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Face dissimilarity judgements are predicted by representational distance in morphable and image-computable models

View ORCID ProfileKamila M. Jozwik, Jonathan O’Keeffe, Katherine R. Storrs, Wenxuan Guo, Tal Golan, Nikolaus Kriegeskorte
doi: https://doi.org/10.1101/2021.04.09.438859
Kamila M. Jozwik
1Department of Psychology, University of Cambridge, Cambridge, UK
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  • ORCID record for Kamila M. Jozwik
  • For correspondence: jozwik.kamila@gmail.com
Jonathan O’Keeffe
2Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
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Katherine R. Storrs
3Department of Experimental Psychology, Justus Liebig University, Giessen, Germany
4Centre for Mind, Brain and Behaviour (CMBB), University of Marburg and Justus Liebig University Giessen, Germany
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Wenxuan Guo
5Zuckerman Mind Brain Behavior Institute, Columbia University, New York, USA
6Department of Psychology, Columbia University, New York, USA
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Tal Golan
5Zuckerman Mind Brain Behavior Institute, Columbia University, New York, USA
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Nikolaus Kriegeskorte
5Zuckerman Mind Brain Behavior Institute, Columbia University, New York, USA
6Department of Psychology, Columbia University, New York, USA
7Department of Neuroscience, Columbia University, New York, USA
8Department of Electrical Engineering, Columbia University, New York, USA
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Abstract

Human vision is attuned to the subtle differences between individual faces. Yet we lack a quantitative way of predicting how similar two face images look, or whether they appear to show the same person. Principal-components-based 3D morphable models are widely used to generate stimuli in face perception research. These models capture the distribution of real human faces in terms of dimensions of physical shape and texture. How well does a “face space” defined to model the distribution of faces as an isotropic Gaussian explain human face perception? We designed a behavioural task to collect dissimilarity and same/different identity judgements for 232 pairs of realistic faces. The stimuli densely sampled geometric relationships in a face space derived from principal components of 3D shape and texture (Basel Face Model, BFM). We then compared a wide range of models in their ability to predict the data, including the BFM from which faces were generated, a 2D morphable model derived from face photographs, and image-computable models of visual perception. Euclidean distance in the BFM explained both similarity and identity judgements surprisingly well. In a comparison against 14 alternative models, we found that BFM distance was competitive with representational distances in state-of-the-art image-computable deep neural networks (DNNs), including a novel DNN trained on BFM identities. Models describing the distribution of facial features across individuals are not only useful tools for stimulus generation. They also capture important information about how faces are perceived, suggesting that human face representations are tuned to the statistical distribution of faces.

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-ND 4.0 International license.
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Posted December 13, 2021.
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Face dissimilarity judgements are predicted by representational distance in morphable and image-computable models
Kamila M. Jozwik, Jonathan O’Keeffe, Katherine R. Storrs, Wenxuan Guo, Tal Golan, Nikolaus Kriegeskorte
bioRxiv 2021.04.09.438859; doi: https://doi.org/10.1101/2021.04.09.438859
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Face dissimilarity judgements are predicted by representational distance in morphable and image-computable models
Kamila M. Jozwik, Jonathan O’Keeffe, Katherine R. Storrs, Wenxuan Guo, Tal Golan, Nikolaus Kriegeskorte
bioRxiv 2021.04.09.438859; doi: https://doi.org/10.1101/2021.04.09.438859

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