The adult human brain remains plastic even after puberty. However, whether first language (L1) training in adults can alter the language network is yet largely unknown. Thus, we conducted a longitudinal training experiment on syntactically complex German sentence comprehension. Sentence complexity was varied by the depth of the center embedded relative clauses (i.e., single or double embedded). Comprehension was tested after each sentence with a question on the thematic role assignment. Thirty adult, native German speakers were recruited for 4 days of training. Magnetoencephalography (MEG) data were recorded and subjected to spectral power analysis covering the classical frequency bands (i.e., theta, alpha, beta, low gamma, and gamma). Normalized spectral power, time-locked to the final closure of the relative clause, was subjected to a two-factor analysis ("sentence complexity" and "training days"). Results showed that for the more complex sentences, the interaction of sentence complexity and training days was observed in Brodmann area 44 (BA 44) as a decrease of gamma power with training. Moreover, in the gamma band (55-95 Hz) functional connectivity between BA 44 and other brain regions such as the inferior frontal sulcus and the inferior parietal cortex were correlated with behavioral performance increase due to training. These results show that even for native speakers, complex L1 sentence training improves language performance and alters neural activities of the left hemispheric language network. Training strengthens the use of the dorsal processing stream with working-memory-related brain regions for syntactically complex sentences, thereby demonstrating the brain's functional plasticity for L1 training.
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http://dx.doi.org/10.1002/hbm.25470 | DOI Listing |
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Faculdade de Letras, Universidade Federal de Minas Gerais.
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View Article and Find Full Text PDFPLoS Biol
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Max Planck Institute for Psycholinguistics, Nijmegen, the Netherlands.
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June 2025
Computer Science Department, Information Technology University of Punjab, Lahore, Pakistan.
Optical character recognition (OCR) is vital in digitizing printed data into a digital format, which can be conveniently used for various purposes. A significant amount of work has been done in OCR for well-resourced languages like English. However, languages like Urdu, spoken by a large community, face limitations in OCR due to a lack of resources and the complexity and diversity of handwritten scripts.
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SCES, Strathmore University, Nairobi, Kenya.
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