  Human observers engage in selective information uptake when classifying
visual patterns. The same is true of deep neural networks, which currently
constitute the best performing artificial vision systems. Our goal is to
examine the congruence, or lack thereof, in the information-gathering
strategies of the two systems. We have operationalized our investigation as a
character recognition task. We have used eye-tracking to assay the spatial
distribution of information hotspots for humans via fixation maps and an
activation mapping technique for obtaining analogous distributions for deep
networks through visualization maps. Qualitative comparison between
visualization maps and fixation maps reveals an interesting correlate of
congruence. The deep learning model considered similar regions in character,
which humans have fixated in the case of correctly classified characters. On
the other hand, when the focused regions are different for humans and deep
nets, the characters are typically misclassified by the latter. Hence, we
propose to use the visual fixation maps obtained from the eye-tracking
experiment as a supervisory input to align the model's focus on relevant
character regions. We find that such supervision improves the model's
performance significantly and does not require any additional parameters. This
approach has the potential to find applications in diverse domains such as
medical analysis and surveillance in which explainability helps to determine
system fidelity.
