用大模型模拟中风后失语症患者的命名错误模式，效果接近真实患者。
Lesioned Multimodal Language Models Reproduce Aphasic Picture-Naming Patterns
- 通过扰动多模态模型的层和参数，模拟不同类型的命名错误。
- 97.8%患者在6个错误类别中匹配，79.5%在全部7类中匹配。
- 为失语症数字孪生提供可量化的建模框架，适合临床研究与康复评估。
中风后失语症常导致系统性命名错误，具有特定模式。本文检验了通用语言模型是否能复现这些错误模式。我们以LLaVA 1.6为基础，通过改变模型层、扰动比例和噪声强度进行控制性损伤测试，分析278名失语症患者（PWA）在费城命名测试中的表现。使用神经分类器将回答分为七类：正确、语义错误、混合错误、无关错误、新词、无回应及正式错语。在不同参数区域中，前六类错误在临床上可比比例下出现，唯正式错语例外。搜索扰动空间发现，97.8%的患者至少在六类中匹配，79.5%在全部七类中匹配。蒙特卡洛基线验证表明，匹配源于类别间结构关联而非单类重叠。结果建立了一个可量化复现个体失语症命名错误的框架，提示语言模型或可作为中风后失语症患者的数字孪生体。
原文摘要 · Abstract (English)
Aphasia following stroke commonly produces systematic naming errors with characteristic profiles, but whether general-purpose language models not designed for clinical simulation can reproduce these patterns remains untested. We investigated (1) whether lesions or controlled perturbations to a multimodal language model can reproduce different types of errors in picture naming, and (2) whether the framework can reproduce the complete error profile of individual persons with aphasia (PWAs). Using LLaVA 1.6, we evaluated perturbation configurations that varied the layer, proportion, and amount of noise applied to model units. We examined 278 PWAs on the Philadelphia Naming Test, classifying responses into seven categories using a validated neural classifier. Six of seven response categories (correct, semantic, mixed, unrelated, neologism, no response errors) emerged at clinically-comparable proportions across distinct parameter space regions, with formal paraphasia being the exception. Searching the perturbation space revealed configurations that reproduced the individual error profile in at least six of seven categories for 97.8% of PWAs and in all seven categories for 79.5% of PWAs. Monte Carlo baselines confirmed that this matching reflects joint inter-category structure rather than marginal overlap. These results establish a quantitative framework for reproducing individual aphasic error patterns in picture naming. They suggest the potential for language models to serve as digital twins of individuals with post-stroke aphasia.
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