  Although Deep Convolutional Networks (DCNs) are approaching the accuracy of
human observers at object recognition, it is unknown whether they leverage
similar visual representations to achieve this performance. To address this, we
introduce Clicktionary, a web-based game for identifying visual features used
by human observers during object recognition. Importance maps derived from the
game are consistent across participants and uncorrelated with image saliency
measures. These results suggest that Clicktionary identifies image regions that
are meaningful and diagnostic for object recognition but different than those
driving eye movements. Surprisingly, Clicktionary importance maps are only
weakly correlated with relevance maps derived from DCNs trained for object
recognition. Our study demonstrates that the narrowing gap between the object
recognition accuracy of human observers and DCNs obscures distinct visual
strategies used by each to achieve this performance.
