Dyslexia, a neurodevelopmental disorder characterized by persistent reading difficulties, is often linked to reduced activity of the visual word form area (VWFA) in the ventral occipito-temporal cortex. Traditional approaches to studying dyslexia, such as behavioral and neuroimaging methods, have provided valuable insights but remain limited in their ability to test causal hypotheses about the underlying mechanisms of reading impairments. In this study, we use large-scale vision-language models (VLMs) to simulate dyslexia by functionally identifying and perturbing artificial analogues of word processing. Using stimuli from cognitive neuroscience, we identify visual-word-form-selective units within VLMs and demonstrate that they predict human VWFA neural responses. Ablating model VWF units leads to selective impairments in reading tasks while general visual and language comprehension abilities remain intact. In particular, the resulting model matches dyslexic humans' phonological deficits without a significant change in orthographic processing, and mirrors dyslexic behavior in font sensitivity. Taken together, our modeling results replicate key characteristics of dyslexia and establish a computational framework for investigating brain disorders.

翻译：阅读障碍是一种以持续性阅读困难为特征的神经发育障碍，常与腹侧枕颞皮层视觉词形区活动减弱相关。研究阅读障碍的传统方法（如行为学和神经影像学方法）虽提供了宝贵见解，但在检验阅读障碍潜在机制的因果假说方面仍存在局限。本研究利用大规模视觉语言模型，通过功能性地识别并扰动词汇处理的人工模拟单元来模拟阅读障碍。我们采用认知神经科学实验刺激，识别出VLM内部具有视觉词形选择性的单元，并证明这些单元能够预测人类VWFA的神经响应。对模型VWF单元进行消融会导致阅读任务出现选择性损伤，而一般视觉与语言理解能力保持完整。特别值得注意的是，由此产生的模型在正字法处理未发生显著变化的情况下，重现了阅读障碍患者的语音加工缺陷，并模拟了阅读障碍者对字体敏感的行为特征。综合而言，我们的建模结果复现了阅读障碍的关键特征，为研究脑部疾病建立了一个计算框架。