RT Journal Article
SR Electronic
T1 Efficient inverse graphics in biological face processing
JF Science Advances
JO Sci Adv
FD American Association for the Advancement of Science
SP eaax5979
DO 10.1126/sciadv.aax5979
VO 6
IS 10
A1 Yildirim, Ilker
A1 Belledonne, Mario
A1 Freiwald, Winrich
A1 Tenenbaum, Josh
YR 2020
UL http://advances.sciencemag.org/content/6/10/eaax5979.abstract
AB 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.