Untangling the Animacy Organization of Occipitotemporal Cortex.

Some of the most impressive functional specializations in the human brain are found in the occipitotemporal cortex (OTC), where several areas exhibit selectivity for a small number of visual categories, such as faces and bodies, and spatially cluster based on stimulus animacy. Previous studies suggest this animacy organization reflects the representation of an intuitive taxonomic hierarchy, distinct from the presence of face- and body-selective areas in OTC. Using human functional magnetic resonance imaging, we investigated the independent contribution of these two factors-the face-body division and taxonomic hierarchy-in accounting for the animacy organization of OTC and whether they might also be reflected in the architecture of several deep neural networks that have not been explicitly trained to differentiate taxonomic relations.

Orthogonal Representations of Object Shape and Category in Deep Convolutional Neural Networks and Human Visual Cortex.

Deep Convolutional Neural Networks (CNNs) are gaining traction as the benchmark model of visual object recognition, with performance now surpassing humans. While CNNs can accurately assign one image to potentially thousands of categories, network performance could be the result of layers that are tuned to represent the visual shape of objects, rather than object category, since both are often confounded in natural images. Using two stimulus sets that explicitly dissociate shape from category, we correlate these two types of information with each layer of multiple CNNs.