@article {4448,
	title = {Efficient inverse graphics in biological face processing},
	journal = {Science Advances},
	volume = {6},
	year = {2020},
	month = {03/2020},
	pages = {eaax5979},
	abstract = {<p>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 {\textquotedblleft}analysis-by-synthesis{\textquotedblright}, 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 {\textquotedblleft}hollow face{\textquotedblright} 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.</p>
},
	doi = {10.1126/sciadv.aax5979},
	url = {https://advances.sciencemag.org/lookup/doi/10.1126/sciadv.aax5979},
	author = {Ilker Yildirim and Mario Belledonne and W. A. Freiwald and Joshua B. Tenenbaum}
}
