Linguistics and Human Brain: A Perspective of Computational Neuroscience
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866911446031400960 |
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| author | Zhang, Fudong Chai, Bo Wu, Yujie Siok, Wai Ting Wang, Nizhuan |
| author_facet | Zhang, Fudong Chai, Bo Wu, Yujie Siok, Wai Ting Wang, Nizhuan |
| contents | Elucidating the language-brain relationship requires bridging the methodological gap between the abstract theoretical frameworks of linguistics and the empirical neural data of neuroscience. Serving as an interdisciplinary cornerstone, computational neuroscience formalizes the hierarchical and dynamic structures of language into testable neural models through modeling, simulation, and data analysis. This enables a computational dialogue between linguistic hypotheses and neural mechanisms. Recent advances in deep learning, particularly large language models (LLMs), have powerfully advanced this pursuit. Their high-dimensional representational spaces provide a novel scale for exploring the neural basis of linguistic processing, while the "model-brain alignment" framework offers a methodology to evaluate the biological plausibility of language-related theories. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_08275 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Linguistics and Human Brain: A Perspective of Computational Neuroscience Zhang, Fudong Chai, Bo Wu, Yujie Siok, Wai Ting Wang, Nizhuan Neurons and Cognition Computation and Language Elucidating the language-brain relationship requires bridging the methodological gap between the abstract theoretical frameworks of linguistics and the empirical neural data of neuroscience. Serving as an interdisciplinary cornerstone, computational neuroscience formalizes the hierarchical and dynamic structures of language into testable neural models through modeling, simulation, and data analysis. This enables a computational dialogue between linguistic hypotheses and neural mechanisms. Recent advances in deep learning, particularly large language models (LLMs), have powerfully advanced this pursuit. Their high-dimensional representational spaces provide a novel scale for exploring the neural basis of linguistic processing, while the "model-brain alignment" framework offers a methodology to evaluate the biological plausibility of language-related theories. |
| title | Linguistics and Human Brain: A Perspective of Computational Neuroscience |
| topic | Neurons and Cognition Computation and Language |
| url | https://arxiv.org/abs/2602.08275 |