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What computational model provides the best explanation of face representations in the primate brain?

Le Chang, Bernhard Egger, Thomas Vetter, Doris Y. Tsao
doi: https://doi.org/10.1101/2020.06.07.111930
Le Chang
1Division of Biology and Biological Engineering, Computation and Neural Systems, Caltech, Pasadena, CA, 91125, USA
2Institute of Neuroscience, Key Laboratory of Primate Neurobiology, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China
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  • For correspondence: lechang@ion.ac.cn dortsao@caltech.edu
Bernhard Egger
4Department of Mathematics and Computer Science, University of Basel, Basel, Switzerland
5Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
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Thomas Vetter
4Department of Mathematics and Computer Science, University of Basel, Basel, Switzerland
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Doris Y. Tsao
1Division of Biology and Biological Engineering, Computation and Neural Systems, Caltech, Pasadena, CA, 91125, USA
3Howard Hughes Medical Institute, Pasadena, CA, 91125, USA
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  • For correspondence: lechang@ion.ac.cn dortsao@caltech.edu
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Summary

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 (Tsao et al., 2006). A previous study reported that single face patch neurons encode axes of a generative model called the “active appearance” model (Chang and Tsao, 2017), which transforms 50-d feature vectors separately representing facial shape and facial texture into facial images (Cootes et al., 2001; Edwards et al., 1998). However, it remains unclear whether this model constitutes the best model for explaining face cell responses. Here, we recorded responses of cells in the most anterior face patch 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.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted June 08, 2020.
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What computational model provides the best explanation of face representations in the primate brain?
Le Chang, Bernhard Egger, Thomas Vetter, Doris Y. Tsao
bioRxiv 2020.06.07.111930; doi: https://doi.org/10.1101/2020.06.07.111930
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What computational model provides the best explanation of face representations in the primate brain?
Le Chang, Bernhard Egger, Thomas Vetter, Doris Y. Tsao
bioRxiv 2020.06.07.111930; doi: https://doi.org/10.1101/2020.06.07.111930

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