  Deep Convolutional Neural Networks (DCNNs) were originally inspired by
principles of biological vision, have evolved into best current computational
models of object recognition, and consequently indicate strong architectural
and functional parallelism with the ventral visual pathway throughout
comparisons with neuroimaging and neural time series data. As recent advances
in deep learning seem to decrease this similarity, computational neuroscience
is challenged to reverse-engineer the biological plausibility to obtain useful
models. While previous studies have shown that biologically inspired
architectures are able to amplify the human-likeness of the models, in this
study, we investigate a purely data-driven approach. We use human eye tracking
data to directly modify training examples and thereby guide the models' visual
attention during object recognition in natural images either towards or away
from the focus of human fixations. We compare and validate different
manipulation types (i.e., standard, human-like, and non-human-like attention)
through GradCAM saliency maps against human participant eye tracking data. Our
results demonstrate that the proposed guided focus manipulation works as
intended in the negative direction and non-human-like models focus on
significantly dissimilar image parts compared to humans. The observed effects
were highly category-specific, enhanced by animacy and face presence, developed
only after feedforward processing was completed, and indicated a strong
influence on face detection. With this approach, however, no significantly
increased human-likeness was found. Possible applications of overt visual
attention in DCNNs and further implications for theories of face detection are
discussed.
