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.