Your browser doesn't support javascript.
loading
FFA and OFA Encode Distinct Types of Face Identity Information.
Tsantani, Maria; Kriegeskorte, Nikolaus; Storrs, Katherine; Williams, Adrian Lloyd; McGettigan, Carolyn; Garrido, Lúcia.
  • Tsantani M; Division of Psychology, Department of Life Sciences, Brunel University London, Uxbridge, UB8 3PH, United Kingdom maria.tsantani@gmail.com lucia.garrido@city.ac.uk.
  • Kriegeskorte N; Zuckerman Mind Brain Behavior Institute, Columbia University, New York, New York 10027.
  • Storrs K; Department of Experimental Psychology, Justus Liebig University, Giessen, 35390, Germany.
  • Williams AL; Division of Psychology, Department of Life Sciences, Brunel University London, Uxbridge, UB8 3PH, United Kingdom.
  • McGettigan C; Speech Hearing and Phonetic Sciences, University College London, London WC1N 1PF, United Kingdom.
  • Garrido L; Division of Psychology, Department of Life Sciences, Brunel University London, Uxbridge, UB8 3PH, United Kingdom maria.tsantani@gmail.com lucia.garrido@city.ac.uk.
J Neurosci ; 41(9): 1952-1969, 2021 03 03.
Article en En | MEDLINE | ID: mdl-33452225
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.SIGNIFICANCE STATEMENT Recent studies using fMRI have shown that several face-responsive brain regions can distinguish between different face identities. It is however unclear whether these different face-responsive regions distinguish between identities in similar or different ways. We used representational similarity analysis to investigate the computations within three brain regions in response to naturalistically varying videos of face identities. Our results revealed that two regions, the fusiform face area and the occipital face area, encode distinct identity information about faces. Although identity can be decoded from both regions, identity representations in fusiform face area primarily contained information about social traits, gender, and high-level visual features, whereas occipital face area primarily represented lower-level image features.
Asunto(s)
Palabras clave

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Encéfalo / Reconocimiento Facial / Modelos Neurológicos Límite: Female / Humans / Male Idioma: En Año: 2021 Tipo del documento: Article

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Encéfalo / Reconocimiento Facial / Modelos Neurológicos Límite: Female / Humans / Male Idioma: En Año: 2021 Tipo del documento: Article