Linguistics and Human Brain: A Perspective of Computational Neuroscience

TL;DR

This paper explores how computational neuroscience and deep learning, especially large language models, can bridge linguistics and neuroscience to understand the neural basis of language processing.

Contribution

It introduces a framework combining linguistic theories, neural data, and deep learning models to study language-brain relationships.

Findings

Deep learning models provide new insights into neural language processing.

Model-brain alignment offers a method to evaluate linguistic theories.

Large language models enable scalable exploration of neural representations.

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.