Symmetrical Viewpoint Representations in Face-Selective Regions Convey an Advantage in the Perception and Recognition of Faces

Abstract

Learning new identities is crucial for effective social interaction. A critical aspect of this process is the integration of different images from the same face into a view-invariant representation that can be used for recognition. The representation of symmetrical viewpoints has been proposed to be a key computational step in achieving view-invariance. The aim of this study was to determine whether the representation of symmetrical viewpoints in face-selective regions is directly linked to the perception and recognition of face identity. In Experiment 1, we measured fMRI responses while male and female human participants viewed images of real faces from different viewpoints (Ϫ90, Ϫ45, 0, 45, and 90°from full-face view). Within the face regions, patterns of neural response to symmetrical views (Ϫ45 and 45°or Ϫ90 and 90°) were more similar than responses to nonsymmetrical views in the fusiform face area and superior temporal sulcus, but not in the occipital face area. In Experiment 2, participants made perceptual similarity judgements to pairs of face images. Images with symmetrical viewpoints were reported as being more similar than nonsymmetric views. In Experiment 3, we asked whether symmetrical views also convey an advantage when learning new faces. We found that recognition was best when participants were tested with novel face images that were symmetrical to the learning viewpoint. Critically, the pattern of perceptual similarity and recognition across different viewpoints predicted the pattern of neural response in face-selective regions. Together, our results provide support for the functional value of symmetry as an intermediate step in generating view-invariant representations.

The impact of stability in appearance on the development of facial representations

“…a difference in background colour or a photographic artefact). This would then yield low cost representations with low generalisability and to poor performance in a subsequent memory test that uses images where the appearance, the view or pictorial artefacts have changed and/or where distractors display gross resemblances with learned faces (for a recent example with viewpoint, see Flack et al, 2019). The same reasoning can also help explain poor performance with new faces briefly encountered in the real world, for example, when one is witnessing a crime.…”

A unifying model for discordant and concordant results in human neuroimaging studies of facial viewpoint selectivity

“…Images provided as input to the model were obtained from two popular databases: Karolinska Directed Emotional Faces (KDEF) (Lundqvist et al, 1998) and Radboud Faces Database (RaFD) (Langner et al, 2010). These databases have been used by previous studies investigating viewpoint-invariant face recognition, and RaFD specifically used in studies combining fMRI and MVPA (Weibert et al, 2018; Flack et al, 2019). Both databases include multiple individuals photographed in the same five viewpoints: −90° (left-profile view), −45°, 0°, 45°, and 90° (right-profile view).…”

Face Perception

“…Haxby et al's model offers a synthesis of results from studies of neural responses using fMRI and older functional models based on behavioural and neuropsychological evidence (Bruce & Young, 1986). Most of this fMRI work used the subtractive method to identify faceresponsive brain regions (Kanwisher, 2017), but later fMRI research using adaptation to properties of face stimuli, or multi-voxel analysis of the patterns of activation, has continued to emphasise the importance of the same regions (Andrews & Ewbank, 2004;Flack et al, 2019;Kovacs, 2020), as have direct measurements from intracerebral recordings (Jonas et al, 2016). What remains less clear is exactly what each of these regions contributes to the processes involved in perceiving faces (Freiwald et al, 2016;Kovacs, 2020;Yovel, 2016).…”