Semantic processing of argument structure during naturalistic story listening: Evidence from computational modeling on fMRI

• Published in: NeuroImage (Orlando, Fla.) Vol. 314; p. 121253
• Main Authors: Xu, Tianze, Li, Jixing, Jiang, Xiaoming
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.07.2025; Elsevier Limited; Elsevier
• Subjects: Adult; Argument structure processing; Brain; Brain - physiology; Brain Mapping - methods; Computational modeling; Computational neuroscience; Computer Simulation; Cortex (parietal); Female; Functional magnetic resonance imaging; Humans; Information processing; Language; Linguistics; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Male; Medical imaging; Models, Neurological; Naturalistic fMRI; Neuroimaging; Roles; Semantics; Speech Perception - physiology; Superior temporal gyrus; Syntax; Temporal gyrus; Young Adult; Naturalistic fMRI; Computational modeling; Syntax; Argument structure processing; Semantics
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2025.121253

•Examined argument structure with naturalistic fMRI data and computational models.•Differentiated syntactic and semantic influences on argument structure processing.•Demonstrated reliance on semantic information in argument structure processing.•Integrated linguistic theories, neural data, and computational methods. A long-standing theoretical debate exists in linguistics concerning argument structure processing, with separationism focusing on syntactic structure and projectionism on semantic properties. To investigate whether argument structure processing is primarily influenced by syntactic structure or semantic properties, this study employed integrative neurocomputational modeling to link brain functions with explicitly defined computational models. We analyzed naturalistic functional magnetic resonance imaging (fMRI) data from participants listening to a story, with a focus on subject noun phrase + verb chunks. The methodological framework integrated a general linear model (GLM) analysis of the fMRI data with computational modeling using natural language processing algorithms. These components were integrated using representational similarity analysis (RSA), allowing us to assess the relatedness of two symbolic computational models—one relying on syntactic information from parse trees and the other based on semantic selectional preference information of verbs—to brain activities. The GLM analysis identified significant neural correlates of argument structure processing largely consistent with previous findings, including the precuneus, the right superior temporal gyrus, and the right middle temporal gyrus. Some deviations from previous studies likely reflect the naturalistic nature of the stimuli and our contrast design. The RSA results favored the model utilizing semantic information—a finding further supported by effects observed in brain regions associated with argument structure processing in the literature and by an additional RSA comparing constructions with varying levels of transitivity. These findings suggest that during naturalistic story listening, humans rely heavily on semantic information to interpret argument structure. This study demonstrates an alternative method to engage with the debate on argument structure, highlighting a collaborative effort between theoretical, neuroscientific, and computational linguistics.