Component-Level Lesioning of Language Models Reveals Clinically Aligned Aphasia Phenotypes

• URL: http://arxiv.org/abs/2601.19723v1
• Date: Tue, 27 Jan 2026 15:47:22 GMT
• Title: Component-Level Lesioning of Language Models Reveals Clinically Aligned Aphasia Phenotypes
• Authors: Yifan Wang, Jichen Zheng, Jingyuan Sun, Yunhao Zhang, Chunyu Ye, Jixing Li, Chengqing Zong, Shaonan Wang,
• Abstract summary: We introduce a component-level framework that simulates aphasia by selectively perturbing functional components in large language models.<n>Our pipeline identifies subtype-linked components for Broca's and Wernicke's aphasia, and induces graded impairments by progressively perturbing the top-k subtype-linked components.<n>Across architectures and lesioning strategies, subtype-targeted perturbations yield more systematic, aphasia-like regressions than size-matched random perturbations.
• Score: 40.41503864764337
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models (LLMs) increasingly exhibit human-like linguistic behaviors and internal representations that they could serve as computational simulators of language cognition. We ask whether LLMs can be systematically manipulated to reproduce language-production impairments characteristic of aphasia following focal brain lesions. Such models could provide scalable proxies for testing rehabilitation hypotheses, and offer a controlled framework for probing the functional organization of language. We introduce a clinically grounded, component-level framework that simulates aphasia by selectively perturbing functional components in LLMs, and apply it to both modular Mixture-of-Experts models and dense Transformers using a unified intervention interface. Our pipeline (i) identifies subtype-linked components for Broca's and Wernicke's aphasia, (ii) interprets these components with linguistic probing tasks, and (iii) induces graded impairments by progressively perturbing the top-k subtype-linked components, evaluating outcomes with Western Aphasia Battery (WAB) subtests summarized by Aphasia Quotient (AQ). Across architectures and lesioning strategies, subtype-targeted perturbations yield more systematic, aphasia-like regressions than size-matched random perturbations, and MoE modularity supports more localized and interpretable phenotype-to-component mappings. These findings suggest that modular LLMs, combined with clinically informed component perturbations, provide a promising platform for simulating aphasic language production and studying how distinct language functions degrade under targeted disruptions.

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