PT  - JOURNAL ARTICLE
AU  - Yildirim, Ilker
AU  - Belledonne, Mario
AU  - Freiwald, Winrich
AU  - Tenenbaum, Josh
TI  - Efficient inverse graphics in biological face processing
AID  - 10.1126/sciadv.aax5979
DP  - 2020 Mar 01
TA  - Science Advances
PG  - eaax5979
VI  - 6
IP  - 10
4099  - http://advances.sciencemag.org/content/6/10/eaax5979.short
4100  - http://advances.sciencemag.org/content/6/10/eaax5979.full
SO  - Sci Adv2020 Mar 01; 6
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.