Zhu, Xun, Watson, David M, Rogers, Daniel et al. (1 more author) (2025) View-symmetric representations of faces in human and artificial neural networks. Neuropsychologia. 109061. ISSN 0028-3932
Abstract
View symmetry has been suggested to be an important intermediate representation between view-specific and view-invariant representations of faces in the human brain. Here, we compared view-symmetry in humans and a deep convolutional neural network (DCNN) trained to recognise faces. First, we compared the output of the DCNN to head rotations in yaw (left-right), pitch (up-down) and roll (in-plane rotation). For yaw, an initial view-specific representation was evident in the convolutional layers, but a view-symmetric representation emerged in the fully-connected layers. Consistent with a role in the recognition of faces, we found that view-symmetric responses to yaw were greater for same identity compared to different identity faces. In contrast, we did not find a similar transition from view-specific to view-symmetric representations in the DCNN for either pitch or roll. These findings suggest that view-symmetry emerges when opposite rotations of the head lead to mirror images. Next, we compared the view-symmetric patterns of response to yaw in the DCNN with corresponding behavioural and neural responses in humans. We found that responses in the fully-connected layers of the DCNN correlated with judgements of perceptual similarity and with the responses of higher visual regions. These findings suggest that view-symmetric representations may be computationally efficient way to represent faces in humans and artificial neural networks for the recognition of identity.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2024 The Authors |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Psychology (York) |
Depositing User: | Pure (York) |
Date Deposited: | 13 Dec 2024 14:20 |
Last Modified: | 01 Jan 2025 04:00 |
Published Version: | https://doi.org/10.1016/j.neuropsychologia.2024.10... |
Status: | Published |
Refereed: | Yes |
Identification Number: | 10.1016/j.neuropsychologia.2024.109061 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:220819 |
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