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Patterns of neural response in face regions are predicted by low-level image properties

Research output: Contribution to journalArticle



Publication details

DateAccepted/In press - 13 Mar 2018
DateE-pub ahead of print - 23 Mar 2018
DatePublished (current) - 1 Jun 2018
Number of pages12
Pages (from-to)199-210
Early online date23/03/18
Original languageEnglish


Models of face processing suggest that the neural response in different face regions is selective for higher-level attributes of the face, such as identity and expression. However, it remains unclear to what extent the response in these regions can also be explained by more basic organizing principles. Here, we used functional magnetic resonance imaging multivariate pattern analysis (fMRI-MVPA) to ask whether spatial patterns of response in the core face regions (occipital face area – OFA, fusiform face area – FFA, superior temporal sulcus – STS) can be predicted across different participants by lower level properties of the stimulus. First, we compared the neural response to face identity and viewpoint, by showing images of different identities from different viewpoints. The patterns of neural response in the core face regions were predicted by the viewpoint, but not the identity of the face. Next, we compared the neural response to viewpoint and expression, by showing images with different expressions from different viewpoints. Again, viewpoint, but not expression, predicted patterns of response in face regions. Finally, we show that the effect of viewpoint in both experiments could be explained by changes in low-level image properties. Our results suggest that a key determinant of the neural representation in these core face regions involves lower-level image properties rather than an explicit representation of higher-level attributes in the face. The advantage of a relatively image-based representation is that it can be used flexibly in the perception of faces.

Bibliographical note

© 2018 Elsevier Ltd. All rights reserved. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy.

    Research areas

  • Face, fMRI, Identity, MVPA, Viewpoint

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