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.

单词类别信息（WCI）被认为是语法学习和处理的基础。然而，目前尚不清楚语言的左半球关键区域，包括前额下回（IFG）和颞上回（STG）的BA 44如何与它们的右半球同系语相互作用以支持基于WCI的句法学习。为了解决这个问题，本研究采用统一的结构方程模型（uSEM）方法来探索这些区域之间的半球内和半球间有效连接，从而为句法学习指定处理WCI的神经基础。建模结果确定了从左BA 44到左STG的独特的左半球内部连接，即更完整的右半球内部网络，以及用于基于WCI的句法学习的特定的正面从右到左半球连接模式。进一步的分析揭示了任务绩效与左BA 44的滞后效应之间的选择性正相关。这些结果集中在具有左BA 44的关键左额颞语言网络及其与左STG的连接上，以进行基于WCI的句法学习，这是正确的同源物也以领域通用的方式促进了该过程。在一起，这些结果为基于WCI的语法学习的关键神经网络提供了新颖的见解。这些结果集中在具有左BA 44及其与左STG的连通性的关键左额颞语言网络上，以用于基于WCI的句法学习，并且通过右同源物以领域通用的方式得到了促进。在一起，这些结果为基于WCI的语法学习的关键神经网络提供了新颖的见解。这些结果集中在具有左BA 44及其与左STG的连通性的关键左额颞语言网络上，以用于基于WCI的句法学习，并且通过右同源物以领域通用的方式得到了促进。在一起，这些结果为基于WCI的语法学习的关键神经网络提供了新颖的见解。