Linguistics and Human Brain: A Perspective of Computational Neuroscience

2026-02-09

AI Summary

This study addresses the methodological gap between abstract linguistic theories and empirical neuroscience data by leveraging the high-dimensional representational space of large language models (LLMs) to formalize the hierarchical and dynamic structure of language into testable neurocomputational models. Employing a model–brain alignment framework, the work evaluates the biological plausibility of linguistic theories through systematic comparison with neural data. By integrating computational modeling, neural data simulation, and empirical analysis, the project establishes verifiable computational pathways linking linguistic hypotheses to underlying neural mechanisms. This approach fosters a deeper integration of theoretical linguistics and cognitive neuroscience, offering a novel paradigm for investigating the neural foundations of language processing.

Technology Category

• Natural Language Processing: (Large) Language Models
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

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.

Problem

• language-brain relationship
• computational neuroscience
• linguistic theory
• neural data
• model-brain alignment

Innovation

• computational neuroscience
• large language models
• model-brain alignment
• linguistic representation
• neural mechanisms