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.

翻译：大型语言模型（LLMs）日益展现出类人的语言行为与内部表征，使其可能成为语言认知的计算模拟器。本研究探讨能否通过系统性操控LLMs，复现局灶性脑损伤后失语症特有的语言产出障碍。此类模型可为康复假说检验提供可扩展的代理工具，并为探索语言功能组织提供受控框架。我们提出一种基于临床的组件级框架，通过选择性扰动LLMs中的功能组件来模拟失语症，并借助统一干预接口将其应用于模块化专家混合模型与密集型Transformer架构。我们的流程（i）识别布罗卡失语与韦尼克失语相关的亚型关联组件，（ii）通过语言探测任务解析这些组件的功能，（iii）通过渐进扰动前k个亚型关联组件诱发梯度损伤，并使用失语商（AQ）汇总的西方失语成套测验（WAB）子项评估结果。在不同架构与损伤策略中，亚型靶向扰动比规模匹配的随机扰动产生更系统化、更接近失语症的回归表现，且专家混合模型的模块化特性支持更局部化、可解释的表型-组件映射。这些发现表明，模块化LLMs结合临床导向的组件扰动，为模拟失语性语言产出及研究特定语言功能在靶向破坏下的退化机制提供了前景广阔的研究平台。