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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

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.

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 Creators:
Chen, Luyao1, 2, Author              
Wu, Junjie3, Author
Hartwigsen, Gesa4, Author              
Zhongshan, Li5, Author
Wang, Peng6, Author              
Feng, Liping1, Author
Affiliations:
1College of Chinese Language and Culture, Beijing Normal University, China, ou_persistent22              
2Department Neuropsychology, MPI for Human Cognitive and Brain Sciences, Max Planck Society, Leipzig, DE, ou_634551              
3Academy of Psychology and Behavior, Tianjin Normal University, China, ou_persistent22              
4Lise Meitner Research Group Cognition and Plasticity, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_3025665              
5School of Foreign Languages and Literature, Beijing Normal University, China, ou_persistent22              
6Methods and Development Group Brain Networks, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_2205650              

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Free keywords: Effective connectivity; Right hemisphere; Syntactic processing; Unified structural equation modeling; Word category information
 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.

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Language(s): eng - English
 Dates: 2020-11-212020-07-122020-12-082020-12-252021-05
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1016/j.jneuroling.2020.100977
 Degree: -

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Title: Journal of Neurolinguistics
  Other : J. Neurolinguist.
Source Genre: Journal
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Publ. Info: Tokyo : Pergamon
Pages: - Volume / Issue: 58 Sequence Number: 100977 Start / End Page: - Identifier: ISSN: 0911-6044
CoNE: https://pure.mpg.de/cone/journals/resource/954926241467