Bridging Brains and Models: MoE-Based Functional Lesions for Simulating and Rehabilitating Aphasia

TL;DR

This paper introduces a modular language model approach to simulate different types of aphasia by lesioning specific components, and demonstrates potential for modeling recovery through retraining, offering a new computational framework for understanding and rehabilitating language disorders.

Contribution

The study presents a novel method to simulate aphasia in language models by lesioning modules, and explores recovery through retraining, bridging computational modeling and clinical rehabilitation.

Findings

Lesioning syntax or semantics modules mimics Broca's and Wernicke's aphasia.

Retraining remaining modules improves linguistic function post-lesion.

Modular LLMs can serve as models for language disorder mechanisms and therapy.

Abstract

The striking alignment between large language models (LLMs) and human brain activity positions them as powerful models of healthy cognition. This parallel raises a fundamental question: if LLMs can model the intact brain, can we lesion them to simulate the linguistic deficits of the injured brain? In this work, we introduce a methodology to model aphasia - a complex language disorder caused by neural injury - by selectively disabling components in a modular Mixture-of-Experts (MoE) language model. We simulate distinct aphasia subtypes, validate their linguistic outputs against real patient speech, and then investigate functional recovery by retraining the model's remaining healthy experts. Our results demonstrate that lesioning functionally-specialized experts for syntax or semantics induces distinct impairments that closely resemble Broca's and Wernicke's aphasia, respectively. Crucially, we show that freezing the damaged experts and retraining the intact ones on conversational data restores significant linguistic function, demonstrating a computational analogue for rehabilitation. These findings establish modular LLMs as a powerful and clinically-relevant potential framework for modeling the mechanisms of language disorders and for computationally exploring novel pathways for therapy.