THE IMPORTANCE OF IMAGE PROPERTIES IN THE NEURAL REPRESENTATION OF FAMILIAR FACES

Abstract

A full understanding of face recognition must involve identifying the visual information that is used to discriminate different identities and how this is represented in the brain. Previous behavioural studies have shown that the recognition of familiar faces is primarily based on differences in surface properties of the image. Our aim was to explore the importance of surface properties and also shape properties in the neural representation of familiar faces. We took a set of face images and measured variance in the shape and surface properties using principal component analysis (PCA). Face-selective regions (FFA, OFA and STS) were defined in an independent localizer scan. We then showed participants a subset of the face images and measured the resulting patterns of neural response using fMRI. Patterns of response to pairs of images were compared to generate a similarity matrix across all faces in each ROI. Corresponding similarity matrices for shape and surface properties were then created by correlating the PCA vectors across pairs of images. The similarity matrices for shape and surface properties were then used to predict the patterns of neural response in each ROI. Patterns of response in the OFA could be predicted by both the shape and surface properties of the face images. However, patterns of response in the FFA and STS could only be predicted by the shape of the face image. The dissociation between the selectivity for shape in the FFA and previous behavioural findings revealing a preeminent role of surface properties in face recognition suggests that, although the FFA may play a role in the recognition of facial identity, this region is not solely responsible for this process.

Meeting abstract presented at VSS 2017