How does parallel language activation affect switch costs during trilingual language comprehension?

ABSTRACT

Declerck, M., Koch, I., Duñabeitia, J., Grainger, J., & Denise, N. [(2019). What absent switch costs and mixing costs during bilingual language comprehension can tell us about language control? Journal of Experimental Psychology Human Perception & Performance.] assumed that the absence of switch costs in bilingual language comprehension may be due to little degree of parallel language activation while substantial degree of it may lead to switch costs. This study tested this assumption by examining the switch costs during Russian-English-Chinese trilinguals’ performing lexical decision tasks in language pairs of L1–L2 in Experiment 1a, L1–L3 in Experiment 1b and L2–L3 in Experiment 1c. We found switch costs in L2 in Experiment 1a and switch costs in both L1 and L3 in Experiment 1b, but no switch costs in both L2 and L3 in Experiment 1c. These results generally provided evidence for the account.

KEYWORDS:

• The parallel language activation account
• language comprehension
• switch costs
• trilinguals

Acknowledgements

This work was supported by China’s National Foundation for Social Science (Grant Number 16BYY085) to Jianlin Chen. We thank all the participants who took part in the experiments.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Ethical declaration

All procedures performed in studies involving human participants were in accordance with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants and their school authority.

Data availability statement

The datasets for this study are available on request.

Notes

1 The p value was calculated in the formula: p = 2 * (1 − pt(abs(t-value), Number of observations − 2)).

Additional information

Funding

This work was supported by China’s National Fundation for Social Science: [grant number 16BYY085].