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The role of a critical left fronto-temporal network with its right-hemispheric homologue in syntactic learning based on word category information

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Chen,  Luyao
College of Chinese Language and Culture, Beijing Normal University, China;
Department Neuropsychology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Hartwigsen,  Gesa
Lise Meitner Research Group Cognition and Plasticity, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Wang,  Peng
Methods and Development Group Brain Networks, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Citation

Chen, L., Wu, J., Hartwigsen, G., Zhongshan, L., Wang, P., & Feng, L. (2021). The role of a critical left fronto-temporal network with its right-hemispheric homologue in syntactic learning based on word category information. Journal of Neurolinguistics, 58: 100977. doi:10.1016/j.jneuroling.2020.100977.


Cite as: https://hdl.handle.net/21.11116/0000-0007-8F5B-3
Abstract
Word category information (WCI) is proposed to be fundamental for syntactic learning and processing. However, it remains largely unclear how left-hemispheric key regions for language, including BA 44 in the inferior frontal gyrus (IFG) and superior temporal gyrus (STG), interact with their right-hemispheric homologues to support the WCI-based syntactic learning. To address this question, this study employed a unified structural equation modeling (uSEM) approach to explore both the intra- and inter-hemispheric effective connectivity among these areas, to specify the neural underpinnings of handling WCI for syntactic learning. Modeling results identified a distinctive intra-left hemispheric connection from left BA 44 to left STG, a more integrated intra-right hemispheric network, and a particular frontal right-to-left hemispheric connectivity pattern for WCI-based syntactic learning. Further analyses revealed a selective positive correlation between task performance and the lagged effect in left BA 44. These results converge on a critical left fronto-temporal language network with left BA 44 and its connectivity to left STG for WCI-based syntactic learning, which is also facilitated in a domain-general fashion by the right homologues. Together, these results provide novel insights into crucial neural network(s) for syntactic learning on the basis of WCI.