Moaz Shoura¹ (), Dirk Walther¹, Adrian Nestor¹; ¹University of Toronto

The other-race effect (ORE) describes the advantage of recognizing faces of one’s own race better than other-race faces. While this effect has been extensively documented, its representational basis remains elusive. This study aims to bridge this gap by employing style-based generative adversarial networks (i.e., styleGAN2), a deep learning technique for generating photorealistic images (Karras et al., 2020), in conjunction with facial image reconstruction to investigate the characteristics and mechanisms underlying the ORE. Specifically, we explored how the ORE manifests in styleGAN2, by analyzing the similarity in face representations between GANs and adult participants. This involved assessing the pairwise visual similarity of GAN-generated face images by East Asian and Caucasian participants (N = 106). We then compared the structure of the human face space with that of the GAN latent face space and of other neural network face models (i.e., VGG16 and InsightFace). Our findings suggest that GANs offer insights into face recognition that are not captured by existing models. Furthermore, by leveraging the representational similarity between GANs and human participants, we were able to reconstruct perceptual face representations associated with viewing East Asian and Caucasian face stimuli. Last, we identified latent vector features associated with the ORE and we visualized systematic differences associated with the perception of other-race faces. In conclusion, this research provides a novel perspective on the ORE by integrating generative deep learning techniques in the behavioral study of face perception. The ability of GANs to complement other models of face space structure and perceptual bias underscores their potential as a tool in the study of face perception. Our findings not only contribute to the theoretical understanding of the ORE but also demonstrate the utility of GANs and image reconstruction in behavioral research.