标题： 大脑和模型中视觉表征的收敛变换 显示英文标题

标题： Convergent transformations of visual representation in brains and models

摘要： 认知神经科学中的一个基本问题是视觉感知是由外部世界的结构还是大脑的内部架构所塑造的。 尽管一些感知变异性可以归因于个体差异，但大脑对自然刺激的反应在个体之间引发了相似的活动模式，这表明存在一种收敛的表征原则。 在此，我们测试这种由感觉向高级内部表征转换的刺激驱动收敛是否在人和深度神经网络（DNNs）中遵循共同的轨迹。 我们引入了一个统一的框架，通过结合跨被试相似性与模型层次结构的对齐来追踪表征流动。 将该框架应用于三个独立的视觉场景感知功能磁共振成像（fMRI）数据集，我们揭示了一个全皮层网络，在个体之间保持一致，分为两条路径：一条是用于场景结构的内侧-腹侧流，另一条是针对社会和生物内容的外侧-背侧流。 这种功能组织被视觉DNN的层次结构所捕捉，但未被语言模型捕捉，这加强了视觉到语义转换的特异性。 这些发现表明，人类和人工视觉中视觉编码存在一种收敛的计算解决方案，该解决方案由外部世界的结构驱动。 显示英文摘要

摘要： A fundamental question in cognitive neuroscience is what shapes visual perception: the external world's structure or the brain's internal architecture. Although some perceptual variability can be traced to individual differences, brain responses to naturalistic stimuli evoke similar activity patterns across individuals, suggesting a convergent representational principle. Here, we test if this stimulus-driven convergence follows a common trajectory across people and deep neural networks (DNNs) during its transformation from sensory to high-level internal representations. We introduce a unified framework that traces representational flow by combining inter-subject similarity with alignment to model hierarchies. Applying this framework to three independent fMRI datasets of visual scene perception, we reveal a cortex-wide network, conserved across individuals, organized into two pathways: a medial-ventral stream for scene structure and a lateral-dorsal stream tuned for social and biological content. This functional organization is captured by the hierarchies of vision DNNs but not language models, reinforcing the specificity of the visual-to-semantic transformation. These findings show a convergent computational solution for visual encoding in both human and artificial vision, driven by the structure of the external world.