Stacchi et al. Cognitive Research: Principles and Implications
https://doi.org/10.1186/s41235-019-0205-0(2020) 5:8

ORIGINAL ARTICLE

Cognitive Research: Principles
and Implications

Open Access

Normative data for two challenging tests offace matching under ecological conditions
Lisa Stacchi1, Eva Huguenin-Elie2, Roberto Caldara1 and Meike Ramon2*

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 suchtests can isolate variables that influence face processing, they usually involve somewhat unrealistic situations andoptimized 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 acrosssubstantial age-related changes in facial appearance, while the FICST (n = 218) assesses the ability to processunfamiliar 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 faceperception (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 scoresneither 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.
Keywords: Superior face processing ability, Face perception, Face matching, Face discrimination, Face recognition,
Natural image variations

Significance Statement
How do humans process unfamiliar faces, and how can
we reliably identify individuals that are most proficientat it? Motivated by its relevance in applied contexts, recent empirical work has sought to answer these questions. Controlled laboratory tests have been developed tounderstand the contribution of different variables anddifferences between observers. However, face processing
tests involving face stimuli derived from ideal and highly
controlled manipulations may not accurately represent
real life. This crucial consideration is often overlooked
when laboratory tests are used to predict real-life* Correspondence: meike.ramon@gmail.com
2
Applied Face Cognition Lab, Department of Psychology, University of
Fribourg, Fribourg, Switzerland
Full list of author information is available at the end of the article

proficiency. The present study followed the rationalethat traditionally used controlled tests should be pairedwith more realistic and ecologically meaningful ones, interms of the images used and tasks performed. Testinglarge and heterogeneous samples, we standardized twoless frequently used tests of facial identity matching: theYearbook Test (YBT) and the Facial Identity Card Sorting Test (FICST). These procedurally simple tests mimic
real-life challenges in face perception as they assess unfamiliar facial identity matching across superficial imagechanges, or substantial age-related changes in facial appearance. Beyond providing normative data, we describe
how performance measured by these tests relates to thatobserved on more commonly used tests of face recognition (the long form of the Cambridge Face Memory Test
(CFMT+)) and perception (the Expertise in Facial© The Author(s). 2020 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, andreproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made.

Stacchi et al. Cognitive Research: Principles and Implications(2020) 5:8

Comparison Test (EFCT) and the Person Identification Challenge test (PICT)). Our findings suggest that:
1) the YBT and FICST are easy to use and are preferable alternatives to pairwise face-matching tasks
which are more prone to speed–accuracy trade-offs
and/or ceiling effects; and 2) challenging and ecologically meaningful tests should complement highly
controlled measures when aiming to identify individuals with superior face processing abilities for real-life
purposes.

BackgroundIn our everyday lives, we effortlessly process constantly
changing facial information. The commonly accepted notion that it is performed in a highly proficient manner underlies a vast body of research and, for the most part, it
coincides with our own personal experiences. However, an
increasing number of studies is challenging the idea thatface processing is generally highly proficient. Extremely
high performance levels are observed in particular for theprocessing of faces of individuals encountered repeatedly ineveryday life—those that are personally familiar to us (for areview see Ramon & Gobbini, 2018). For unfamiliar faces,on the other hand, our ability to process facial identity iscomparably more prone to error (e.g., Bruce, Henderson,
Newman, & Burton, 2001; Burton, Wilson, Cowan, &Bruce, 1999; Hancock, Bruce, & Burton, 2000; Jenkins &Burton, 2011; Jenkins, White, Van Montfort, & Burton,
2011; Megreya & Burton, 2006; Ramon & Gobbini, 2018;
Ramon & Van Belle, 2016; Young & Burton, 2017) and varies considerably between individuals (e.g., Bate & Dudfield,
2019; Bate, Portch, Mestry, & Bennetts, 2019; Bruce, Bindemann, & Lander, 2018; Fysh, 2018; White, Kemp, Jenkins,
Matheson, & Burton, 2014).
Decades of research has focused on studying normal
face processing skills, as well as individuals exhibiting
deficient face processing abilities (i.e., developmental
prosopagnosia; for a recent review see Geskin &Behrmann, 2018). However, more recently increasing
interest has been directed toward individuals with remarkable face processing abilities—so-called superrecognizers (SRs; e.g., Ramon, Bobak, & White, 2019a,
2019b). Understanding superior face processing skills
is important from both a fundamental scientific aswell as an applied perspective. Theoretically, this work
has led to face processing being considered as a spectrum
rather than supporting a dichotomous distinction between
normal and dysfunctional abilities (Russell, Duchaine, &Nakayama, 2009). From a practical perspective, investigation of the abilities of SRs can provide valuable information for the optimization of automatic face processing or
deployment of personnel in security-critical settings(Ramon et al., 2019a, 2019b), such as criminal investigation (Ramon, 2018a).

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However, a fundamental challenge continues to exist:
How do we reliably identify high performing individuals
within the general population? Addressing this issue requires consideration of aspects that have been expressedby scientists and practitioners alike (cf. Bate et al., 2019;
Moreton, Pike, & Havard, 2019; Ramon et al., 2019a,
2019b; Robertson & Bindemann, 2019). Two major factors are: 1) assessment of different subprocesses or processing levels in face cognition (Ramon et al., 2019a;
Ramon & Gobbini, 2018); and 2) the degree to which
this process-dependent behavior observed experimentally translates into extremely varied and constantly
changing applied settings (cf. e.g., Bate et al., 2019;
Ramon, 2018a). These two factors are discussed below.
Assessment of face perception versus recognition:
procedural considerations

Concerning the first factor, distinct subprocesses involved in face cognition can be investigated through specific tasks that are (ideally) designed to capture them ina reliable manner. In the context of unfamiliar face processing, the most commonly assessed processes include
face perception and face recognition. These are separate
processes that require careful terminological distinction(cf. Ramon, 2018a; Ramon et al., 2019a; Ramon & Gobbini, 2018) and should not be used interchangeably or
considered analogous to other processes (such as face
identification; see Noyes, Hill, & O’Toole, 2018; Phillips
et al., 2018).
Tests of face perception can involve simultaneous
matching (or discrimination) among image pairs(e.g., Fysh, 2018; Ramon & Rossion, 2010), triplets(e.g., Barton, Press, Keenan, & O’Connor, 2002;
Busigny, Joubert, Felician, Ceccaldi, & Rossion,2010), 1-to-n matching scenarios (e.g., Bruce et al.,
1999; Pachai, Sekuler, Bennett, Schyns, & Ramon,
2017; Rezlescu, Pitcher, & Duchaine, 2012), or cancomprise delayed matching (e.g., Ramon, Busigny, &Rossion, 2010; Ramon & Van Belle, 2016). In thecontext of such laboratory-based face perception
tests, consideration of both accuracy and reaction
times (RTs) is necessary to accurately characterize
individual face processing abilities. This is especially
important for tests that 1) involve behavioral decisions recorded for individual trials with long or unlimited duration, and/or 2) are insufficiently
calibrated to clear two standard deviations from thecontrol mean (e.g., the Glasgow Face Matching Test
(GFMT); Bate et al., 2018; Bobak et al, 2016a; Burton, White, & McNeill, 2010; Fysh, 2018; Fysh &Bindemann, 2018; Robertson et al., 2016; White,
Phillips, Hahn, Hill, & O’Toole, 2015). To illustrate,time-consuming piecemeal matching strategies may
enable normal performance levels in terms ofStacchi et al. Cognitive Research: Principles and Implications(2020) 5:8

accuracy even in highly impaired clinical populations, such individuals suffering from prosopagnosia(cf. Marotta, McKeeff, & Behrmann, 2002; White,
Rivolta, Mike Burton, Al-Janabi, & Palermo, 2017).
Tests of face recognition involve intentional learning ofpreviously unfamiliar identities, which are later recognized
as “old” among novel ones (e.g., Bate et al., 2018; Bobak
et al., 2016b; Russell et al., 2009). In the context of such
face recognition tests, where encoding duration is classically predetermined, longer decision times cannot compensate for an inefficiently encoded face stimulus. Given thedecreased likelihood of speed–accuracy trade-offs in facerecognition tests, RTs are oftentimes not considered (e.g.,
Blais, Jack, Scheepers, Fiset, & Caldara, 2008).
To summarize, while face perception is usuallyassessed via matching of faces presented simultaneously,
face recognition involves distinguishing experimentallylearned identities from entirely novel ones. Importantly,
observers can differ in their unique abilities exhibited
across these distinguishable subprocesses (Fysh, 2018).
Consequently, a comprehensive empirical understandingof the face processing abilities of individuals requires assessment across various levels of processing. In applied
settings, on the other hand, this may not be required as,
depending on the area of intended deployment, assessment of an ability confined to a specific type of task
might be entirely sufficient (Bate et al., 2019; Moreton
et al., 2019; Robertson & Bindemann, 2019).
Relationship between performance in the laboratory andthe real world

The second factor concerns the relationship between experimentally observed process-dependent behavior andskills that are relevant in extremely varied and constantly
changing real-life settings (Moreton et al., 2019; Ramonet al., 2019a, 2019b). For the majority, empirical studies
aim to characterize different aspects of face processing
in a highly controlled manner. For instance, psychophysical studies of identity matching have been conducted tobetter understand the specific contribution of certain
controllable factors—for example, low spatial frequency
information or orientation tuning (Pachai, Sekuler, &Bennett, 2013; Pachai et al., 2017; Watier & Collin, 2009;see also Papinutto, Lao, Ramon, Caldara, & Miellet,2017). Notwithstanding the informative theoretical valueof evidence provided through such rigorous experiments,
the degree to which they characterize or reflect realworld proficiency in facial identity processing remains
unknown (Ramon et al., 2019a, 2019b).
Indeed, other tests have been designed with theintention of assessing behavior that is pertinent to real
life. One frequently used paradigm involves simultaneous identity matching in a two-alternative forcedchoice (2AFC) scenario. This is thought to parallel

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identity verification at, for example, a passport control
point where naturally occurring variation in ambient images is known to affect performance (Burton et al., 1999;
Megreya & Burton, 2006). Unfortunately, some of thetests used to identify SRs (e.g., the GFMT) lack sensitivity, making them inappropriate to identify highly proficient face processing abilities (Bobak et al., 2016b;
Ramon et al., 2019a, 2019b). This situation is further exacerbated by two additional aspects. First, 2AFC paradigms, including for example the Expertise in Facial
Comparison Test (EFCT) and the Person Identification
Challenge Test (PICT) (White et al., 2015), have beenused to further probe individual differences in facematching (Phillips et al., 2018). Although developed toassess performance under challenging situations (Whiteet al., 2015), the EFCT and PICT involve face matchingunder optimal viewing conditions, in other words using
full-frontal face images—a necessary requirement tomeet the original goal of comparing human and machine
performance (Phillips & O'Toole, 2014). Additionally, as
they require observers to make simple same/different decisions, they involve a constant probability of correct responses on a trial-to-trial basis. Most importantly,
however, the “pedestrian notion” of speed–accuracy
trade-offs (Heitz, 2014; see also Luce, 1986) is commonly
not considered; observers can obtain high test scores atthe expense of prolonged RTs, which are not reported
when the EFCT and PICT are used (e.g., Phillips et al.,2018; for a similar approach adopted in the context of
pairwise face-matching tests see also Bobak et al, 2016c;
Robertson et al., 2016).
The solution: standardization of challenging andecological valuable laboratory tests

In the context of identifying individuals that could provide a substantial contribution in applied settings, a
growing body of literature has expressed the need formore ecological and challenging assessment of face processing abilities (Bate et al., 2018, 2019; Lander, Bruce, &Bindemann, 2018; Moreton et al., 2019; Ramon et al.,2019a; Robertson & Bindemann, 2019). We aim to contribute to filling this void concerning the assessment offacial identity processing under both realistic and challenging conditions. To this end, we tested a large groupof individuals of all ages (N = 252) with previously reported tests of facial identity matching. These tests tap
into invariance of facial representations by measuring facial identity matching ability across two dimensions thatare pertinent to real life challenges. The YBT (for examples see Fig. 1b) captures identity matching across significant age-dependent changes in facial appearance (i.e.,
25 years; Bruck et al., 1991). The FICST (see Fig. 1a)probes the ability of telling together and telling apartidentities across superficial image variations (lighting,Stacchi et al. Cognitive Research: Principles and Implications(2020) 5:8

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Fig. 1 Examples of stimulus material presented in the tests of face processing administered in this study. Observers completed tests of facematching involving naturally occurring a image-related (FICST; Jenkins et al., 2011), or b aging-related variations (YBT; Bruck, Cavanagh, & Ceci,
1991). Additional measures included previously established tests of c, d face discrimination (EFCT and PICT; White et al., 2015), and e recognition(CFMT+; Russell et al., 2009). Note that these illustrations are for demonstration purposes and may differ from the stimulus material used (detailed
descriptions are provided in the methods section). Consent/rights for publication obtained for individuals depicted

make-up, hairstyle, etc.; Andrews, Jenkins, Cursiter, &Burton, 2015; Jenkins et al., 2011). Because of the natureof the tasks and face stimuli, these two tests mimic
real-world challenges in face processing. The YBT, forinstance, resembles the situation of encountering acquaintances or friends after considerable time periods.
In a policing context, it might translate into scenarios
where comparison images of alleged criminals in aline-up are dated and experts or witnesses are required to disregard age-related changes. The task andface stimuli used in the FICST, on the other hand,
could resemble a situation where police officers are
required to determine whether footage from multiple
crime scenes depicts the same individual, or whether
image material viewed in the context of child abduction or abuse depicts the same victim(s). In this case,face processing would not only be challenging due tovariation in image quality, but also due to potential
disguises, or time periods between image acquisitions(Megreya, Sandford, & Burton, 2013). Additionally,
image ambiance is inherently larger in the FICST due
to the presence of 20 images per identity (versus twoin the PICT and EFCT), and in both the FICST and
YBT faces are depicted in greyscale and from different
viewpoints (as opposed to full-frontal color images in the
EFCT and PICT; see Fig. 1). Note that the EFCT and

PICT stimuli were presented in color. Finally, instead ofrequiring simple same/different decisions between two
faces as in the EFCT and PICT, the FICST and YBT involve a substantially greater number of possible responses.
In the FICST, participants are blind to the number ofidentities portrayed in the 40 pictures they are supposed
to sort. In the YBT, observers have to match five target
pictures portraying young adults with the corresponding
five probe images, which are presented along with five distractors (Bruck et al., 1991). Consequently, the FICST and
YBT include a broader decisional space, which has been
experimentally shown to increase task difficulty (Ramon,
Sokhn, & Caldara, 2019; Ramon, Sokhn, Lao, & Caldara,2018), and they also resemble more challenging real-world
situations beyond identity verification.
In light of the aforementioned considerations, the goalof this study was twofold. We aimed to provide normative data for the YBT and FICST, which in our opinion
are more ecologically meaningful and challenging testsof unfamiliar face matching (i.e., perception) than the
EFCT and PICT. Beyond this, we sought to determine
the relationship between performance measures provided by the YBT and FICST, as well as the EFCT and
PICT, with the most commonly used tool to assess facecognition, the long form of the Cambridge Face Memory
Test (CFMT+; Russell et al., 2009). Note that, althoughStacchi et al. Cognitive Research: Principles and Implications(2020) 5:8

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the CFMT+ represents a test of face recognition, it remains the most commonly used means to identify superior face processing skills (cf. Bobak et al, 2016b; Noyes,
Phillips, & O'Toole, 2017; Ramon et al., 2019a).
To anticipate our findings, and in line with previous
work, our results demonstrate that facial identity matching is considerably impacted by superficial image variations and age-related changes in appearance.
Importantly, across all tests of face perception reported,which rely on simultaneous matching of facial identity
displayed in natural images (i.e., involve no memory
component), the EFCT and PICT were the least challenging. Based on these observations, we advocate for increased use of more challenging measures that involve
manipulations that are pertinent to real-life settings,
such as the YBT and FICST. Compared to 2AFC scenarios, in these tests time-consuming strategies are less effective for achieving high-performance accuracy. The
conditions under which tools are developed (cf. EFCT
and PICT; probing face matching under ideal visual conditions as required per dated automatic face processing
solutions) have to be carefully considered in combination with the real-life roles in which they are deployed(for example, identity verification encountered by passport control officers, doormen, or cashiers).

Fribourg who received course credit for participation as
well as individuals from a wide range of professional fields
and with varying levels of education (family, friends,
colleagues and acquaintances of the experimenters) who
participated voluntarily without compensation. All procedures were approved by the local Ethics Committee andconducted following the tenets of the Declaration of
Helsinki (Puri, Suresh, Gogtay, & Thatte, 2009). Written
informed consent was obtained from each participant.

Methods

Tests of face perception standardized in this study
Yearbook Test

Participants

A total of 252 observers (150 females; age range 18–80
years) completed the aforementioned YBT. Of this cohort, 218 additionally completed the FICST. In addition
to the YBT and FICST, 181 observers completed the
CFMT+, EFCT, PICT, and Cambridge Face Perception
Test (CFPT). Detailed demographic information for eachsample is provided in Table 1. This entire cohort included (under)graduate students from the University of
Table 1 Demographic information for all samples reported
YBT only
Sample size (all/male/female)N = 252/101/151

Handedness (right/left/ambidextrous)

224/26/2

Mean age (SD), range (years)

29.0 (12.7), 18–80

FICST only
Sample size (all/male/female)N = 218/98/120

Handedness (right/left/ambidextrous)

195/21/2

Mean age (SD), range (years)

29.8 (13.1), 18–80

All tests
Sample size (all/male/female)N = 181/78/103

Handedness (right/left/ambidextrous)

161/19/1

Mean age (SD), range (years)

29.2 (13.1), 18–80

FICST Facial Identity Card Sorting Test, SD standard deviation, YBTYearbook Test

Materials and procedures

Each observer completed the paper-based YBT; if participation was not terminated, additional tests were administered. These included the paper-based FICST, as
well as four computer-based tests (CFMT+, EFCT, PICTand CFPT) which were completed (in randomized order)using the standalone Java applications provided by theauthors of the original studies. Performance measures
were recorded in keeping with previously adopted procedures, or involved additional measures (e.g., RT registration) as detailed below. Examples of stimulus materialpresented in each experiment are displayed in Fig. 1.
Note that since, currently, the YBT and FICST are relatively infrequently used, we provide a list of aspects requiring careful consideration for appropriate application.

This test of face perception involves the original material
reported in Bruck et al. (1991), which differs from thedemonstration provided in Fig. 1. The YBT was originally designed to test recognition and identification of
personally familiar faces (former classmates from highschool) 25 years later at a high school reunion (Brucket al., 1991). We used this test as a measure of simultaneous unfamiliar face matching across age-relatedchanges in facial appearance. The material consists ofeight pages portraying images of same-gendered individuals. On each page, five (younger) target identities are
depicted on the left, along with 10 probe images on theright (see Fig. 1b). Probe images comprised the five target and foil identities of the same age (i.e., all 25 years
older than target identities), respectively. Images (220 ×
350 mm) included variations in viewpoint and depicted
paraphernalia and external cues. Contrary to the original
study (Bruck et al., 1991), none of the participants werefamiliar with any of the depicted identities. Therefore,
participany4csxts were instructed to neglect the middle
columns designed for familiar face recognition and identification, and to simply indicate (by providing a number) which of the probe images corresponded to a given
target face. For each observer, we report the raw scoreof correctly matched identities. Note that only 35 items
could be analyzed as the remaining five were notStacchi et al. Cognitive Research: Principles and Implications(2020) 5:8

identifiable by the authors of the YBT (personal communication, Bruck & Cavanaugh, 2018).
Facial Identity Card Sorting Test

This face perception test involves a simultaneous cardsorting procedure as reported by Jenkins et al. (2011), andrepresents a measure of face matching across imagerelated variation in facial appearance. A selection of 40
images, half of which depict one of two Dutch celebrities(Chantel Janzen and Bridget Maasland), kindly provided
by Jenkins and Burton (personal communication, 2017)
were used as stimulus material (multiple sets of 40 laminated greyscale images sized 38 x 50 mm). The twenty‘ambient images’ (Jenkins et al., 2011; Sutherland et al.,2013) of each individual entailed natural face variability
that occurs in real life as they were taken from publicly
available photographs of each celebrity found on the internet. When instructed to group 40 such ambient images by
the number of perceived identities, observers who are unfamiliar with the depicted identities report perceiving a
median of 7.5 identities (mode 9; range 3–16; Jenkins
et al., 2011; see also Andrews et al., 2015). Here, the 40
image cards were randomly positioned (in upright orientation) in front of observers who had unlimited time togroup them according to the number of identities theysubjectively perceived as distinct, with each group of images representing a single individual (for an example ofsimilar image variations, see Fig. 1a). The experiment
ended if observers spontaneously and correctly grouped
the images correctly into two categories. However, if observers formed a different number of groups, they completed a second sorting phase. In phase 2, the cards wereagain mixed and spread out randomly; however, observers
were now instructed to separate the 40 images into twogroups. The sorting responses of the observers were
photographed; for each phase, the number of piles andtotal number of errors was registered (note that an identity match was considered erroneous if it did not correspond to a given group’s predominant identity). Analyses
were performed on a composite score calculated based on
both measures obtained during the first sorting phase (seebelow).
Optimal YBT and FICST application: methodologicalconsiderations

Based on our experience, we point out three aspects that
require careful consideration to ensure the most efficientand informative application of the YBT and FICST. The
first pertains to the number of trials contained in the
YBT, relative to the number of currently analyzable trials. As mentioned, the authors of this test were able toprovide the correct responses for 35 of the total of 40
items. Currently, efforts are underway to determine themost probable correct responses for the missing items

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using a wisdom of crowd approach (cf. Balsdon, Summersby, Kemp, & White, 2018; Phillips et al., 2018) bytesting a large number of high-performing individuals toensure maximal validity of such data-driven estimates.
Once generated, the normative data provided here canbe re-evaluated to include responses for the currently
not considered items, and the test could be employed in
two versions.
The second consideration relates to application of the
FICST. Naturally, it is imperative that observers have noprior information regarding the number of identities displayed, in other words they should not have participatedin an experiment with a similar procedural setup. As evident from the data reported for phase 2, observers areclearly more proficient when provided with top-down
information regarding the decisional space in which
identity matches are to be provided (cf. Ramon, 2018a;
Ramon et al., 2018; Ramon et al., 2019c). Relatedly, attention must also be paid the possibility of observers being familiar with the two Dutch celebrities depicted.
Such knowledge would serve as an effective prior facilitating identity grouping (see Andrews et al., 2015; Jenkins et al., 2011). Even if different versions of this test
were created in the future that involved different set
sizes with varied numbers of individuals depicted, priorcompletion or knowledge of the original FICST may decrease the initially observed effect of identity separationand grouping reported here and elsewhere (Andrews
et al., 2015; Jenkins et al., 2011).
A final, seemingly trivial, consideration concerns thequality of image material used both for the YBT and
FICST. As noted, we were kindly provided with a physical copy of the original YBT used by Bruck et al. (1991);
a large number of digital image copies of both Dutch celebrities were provided to recreate a version akin to the
FICST reported by Jenkins et al. (2011). We observed
quality and appearance variations in the reproduction
and duplication process. To ensure that observers wereconfronted with identical image material, we opted foruse of the FICST material (printed and laminated for repeated usage) and reproduced the YBT based on its
scanned version using the same printer (and printer settings). Researchers interested in using this material
should contact the corresponding author to receive areplica to be used without further reproduction until thepotential impact of reproduction-related quality variations on observers’ performance is established.
Commonly reported tests of face recognition andperception included in this study
Long form of the Cambridge Face Memory Test

This computer-based test was developed by Russell et al.
(2009) with the aim of assessing face recognition for experimentally learned identities. This computer-based test

Stacchi et al. Cognitive Research: Principles and Implications(2020) 5:8

comprises increasingly difficult parts (see Fig. 1e), in
which grayscale cropped male face stimuli are presented(6 target identities, 46 distractors). Participants encode
images of target identities from different viewpoints before subsequently selecting the target identity among
two distractors. As this test progresses, the trials become
increasingly more difficult, as illumination, orientationand information availability are manipulated (see Fig.
1e). For each observer, we recorded the number of correct responses obtained across the total of 102 items of
the CFMT+.
Expertise in Facial Comparison Test

This test of face perception was reported by White et al.
(2015) and assesses face discrimination ability measured
in the context of a computer-based simultaneous matching task. Half of the (total of 84) trials depict the same
identity; the remainder involve pairs of identities thatwere deliberately “similar looking” (White et al., 2015).
To better approximate its more recent application (cf.
Noyes et al., 2018; Phillips et al., 2018), observers provided binary same/different responses for color uncropped faces with varied facial expressions and on
different backgrounds (see Fig. 1c), which were presented for a maximum of 30 s (versus 5 or 7 scale responses, and 2-s presentation durations; cf. White et al.,2015).1 For each observer, we recorded accuracy and
RTs. As discussed below (see “Analyses”), we deem consideration of both accuracy and RTs at the individuallevel (as opposed to merely the group level; e.g., Whiteet al., 2017) especially relevant when aiming to provide
normative data from a large and heterogeneous sample
against which individual performance can be reliably
compared.
Person Identification Challenge Test

This test of face perception involves the same procedureand experimental settings, and hence process, as the
EFCT (White et al., 2015). The only difference is thatthe face stimuli portray individuals from a greater distance, and therefore display more external cues and environmental information. This test involved the same
testing procedure (presentation duration, responses registered) as described above for the EFCT.
In addition to these three frequently applied tests, andas mentioned above, observers also completed the CFPT
(Duchaine, Germine, & Nakayama, 2007), a test of faceperception that involves sorting upright and inverted
faces according to their similarity with a simultaneously
1

We adopted this long presentation duration to ensure that decisions
were made based on perceptual matching, and not contaminated bydecisional processes or memory after stimulus offset. Additionally, we
reasoned that this presentation duration exceeds the time required byhealthy observers to perform this task.

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presented target face. This test was not intended to beconsidered in our analyses; it was employed to enable
our students to independently formulate and probe hypotheses using the data from the total database.
Analyses
The need for computation of composite scores

To take into account the behavioral measures registered
for a given test, composite performance scores were calculated for all tests with the exception of the YBT and
CFMT+, for which only raw scores (i.e., number of correctly matched or recognized faces) were considered.
For the FICST, perfect performance involves separating the 40 images into two piles without inclusion errors. While previous studies considered only the numberof piles as an index of performance (Andrews et al.,2015; Jenkins et al., 2011), here we calculated a composite score to consider both possible sources of errors (inclusion and separation). This allows distinction between
observers, who randomly versus accurately create two
identity piles. Any number of piles exceeding two wasconsidered as an additional mistake(s). The FICST
scores of the observers were computed by subtracting
two from the sum of piles and errors. Thus, a score ofzero corresponds to perfect performance; the higher the
FICST score, the less proficient an observer’s performance for grouping images by identity.
Note that for the YBT, FICST and CFMT+, RTs were
not considered as informative for the following reasons.
First, for the YBT and FICST, provided responses are
not based on discrete pairs. Moreover, identity matchdecisions can be changed. Furthermore, completed identity matches (correct or incorrect) determine the natureof subsequent responses. For the CFMT+, the presentation of the to-be-learned faces is fixed, while recognition
decisions provided for the test face trio is unlimited. As
in this context performance is determined by encoding
proficiency and recognition ability, longer decision times
will not facilitate recognition performance, and hence nosystematic speed–accuracy trade-off is expected.
From a procedural perspective, the EFCT and PICT
are the least complex compared to the YBT, FICST and
CFMT+. Both require binary, perceptually based decisions for face stimuli presented simultaneously without
time constraints. In such an experimental context, we
reasoned that consideration of RTs is imperative. Under
simultaneous matching conditions observers are more
likely to display speed–accuracy trade-offs compared to,for example, recognition tasks. Even highly impaired
prosopagnosic individuals can achieve accurate performance at the expense of vastly prolonged RTs, which is
why it is common practice to take into account RTs instudies of both healthy and impaired individuals (e.g.,Behrmann, Avidan, Marotta, & Kimchi, 2005; Biotti

Stacchi et al. Cognitive Research: Principles and Implications(2020) 5:8

et al., 2017; Lao, Vizioli, & Caldara, 2013; Marotta et al.,
2002; Tarr, 2003; White et al., 2017). Indeed, according
to many, “the notion of speed–accuracy tradeoff (SAT)
is pedestrian” (Heitz, 2014; see also Luce, 1986).
The highly heterogeneous distribution of RTs observed
for the EFCT and PICT supports this notion and suggests that many healthy observers may indeed show
speed–accuracy trade-offs. Leaving aside the implications for studies that did not consider RTs and therefore
neglect the impact of speed–accuracy trade-offs (e.g.,
Bobak et al., 2016a; Bobak et al., 2016b; Bobak et al.,2016c; Burton et al., 2010; Noyes et al., 2018; Phillips
et al., 2018; Robertson et al., 2016; White et al., 2015),
here we opted to consider both speed and accuracy bycomputing the inverse efficiency (IE) score of the observers (Townsend & Ashby, 1978). This is crucial given
our goal of providing normative data against which individual performance can be compared in order to determine potentially superior performance.
Statistics
Behavioral measures considered

Behavioral responses from all five tests were subject todescriptive and correlation analyses. Descriptive statistics
are provided separately for all measured behavioral responses, as well as composite scores. Correlation analyses were performed on the YBT and CFMT+ raw
scores, and (following the aforementioned sources of errors and potential speed–accuracy trade-offs) the FICST,EFCT and PICT composite scores.

Page 8 of 17

observers correctly matched 8.8 ± 4.0 (median 8.0) of the
35 analyzable items (see “Methods”).
Normative data for the Facial Identity Card Sorting Test

The normative data obtained from 218 participants arereported in Table 3 and visualized in Fig. 2b. On average, during phase 1 observers perceived 7.6 ± 4.9 (median 7) identities and made 2.4 ± 3.3 (median 1) errors.
In phase 2, the average number of errors was 3.4 ± 4.4(median 2).
Normative data for observers who participated in all fivetests

Figure 3 and Table 4 summarize the normative data obtained for the subsample of 181 observers who participated in all tests (YBT, FICST, CFMT+, EFCT and
PICT). Mirroring the observations made for the larger
sample (see Table 1), in the YBT observers correctly
matched 8.8 ± 3.9 (median 8.0) of the 35 items. For the
FICST, as in the larger sample (see Table 2), in phase 1
observers perceived on average 7.4 ± 4.6 (median 7)
identities and made 2.5 ± 3.4 (median 1) errors; in phase
2 they made on average 3.4 ± 4.4 (median 2) errors. For
the CFMT+, observers achieved an average raw score of
66 (median 65). The results for the EFCT and PICT (seeTable 4) were similar, although accuracy scores werehigher for the EFCT than the PICT, leading to comparatively higher IE scores (i.e., inferior performance) for the
PICT as compared to the EFCT.
Correlation analyses

Analyses of high-performing individuals

Additionally, in order to determine the predictive value of agiven test, we investigated how the top performers on eachtest scored on the remaining ones. First, and independently
for each of the five tests, we identified those participants
whose scores were located in the top 5% (n = 9). We thencomputed each subsample’s mean performance and retained
the value as a reference average. Finally, we extracted thescore of each subsample for each respective other test andcompared the mean to each test’s reference average.

Results
Descriptive analyses
Normative data for the Yearbook Test

The normative data collected from 252 observers are reported in Table 2 and visualized in Fig. 2a. On average,
Table 2 Normative data—number of correct identities achievedin the Yearbook Test
All participants (N = 252)
Mean (SD)

8.8 (4.0)

Median8

SD standard deviation

Table 5 summarizes the results of Spearman correlations
computed between the five tests, which are visualized inFig. 4. These analyses revealed the following significant
relationships (Bonferroni-corrected for multiple comparisons). First, we observed a significant positive relationship between the YBT and CFMT+ raw scores; greaternumbers of correctly matched identities in the YBT wereassociated with more items recognized in the CFMT+.
Second, a positive relationship also emerged between the
IE scores obtained for EFCT and PICT; better EFCT
performance (i.e., lower IE scores) was associated with
better PICT performance. Third, we found a significant
negative relationship between YBT and FICST scores,
as well as between the YBT and PICT, and YBT and
EFCT IE scores. These correlations indicate that
greater numbers of correctly matched YBT items wereassociated with both less errors and piles in the
FICST, and better performance for the PICT and
EFCT. Finally, EFCT IE scores also correlated negatively with CFMT+ raw scores, indicating that higher
CFMT+ performance was associated with more proficient EFCT performance. None of the remaining relationships reached significance.

Stacchi et al. Cognitive Research: Principles and Implications(2020) 5:8

Page 9 of 17

Fig. 2 Distribution of scores obtained for the a Yearbook Test (YBT) and b Facial Identity Card Sorting Test (FICST). Across both tasks, low andhigh scores are represented on the left and right, respectively

Analyses of top performing individuals

Table 6 summarizes the following: 1) reference scorescomputed across the top 5% performing individuals
identified independently per test (n = 9); 2) their respective performance on the remaining tests; and 3) the meanand standard deviation for each test based on the normative sample. For the YBT, high-performing individuals
identified via the CFMT+ and FICST performed moresimilarly to the reference score compared to the top performers identified via the EFCT and PICT (Table 6,
Fig. 5a). Moreover, CFMT+ and FICST high performers
obtained an average YBT score that was one standard
deviation above the mean of the normative sample. Focusing on FICST performance patterns, the means of
the top performers on CFMT+ and YBT were moresimilar to the reference mean of the top performers on
FICST compared to those of the high performers on
EFCT and PICT (Table 6, Fig. 5b). Additionally, with respect to the FICST normative average, top performers
on CFMT+, YBT and EFCT scored better, but within
one standard deviation of the mean. Focusing on the
CFMT+, YBT and FICST, high performers scored above
the normative average and more similarly to the reference mean of the top performers on CFMT+. This wascontrasted by the top performers on PICT and EFCT,
who also exhibited average performance that was below
the CFMT+ normative average. Finally, top performers
identified for the EFCT and PICT performed better onthese tests than the normative sample and more similarly to the reference mean of the top performers on

Stacchi et al. Cognitive Research: Principles and Implications(2020) 5:8

Table 3 Normative data—numbers of piles and errors
expressed in the Facial Identity Card Sorting Test (FICST)All participants (N = 218)
Phase 1
Number of piles
Mean (SD)

7.6 (4.9)

Median7

Total number of errors
Mean (SD)

2.4 (3.3)

Median1

FICST score
Mean (SD)

8.0 (5.3)

Median

8.0

Page 10 of 17

Caldara, 2019). A growing body of work has focused on
identifying and characterizing individuals with remarkable
face processing abilities, the so-called SRs (Ramon et al.,2019a, 2019b; Russell et al., 2009). Here, we aimed to contribute to advancing knowledge of this population bystandardizing two procedurally simple but highly challenging behavioral face perception tasks. Both involve
two ecologically relevant aspects of face cognition byprobing invariant facial identity matching across naturally occurring conditions that are commonly
neglected—aging and image ambiance. These tests arepromising solutions to fill the current void regarding
highly sensitive applied screening tests suitable for
identifying individuals with superior face processing
skills (see also Ramon et al., 2019a, 2019b).

Phase 2
Total number of errors
Mean (SD)

3.4 (4.4)

Median2Note that the FICST scores computed for phase 1 were considered in thesubsequent analyses
SD standard deviationthese tests than high performing individuals identified via
the CFMT+, YBT and FICST (Additional file 1: Figure S1).

Discussion
Despite decades of research, the ability of humans toprocess facial identity continues to attract considerable attention. While group studies have previously dominatedthe field, more recently an increasing interest in individual
differences has emerged, both at the behavioral and neurallevel (cf. Bruce et al., 2018; Stacchi, Liu-Shuang, Ramon, &The YBT and FICST as novel face perception tests to
identify superior face processing

Arguably, one of the most commonly employed measures of face processing is the CFMT+ (Russell et al.,2009). This test of face recognition, in which high performance can result from superior face perception or
memory, has been frequently deployed to identify individuals with superior face processing abilities (e.g. Bobak
et al., 2016; Noyes et al., 2017; Ramon et al., 2019a). Facematching tests created as alternative assessment tools
for face perception (e.g., the GFMT; Burton et al., 2010;
White et al., 2015) unfortunately often lack sensitivity(Bate et al., 2018; Bobak et al., 2016; Fysh, 2018; Fysh &Bindemann, 2018) or do not consider potential speed–
accuracy trade-offs, which are particularly relevant whenobservers have unlimited processing time to perform
simple binary decisions. Such procedural aspects are

Fig. 3 Distributions of scores obtained for observers who participated in the Yearbook Test (YBT), Facial Identity Card Sorting Test (FICST), longform of the Cambridge Face Memory Test (CFMT+), Expertise in Facial Comparison Test (EFCT) and Person Identification Challenge Test (PICT).
Note that scores representing low and high performance are represented on the left and right of the x axes, respectively (i.e., high inverse
efficiency scores (IES) for the EFCT and PICT indicate low performance); for visualization purposes one extreme value was removed for the CFMT+and PICT, respectively

Stacchi et al. Cognitive Research: Principles and Implications(2020) 5:8

Table 4 Normative data for participants who completed the
YBT, FICST, CFMT+, EFCT and PICT

Table 5 Correlation coefficients computed for the CFMT+, YBT,FICST, EFCT and PICT scores

All participants (N = 181)
YBT

CFMT+
CFMT+

Mean (SD)

8.8 (3.9)

YBTMedian

8

FICST

FICST

PICT

Number of piles
7.4 (4.6)

Median7

Total number of errors
Mean (SD)

2.5 (3.4)

Median1

FICST score
Mean (SD)

8.0 (5.1)

Median8

Phase 2
Total number of errors
Mean (SD)

3.4 (4.4)

Median2

CFMT+ raw score
Mean (SD)65.9 (11.9)

Median65

EFCT77.9 (8.2)

Median accuracy

78.6

Mean RT in s (SD)

4.7 (2.7)

Median RT in s

4.0

EFCT inverse efficiency score
Mean (SD)

6.0 (3.5)

Median5.1

PICT
Mean accuracy in % (SD)

YBT

FICST

PICT

EFCT

.46*

−.19

−.10

−.24*

−.37*

−.26*

−.24*

.06

.12
.78*

EFCT

Mean (SD)

Mean accuracy in % (SD)Page 11 of 1772.8 (11.0)

Median accuracy

72.5

Mean RT in s (SD)

4.9 (3)

Median RT in s

4.1

PICT inverse efficiency score
Mean (SD)

6.9 (5.1)

Median5.8

CFMT+ long form of the Cambridge Face Memory Test, EFCT Expertise in Facial
Comparison Test, FICST Facial Identity Card Sorting Test, PICT Person
Identification Challenge Test, RT reaction time, SD standard deviation, YBTYearbook Testoften neglected, and there is currently no accepted “goldstandard test” of superior face processing ability (Ramonet al., 2019a, 2019b).
In light of the shortcoming of tests currently used to
identify individuals with superior face perception, here

Asterisks indicate significant correlations (Bonferroni-corrected; p < .005); notethat for the FICST, PICT and EFCT lower values indicate better performance(see “Methods” section)
CFMT+ long form of the Cambridge Face Memory Test, EFCT Expertise in Facial
Comparison Test, FICST Facial Identity Card Sorting Test, PICT Person
Identification Challenge Test, YBT Yearbook Test

we aimed to provide normative data for the YBT (Brucket al., 1991) and the FICST (Jenkins et al., 2011). We
deem both to be ecologically meaningful and challenging
tests of face perception which are not affected by the
aforementioned procedural considerations. The YBTassesses unfamiliar face matching across substantial
age-related changes, while the FICST demonstrates
the effect of superficial or ambient image changes onperception of facial identity (see Fig. 1). Both tests
were standardized on a large and heterogeneous cohort. To promote their respective usage, the data for
all cohorts and tests reported here are made publicly
available on the public repository Open Science
Framework (https://osf.io/kagtr/).
A comparable number of observers achieved perfectscores for the FICST and fell within the 95th percentile
for the YBT, respectively. However, for the YBT, no observer achieved the highest possible score of 35 correct
identity matches. Thus, the ecologically relevant andchallenging face matching tests standardized here arecharacterized by higher task difficulty than tests thathave previously been employed as “standard screening”(Noyes et al., 2018) tools, despite their aforementionedlack of sensitivity in both healthy observers and individuals with developmental prosopagnosia (e.g., Whiteet al., 2017). Conversely, floor effects, which are a common problem when aiming to calibrate challenging tasks
suitable for identifying high performance (cf. Crowds
matching test; Bate et al., 2018), were found for neither
the YBT nor FICST. Thus, we believe that these tests
provide much needed alternatives of unfamiliar facematching that are easy to use across varied contexts andin pursuit of the goal of identifying superior face perception skills.
Evaluation of the YBT and FICST as predictors of faceprocessing skill: comparison with CFMT+, EFCT and PICTTo determine the relationship with previously deployed
tests of face cognition, the YBT and FICST were compared with the most commonly used measure of face(2020) 5:8

Stacchi et al. Cognitive Research: Principles and Implications

Page 12 of 17

Fig. 4 Significant correlations between measures. Visualized here are the significant correlations (see Table 5) between the Yearbook Test (YBT)and Facial Identity Card Sorting Test (FICST), long form of the Cambridge Face Memory Test (CFMT+), Person Identification Challenge Test (PICT)
and Expertise in Facial Comparison Test (EFCT) (top row to bottom left), as well as EFCT and CFMT+ (bottom middle), and PICT (bottom right),
respectively. Red lines indicate the median of each test. IES inverse efficiency scorerecognition (CFMT+; Russell et al., 2009) and two frequently reported tests of face perception (the EFCT and
PICT; White et al., 2015). As used recently, here the
EFCT and PICT were employed under unconstrained
viewing conditions (Noyes et al., 2018; Phillips et al.,Table 6 Top performers mean performance for each of the fiveadministered tests
Top 5% performers
CFMT+

YBT

FICST

EFCT

PICT

Mean ±
standard
deviationof thenormative
data (n =181)Median ofthenormative
data (n =
181)

CFMT+

88.9

75.8

75

64.1

64.8

65.9 ± 11.9

65

YBT

13.9

17.8

13.3

9.0

7.7

8.8 ± 3.9

8

FICST

4.4

4.0

0.0

6.3

10.2

8.0 ± 5.1

8

EFCT

4.9

4.2

5.2

1.9

2.0

6.0 ± 3.5

5.1

PICT

5.9

4.8

6.3

2.4

2.2

6.9 ± 5.1

5.8

Reference mean scores computed for the top performers of a given test are
shown in bold
CFMT+ long form of the Cambridge Face Memory Test, EFCT Expertise in Facial
Comparison Test, FICST Facial Identity Card Sorting Test, PICT Person
Identification Challenge Test, YBT Yearbook Test

2018). The majority of studies involving the latter facematching tests have focused exclusively on accuracy
scores obtained in highly homogeneous and/or small
samples of young observers, but did not consider RTs(e.g., Bobak et al., 2016; Noyes et al., 2018; Phillips
et al., 2018; Robertson et al., 2016). On the contrary,here we considered both accuracy and RTs. Aiming
to account for the “pedestrian notion” of speed–accuracy trade-offs (Heitz, 2014; see also Luce, 1986),
which can be found in healthy as well as in abnormal
populations (Behrmann et al., 2005; Biotti et al., 2017;
Geskin & Behrmann, 2018; Herzmann, Danthiir,
Schacht, Sommer, & Wilhelm, 2008; Marotta et al.,
2002; Ramon et al., 2010; Ramon & Rossion, 2010;
Tarr, 2003; White et al., 2017), and which may varyacross age (Hildebrandt, Herzmann, Sommer, & Wilhelm, 2010), analyses were performed on composite
scores comprising both measures. Thus, alongsideproviding normative data for the YBT and FICST,
their respective value was assessed relative to tests
that have been previously used as “standard screening” tests (Bobak et al., 2016; Noyes et al., 2018) ofindividual face processing skills.

Stacchi et al. Cognitive Research: Principles and Implications

Fig. 5 (See legend on next page.)(2020) 5:8

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Stacchi et al. Cognitive Research: Principles and Implications(2020) 5:8

Page 14 of 17(See figure on previous page.)
Fig. 5 Yearbook Test (YBT) and Facial Identity Card Sorting Test (FICST) scores of the top performers identified independently per test. Visualized
here are performance levels of individuals identified as the top performers (colored symbols) for the YBT (red circles), FICST (blue crosses), longform of the Cambridge Face Memory Test (CFMT+) (green squares), Expertise in Facial Comparison Test (EFCT) (purple triangles) and Person
Identification Challenge Test (PICT) (orange asterisks), and their relative location among observations made (grey dots) for the a YBT and b FICST.
High performers identified based on the CFMT+, YBT and FICST are more consistently located above the YBT and FICST median (black dashedhorizontal line), thereby exhibiting better performance than individuals identified as top performers based on the EFCT and PICTAs noted, the distribution of data observed for the
YBT and FICST demonstrate that they indeed provide
challenging tools to assess unfamiliar facial identity
matching. Interestingly, the highest correlation was
found between the YBT and the procedurally distinct
face recognition test CFMT+ (Russell et al., 2009), andthe YBT and FICST. Weaker correlations emerged between the YBT and PICT, and YBT and EFCT, respectively. On the other hand, the FICST correlated withneither the EFCT nor PICT which, likely due to theirprocedural similarity, correlated highly with one
another.
Based on the process measured, one might expect the
YBT and FICST, which both tap into face perception, tocorrelate more strongly than the YBT and CFMT+,
which assesses face recognition. Interestingly, instead,the CFMT+ and YBT correlated more strongly than the
YBT and FICST. In our opinion, these findings highlightthe need to carefully consider procedural differences
across tests. Specifically, which process a given test measures might not be the only element to consider. For example, despite assessing two distinct processes, the
CFMT+ and YBT both require individuals to find a target identity among multiple alternatives. On the other
hand, while both the FICST and YBT assess face perception, due to the incorporated manipulations they involve
distinct tasks, highly different face stimuli, and fundamentally different decisional spaces (Ramon et al., 2018;
Ramon et al., 2019c).
These findings strengthen the concept that face cognition is highly complex and that measurements of proficiency depend on several elements. Specifically, ourresults show that the YBT, FICST and CFMT+ capture
different, albeit related, aspects of face processing. This
makes their combined application a promising tool for
initial screening of superior face processing abilities.
This assessment would provide: 1) a first indication ofan observer’s proficiency level; 2) the direction in whichfurther testing should proceed; and 3) potential appliedfields in which an individual may be ideally deployed.
Overall, the stronger relationship observed between
YBT, FICST and CFMT+ is further supported by investigation of test scores of observers identified independently per test as top performers. High-performing
individuals identified via the YBT, FICST and CFMT+
scored more similarly across these three tests than

individuals identified as top performers in the EFCT and
PICT (Fig. 5). This suggests that, in comparison to the
YBT, the EFCT and PICT have comparatively limited
value in terms of predicting individuals’ face recognition
ability when speed–accuracy trade-offs are taken into account in large and heterogeneous samples. Moreover,the EFCT and PICT cannot predict invariant perceptionof facial identity across highly varied photographs asmeasured by the FICST (Jenkins et al., 2011; Sutherland
et al., 2013). Finally, considering the challenges inherent
to the YBT and FICST, and in contrast to the EFCT and
PICT, observers are less likely to excel by using strategies that can go unnoticed when only accuracy scores
are considered (e.g., Noyes et al., 2018; Phillips et al.,2018) and speed–accuracy trade-offs are not accountedfor (cf.Heitz, 2014 ; Luce, 1986).
We emphasize that in order to screen for potentially superior face processing skills multiple ecologically meaningful and challenging tasks must be employed to assess
distinct aspects of face processing (Bate et al., 2018; Fysh,
2018; Herzmann et al., 2008; Ramon, 2018a; Ramon et al.,2019a, 2019b). To this end, accuracy scores recorded forface matching under “best-case scenarios” (Fysh, 2018) areinsufficient, including, for example, moderate time periods
between image creation, or the use of well-lit frontal faces
as required for reliable computer-based identity matching(Phillips, Yates, Beveridge, & Givens, 2017; Phillips et al.,2018). Especially when tasks require simple same/different
decisions under virtually no time constraints (e.g., 30 s
here, or even 3 months in Phillips et al. (2018)), high performance accuracy cannot provide a reliable basis to identify or validate allegedly superior abilities. To maximize
test sensitivity, all relevant behavioral measures obtainable(in other words accuracy and RTs) need to be considered in combination, especially in the context of procedurally simple matching tasks with unconstrained
viewing conditions.
Based on our observations, we suggest that the YBTand FICST applied in combination with the CFMT+
may serve as a collection of procedurally simple screening tests of face perception and recognition. Considering
the different sources of variation implemented acrossboth tests, they may serve as powerful tools for screening of employees in applied professional settings, such as
law enforcement (Ramon, 2018a; Ramon et al., 2019a,
2019b). The current findings are a step towards theStacchi et al. Cognitive Research: Principles and Implications(2020) 5:8

expressed need for a feasible framework to assess ecologically relevant face processing skills (Moreton et al.,2019; Ramon et al., 2019a, 2019b).
Future directions and conclusions

Having provided normative data for two challenging andecologically meaningful tests of facial identity matching,
the question is how should these measures be validated
and further developed? The next steps will lie in establishing how previously reported, empirically and professionally identified SRs perform in the YBT and the
FICST, and establishing how performance in the YBTand FICST relates to performance in real-life settings,
such as law enforcement (Ramon, 2018a; Ramon et al.,2019a, 2019b; Ramon, 2019).
Further improvements can also be made regarding implementation of the YBT. On the one hand, a wisdom of
crowds approach (cf. Balsdon et al., 2018; Phillips et al.,2018) to determine the correct responses for the currently missing five items for their subsequent inclusion
will be beneficial to increase the test’s reliability. On theother hand, considering the demanding nature of thistest, one fruitful endeavor is the development of ashorter screening version. To this end we have recently
tested 146 individuals who completed five other previously standardized test including the CFMT+, the Kent
Face Matching Test (long version; Fysh & Bindemann,2018), FICST, 1 in 10 (Bobak et al., 2016; Bruce et al.,1999) and Models Memory Test (Bate et al., 2018), in
addition to a 10-item version of the YBT (YBT-10; Fysh,
Stacchi, & Ramon, in preparation; this test will be madeavailable on request). Aligning with the data reported
here for the YBT (~25% of the target identities correctly
matched), observers correctly matched 3–4 items (i.e.,37%) in the abridged YBT-10.
Additionally, alternate versions of the FICST could be
developed. For example, to account for sex-dependent
differences in variation across ambient images, parallel
male and female versions could be included, as well asother-race versions (see Tüttenberg & Wiese, 2019). Asprocessing proficiency deteriorates with other-race faces,
assessing how the ability of top performers varies
across races might provide information valuable for
deployment in situations that require processing offaces from varied ethnic backgrounds (e.g., specialized
thefts committed almost exclusively by a specificnationality; Green, personal communication (2019)).
A final practical aspect to consider lies in developing
computer-based versions of the YBT and FICST implemented as paper-based tests here. Currently, the AFC
Laboratory is developing an online platform (Ramon &Docherty, in preparation) that will include, among othertests, digital versions of the YBT and FICST. This will
enable screening of greater numbers of individuals orPage 15 of 17

preselected cohorts, conducting item analyses, andexploiting information related to response times.
Independently of these future directions, our results
emphasize the need to assess face processing abilities
from various angles and considering both test-specificprocedural constraints as well as the specific goal(s) ofassessment. Complementing highly controlled tests, suchas the CFMT+, with more realistic and challenging tasks
can provide a more comprehensive and targeted understanding of observers’ abilities, and potentially higher
predictive power for performance in applied settings.

Supplementary information
Supplementary information accompanies this paper at https://doi.org/10.
1186/s41235-019-0205-0.
Additional file 1: Figure S1. CFMT+, EFCT and PICT scores of the top
performers identified independently per test. Visualized here are
performance levels of individuals identified as the top performers(colored markers) based on the YBT (red circles), FICST (blue crosses),
CFMT+ (green squares), EFCT (purple triangles), and PICT (orange
asterisks), and their relative location among all observations made (greydots) for the a CFMT+, b EFCT and c PICT.

Abbreviations
2AFC: Two-alternative forced-choice; CFMT+: Long form of the Cambridge
Face Memory Test; CFPT: Cambridge Face Perception Test; EFCT: Expertise in
Facial Comparison Test; FICST: Facial Identity Card Sorting Test;
GFMT: Glasgow Face Matching Test; IE: Inverse efficiency; PICT: Person
Identification Challenge Test; RT: Reaction time; SR: Super-recognizer;
YBT: Yearbook Test
Acknowledgements
LS and MR thank the authors of the YBT, FICST, EFCT, PICT and CFMT+ for
kindly providing their original tests. We also thank all observers for their
participation, and the following former bachelor students for their help
during data acquisition: Neda Aghabozorgtar, Sarah Auderset, David
Corminboeuf, Pauline Equey, Yasmina Gavillet, Lisa Genre, Adrien Kolly,
Serena Malagnino, Lavinia Pedrini, Samuel Rossier and Jeremiah Schenk.
Authors’ contributions
LS contributed to research design, performed the research, analyzed andinterpreted the data, and wrote the manuscript. EH-E performed the research, analyzed data, and contributed to the manuscript preparation. RC
provided resources to conduct the work, and input regarding data interpretation. MR designed and performed the research, analyzed and interpreted
the data, and was the major contributor in writing the manuscript. All authors read and approved the final manuscript.
Authors’ information
LS and EH-E are doctorate and bachelor students at University of Fribourg,
Switzerland (UNIFR), respectively. Meike Ramon is a Principal Investigator andhead of the Applied Face Cognition Lab, supported by a Swiss National Science Foundation PRIMA (Promoting Women in Academia) grant(PR00P1_179872).
Funding
MR is funded by the Swiss National Science Foundation.
Availability of data and materials
The datasets obtained and/or analyzed in this study will be made publicly
available on the OSF. Test material used here will be made available
provided permission is obtained from the original authors.

Stacchi et al. Cognitive Research: Principles and Implications(2020) 5:8

Ethics approval and consent to participate
All procedures were approved by the local Ethics Committee (reference
number 425).

Consent for publication
Consent for publication was obtained for all individuals whose data or
likenesses are reported in the manuscript.

Competing interests
The authors declare that they have no competing interests.
Author details
1
Eye and Brain Mapping Laboratory, Department of Psychology, University of
Fribourg, Fribourg, Switzerland. 2Applied Face Cognition Lab, Department of
Psychology, University of Fribourg, Fribourg, Switzerland.
Received: 8 January 2019 Accepted: 29 December 2019

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