  Feedforward Convolutional Neural Networks (ffCNNs) have become
state-of-the-art models both in computer vision and neuroscience. However,
human-like performance of ffCNNs does not necessarily imply human-like
computations. Previous studies have suggested that current ffCNNs do not make
use of global shape information. However, it is currently unclear whether this
reflects fundamental differences between ffCNN and human processing or is
merely an artefact of how ffCNNs are trained. Here, we use visual crowding as a
well-controlled, specific probe to test global shape computations. Our results
provide evidence that ffCNNs cannot produce human-like global shape
computations for principled architectural reasons. We lay out approaches that
may address shortcomings of ffCNNs to provide better models of the human visual
system.
