthographic markers. Sublexical frequencies of occurrence affected reaction times only for unmarked pseudo-words in both experiments, with greater effects in naming. Our results indicate that bilinguals rely on continuous language-specific statistics at sublexical and lexical levels to infer language membership. Implications are discussed with respect to models of bilingual visual word recognition.

Type
Research Article
Copyright
Copyright © Cambridge University Press 2015 

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Footnotes

*

We thank Frederike Albers and Ulrike Schlickeiser for assistance in data collection and analysis of the voice files, Carsten Schliewe for technical assistance, and Assaf Breska for fruitful discussions of the manuscript. This research was supported by the Deutsche Forschungsgemeinschaft (GRK1589/1, doctoral scholarship to YO).

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