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Bilingual language interference initiates error detection: Evidence from language intrusions*

Published online by Cambridge University Press:  01 August 2016

MATHIEU DECLERCK ,
KRISTIN LEMHÖFER  and
JONATHAN GRAINGER
MATHIEU DECLERCK*
Affiliation:
Laboratoire de Psychologie Cognitive, Aix-Marseille Université and Centre National de la Recherche Scientifique, Marseille, France
KRISTIN LEMHÖFER
Affiliation:
Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands
JONATHAN GRAINGER
Affiliation:
Laboratoire de Psychologie Cognitive, Aix-Marseille Université and Centre National de la Recherche Scientifique, Marseille, France
*
Address for correspondence: Mathieu Declerck, Aix-Marseille Université, Centre St. Charles, 3 place Victor Hugo, 13331 Marseille, France[email protected]
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Abstract

In the current study, we investigated bilingual error detection by measuring the repair rate of language intrusions (i.e., involuntary production of nontarget language words) that arose while bilinguals produced sentences in a language switching context. This allowed us to compare two prominent accounts of error detection in a bilingual setting. According to the conflict monitoring account, error detection is initiated by interference. Since language switching increases bilingual language interference, error detection should be better in switch relative to repetition trials. According to the perceptual loop theory, error detection is based on language comprehension. Since language switching is known to impair language comprehension, it follows that error detection should be worse in switch relative to repetition trials. The results showed that the repair rate of language intrusions was higher in switch than repetition trials, thus providing evidence that bilingual language interference instigates error detection, in line with the conflict monitoring account.

Keywords

Error detectionconflict monitoringperceptual loop theorylanguage switching
Type
Research Article
Information
Bilingualism: Language and Cognition , Volume 20 , Issue 5 , November 2017 , pp. 1010 - 1016
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Copyright
Copyright © Cambridge University Press 2016 

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Footnotes

*

The authors would like to thank Matthew Graves for putting us in contact with the participants, and Stéphane Dufau for generating the pre-recorded example of the network task. This research was supported by funding from the French National Research Council (ANR-11-LABX-0036, ANR-11-IDEX-0001-02).

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