Human object-selective cortex shows a large-scale organization characterized by the high-level properties of both animacy and object size. To what extent are these neural responses explained by primitive perceptual features that distinguish animals from objects and big objects from small objects? To address this question, we used a texture synthesis algorithm to create a class of stimuli—texforms—which preserve some mid-level texture and form information from objects while rendering them unrecognizable. We found that unrecognizable texforms were sufficient to elicit the large-scale organizations of object-selective cortex along the entire ventral pathway. Further, the structure in the neural patterns elicited by texforms was well predicted by curvature features and by intermediate layers of a deep convolutional neural network, supporting the mid-level nature of the representations. These results provide clear evidence that a substantial portion of ventral stream organization can be accounted for by coarse texture and form information without requiring explicit recognition of intact objects.

人体对对象的选择性皮层显示了一个大规模组织，其特征是具有生命力和对象大小的高级属性。这些神经反应在多大程度上由原始的感知特征来解释，这些特征将动物与物体区别开来，将大物体与小物体区别开来？为了解决这个问题，我们使用纹理合成算法来创建一类刺激-texforms-保留一些中级纹理并从对象中形成信息，同时使它们无法识别。我们发现无法识别的texforms足以引起整个腹侧通路的对象选择性皮质的大规模组织。此外，通过曲率特征和深层卷积神经网络的中间层可以很好地预测由texforms引发的神经模式中的结构，支持陈述的中层性质。这些结果提供了明确的证据，表明腹流组织的很大一部分可以由粗糙的纹理和形式信息来解决，而无需明确识别完整的物体。