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Efficient inverse graphics in biological face processing
Name
sciadv.aax5979.pdf
Description
Published version
Size
3.16 MB
Format
Adobe PDF
Checksum (MD5)
cb84fe272c69f766926a2bd1d8e74b8c
Author(s)
Yildirim, Ilker
Belledonne, Mario
Freiwald, Winrich
Tenenbaum, Josh
Date Issued
2020
Journal
Science Advances
Publisher
American Association for the Advancement of Science (AAAS)
Citation
Yildirim, Ilker, Belledonne, Mario, Freiwald, Winrich and Tenenbaum, Josh. 2020. "Efficient inverse graphics in biological face processing." Science Advances, 6 (10).
Version
Final published version
Abstract
© 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC). Vision not only detects and recognizes objects, but performs rich inferences about the underlying scene structure that causes the patterns of light we see. Inverting generative models, or “analysis-by-synthesis”, presents a possible solution, but its mechanistic implementations have typically been too slow for online perception, and their mapping to neural circuits remains unclear. Here we present a neurally plausible efficient inverse graphics model and test it in the domain of face recognition. The model is based on a deep neural network that learns to invert a three-dimensional face graphics program in a single fast feedforward pass. It explains human behavior qualitatively and quantitatively, including the classic “hollow face” illusion, and it maps directly onto a specialized face-processing circuit in the primate brain. The model fits both behavioral and neural data better than state-of-the-art computer vision models, and suggests an interpretable reverse-engineering account of how the brain transforms images into percepts.
Terms of Use
Creative Commons Attribution NonCommercial License 4.0
Persistent DSpace Link
DOI of Published Version
10.1126/SCIADV.AAX5979
