  For a considerable time, deep convolutional neural networks (DCNNs) have
reached human benchmark performance in object recognition. On that account,
computational neuroscience and the field of machine learning have started to
attribute numerous similarities and differences to artificial and biological
vision. This study aims towards a behavioral comparison of visual core object
recognition performance between humans and feedforward neural networks in a
classification learning paradigm on an ImageNet data set. For this purpose,
human participants (n = 65) competed in an online experiment against different
feedforward DCNNs. The designed approach based on a typical learning process of
seven different monkey categories included a training and validation phase with
natural examples, as well as a testing phase with novel, unexperienced shape
and color manipulations. Analyses of accuracy revealed that humans not only
outperform DCNNs on all conditions, but also display significantly greater
robustness towards shape and most notably color alterations. Furthermore, a
precise examination of behavioral patterns highlights these findings by
revealing independent classification errors between the groups. The obtained
results show that humans contrast strongly with artificial feedforward
architectures when it comes to visual core object recognition of manipulated
images. In general, these findings are in line with a growing body of
literature, that hints towards recurrence as a crucial factor for adequate
generalization abilities.
