کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
7286425 | 1474121 | 2016 | 13 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
The effect of domain-general inhibition-related training on language switching: An ERP study
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کلمات کلیدی
موضوعات مرتبط
علوم زیستی و بیوفناوری
علم عصب شناسی
علوم اعصاب شناختی
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چکیده انگلیسی
Previous studies have demonstrated that inhibitory control ability could be improved by training, and the Inhibitory Control (IC) Model implies that enhanced domain-general inhibition may elicit certain changes in language switch costs. In the present study, we aimed to examine the effects of domain-general inhibition training on performance in a language switching task, including which phase of domain-general inhibitory control benefits from training during an overt picture naming task in L1 and L2, using the event-related brain potentials (ERPs). Results showed that the language switch costs of bilinguals with high inhibitory control (high-IC) were symmetrical in both pretest and posttest, and those of bilinguals with low inhibitory control (low-IC) were asymmetrical in the pretest, but symmetrical in the posttest. Moreover, the high-IC group showed a larger LPC (late positive component) for L2 switch trials than for L1 trials in both pretest and posttest. In contrast, the low-IC group only exhibited a similar pattern of LPC in the posttest, but not in the pretest. These results indicate that inhibition training could increase the efficiency of language switching, and inhibitory control may play a key role during the lexical selection response phase. Overall, the present study is the first one to provide electrophysiological evidence for individual differences in the domain-general inhibition impact on language switching performance in low-proficient bilinguals.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Cognition - Volume 146, January 2016, Pages 264-276
Journal: Cognition - Volume 146, January 2016, Pages 264-276
نویسندگان
Huanhuan Liu, Lijuan Liang, Susan Dunlap, Ning Fan, Baoguo Chen,