Explaining face representation in the primate brain using different computational models

Chang L, Egger B, Vetter T, Tsao DY (2021)

Publication Type: Journal article

Publication year: 2021

Journal

Current Biology Elsevier (Cell Press)

Book Volume: 31

Pages Range: 2785-2795.e4

Journal Issue: 13

DOI: 10.1016/j.cub.2021.04.014

Abstract

Understanding how the brain represents the identity of complex objects is a central challenge of visual neuroscience. The principles governing object processing have been extensively studied in the macaque face patch system, a sub-network of inferotemporal (IT) cortex specialized for face processing. A previous study reported that single face patch neurons encode axes of a generative model called the “active appearance” model, which transforms 50D feature vectors separately representing facial shape and facial texture into facial images. However, a systematic investigation comparing this model to other computational models, especially convolutional neural network models that have shown success in explaining neural responses in the ventral visual stream, has been lacking. Here, we recorded responses of cells in the most anterior face patch anterior medial (AM) to a large set of real face images and compared a large number of models for explaining neural responses. We found that the active appearance model better explained responses than any other model except CORnet-Z, a feedforward deep neural network trained on general object classification to classify non-face images, whose performance it tied on some face image sets and exceeded on others. Surprisingly, deep neural networks trained specifically on facial identification did not explain neural responses well. A major reason is that units in the network, unlike neurons, are less modulated by face-related factors unrelated to facial identification, such as illumination.

Authors with CRIS profile

Bernhard Egger

Involved external institutions

California Institute of Technology (Caltech) US United States (USA) (US) Universität Basel CH Switzerland (CH)

How to cite

APA:

Chang, L., Egger, B., Vetter, T., & Tsao, D.Y. (2021). Explaining face representation in the primate brain using different computational models. Current Biology, 31(13), 2785-2795.e4. https://dx.doi.org/10.1016/j.cub.2021.04.014

MLA:

Chang, Le, et al. "Explaining face representation in the primate brain using different computational models." Current Biology 31.13 (2021): 2785-2795.e4.

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