Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

The neural code for ‘face cells’ is not face specific

View ORCID ProfileKasper Vinken, View ORCID ProfileTalia Konkle, View ORCID ProfileMargaret Livingstone
doi: https://doi.org/10.1101/2022.03.06.483186
Kasper Vinken
1Department of Neurobiology, Harvard Medical School, Boston, MA 02115
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Kasper Vinken
  • For correspondence: kasper_vinken@hms.harvard.edu
Talia Konkle
2Department of Psychology, Harvard University, Cambridge, MA 02478
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Talia Konkle
Margaret Livingstone
1Department of Neurobiology, Harvard Medical School, Boston, MA 02115
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Margaret Livingstone
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Preview PDF
Loading

1 Abstract

‘Face cells’ are visual neurons that selectively respond more to faces than other objects. Clustered together in inferotemporal cortex, they are thought to form a network of modules specialized in face processing by encoding face-specific features. Here we reveal that their category selectivity is instead captured by domain-general attributes. Analyzing neural responses in and around macaque face patches to hundreds of objects, we discovered graded tuning for non-face objects that was more predictive of face preference than was tuning for faces themselves. The relationship between category-level face selectivity and image-level non-face tuning was not predicted by color and simple shape properties, but by domain-general information encoded in deep neural networks trained on object classification. These findings contradict the long-standing assumption that face cells owe their category selectivity to face-specific features, challenging the prevailing idea that visual processing is carried out by discrete modules, each specialized in a semantically distinct domain.

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. It is made available under a CC-BY-NC 4.0 International license.
Back to top
PreviousNext
Posted March 07, 2022.
Download PDF
Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
The neural code for ‘face cells’ is not face specific
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
The neural code for ‘face cells’ is not face specific
Kasper Vinken, Talia Konkle, Margaret Livingstone
bioRxiv 2022.03.06.483186; doi: https://doi.org/10.1101/2022.03.06.483186
Reddit logo Twitter logo Facebook logo LinkedIn logo Mendeley logo
Citation Tools
The neural code for ‘face cells’ is not face specific
Kasper Vinken, Talia Konkle, Margaret Livingstone
bioRxiv 2022.03.06.483186; doi: https://doi.org/10.1101/2022.03.06.483186

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Neuroscience
Subject Areas
All Articles
  • Animal Behavior and Cognition (4654)
  • Biochemistry (10298)
  • Bioengineering (7614)
  • Bioinformatics (26189)
  • Biophysics (13445)
  • Cancer Biology (10620)
  • Cell Biology (15333)
  • Clinical Trials (138)
  • Developmental Biology (8452)
  • Ecology (12754)
  • Epidemiology (2067)
  • Evolutionary Biology (16761)
  • Genetics (11356)
  • Genomics (15399)
  • Immunology (10548)
  • Microbiology (25040)
  • Molecular Biology (10151)
  • Neuroscience (54090)
  • Paleontology (398)
  • Pathology (1655)
  • Pharmacology and Toxicology (2877)
  • Physiology (4314)
  • Plant Biology (9196)
  • Scientific Communication and Education (1579)
  • Synthetic Biology (2541)
  • Systems Biology (6752)
  • Zoology (1452)