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Linguistics and Human Brain: A Perspective of Computational Neuroscience

Fudong Zhang, Bo Chai, Yujie Wu, Wai Ting Siok, Nizhuan Wang

TL;DR

This paper addresses the challenge of linking abstract linguistic theory to neural mechanisms by advocating computational neuroscience as an integrative bridge. It argues that large language models (LLMs) offer a scalable, testable computational space for model–brain alignment, while neural coding and multi-modal data provide a rigorous empirical framework. The authors survey classical linguistic theories, delineate four methodological pillars (measurement, embeddings, evolving models, neural coding), and review recent progress in LLM-driven neural alignment across cross-modal, inter-brain, hierarchical, and learning-dynamics domains, also outlining four future directions to strengthen structure–function mapping, real-time interaction, multimodal data, and evaluation. They emphasize the need for causal interventions and brain-grounded constraints to move beyond correlational findings toward mechanistic explanations, with potential impact on brain–computer interfaces and brain-inspired language models.

Abstract

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.

Linguistics and Human Brain: A Perspective of Computational Neuroscience

TL;DR

This paper addresses the challenge of linking abstract linguistic theory to neural mechanisms by advocating computational neuroscience as an integrative bridge. It argues that large language models (LLMs) offer a scalable, testable computational space for model–brain alignment, while neural coding and multi-modal data provide a rigorous empirical framework. The authors survey classical linguistic theories, delineate four methodological pillars (measurement, embeddings, evolving models, neural coding), and review recent progress in LLM-driven neural alignment across cross-modal, inter-brain, hierarchical, and learning-dynamics domains, also outlining four future directions to strengthen structure–function mapping, real-time interaction, multimodal data, and evaluation. They emphasize the need for causal interventions and brain-grounded constraints to move beyond correlational findings toward mechanistic explanations, with potential impact on brain–computer interfaces and brain-inspired language models.

Abstract

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.
Paper Structure (28 sections, 4 figures, 3 tables)

This paper contains 28 sections, 4 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Theoretical Foundations of Linguistics
  3. Frameworks of Classical Linguistic Theories
  4. Cross-Cultural Evolution of Linguistic Theories
  5. Challenges in Classical Linguistic Theories
  6. Computational Neuroscience Methods for Linguistics
  7. Methodology in Computational Neuroscience
  8. Brain Activity Measurement for Linguistic Tasks
  9. Word Embedding
  10. The Evolving Language Models
  11. Neural Coding
  12. "Model–Brain Alignment" Workflow from Computational Neuroscience in Linguistic Case
  13. Foundational Insights and Framework Shifts in Language-Brain Computational Research
  14. Early Model Exploration of Predictability and Structural Features
  15. LLM-Driven Framework for Neural Alignment
  16. ...and 13 more sections

Figures (4)

  • Figure 1: Classical Linguistic Theories and Challenges for Bridging Linguistics and Neuroscience.
  • Figure 2: The Association between Linguistics and Neuroscience from the Perspective of Computational Neuroscience.
  • Figure 3: Computational Neuroscience Workflow of Each Component Collaboration in the Linguistic Case Study.
  • Figure 4: Correspondence between Current Limitations and Future Research Perspectives in the Study of Neural Mechanisms of Language.