Evidence map›Paper›PMID 33452225›Full record

ArticleThe Journal of neuroscience : the official journal of the Society for Neuroscience2021

FFA and OFA Encode Distinct Types of Face Identity Information.

Maria Tsantani, Nikolaus Kriegeskorte, Katherine Storrs, Adrian Lloyd Williams, Carolyn McGettigan, Lúcia Garrido

Open access · bronzeAbstract read
In one paragraph

Article in The Journal of neuroscience : the official journal of the Society for Neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
33citing papers in PubMed, 1 pooled it
7.1field-weighted citation impact, top 2% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

33 citing papers in PubMed, 1 synthesis or guideline pooled it, 83 citations in OpenAlex.

  1. Pooled it
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  3. Article
  4. The state of modelling face processing in humans with deep learning.British journal of psychology (London, England : 1953) · 2026
    Review
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  8. Conceptual Knowledge Shapes the Neural Representations of Learned Faces in Non-Visual Regions of the Brain.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025
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  20. Modeling naturalistic face processing in humans with deep convolutional neural networks.Proceedings of the National Academy of Sciences of the United States of America · 2023
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 4 institutions in 3 countries.

Maria TsantaniDivision of Psychology, Department of Life Sciences, Brunel University London, Uxbridge, UB8 3PH, United Kingdom maria.tsantani@gmail.com lucia.garrido@city.ac.uk.
Nikolaus KriegeskorteZuckerman Mind Brain Behavior Institute, Columbia University, New York, New York 10027.
Katherine StorrsDepartment of Experimental Psychology, Justus Liebig University, Giessen, 35390, Germany.
Adrian Lloyd WilliamsDivision of Psychology, Department of Life Sciences, Brunel University London, Uxbridge, UB8 3PH, United Kingdom.
Carolyn McGettiganSpeech Hearing and Phonetic Sciences, University College London, London WC1N 1PF, United Kingdom.
Lúcia GarridoDivision of Psychology, Department of Life Sciences, Brunel University London, Uxbridge, UB8 3PH, United Kingdom maria.tsantani@gmail.com lucia.garrido@city.ac.uk.
Brunel University of London · GBJustus-Liebig-Universität Gießen · DEMIND Research Institute · USUniversity College London · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Faces of different people elicit distinct fMRI patterns in several face-selective regions of the human brain. Here we used representational similarity analysis to investigate what type of identity-distinguishing information is encoded in three face-selective regions: fusiform face area (FFA), occipital face area (OFA), and posterior superior temporal sulcus (pSTS). In a sample of 30 human participants (22 females, 8 males), we used fMRI to measure brain activity patterns elicited by naturalistic videos of famous face identities, and compared their representational distances in each region with models of the differences between identities. We built diverse candidate models, ranging from low-level image-computable properties (pixel-wise, GIST, and Gabor-Jet dissimilarities), through higher-level image-computable descriptions (OpenFace deep neural network, trained to cluster faces by identity), to complex human-rated properties (perceived similarity, social traits, and gender). We found marked differences in the information represented by the FFA and OFA. Dissimilarities between face identities in FFA were accounted for by differences in perceived similarity, Social Traits, Gender, and by the OpenFace network. In contrast, representational distances in OFA were mainly driven by differences in low-level image-based properties (pixel-wise and Gabor-Jet dissimilarities). Our results suggest that, although FFA and OFA can both discriminate between identities, the FFA representation is further removed from the image, encoding higher-level perceptual and social face information.

Indexed as

Models, NeurologicalBrainBrain MappingFacial RecognitionFemaleHumansImage Processing, Computer-AssistedMagnetic Resonance ImagingMaleface identityface processingFFAOFArepresentational similarity analysis

Identifiers

PMID33452225
PMCPMC7939092
OpenAlexW3025336292

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.