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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.
