用计算神经科学连接语言理论与脑数据，验证语言处理的神经机制。
Linguistics and Human Brain: A Perspective of Computational Neuroscience
- 构建可验证的神经模型，将语言层级结构映射到脑活动。
- 利用大语言模型的高维表征探索语言神经基础。
- 通过模型-脑对齐评估语言理论的生物学合理性，适合跨学科研究者。
揭示语言与大脑的关系需弥合语言学抽象理论框架与神经科学实证数据之间的方法鸿沟。计算神经科学作为交叉基石，通过建模、仿真与数据分析，将语言的层级动态结构形式化为可检验的神经模型，实现语言假说与神经机制间的计算对话。近年来深度学习的发展，特别是大语言模型（LLMs）的突破，有力推动了这一进程。其高维表征空间为探索语言处理的神经基础提供了新尺度，而‘模型-脑对齐’框架则提供了一种评估语言相关理论生物合理性的方法论。该方法促进了语言学与神经科学的深度融合。
原文摘要 · Abstract (English)
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.
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