Mid-level visual features underlie the high-level categorical organization of the ventral stream

• Published in: Proceedings of the National Academy of Sciences - PNAS 2018-09, Vol.115 (38), p.E9015-E9024
• Main Authors: Long, Bria, Yu, Chen-Ping, Konkle, Talia
• Format: Article
• Language: English
• Subjects: Adult; Artificial neural networks; Biological Sciences; Brain Mapping - methods; Curvature; Female; Functional Neuroimaging - methods; Healthy Volunteers; Humans; Level (quantity); Magnetic Resonance Imaging - methods; Male; Models, Neurological; Neural networks; Neurology; Object recognition; Pattern Recognition, Visual - physiology; Photic Stimulation - methods; PNAS Plus; Recognition; Responses; Texture recognition; Visual cortex; Visual Cortex - physiology; Visual Pathways - physiology; Visual perception; Young Adult
• ISSN: 0027-8424; 1091-6490
• DOI: 10.1073/pnas.1719616115

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.