The cortical architecture representing the linguistic hierarchy of the conversational speech

IF 4.7 2区 医学 Q1 NEUROIMAGING
Ruhuiya Aili , Siyuan Zhou , Xinran Xu , Xiangyu He , Chunming Lu
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引用次数: 0

Abstract

Recent studies demonstrate that the brain parses natural language into smaller units represented in lower-order regions and larger units in higher-order regions. Most of these studies, however, have been conducted on unidirectional narrative speech, leaving the linguistic hierarchy and its cortical representation in bidirectional conversational speech unexplored. To address this gap, we simultaneously measured brain activity from two individuals using functional near-infrared spectroscopy (fNIRS) hyperscanning while they engaged in a naturalistic conversation. Using a Pre-trained Language Model (PLM) and Representational Similarity Analysis (RSA), we demonstrated that conversational speech, jointly produced by two interlocutors in a turn-taking manner, exhibits a linguistic hierarchy, characterized by a boundary effect between linguistic units and an incremental context effect. Furthermore, a gradient pattern of shared cortical representation of the linguistic hierarchy was identified at the dyadic rather than the individual level. Interpersonal neural synchronization (INS) in the left superior temporal cortex was associated with turn representation, whereas INS in the medial prefrontal cortex was linked to topic representation. These findings further validated the distinctiveness of linguistic units of different sizes. Together, our results provide original evidence for the linguistic hierarchy and the underlying cortical architecture during a naturalistic conversation, extending the hierarchical nature of natural language from unidirectional narrative speech to bidirectional conversational speech.
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来源期刊
NeuroImage
NeuroImage 医学-核医学
CiteScore
11.30
自引率
10.50%
发文量
809
审稿时长
63 days
期刊介绍: NeuroImage, a Journal of Brain Function provides a vehicle for communicating important advances in acquiring, analyzing, and modelling neuroimaging data and in applying these techniques to the study of structure-function and brain-behavior relationships. Though the emphasis is on the macroscopic level of human brain organization, meso-and microscopic neuroimaging across all species will be considered if informative for understanding the aforementioned relationships.
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