ISSN 1671-3710
CN 11-4766/R

Advances in Psychological Science ›› 2023, Vol. 31 ›› Issue (5): 747-758.doi: 10.3724/SP.J.1042.2023.00747

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Brain mechanism underlying learning Chinese as a second language

ZHANG Caihui1,2, YE Jianqiao2,3, YANG Jing4()   

  1. 1Bilingual Cognition and Development Lab, Center for Linguistics and Applied Linguistics, Guangdong University of Foreign Studies, Guangzhou 510420, China
    2Faculty of English Language and Culture, Guangdong University of Foreign Studies, Guangzhou 510420, China
    3Bluesail Surgical Co., Ltd., Shanghai 201318, China
    4School of International Studies, Zhejiang University, Hangzhou 310058, China
  • Received:2022-07-13 Online:2023-05-15 Published:2023-02-13
  • Contact: YANG Jing


With the fast growth of the Chinese economy, the Chinese language has become one of the most widely spoken world languages. There is a steady growth of empirical studies on the neural mechanisms underlying the learning of Chinese as a second language (L2). Yet, research on the specific brain mechanisms and the corresponding theoretical models for Chinese L2 learning are still in their infancy. Research in the past two decades has revealed that: 1) Chinese tone learning relies on the brain areas of the right superior temporal gyrus and the inferior frontal gyrus when learners are at a lower L2 proficiency, and then shifts to the left superior temporal gyrus as they reach advanced proficiency; 2) Chinese character learning is related to the inferior frontal gyrus and the right fusiform gyrus, whereas Chinese phonological learning is closely related to the left temporal-parietal areas; 3) Overall, Chinese L2 learning relies more on right-hemisphere brain regions (e.g., inferior frontal gyrus, fusiform gyrus) at the early stages of L2 learning, and the reliance decreases with the improvement of L2 competence.
To sum up, Chinese L2 learning undergoes a dynamic neural change from an early stage of right-hemisphere reliance to a later stage of left-lateralization or bilateralization. The findings support the Assimilation Hypothesis in the Assimilation-Accommodation Hypothesis (Perfetti & Liu, 2005) which argues that extra right-hemisphere brain regions are activated in L2 learning when the typical left-hemisphere regions for first language processing cannot adapt to L2 input processing. The findings above also support the Dynamic Restructuring Model (Pliatsikas et al., 2020), which states that learners’ brain functions and neural structures go through dynamic changes at different stages of L2 learning. Furthermore, L2 learning strategies and learners’ auditory perception abilities are found to influence brain functions, neural structures, and connectivity networks. These findings are in line with the prediction of the Unifying the Bilingual Experience Trajectories model (DeLuca et al., 2020) which claims that bilinguals’ linguistic and cognitive representations are strongly influenced by their language experiences (e.g., language typology, language usage, code-switching frequency, proficiency, and age of acquisition). The current theoretical models of L2 Chinese learning can be further strengthened by considering factors such as L1-L2 characteristics, individual differences, learning strategies, and learning contexts.
Future research on Chinese L2 learning can investigate learners of varying characteristics (e.g., young learners and high-proficiency learners), triangulate research paradigms, and synthesize behavioural, functional and structural brain imaging data of language processing and production. Meanwhile, new neuroscience techniques have created the opportunity to investigate the socio-cognitive mechanisms of L2 learning under different interaction situations, such as teacher-student, student-student, and computer-student interactions. More future research in this field can advance the current theoretical models and understanding of neuroplasticity in Chinese L2 learning.

Key words: Chinese as a second language, magnetic resonance imaging, brain function, brain structure, functional brain network

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