Article published In: Words in the World
Edited by Laura Teddiman, Lori Buchanan and Hamad Al-Azary
[The Mental Lexicon 19:1] 2024
► pp. 111–125
Processing costs in Cantonese-Latin script-mixing
Published online: 10 January 2025
https://doi.org/10.1075/ml.24021.tam
https://doi.org/10.1075/ml.24021.tam
Abstract
An emerging trend among young Cantonese speakers is to script-mix morphographic Chinese characters with Latin
graphemes in social media exchanges, uncommon in traditional Chinese contexts. Results of a self-paced reading experiment with
Cantonese speakers are reported to determine whether script-mixing incurs processing costs, and if so, whether these can be
attributed to Inhibitory Control of one of the two scripts or to Dual Activation of both scripts but with slower lexical access
within the non-dominant script. Sentences were presented either entirely in Chinese characters or had one region presented in
Latin graphemes. Processing costs arose only at the switch from Latin graphemes back to Chinese characters, pointing to the
involvement of Inhibitory Control. Further, these costs only appeared in a subset of grammatical categories, potentially
coinciding with parsing uncertainties. As such, a combination of script-mixing and parsing complexities could be seen to result in
processing costs in certain sentential positions.
Article outline
- Introduction
- Methods
- Participants
- Materials and procedure
- Results
- Discussion
- Conclusion
- Replication package
- Acknowledgements
References
References (28)
Alvarez, R. P., Holcomb, P. J., & Grainger, J. (2003). Accessing
word meaning in two languages: An event-related brain potential study of beginning
bilinguals. Brain and
Language, 87(2), 290–304.
Baayen, R. H., Davidson, D. J., & Bates, D. M. (2008). Mixed-effects
modeling with crossed random effects for subjects and items. Journal of Memory and
Language, 59(4), 390–412.
Bates, D., Mächler, M., Bolker, B., & Walker, S. (2015). Fitting linear mixed-effects models using lme4. Journal of Statistical
Software, 67(1), 1–48.
Bultena, S., Dijkstra, T., & van Hell, J. (2015). Language
switch costs in sentence comprehension depend on language dominance: Evidence from self-paced
reading*. Bilingualism: Language and
Cognition, 18(3), 453–469.
Costa, A., & Santesteban, M. (2004). Lexical
access in bilingual speech production: Evidence from language switching in highly proficient bilinguals and L2
learners. Journal of Memory and
Language, 50(4), 491–511.
Cruz, L. (2022). Is
AoA a relevant variable to account for code-switching processing? Poster presented
at IMPRS Conference on Interdisciplinary Approaches in the Language
Sciences. Nijmegen, Netherlands.
Dijkstra, T., & van Heuven, W. J. B. (1998). The
BIA model and bilingual word recognition. In J. Grainger & A. M. Jacobs (Eds.), Localist
connectionist approaches to human
cognition (pp. 189–225). Lawrence Erlbaum Associates Publishers.
Filipović-Đurđević, D., Milin, P., & Feldman, L. B. (2013). Bi-alphabetism:
A window on phonological
processing. Psihologija, 46(4), 421–438.
Green, D. W. (1998). Mental
control of the bilingual lexico-semantic system. Bilingualism: Language and
Cognition, 1(2), 67–81.
Grosjean, F. (2008). Studying
Bilinguals. Oxford University Press. [URL].
Jegerski, J. (2014). Self-paced
reading. In J. Jegerski & B. VanPatten (Eds.), Research
Methods in Second Language
Psycholinguistics (pp. 20–49). Routledge.
Kuznetsova, A., Brockhoff, P. B., & Christensen, R. H. B. (2017). lmerTest
package: Tests in linear mixed effects models. Journal of Statistical
Software, 82(1), 1–26.
Lee, J. (2015). PyCantonese:
Cantonese linguistic research in the age of big data [Talk]. [URL]
Lenth, R. V. (2024). emmeans:
Estimated Marginal Means, aka Least-Squares Means (R package version 1.10.4). [URL]
Leung, C. H., & Chan, W. T. Y. (2016). Sociolinguistic phenomenon of code mixing in Hong Kong: From a perspective of marketing communications. Humanities & Social Sciences Reviews, 4(1), 20–26.
Levelt, W. J. (1992). Accessing
words in speech production: Stages, processes and
representations. Cognition, 42(1–3), 1–22.
Li, C. N., & Thompson, S. A. (1981). Mandarin
Chinese: A functional reference grammar. University of California Press.
Luoma, A. (2019). Linguistic
Diversity in the Digital Age [MA Thesis]. University of Helsinki.
Mosca, M., & De Bot, K. (2017). Bilingual
language switching: Production vs. Recognition. Frontiers in
psychology, 81, 934.
Proverbio, A. M., Leoni, G., & Zani, A. (2004). Language
switching mechanisms in simultaneous interpreters: An ERP
study. Neuropsychologia, 42(12), 1636–1656.
R Core Team. (2019). R: A Language and
Environment for Statistical Computing. R Foundation for Statistical Computing. [URL]
Song, J. J. (2015). Language
policies in North and South Korea. In L. Brown & J. Yeon (Eds.), The
Handbook of Korean
Linguistics (pp. 477–491). Wiley-Blackwell.
Srivastava, A., Bali, K., & Choudhury, M. (2020). Understanding
Script-Mixing: A Case Study of Hindi-English Bilingual Twitter Users. Proceedings of the The
4th Workshop on Computational Approaches to Code
Switching, 36–44. [URL]
Tamaoka, K. (1991). Psycholinguistic
nature of the Japanese orthography. Studies in Language and
Literature, 11(1), 49–82.
Wang, X. (2015). Language
control in bilingual language comprehension: Evidence from the maze task. Frontiers in
Psychology, 61, 1179.
Wickham, H., François, R., Henry, L., & Müller, K. (2020). dplyr:
A Grammar of Data Manipulation. [URL]