  Deep convolutional neural networks (DCNNs) and the ventral visual pathway
share vast architectural and functional similarities in visual challenges such
as object recognition. Recent insights have demonstrated that both hierarchical
cascades can be compared in terms of both exerted behavior and underlying
activation. However, these approaches ignore key differences in spatial
priorities of information processing. In this proof-of-concept study, we
demonstrate a comparison of human observers (N = 45) and three feedforward
DCNNs through eye tracking and saliency maps. The results reveal fundamentally
different resolutions in both visualization methods that need to be considered
for an insightful comparison. Moreover, we provide evidence that a DCNN with
biologically plausible receptive field sizes called vNet reveals higher
agreement with human viewing behavior as contrasted with a standard ResNet
architecture. We find that image-specific factors such as category, animacy,
arousal, and valence have a direct link to the agreement of spatial object
recognition priorities in humans and DCNNs, while other measures such as
difficulty and general image properties do not. With this approach, we try to
open up new perspectives at the intersection of biological and computer vision
research.
