Keywords
Visual Expertise, Neurocomputational models of vision, Deep Learning
Abstract
We report on preliminary results of an anatomically-inspired deep learning model of the visual system and its role in explaining the face inversion effect. Contrary to the generally accepted wisdom, our hypothesis is that the face inversion effect can be accounted for by the representation in V1 combined with the reliance on the configuration of features due to face expertise. We take two features of the primate visual system into account: 1) The foveated retina; and 2) The log-polar mapping from retina to V1. We simulate acquisition of faces, etc., by gradually increasing the number of identities the network learns. We find that the more faces the network knows, the more the network shows the face inversion effect. In contrast, a standard convolutional network’s inversion performance drops to nearly 0 in the same situation.
Start Date
13-5-2022 9:50 AM
End Date
13-5-2022 10:15 AM
Included in
Cognition and Perception Commons, Cognitive Neuroscience Commons, Cognitive Science Commons, Computational Neuroscience Commons
Visual Expertise in an Anatomically-inspired Model of the Visual System
We report on preliminary results of an anatomically-inspired deep learning model of the visual system and its role in explaining the face inversion effect. Contrary to the generally accepted wisdom, our hypothesis is that the face inversion effect can be accounted for by the representation in V1 combined with the reliance on the configuration of features due to face expertise. We take two features of the primate visual system into account: 1) The foveated retina; and 2) The log-polar mapping from retina to V1. We simulate acquisition of faces, etc., by gradually increasing the number of identities the network learns. We find that the more faces the network knows, the more the network shows the face inversion effect. In contrast, a standard convolutional network’s inversion performance drops to nearly 0 in the same situation.