Cited by 644Face Recognition and PerceptionFace recognition and analysisVisual perception and processing mechanisms

The Code for Facial Identity in the Primate Brain

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Chang, Le, Tsao, Doris Y.·2017·Cell

Abstract

Primates recognize complex objects such as faces with remarkable speed and reliability. Here, we reveal the brain's code for facial identity. Experiments in macaques demonstrate an extraordinarily simple transformation between faces and responses of cells in face patches. By formatting faces as points in a high-dimensional linear space, we discovered that each face cell's firing rate is proportional to the projection of an incoming face stimulus onto a single axis in this space, allowing a face cell ensemble to encode the location of any face in the space. Using this code, we could precisely decode faces from neural population responses and predict neural firing rates to faces. Furthermore, this code disavows the long-standing assumption that face cells encode specific facial identities, confirmed by engineering faces with drastically different appearance that elicited identical responses in single face cells. Our work suggests that other objects could be encoded by analogous metric coordinate systems. PAPERCLIP.

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  • Prior investigations have provided evidence for neurons that encode another animal's familiarity or dominance.

    “They have also provided evidence for neurons that encode another animals’ familiarity or dominance 12,13”
  • A brain-computational model of face recognition will have to explain the spatial clusters of face-selective units and the selectivities and invariances observed with fMRI and invasive recordings.

    “A brain-computational model of face recognition16 will have to explain the spatial clusters of face-selective units and the selectivities and invariances observed with fMRI17,18 and invasive recordings14,19.”

    From Cognitive computational neuroscience (2018) · cites this paper as [19]

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Research works printed but not linked: 2

  1. Lin H, and Tegmark M (2016). Why does deep and cheap learning work so well? arXiv.
  2. Moeller S, Freiwald WA, and Tsao DY (2008a). A direct link between face-selective regions in temporal and prefrontal cortex. Paper presented at: Society for Neuroscience Conference (Washington D.C.).

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Authors
Chang, L., & Tsao, D. Y.
Affiliations
California Institute of Technology (US)
Corresponding author
  • Chang, Le
  • Tsao, Doris Y.
Venue
Cell
Publisher
Elsevier BV
Published
2017-06
Volume
169
Issue
6
Pages
1013-1028.e14
ISSN
0092-8674 (ISSN-L)
OpenAlex
W2621150162
Cited by
644
References
35
Citations by year
2026: 45 · 2025: 61 · 2024: 51 · 2023: 75 · 2022: 73 · 2021: 76 · 2020: 115 · 2019: 53 · 2018: 74 · 2017: 21
FWCI
41.26
Type
journal article
Language
en
Open access
Locations
  • Cell (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
  • CaltechAUTHORS (California Institute of Technology) (submittedVersion)
Funders
  • NIH (1R01EY019702)
  • Howard Hughes Medical Institute
  • Tianqiao and Chrissy Chen Institute for Neuroscience at Caltech
  • Swartz Foundation (# 2013-36)
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Face Recognition and Perception
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
Keywords
face identity, face recognition (cognition), face patches, population coding, linear dimensionality reduction, macaque visual cortex, object recognition
MeSH terms
Facial Recognition, Animals, Humans, Macaca, Magnetic Resonance Imaging, Male, Models, Neurological, Neurons, Temporal Lobe

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