In a number of high-profile criminal cases, offenders have used hyper-realistic face masks (Fig. 1) to transform their facial appearance, leading police to pursue suspects who looked nothing like the actual offenders (e.g. different race or age; Bernstein, 2010). In a separate incident, an airline passenger wearing a hyper-realistic mask boarded an international flight without the deception being noticed (Zamost, 2010). These cases suggest that, in practical settings, hyper-realistic face masks can be difficult to distinguish from real faces. Experimental evidence bears out this conclusion. In a series of studies (Sanders et al., 2017), we examined incidental detection of unexpected but attended hyper-realistic masks in both photographic and live presentations. In all of these studies, viewers accepted hyper-realistic masks as real faces. These findings extend a tradition of research into realism of artificial stimuli. The Uncanny Valley phenomenon originally considered a range of human-like stimuli from puppets to robots (Mori, 1970; Mori, MacDorman, & Kageki, 2012). In recent years, the focus has shifted somewhat to computer-generated images (e.g. Nightingale, Wade, & Watson, 2017), but the very success of computer graphics has raised awareness that on-screen images may be digitally generated or enhanced. One of the interesting aspects of hyper-realistic masks is that they also fool the eye in the physical world (Sanders et al., 2017), where digital image manipulation has not yet encroached.
The finding that spontaneous mask detection is unreliable suggests that specific measures may be required if detection rates are to be improved. Here we pursue an individual differences approach to the problem. Over the last decade, individual differences have become an important topic in face perception research, not least because they suggest a route to improving performance in applied settings. For face identification, the range of ability is bracketed by two extremes. At the high end, super-recognizers who rarely make errors (Bobak, Bennetts, Parris, Jansari, & Bate, 2016; Robertson, Noyes, Dowsett, Jenkins, & Burton, 2016; Russell, Duchaine, & Nakayama, 2009), and at the low end, people with developmental prosopagnosia who rarely exceed chance performance (Behrmann & Avidan, 2005; Duchaine & Nakayama, 2005). Between these extremes, there is a spectrum of ability on standardized face identification tests (e.g. Burton, White, & McNeill, 2010; Duchaine & Nakayama, 2006).
These findings have led some researchers to suggest that personnel selection could play a useful role in optimizing occupational face recognition (White, Kemp, Jenkins, Matheson, & Burton, 2014). For example, Metropolitan Police super-recognizers have been found to score unusually high on a range of face identification tests (Robertson et al., 2016).
For mask detection, the cognitive situation is somewhat different. Here the challenge is not individuation at the subordinate level (Rosch, Mervis, Gray, Johnson, & Boyes-Braem, 1976), but rather categorization at the basic level, albeit for the unusual case where one basic category (masks) deliberately mimics the other (faces). As the current task involves face/non-face categorization, it arguably has more in common with face detection than with face identification (see Bindemann & Lewis, 2013, for a careful dissection of these issues).
The analogy with face detection may have some broad predictive value for the present case. Large individual differences in face detection ability have recently been reported (Robertson, Jenkins, & Burton, 2017) and they appear to dissociate from face identification ability. However, one important difference is that face detection hinges on the presence or absence of a face-like pattern (e.g. two eyes above a nose above a mouth). That criterion will not help the viewer in the current task, as hyper-realistic face masks and real faces both present face-like patterns. Thus, the intuition is that hyper-realistic mask detection will require finer discrimination than face detection tasks demand.
As yet, very little is known about individual differences in this finer perceptual task. For example, we do not know the expected range of ability. Nor do we know any factors that might differentiate high performers from low performers. The present studies address these issues by asking whether some people are better than others at categorizing masks and faces, and what they may be doing that allows them to perform well. The overarching aim is to establish whether an individual differences approach might be as useful in hyper-realistic mask detection as it has been in face identification.
We begin in Experiment 1 by comparing detection of low-realism and high-realism masks in the context of real faces. In Experiment 2, we eliminated low-realism masks to focus participants on the harder comparison (high-realism masks vs real faces). Finally, we undertook an image analysis to compare use of information for high- and low-accuracy subgroups.