Normative data for two challenging tests of face matching under ecological conditions

Abstract

Background: Unfamiliar face processing is an ability that varies considerably between individuals. Numerous studies have aimed to identify its underlying determinants using controlled experimental procedures. While such tests can isolate variables that influence face processing, they usually involve somewhat unrealistic situations and optimized face images as stimulus material. As a consequence, the extent to which the performance observed under laboratory settings is informative for predicting real-life proficiency remains unclear. Results: We present normative data for two ecologically valid but underused tests of face matching: the Yearbook Test (YBT) and the Facial Identity Card Sorting Test (FICST). The YBT (n = 252) measures identity matching across substantial age-related changes in facial appearance, while the FICST (n = 218) assesses the ability to process unfamiliar facial identity despite superficial image variations. To determine the predictive value of both tests, a subsample of our cohort (n = 181) also completed a commonly used test of face recognition and two tests of face perception (the long form of the Cambridge Face Memory Test (CFMT+), the Expertise in Facial Comparison Test (EFCT) and the Person Identification Challenge Test (PICT)). Conclusions: Focusing on the top performers identified independently per test, we made two important observations: 1) YBT and FICST performance can predict CFMT+ scores and vice versa; and 2) EFCT and PICT scores neither reliably predict superior performance in ecologically meaningful and challenging tests of face matching, nor in the most commonly used test of face recognition. These findings emphasize the necessity for using challenging and ecologically relevant, and thus highly sensitive, tasks of unfamiliar face processing to identify high-performing individuals in the normal population.

Understanding human individuation of unfamiliar faces with oddball fast periodic visual stimulation and electroencephalography

“…In the same vein, behavioral studies point to a large degree of inter‐individual variability in the unfamiliar FI ability of neurotypical human adults (e.g., Bindemann et al., 2012; Bowles et al., 2009; McCaffery et al., 2018; Stacchi et al., 2020; Wilmer et al., 2010), but the source of this variability remains unknown and may essentially reflect general task factors, as noted above. Hence, finding a significantly higher correlation of scores between monozygotic twins than dizygotic twins at the CFMT (Wilmer et al., 2010) should not be taken as evidence for a high genetic contribution specific to FI ability.…”

“…However, as discussed earlier in this review (Section 3.2), explicit behavioral performance at a (single) test such as the CFMT or the BFRT depends on many general processes beyond FI. Hence, behavioral tests of FI in the normal adult population are also only weakly correlated with one another (e.g., r = .45 between the CFMT and GFMT in McCaffery et al., 2018; see also Stacchi et al., 2020 for weak to no correlations between several behavioral face recognition tests). Importantly, these weak correlations do not necessarily mean that the two measures (behavior/EEG, or behavior/behavior) reflect different FI processes (e.g., “face perception” vs. “face memory”).…”

“…As for the BFRT, it has long been known that brain‐damaged patients with substantial impairment for language comprehension show a notably high frequency of defect at this test (Hamsher, Levin, & Benton, 1979). As a consequence of this uncertain validity, it is unclear whether the large inter‐individual variability observed with these tests (e.g., in the CFMT: Bowles et al., 2009; Palermo et al., 2017; CFMT and GFMT: McCaffery, Robertson, Young, & Burton, 2018; BFRT: Rossion & Michel, 2018) or with other explicit behavioral measures of FI (e.g., Bindemann, Avetisyan, & Rakow, 2012; Estudillo & Bindemann, 2014; Stacchi, Huguenin‐Elie, Caldara, & Ramon, 2020; Wilhelm et al, 2010) in the normal population reflects genuine variability at the FI function or variability of these general factors. As performance is only modestly correlated across different behavioral tests in the normal adult population (e.g., McCaffery et al., 2018; Stacchi et al., 2020; Wilhelm et al, 2010), the weight of general factors involved in these various tasks could be predominant.…”

“…For all these reasons, despite their usefulness in fundamental research and clinical evaluation, behavioral tests relying on explicit instructions and observer responses are limited at adequately capturing the FI function. This makes it particularly challenging to compare FI ability between individuals (see Stacchi et al., 2020), as well as to assess this ability across development and in clinical populations. For instance, these tests are often not suitable to capture the development of FI in children: Instead of being used in their original format, they have to be adapted to test children from 5 years of age (e.g., the “CFMT‐C,” Croydon, Pimperton, Ewing, Duchaine, & Pellicano, 2014), yet again, general developmental factors and stimulus limitations could still heavily influence performance (Croydon et al., 2014).…”

“…The last two decades have seen an enormous amount of research devoted to study inter‐individual differences in facial identity recognition, this function being most often probed by explicit behavioral measures of individuation of unfamiliar faces (with various task requirements; Stacchi et al., 2020; Wilmer, 2017). As noted above in the present review, the sources of this variation remain, however, unknown, and may have little to do with specific visual processes involved in FI.…”