Proceedings of the Annual Meeting of the Cognitive Science Society

The face-inversion effect, or the drastic decrease in accuracyseen when a participant is asked to identify inverted faces whencompared to upright faces, is an effect that is not found in objectinversion. Here we suggest a new explanation of this effect usingcomputational models to show that the phenomenon can beexplained by the anatomical mapping from the visual field toprimary visual cortex. We propose that the way inverted faces aremapped onto the cortex is fundamentally different from the wayupright faces are mapped. Our work first shows the advantages ofthis mapping due to its scale and rotation invariance when used asinput to a convolutional neural network. We train the network toperform recognition tasks and show it exhibits scale andrealistically constrained rotation invariance. We then confirm thatthe decline in accuracy seen when a participant is asked to identifyinverted faces is not seen in the network with inverted objectrecognition tasks. With the support of these two findings, we testthe face-inversion effect on our network and are able to show theunique decline in accuracy, suggesting that the way the visual fieldis mapped onto the primary visual cortex is a key facet in themanifestation of this effect.