A highly sensitive famous face recognition paradigm for prosopagnosia screening

Famous face recognition tasks have traditionally been used to diagnose prosopagnosia, offering striking examples of the inability to recognise highly familiar faces. Yet, their popularity has dwindled with the development of standardised unfamiliar face recognition tasks that are less cumbersome to administer and can readily be implemented online. Here, we argue that there is a danger of omitting measures of familiar face recognition from prosopagnosia screening: not only may this challenge the very definition of the condition, but, with some adjustments, famous face recognition tasks can continue to offer highly sensitive measures of everyday face recognition ability. Thus, we developed and evaluated an online, automated famous face recognition paradigm that can readily be implemented into large-scale screening programmes. This task improves on previous designs by (a) eliminating extrinsic cues to identity by including distractor as well as familiar faces, (b) supporting the use of unseen rather than "iconic" images of celebrities, and (c) offering a method for automated scoring. Multiple versions of the task were found to have high sensitivity in the detection of developmental prosopagnosia. When required, sub-scores collected from the same paradigm can be used to assess performance at different stages of recognition and identification, helping to probe more precise loci of impairment. The latter is important to guide the diagnosis of more complex cases and, potentially, their remediation.