The ability to connect the form and meaning of a concept, known as word retrieval, is fundamental to human communication. While various input modalities could lead to identical word retrieval, the exact neural dynamics supporting this convergence relevant to daily auditory discourse remain poorly understood. Here, we leveraged neurosurgical electrocorticographic (ECoG) recordings from 48 patients and dissociated two key language networks that highly overlap in time and space integral to word retrieval. Using unsupervised temporal clustering techniques, we found a semantic processing network located in the middle and inferior frontal gyri. This network was distinct from an articulatory planning network in the inferior frontal and precentral gyri, which was agnostic to input modalities. Functionally, we confirmed that the semantic processing network encodes word surprisal during sentence perception. Our findings characterize how humans integrate ongoing auditory semantic information over time, a critical linguistic function from passive comprehension to daily discourse.
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http://dx.doi.org/10.1101/2024.05.15.594403 | DOI Listing |
Sci Rep
January 2025
School of Economics and Management, Beijing jiaotong University, Shandong, 264401, China.
Text Graph Representation Learning through Graph Neural Networks (TG-GNN) is a powerful approach in natural language processing and information retrieval. However, it faces challenges in computational complexity and interpretability. In this work, we propose CoGraphNet, a novel graph-based model for text classification, addressing key issues.
View Article and Find Full Text PDFFront Behav Neurosci
December 2024
Basque Center on Cognition, Brain and Language (BCBL), Donostia-San Sebastián, Spain.
Psychol Res
December 2024
Dpto Metodología and ERI-Lectura, Universitat de València, Av. Blasco Ibáñez, 21; 46010, Valencia, Spain.
Brand names typically maintain a distinctive letter case (e.g., IKEA, Google).
View Article and Find Full Text PDFSci Rep
December 2024
Department of CSE, Adama Science and Technology University, Oromia, Ethiopia.
Afaan Oromo is a resource-scarce language with limited tools developed for its processing, posing significant challenges for natural language tasks. The tools designed for English do not work efficiently for Afaan Oromo due to the linguistic differences and lack of well-structured resources. To address this challenge, this work proposes a topic modeling framework for unstructured health-related documents in Afaan Oromo using latent dirichlet allocation (LDA) algorithms.
View Article and Find Full Text PDFEur J Phys Rehabil Med
December 2024
Laboratory of Neuropsychology, Department of Neurorehabilitation Sciences, IRCCS Istituto Auxologico Italiano, Milan, Italy.
Background: The defective spoken output of persons with aphasia has anomia as a main clinical manifestation. Improving anomia is therefore a main goal of any language treatment.
Aim: This study assessed the effectiveness of a novel, 2-week, rehabilitation protocol (PHOLEXSEM), focused on PHonological, SEmantic, and LExical deficits, aiming at improving lexical retrieval, and, generally, spoken output.
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