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Testing the effect of depth on the perception of faces in an online study

MPS-Authors
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Hofmann,  Simon       
Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Koushik,  Abhay       
Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;
Max Planck School of Cognition;

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Klotzsche,  Felix       
Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Nikulin,  Vadim V.       
Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Villringer,  Arno       
Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Gaebler,  Michael       
Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Citation

Hofmann, S., Koushik, A., Klotzsche, F., Nikulin, V. V., Villringer, A., & Gaebler, M. (2022). Testing the effect of depth on the perception of faces in an online study. In Proceedings of the 2022 Conference on Cognitive Computational Neuroscience.


Cite as: https://hdl.handle.net/21.11116/0000-000B-06D3-0
Abstract
Faces are socially relevant stimuli that can be distinguished by the spatial arrangements of their visual features. However, face perception has been mostly investigated with static 2D images, which differs from everyday life experience. In an online study, we investigate face perception in two viewing conditions (2D & 3D). We compare the cognitive face space for these conditions, by modeling the acquired human similarity ratings with similarity matrices computed from physical face attributes and feature maps of deep learning-based face recognition models. Lastly, we fit these models to the human similarity judgements to explore relevant facial features between the viewing conditions. Unveiling differences between 2D and 3D perception of faces will further our understanding on the role of stimulus presentation on face processing.