Spoken language comprehension is known to involve a large left-dominant network of fronto-temporal brain regions, but there is still little consensus about how the syntactic and semantic aspects of language are processed within this network. In an fMRI study, volunteers heard spoken sentences that contained either syntactic or semantic ambiguities as well as carefully matched low-ambiguity sentences. Results showed ambiguity-related responses in the posterior left inferior frontal gyrus (pLIFG) and posterior left middle temporal regions. The pLIFG activations were present for both syntactic and semantic ambiguities suggesting that this region is not specialised for processing either semantic or syntactic information, but instead performs cognitive operations that are required to resolve different types of ambiguity irrespective of their linguistic nature, for example by selecting between possible interpretations or reinterpreting misparsed sentences. Syntactic ambiguities also produced activation in the posterior middle temporal gyrus. These data confirm the functional relationship between these two brain regions and their importance in constructing grammatical representations of spoken language.
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http://dx.doi.org/10.1016/j.neuropsychologia.2009.12.035 | DOI Listing |
Sci Rep
January 2025
EIAS Data Science Lab, College of Computer and Information Sciences, Prince Sultan University, 11586, Riyadh, Saudi Arabia.
During the Covid-19 pandemic, the widespread use of social media platforms has facilitated the dissemination of information, fake news, and propaganda, serving as a vital source of self-reported symptoms related to Covid-19. Existing graph-based models, such as Graph Neural Networks (GNNs), have achieved notable success in Natural Language Processing (NLP). However, utilizing GNN-based models for propaganda detection remains challenging because of the challenges related to mining distinct word interactions and storing nonconsecutive and broad contextual data.
View Article and Find Full Text PDFBrain Lang
January 2025
School of Communication Sciences, Beijing Language and Culture University, Beijing 100083, China.
How our brain integrates single words into larger linguistic units is a central focus in neurolinguistic studies. Previous studies mainly explored this topic at the semantic or syntactic level, with few looking at how cortical activities track word sequences with different levels of semantic correlations. In addition, prior research did not tease apart the semantic factors from the syntactic ones in the word sequences.
View Article and Find Full Text PDFBrain Struct Funct
January 2025
CHRIST (Deemed to be University), Bangalore, Karnataka, India.
In this investigation, we delve into the neural underpinnings of auditory processing of Sanskrit verse comprehension, an area not previously explored by neuroscientific research. Our study examines a diverse group of 44 bilingual individuals, including both proficient and non-proficient Sanskrit speakers, to uncover the intricate neural patterns involved in processing verses of this ancient language. Employing an integrated neuroimaging approach that combines functional connectivity-multivariate pattern analysis (fc-MVPA), voxel-based univariate analysis, seed-based connectivity analysis, and the use of sparse fMRI techniques to minimize the interference of scanner noise, we highlight the brain's adaptability and ability to integrate multiple types of information.
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December 2024
Faculty of Arts and Humanities, University of Macau, Macau SAR 999078, China.
Background/objectives: Previous studies have examined the role of working memory in cognitive tasks such as syntactic, semantic, and phonological processing, thereby contributing to our understanding of linguistic information management and retrieval. However, the real-time processing of phonological information-particularly in relation to suprasegmental features like tone, where its contour represents a time-varying signal-remains a relatively underexplored area within the framework of Information Processing Theory (IPT). This study aimed to address this gap by investigating the real-time processing of similar tonal information by native Cantonese speakers, thereby providing a deeper understanding of how IPT applies to auditory processing.
View Article and Find Full Text PDFNeural Netw
December 2024
Hubei Key Laboratory of Smart Internet Technology, School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, 430074, China. Electronic address:
Document-level event causality identification (ECI) aims to detect causal relations in between event mentions in a document. Some recent approaches model diverse connections in between events, such as syntactic dependency and etc., with a graph neural network for event node representation learning.
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