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Language learning and use relies on domain-specific, domain-general cognitive and sensory-motor functions. Using fMRI during story listening and behavioral tests, we investigated brain-behavior associations between linguistic and non-linguistic measures in individuals with varied multilingual experience and reading skills, including typical reading participants (TRs) and dyslexic readers (DRs). Partial Least Square Correlation revealed a main component linking cognitive, linguistic, and phonological measures to amodal/associative brain areas. A second analysis only in TRs revealed a stronger association between cognitive, linguistic, literacy and phonological skills within the same brain network as in the full sample, suggesting better speech-print convergence in TRs. In this sample, an additional component involving speed, automatization, and lexical access was associated with less involvement in unimodal, lower-level auditory, and motor brain areas. The complementarity between the two components likely reflects TRs' reduced reliance on lower-level sensorimotor regions and greater engagement of higher-level cortices and skills. Overall, our work suggests convergence between behavioral measures of linguistic, domain-general cognitive and domain-specific non-linguistic skill, and between these behavioral measures and neural processing of language. This convergence is greater in TRs, suggesting more integrated processing in this group. Our work advocates a comprehensive, multimodal approach to understanding individual differences in language abilities and experience.
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http://dx.doi.org/10.1093/cercor/bhaf231 | DOI Listing |
Cognition
September 2025
Department of Linguistics, University of Ottawa, Ottawa, ON K1N 6N5, Canada. Electronic address:
This research examines how adults process and integrate a combination of higher-level semantic cues (i.e., semantic context) which are followed by lower-level acoustic cues (i.
View Article and Find Full Text PDFNat Comput Sci
September 2025
Department of Linguistics, Stanford University, Stanford, CA, USA.
Acta Psychol (Amst)
September 2025
Univ. Lille, CNRS, UMR 9193 - SCALab - Sciences Cognitives et Sciences Affectives, F-59000 Lille, France; Univ. Lille, Inria, CNRS, Centrale Lille, UMR 9189 - CRIStAL, F-59000 Lille, France. Electronic address:
Dialogue is an ideal setting for changing linguistic representations thanks to the repeated use of new words and meanings. Two experiments were conducted to examine the extent to which new semantic relationships created during dialogue may change preexisting representations in long-term semantic memory after a dialogue. For this purpose, we developed an interactive agreement referential task to create new semantic relationships in dialogue between two words by associating them to a single picture.
View Article and Find Full Text PDFCereb Cortex
August 2025
Faculty of Psychology and Education Science, Department of Psychology, University of Geneva, Chemin des Mines 9, Geneva, 1202, Switzerland.
Language learning and use relies on domain-specific, domain-general cognitive and sensory-motor functions. Using fMRI during story listening and behavioral tests, we investigated brain-behavior associations between linguistic and non-linguistic measures in individuals with varied multilingual experience and reading skills, including typical reading participants (TRs) and dyslexic readers (DRs). Partial Least Square Correlation revealed a main component linking cognitive, linguistic, and phonological measures to amodal/associative brain areas.
View Article and Find Full Text PDFDisabil Rehabil Assist Technol
September 2025
International Communication College, Jilin International Studies University, Changchun, Jilin, China.
Background: Conventional automated writing evaluation systems typically provide insufficient support for students with special needs, especially in tonal language acquisition such as Chinese, primarily because of rigid feedback mechanisms and limited customisation.
Objective: This research develops context-aware Hierarchical AI Tutor for Writing Enhancement(CHATWELL), an intelligent tutoring platform that incorporates optimised large language models to deliver instantaneous, customised, and multi-dimensional writing assistance for Chinese language learners, with special consideration for those with cognitive learning barriers.
Methods: CHATWELL employs a hierarchical AI framework with a four-tier feedback mechanism designed to accommodate diverse learning needs.