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Deep convolutional neural networks (DCNNs) and the ventral visual pathwayshare vast architectural and functional similarities in visual challenges suchas object recognition. Recent insights have demonstrated that both hierarchicalcascades can be compared in terms of both exerted behavior and underlyingactivation. However, these approaches ignore key differences in spatialpriorities of information processing. In this proof-of-concept study, wedemonstrate a comparison of human observers (N = 45) and three feedforwardDCNNs through eye tracking and saliency maps. The results reveal fundamentallydifferent resolutions in both visualization methods that need to be consideredfor an insightful comparison. Moreover, we provide evidence that a DCNN withbiologically plausible receptive field sizes called vNet reveals higheragreement with human viewing behavior as contrasted with a standard ResNetarchitecture. We find that image-specific factors such as category, animacy,arousal, and valence have a direct link to the agreement of spatial objectrecognition priorities in humans and DCNNs, while other measures such asdifficulty and general image properties do not. With this approach, we try toopen up new perspectives at the intersection of biological and computer visionresearch.

Comparing object recognition in humans and deep convolutional neural networks -- An eye tracking study