The Face of Image Reconstruction: Progress, Pitfalls, Prospects. Issue 9 (September 2020) - Nestor, Adrian; Lee, Andy C.H.; Plaut, David C.; Behrmann, Marlene
Abstract : Recent research has demonstrated that neural and behavioral data acquired in response to viewing face images can be used to reconstruct the images themselves. However, the theoretical implications, promises, and challenges of this direction of research remain unclear. We evaluate the potential of this research for elucidating the visual representations underlying face recognition. Specifically, we outline complementary and converging accounts of the visual content, the representational structure, and the neural dynamics of face processing. We illustrate how this research addresses fundamental questions in the study of normal and impaired face recognition, and how image reconstruction provides a powerful framework for uncovering face representations, for unifying multiple types of empirical data, and for facilitating both theoretical and methodological progress. Highlights: The visual content of human face representations can be derived through image reconstruction at an unprecedented level of detail. The neural underpinnings of psychological face representations can be elucidated via image reconstruction from behavioral and multiple types of neural data. Facial image reconstruction capitalizes on the structure of face space and also serves to validate its representational properties. The recovery of face representations from both perception and memory can help to clarify the relationship between these two components of cognition. Image reconstruction can pinpoint the loss of information and/or misrepresentations in face perception across a broad population, including individuals with deficits in face processing.