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Determiner-noun code-switching in Spanish heritage speakers*

Published online by Cambridge University Press:  09 September 2015

SARAH FAIRCHILD*
Affiliation:
Department of Psychological & Brain Sciences, University of Delaware, USA
JANET G. VAN HELL
Affiliation:
Department of Psychology, The Pennsylvania State University, USA Behavioural Science Institute, Radboud University, Nijmegen, the Netherlands
*
Address for correspondence: Sarah Fairchild, Department of Psychological & Brain Sciences, University of Delaware, 401 Wolf Hall, Newark, DE 19716, USAsarahcfairchild@gmail.com

Abstract

Code-switching is prevalent in bilingual speech, and follows specific syntactic constraints. Several theories have been proposed to explain these constraints, and in this paper we focus on the Minimalist Program and the Matrix Language Frame model. Using a determiner-noun picture naming paradigm, we tested the ability of these theories to explain determiner-noun code-switches in Spanish–English bilinguals. The Minimalist Program predicts that speakers will use the determiner from the gendered language, whereas the Matrix Language Frame model predicts that the determiner will come from the language that dominates the syntactic structure in a code-switched utterance. We observed that the bilinguals had slowest naming times and decreased accuracy in Spanish determiner - English noun conditions (‘el dog’), and that adding a Matrix Language did not modulate this pattern. Although our results do not align with either theory, we conclude that they can be explained by the WEAVER++ model of speech production.

Type
Research Article
Copyright
Copyright © Cambridge University Press 2015 

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Footnotes

*

We thank Kaitlyn Litcofsky, Delaney Wilson, Susanna Cochrane, and Jeremy Todd for assistance with the research, and Alvaro Villegas and Miguel Curcho for help with the Spanish translation of the materials. This research was supported by NSF grant OISE-0968369 to Janet van Hell, Judith Kroll, and Paola Dussias, and NSF grant BCS-1349110 to Janet van Hell and Ping Li.

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