%0 Journal Article %J Science Advances %D 2020 %T Efficient inverse graphics in biological face processing %A Ilker Yildirim %A Mario Belledonne %A W. A. Freiwald %A Joshua B. Tenenbaum %X

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.

%B Science Advances %V 6 %P eaax5979 %8 03/2020 %G eng %U https://advances.sciencemag.org/lookup/doi/10.1126/sciadv.aax5979 %N 10 %! Sci. Adv. %R 10.1126/sciadv.aax5979