  Deep convolutional neural networks follow roughly the architecture of
biological visual systems and have shown a performance comparable to human
observers in object recognition tasks. In this study, I tested three pretrained
deep neural networks in visual search for simple visual features, and for
feature configurations. The results reveal a qualitative difference from human
performance. It appears that there is no clear difference between searches for
simple features that pop out in experiments with humans, and for feature
configurations that exhibit strict capacity limitations in human vision. Both
types of stimuli reveal comparable capacity limitations in the neural networks
tested here.
