Humans have an impressive ability to rapidly process global information in natural scenes to infer their category. Yet, it remains unclear whether and how scene categories observed dynamically in the natural world are represented in cerebral cortex beyond few canonical scene-selective areas. To address this question, here we examined the representation of dynamic visual scenes by recording whole-brain blood oxygenation level-dependent (BOLD) responses while subjects viewed natural movies. We fit voxelwise encoding models to estimate tuning for scene categories that reflect statistical ensembles of objects and actions in the natural world. We find that this scene-category model explains a significant portion of the response variance broadly across cerebral cortex. Cluster analysis of scene-category tuning profiles across cortex reveals nine spatially-segregated networks of brain regions consistently across subjects. These networks show heterogeneous tuning for a diverse set of dynamic scene categories related to navigation, human activity, social interaction, civilization, natural environment, non-human animals, motion-energy, and texture, suggesting that the organization of scene category representation is quite complex.

人类在自然场景中快速处理全局信息以推断其类别的能力令人印象深刻。然而，目前尚不清楚在自然界中动态观察到的场景类别是否以及如何在大脑皮层中表现出少数典型的场景选择区域。为了解决这个问题，我们在这里通过记录受试者观看自然电影时的全脑血氧水平依赖 (BOLD) 反应来检查动态视觉场景的表示。我们拟合体素编码模型来估计场景类别的调整，这些场景类别反映了自然世界中对象和动作的统计集合。我们发现这个场景类别模型解释了大脑皮层广泛的响应方差的很大一部分。跨皮层的场景类别调整配置文件的聚类分析揭示了九个空间分离的大脑区域网络在受试者中始终如一。这些网络显示出与导航、人类活动、社会互动、文明、自然环境、非人类动物、运动能量和纹理相关的各种动态场景类别的异构调整，这表明场景类别表示的组织非常复杂的。