The defining characteristic of development prosopagnosia is severe difficulty recognising familiar faces in everyday life. Numerous studies have reported that the condition is highly heterogeneous in terms of both presentation and severity with many mixed findings in the literature. I will present behavioural data from a large face processing test battery (n = 24 DPs) as well as some early findings from a larger survey of the lived experience of individuals with DP and discuss how insights from individuals' real-world experience can help to understand and interpret lab-based data.

Developmental prosopagnosia is characterised by severe, lifelong difficulties when recognising facial identity. Most researchers require prosopagnosia cases exhibit ultra-conservative levels of impairment on the Cambridge Face Memory Test before they include them in their experiments. This results in the majority of people who believe that they have this condition being excluded from the scientific literature. In this talk I outline the many issues that will afflict prosopagnosia research if this continues, and show that these excluded cases do exhibit impairments on all commonly used diagnostic tests when a group-based method of assessment is utilised. I propose a paradigm shift away from cognitive task-based approaches to diagnosing prosopagnosia, and outline a new way that researchers can investigate this condition.

The nature of facial information that is stored by humans to recognise large amounts of faces is unclear despite decades of research in the field. To complicate matters further, little is known about how representations may evolve as novel faces become familiar, and there are large individual differences in the ability to recognise faces. I will present a theory I am developing and that assumes that facial representations are cost-efficient. In that framework, individual facial representations would incorporate different diagnostic features in different faces, regardless of familiarity, and would evolve depending on the relative stability in appearance over time. Further, coarse information would be prioritised over fine details in order to decrease storage demands. This would create low-cost facial representations that refine over time if appearance changes. Individual differences could partly rest on that ability to refine representation if needed. I will present data collected in the general population and in participants with developmental prosopagnosia. In support of the proposed view, typical observers and those with developmental prosopagnosia seem to rely on coarse peripheral features when they have no reason to expect someone’s appearance will change in the future.

Not all individuals are equally competent at recognizing the faces they interact with. Revealing how the brains of different individuals support variations in this ability is a crucial step to develop an understanding of real-world human visual behaviour. In this talk, I will present findings from a large high-density EEG dataset (>100k trials of participants processing various stimulus categories) and computational approaches which aimed to characterise the brain representations behind real-world proficiency of “super-recognizers”—individuals at the top of face recognition ability spectrum. Using decoding analysis of time-resolved EEG patterns, we predicted with high precision the trial-by-trial activity of super-recognizers participants, and showed that evidence for face recognition ability variations is disseminated along early, intermediate and late brain processing steps. Computational modeling of the underlying brain activity uncovered two representational signatures supporting higher face recognition ability—i) mid-level visual & ii) semantic computations. Both components were dissociable in brain processing-time (the first around the N170, the last around the P600) and levels of computations (the first emerging from mid-level layers of visual Convolutional Neural Networks, the last from a semantic model characterising sentence descriptions of images). I will conclude by presenting ongoing analyses from a well-known case of acquired prosopagnosia (PS) using similar computational modeling of high-density EEG activity.

Over the past decade, the domain of face identity processing has seen a surging interest in inter-individual differences, with a focus on individuals with superior skills, so-called Super-Recognizers (SRs; Ramon et al., 2019; Russell et al., 2009). Their study can provide valuable insights into brain-behavior relationships and advance our understanding of neural functioning. Despite a decade of research, and similarly to the field of developmental prosopagnosia, a consensus on diagnostic criteria for SR identification is lacking. Consequently, SRs are currently identified either inconsistently, via suboptimal individual tests, or via undocumented collections of tests. This state of the field has two major implications. Firstly, our scientific understanding of SRs will remain at best limited. Secondly, the needs of government agencies interested in deploying SRs for real-life identity verification (e.g., policing) are unlikely to be met. To counteract these issues, I suggest the following action points. Firstly, based on our and others’ work suggesting novel and challenging tests of face cognition (Bobak et al., 2019; Fysh et al., in press; Stacchi et al., 2019), and my collaborations with international security agencies, I recommend novel diagnostic criteria for SR identification. These are currently being used to screen the Berlin State Police’s >25K employees before identifying SRs via bespoke testing procedures we have collaboratively developed over the past years. Secondly, I introduce a cohort of SRs identified using these criteria, which is being studied in-depth using behavioral methods, psychophysics, eye-tracking, and neuroimaging. Finally, I suggest data acquired for these individuals should be curated to develop and share best practices with researchers and practitioners, and to gain an accurate and transparent description of SR cases to exploit their informative value.