[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-86280-en":3,"doc-seo-86280-105":29,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},86280,1099514068365,"Aurelia","https://ap-avatar.wpscdn.com/avatar/10000253d8d9f28188e?_k=1776742907772140068",8,"Research & Report","Lesioned Multimodal Language Models Reproduce Aphasic Picture-Naming Patterns","Aphasia after stroke often causes systematic naming errors with stable profiles, yet it remains unclear whether general-purpose language models can recreate these clinical patterns. A study evaluates whether lesions or controlled perturbations applied to a multimodal model reproduce seven categories of picture-naming errors. Using LLaVA 1.6 and a dataset of 278 PWAs on the Philadelphia Naming Test, response distributions match clinically comparable proportions across most categories, enabling individual-level error-profile reconstruction.","Lesioned Multimodal Language Models Reproduce Aphasic Picture-Naming Patterns  \nYong Yang1· *, Xiang Guan1, Sophie Arheix-Parras4, Saeed Ahmadi², Roger Newman-Norlund3·4, Leonardo Bonilha5, Christopher Rorden⁴, Julius  \nFridriksson2·3, Rutvik H. Desai4, Srihari Nelakuditi1  \n1Department of Computer Science and Engineering, University of South Carolina; 2Department of Communication Sciences and Disorders, University of South Carolina; 3ALLT.AI, LLC; 4Department of Psychology, University of South Carolina; 5Department of Neurology, School of  \nMedicine, University of South Carolina  \n*[Corresponding author: yongy@email.sc.edu](Corresponding author: yongy@email.sc.edu)  \nKey Words: Large Language Models, aphasia, picture naming, artificial perturbations, error pattern reproduction, neural classifier  \nAbstract  \nAphasia following stroke commonly produces systematic naming errors with characteristic profiles, but whether generalpurpose 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.  \nSignificance Statement  \nWhile artificial intelligence language models exhibit a wide range of capabilities, it is not clear whether they can serve as analogs of language processing in the human brain. Here, we tested whether ablations to language models can produce deficits that are similar to those found in post-stroke aphasia, using a picture naming task administered to a large cohort of persons with aphasia with varied error profiles. We demonstrate that controlled perturbations to a general-purpose multimodal language model reproduce the same error patterns observed in stroke survivors, with different regions of the lesion space associated with different error types. Formal paraphasias are a partial exception, arising in the model at substantially lower rates than in the patients whose error profiles they dominate. Moreover, we show that without any individual fine-tuning, specific perturbations to the model can produce complete error profiles of individual stroke survivors. The work establishes a quantitative framework for computationally characterizing individual aphasic picture-naming profiles. It demonstrates the potential of language models not just as powerful computational tools, but to serve as cognitive models of language processing in the human brain, with possible applications in testing and development of therapies in aphasia.  \nIntroduction  \nAphasia following stroke produces systematic rather than random error patterns, with specific lesion characteristics yielding predictable failure modes (Fridriksson et al., 2022) . These characteristic breakdown patterns have been extensive","cbCaiqzmaqmSWk4y","https://ap.wps.com/l/cbCaiqzmaqmSWk4y","pdf",1977509,1,33,"English","en",105,"# Abstract\n# Significance Statement\n# Introduction\n## Research Questions\n## Expressiveness and Error-Profile Matching","[{\"question\":\"What is the main goal of the study on multimodal language models and aphasia?\",\"answer\":\"To test whether controlled lesions or perturbations to a general-purpose multimodal language model can reproduce the naming error patterns seen in post-stroke aphasia, including individual persons’ complete error profiles.\"},{\"question\":\"How were aphasic naming errors measured and categorized?\",\"answer\":\"Responses were drawn from the Philadelphia Naming Test and classified into seven error categories using a validated neural classifier.\"},{\"question\":\"How effectively did the perturbation framework match individual patients’ error profiles?\",\"answer\":\"Perturbation configurations reproduced an individual’s error profile in at least six of seven categories for 97.8% of PWAs and matched all seven categories for 79.5% of 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is the main goal of the study on multimodal language models and aphasia?","Question",{"text":75,"@type":76},"To test whether controlled lesions or perturbations to a general-purpose multimodal language model can reproduce the naming error patterns seen in post-stroke aphasia, including individual persons’ complete error profiles.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How were aphasic naming errors measured and categorized?",{"text":80,"@type":76},"Responses were drawn from the Philadelphia Naming Test and classified into seven error categories using a validated neural classifier.",{"name":82,"@type":73,"acceptedAnswer":83},"How effectively did the perturbation framework match individual patients’ error profiles?",{"text":84,"@type":76},"Perturbation configurations reproduced an individual’s error profile in at least six of seven categories for 97.8% of PWAs and matched all seven categories for 79.5% of 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