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Although Deep Convolutional Networks (DCNs) are approaching the accuracy ofhuman observers at object recognition, it is unknown whether they leveragesimilar visual representations to achieve this performance. To address this, weintroduce Clicktionary, a web-based game for identifying visual features usedby human observers during object recognition. Importance maps derived from thegame are consistent across participants and uncorrelated with image saliencymeasures. These results suggest that Clicktionary identifies image regions thatare meaningful and diagnostic for object recognition but different than thosedriving eye movements. Surprisingly, Clicktionary importance maps are onlyweakly correlated with relevance maps derived from DCNs trained for objectrecognition. Our study demonstrates that the narrowing gap between the objectrecognition accuracy of human observers and DCNs obscures distinct visualstrategies used by each to achieve this performance.

What are the visual features underlying human versus machine vision?