Quantum mechanics

Object detection

Neuroscience

Set (abstract data type)

Computer science

Visual cortex

Physics

Psychology

Pattern recognition (psychology)

Politics

Image (mathematics)

Political science

Object (grammar)

Categorization

Human visual system model

Artificial intelligence

Representation (politics)

Observer (physics)

Programming language

Cognitive neuroscience of visual object recognition

Computer vision

Proceedings of the National Academy of Sciences

Significance Humans are exquisitely sensitive to the spatial arrangement of visual features in objects and scenes, but not in visual textures. Category-selective regions in the visual cortex are widely believed to underlie object perception, suggesting such regions should distinguish natural images of objects from synthesized images containing similar visual features in scrambled arrangements. Contrarily, we demonstrate that representations in category-selective cortex do not discriminate natural images from feature-matched scrambles but can discriminate images of different categories, suggesting a texture-like encoding. We find similar insensitivity to feature arrangement in Imagenet-trained deep convolutional neural networks. This suggests the need to reconceptualize the role of category-selective cortex as representing a basis set of complex texture-like features, useful for a myriad of behaviors.

Texture-like representation of objects in human visual cortex