UNSW Face Test: A screening tool for super-recognizers

Abstract

We present a new test–the UNSW Face Test (www.unswfacetest.com)–that has been specifically designed to screen for super-recognizers in large online cohorts and is available free for scientific use. Super-recognizers are people that demonstrate sustained performance in the very top percentiles in tests of face identification ability. Because they represent a small proportion of the population, screening large online cohorts is an important step in their initial recruitment, before confirmatory testing via standardized measures and more detailed cognitive testing. We provide normative data on the UNSW Face Test from 3 cohorts tested via the internet (combined n = 23,902) and 2 cohorts tested in our lab (combined n = 182). The UNSW Face Test: (i) captures both identification memory and perceptual matching, as confirmed by correlations with existing tests of these abilities; (ii) captures face-specific perceptual and memorial abilities, as confirmed by non-significant correlations with non-face object processing tasks; (iii) enables researchers to apply stricter selection criteria than other available tests, which boosts the average accuracy of the individuals selected in subsequent testing. Together, these properties make the test uniquely suited to screening for super-recognizers in large online cohorts.

The effect of face masks and sunglasses on identity and expression recognition with super-recognizers and typical observers

“…We had access to the GFMT scores and CFMT+ scores for both our practised control and super-recognizer group, and found significant correlations between performance on our tasks and the GFMT ( figure 4 ), and our tasks and the CFMT+ ( figure 5 ). This is consistent with the idea that performance on face tests tends to correlate with each other at the group level [ 89 – 91 ]. The scatterplots also clearly demonstrate spread in performance at the individual level for participants in each group.…”

Are super‐face‐recognisers also super‐voice‐recognisers? Evidence from cross‐modal identification tasks

“…Not surprisingly therefore, the proportion of participants achieving >1.5 criterion for bells was considerably lower than those achieving criterion for voices (Table 4). These voice-specific results suggest parallels with the facespecific effects shown in super-face-recognisers (e.g., Bobak, Bennetts, et al, 2016;Dunn et al, 2020), as many do not display superiority on other visual processing tasks. The lack of any between-groups bells effects also suggest that the voice-specific advantages found in the current research are unlikely to be associated with motivational factors.…”

“…These lie on a continuum ranging from developmental prosopagnosics (e.g., Knutson et al, 2011; McConachie, 1976), who display dysfunctional familiar face recognition; to super‐recognisers, who display outstanding unfamiliar face recognition (e.g., Bate et al, 2018; Bobak, Bennetts, et al, 2016; Bobak, Hancock, & Bate, 2016). These individual differences are heritable and face‐specific, in that only weak correlations are found with non‐face object recognition and other visual processing skills (e.g., Dunn et al, 2020; McCaffery et al, 2018; Royer et al, 2018; Verhallen et al, 2017; Wilhelm, Herzmann, et al, 2010; Wilmer, Germine, et al, 2010; Yovel et al, 2014).…”

GFMT2: A psychometric measure of face matching ability

“…Bobak et al 2016), and professional groups displaying high levels of accuracy in face recognition tasks (Phillips et al 2018). While researchers have produced challenging tests to address specific research goals (Fysh & Bindemann, 2017;White et al 2015;Dunn et al 2020), there is now a need for a standard lab-based test with known psychometric properties that enables comparison across high performers, typical performers and low performers. To make the GFMT2 more challenging than the original test, we select image pairs that require participants to match identity across variations in head angle, pose, expression and subject-to-camera distance.…”