  Object recognition is a primary function of the human visual system. It has
recently been claimed that the highly successful ability to recognise objects
in a set of emergent computer vision systems---Deep Convolutional Neural
Networks (DCNNs)---can form a useful guide to recognition in humans. To test
this assertion, we systematically evaluated visual crowding, a dramatic
breakdown of recognition in clutter, in DCNNs and compared their performance to
extant research in humans. We examined crowding in three architectures of DCNNs
with the same methodology as that used among humans. We manipulated multiple
stimulus factors including inter-letter spacing, letter colour, size, and
flanker location to assess the extent and shape of crowding in DCNNs. We found
that crowding followed a predictable pattern across architectures that was
different from that in humans. Some characteristic hallmarks of human crowding,
such as invariance to size, the effect of target-flanker similarity, and
confusions between target and flanker identities, were completely missing,
minimised or even reversed. These data show that DCNNs, while proficient in
object recognition, likely achieve this competence through a set of mechanisms
that are distinct from those in humans. They are not necessarily equivalent
models of human or primate object recognition and caution must be exercised
when inferring mechanisms derived from their operation.
