British Journal of Psychology (2019)
©2019 The Authors. British Journal of Psychology published by
John Wiley &Sons Ltd on behalf of British Psychological Society
Super-recognizers: From the lab to the world and
, Anna K. Bobak
* and David White
Applied Face Cognition Lab, University of Fribourg, Switzerland
Psychology, Faculty of Natural Sciences, University of Stirling, UK
UNSW Sydney, New South Wales, Australia
The recent discovery of individuals with superior face processing ability has sparked
considerable interest amongst cognitive scientists and practitioners alike. These ‘Super-
recognizers’ (SRs) offer clues to the underlying processes responsible for high levels of
face processing ability. It has been claimed that they can help make societies safer and
fairer by improving accuracy of facial identity processing in real-world tasks, for example
when identifying suspects from Closed Circuit Television or performing security-critical
identity veriﬁcation tasks. Here, we argue that the current understanding of superior face
processing does not justify widespread interest in SR deployment: There are relatively
few studies of SRs and no evidence that high accuracy on laboratory-based tests translates
directly to operational deployment. Using simulated data, we show that modest accuracy
beneﬁts can be expected from deploying SRs on the basis of ideally calibrated laboratory
tests. Attaining more substantial beneﬁts will require greater levels of communication and
collaboration between psychologists and practitioners. We propose that translational
and reverse-translational approaches to knowledge development are critical to advance
current understanding and to enable optimal deployment of SRs in society. Finally, we
outline knowledge gaps that this approach can help address.
Super-recognizers (SRs) are individuals who are extremely proﬁcient at processing facial
identity. In the past decade, it has become clear that people vary in their proﬁciency on
laboratory-based tasks of facial identity processing (see, e.g., Lander, Bruce, & Bindemann,
2018 for a review). These tests, which typically require participants to discriminate
between or recognize previously unfamiliar faces, have demonstrated that face
processing ability is characterized by large individual differences with some individuals
attaining high performance (e.g., Bobak, Pampoulov, & Bate, 2016; Bowles et al., 2009).
Moreover, such inter- individual differences have been linked to stable genetic factors
(Shakeshaft & Plomin, 2015; Wilmer et al., 2010).
These discoveries followed decades of empirical work, showing that people in general
are poor at processing facial identity of unfamiliar, compared to familiar individuals (e.g.,
Hancock, Bruce, & Burton, 2001). More recently, studies with professionals trained to
perform security-critical identity veriﬁcation tasks have shown that they perform no
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and
reproduction in any medium, provided the original work is properly cited.
*Correspondence should be addressed to Anna K. Bobak, Psychology, Faculty of Natural Sciences, University of Stirling, Stirling
FK9 4LA, UK (email: firstname.lastname@example.org).
All authors contributed equally to this work.
better than students on tasks that are representative of their daily work (Wirth & Carbon,
2017; White, Kemp, Jenkins, Matheson, & Burton, 2014; cf., Figure 1). SRs have been
viewed as a solution to this problem, and there is increasing interest in deploying SRs in
real-world settings that stand to beneﬁt from their superior ability, such as policing,
national security, and surveillance. For instance, individuals selected based on their face
processing abilities have been deployed within the London Metropolitan Police (Davis,
Forrest, Treml, & Jansari, 2018; Davis, Lander, Evans, & Jansari, 2016; Robertson, Noyes,
Dowsett, Jenkins, & Burton, 2016), as well as the Police in Cologne, Germany.
been reported to have assisted investigations of several high-proﬁle cases, for example,
Alice Gross’s murder in the United Kingdom,
the recent Novichok poisonings in
and the mass assaults on women in Cologne (Germany) on New Year’s
In concert with the widespread media coverage of SRs in such operational
deployments, other initiatives have emerged. The resulting rapid translation of limited
Figure 1. Super-Recognizer identiﬁcation in the lab, and potential for deployment in the real world. In
laboratory settings (left box), superior face processing abilities are commonly assessed with experimental
paradigms involving (top to bottom) simultaneous discrimination of pairs of stimuli (Robertson et al.,
2016; Phillips et al., 2018), simultaneous one-to-many matching (Bruce et al., 1999), and memory
paradigms designed to assess learning of facial identity using videos (left: Bobak et al., 2016, pics.stir.ac.uk)
and static images (right: Russell et al., 2009). In the real-world, SRs are selected using lab-based tests and
“on the job performance” (e.g. Davis et al., 2016), and have supported criminal investigations (Ramon,
2018a). They could be deployed in a diverse range of operational law enforcement and security settings
(right box), including (top to bottom) e.g. passport control, investigative purposes (left image: West
Midlands Police, https://www.flickr.com/photos/westmidlandspolice/39164763734/; right: Landespolizei
Schleswig-Holstein Filmgruppe), or crowd surveillance (left: Community Safety Glasgow; right:
Landespolizei Schleswig-Holstein Filmgruppe).
2Meike Ramon et al.
scientiﬁc evidence into applied practice in this area has sometimes led to an
overstatement of the beneﬁts of deploying SRs and unsubstantiated claims. For example,
one professional agency recently claimed that ‘Super recognisers can remember 80% of
faces they have seen. The average person can only remember about 20% of faces they have
and assure their staff’s high ability through ‘vigorous and continued training’.
Another professional association
offers membership accreditation to practice as a SR.
Such claims and offers are not corroborated by the limited number of studies of SRs
available to date. These have thus far documented a 5–17% point advantage depending on
the empirical test used (Davis et al., 2016; Robertson, Jenkins, & Burton, 2017).
Additionally, several studies report that professionals, whose jobs require frequent image
matching, are no better than inexperienced student control participants (Bruce,
Bindemann, & Lander, 2018; see also Papesh, 2018; White et al., 2014). Finally, it is
unclear what an accreditation to practice as an SR entails and in what capacity the
associates are encouraged to operate.
Here, we argue that the current level of scientiﬁc understanding of superior face
processing abilities does not yet warrant broad placement of SRs in diverse operational
settings. We brieﬂy outline the present state of scientiﬁc knowledge, before highlighting
key shortcomings that limit our understanding of the potential beneﬁt of SR deployment.
These shortcomings can be attributed to the limited number of available studies examining
exclusively the SR population (Table 1) and, hence, our insufﬁcient understanding of the
functional basis of superior face processingskills. Additionally, we currently lack a detailed
understanding of the real-world tasks that SRs are expected to perform and whether
laboratory-based tests capture the real-world abilities of interest (see Figure 1).
We propose that solving these problems requires researchers and practitioners to
approach this growing ﬁeld of research in a fundamentally different way. The emergence
of effective strategies for selecting and deploying individuals with superior face
processing abilities requires regular communication between scientists and practitioners.
Speciﬁcally, we suggest that future research in this area should incorporate a feedback
loop encompassing translational and reverse-translational research –from the lab to the
world and back again (cf. Ledford, 2008). This is critical for developing robust theory
that transfers to an understanding of real-world tasks and for streamlining recruitment
processes and legal guidelines to support the deployment of SRs in society.
Identifying superior face processing –A solution to real-world problems?
The concept of SRs was introduced in the seminal work of Russell, Duchaine, and
Nakayama (2009) and Russell, Chatterjee, and Nakayama (2012). These researchers found
that, relative to a control sample, a group of individuals who self-identiﬁed or were singled
out by acquaintances as possessing superior face recognition skills achieved high scores
on three tests: the Cambridge Face Memory Test Long Form (CFMT+), Cambridge Face
Perception Test (CFPT), and the Before They Were Famous Test (see also Noyes, Phillips,
& O’Toole, 2017a for a summary of these tests). Two of these tests (CFMT+, CFPT) were
originally developed for the purpose of assessing the face processing performance of
people with impaired ability (developmental prosopagnosia; DP). The limited number of
http://superrecognisersinternational.com/agency (Date accessed: 31/11/2018).
https://www.linkedin.com/pulse/afr-human-veriﬁcation-centres-kenneth-long-fsra-qii/?published=t (Date accessed: 31/11/2018).
https://www.associationofsuperrecognisers.org/mission (Date accessed: 31/11/2018).
Super-recognizers: From the laboratory to the world and back again 3
Table 1. Abilities assessed in studies of superior face processing skill
face processing Other
Russell et al., 2009 X CFMT+CFPT BTWF CFPT IE X X X X
Russell et al., 2012 X CFMT+CFPT X X X X X X
Bobak et al., 2016a X CFMT+,
1-in-10 test X X X X X X
Bobak et al., 2016b X CFMT+CFPT, MFMT,
XX X XX X
Bobak et al., 2016c WTAR,
X CFPT IE,
X X GBI
Bobak et al., 2016d X CFMT+CFPT X X X X X Self-report,
Davis et al., 2016 X CFMT+,
FFRT X Object
Robertson et al., 2016 X X MFMT, GFMT PLT X X X X X
Bobak et al., 2017 X CFMT+CFPT X X X X X Eye-tracking
Bennetts et al., 2017 WASI CFMT+CFPT,
Bate et al., 2018 CFMT+, MMT PMT, CMT X X X X X X
Davis et al., 2018 X CFMT+, SFCT X X X X IPIP, NASA-TLI,
Phillips et al., 2018 X X Matching of
XX X XX X
Belanova et al., 2018 X CFMT+, AFRT,
X X X X X X EEG
Note. AFRT (Adults Face Recognition Test, Belanvova et al., 2018); BORB (Birmingham Object Recognition Battery, Humphreys & Riddoch, 1993); BTWF (Before They Were Famous, Russell et al., 2009); CBT (Change Blindness
Test, Smart et al., 2014); CCMT (Cambridge Car Memory Test; Dennett et al., 2011); CFE (Composite Face Effect Robbins & McKone, 2007); CFMT+(Cambridge Face Memory Test Long Form; Russell et al., 2009); CFPT
(Cambridge Face Perception Test; Duchaine et al., 2007); CMT (Crowd Matching Test; Bate et al., 2018); Ekman 60 (Ekman 60 faces test; Young et al., 2002); FFRT (Famous Face Recognition Test; Lander et al., 2001); GFMT
(Glasgow Face Matching Test; Burton et al., 2010); Global Bias Index (Navon, 1977); IE (Inversion Effect); IFRT (Infant Face Recognition Test, Belanova et al., 2018) IPIP (International Personality Item Pool Representation of the
; Goldberg, 1998); MFMT (Models Face Matching Test; Dowsett & Burton, 2015); MMT (Models Matching Test, Bate et al., 2018); NASA-TLI (National Aeronautics and Space Administration Task Load Index Hart &
Staveland, 1988); Old/New UFMT (Old/New Unfamiliar Memory Test, Davis et al., 2016); SFCT (Spotting Face in a Crowd Test, Davis et al., 2018); PFPB(Philadelphia Face Perception Battery; Thomas et al., 2008); PLT (Pixelated
Lookalike Test; Robertson et al., 2016); PMT (Pairs Matching Test; Bate et al., 2018); RMITE (Reading theMind in The Eyes; Baron Cohen et al., 2001); SIAS (Social Interaction Anxiety Scale, Mattick & Clarke, 1998); SMT (Sequential
Matching Task); STAI-T (State Trait Anxiety Inventory- Trait; Spielberger et al., 1983); WASI (Wechsler abbreviated Scale of Intelligence; Wechsler 1999); WTAR (Wechsler Test of Adult Reading; Holdnack, 2001).
4Meike Ramon et al.
studies that have emerged since has primarily aimed to establish whether individuals who
excel at these tests also outperform controls at other tasks of face and object processing
(for a comprehensive summary of SR studies published to date, see Table 1).
Three important aspects are shared by most laboratory-based studies on this topic.
First, SRs have been identiﬁed based on measures originally designed to test face
processing at the low-performing end or normal range of the ability continuum, and it is
not clear whether these measures are equally suited to identify high-performing
individuals. Second, while SRs as a group tend to outperform groups of non-SR controls,
individual SRs’ performance can be within the average range, and individual SRs present
with heterogeneous patterns of performance across different face processing tests (e.g.,
Bate et al., 2018; Bobak, Hancock, & Bate, 2016; Phillips et al., 2018; Ramon & Bobak,
2017). This mirrors ﬁndings from individuals with DP who lie at the opposite end of the
ability spectrum and present with profound deﬁcits in face processing. As a result, this
continues to lack a consensus on appropriate diagnostic criteria (see
Geskin & Behrmann, 2017). Third, the tests that are used to identify SRs are not
representative of the diverse operational tasks that they could encounter if professionally
deployed. For example, face images used in standardized tests are classically captured in
controlled environmental conditions (e.g., optimal and consistent camera settings) and
involve experimental manipulations that are unlike naturally occurring variations (e.g.,
noise masking, and hair and contour removal). As a result, these tasks may not incorporate
those challenges in identity processing that occur in real-life environments (see Figure 1;
cf. Bate et al., 2018; Jenkins, White, Van Montfort, & Burton, 2011).
Although previous studies have provided valuable empirical insights, both the
cognitive and perceptual basis of superior face processing, as well as the potential
translation of laboratory-based to real-life performance, remain uncertain. As we outline in
the following sections, the development of scientiﬁc understanding and solutions is
hindered by the current lack of appropriate diagnostic criteria for SR identiﬁcation and
evidence-based guidelines for effective SR deployment. We argue that a main factor
contributing to this status quo is that no studies to date offer a task analysis of the role(s)
that SRs play in various organizations. Consequently, the tests used to identify and recruit
SRs are not optimized for the speciﬁc requirements of varied applied purposes.
We expand on previous ﬁndings and recommendations (see Noyes et al., 2017b for a
recent review), by proposing a framework to assess individual performance using
measures that translate directly to the ‘process(es) of interest’, that is, those required in
real-life settings. The diverse real-world tasks SRs are (potentially) expected to perform
(see Figure 1) underscore the need to develop selection measures that capture abilities
pertinent to these tasks speciﬁcally. We contend that greater communication between
scientists and practitioners is required to meet the increasing demands for SR deployment
in applied settings and to ensure that scientiﬁc understanding in this area keeps pace with
developments occurring outside the laboratory.
Quantifying the potential beneﬁts of SRs in applied settings
The goal of selecting and deploying SRs in applied settings is to improve the reliability of
human performance in real-life tasks involving processing of facial identity. The hope is
that such improvements would make societies safer and fairer by, for example, preventing
Super-recognizers: From the laboratory to the world and back again 5
terrorist events, identity fraud, and wrongful convictions (Figure 1). However, the
ultimate success of any recruitment measure depends on its correlation with performance
in real-world tasks. We conducted a Monte Carlo simulation to characterize the
relationship between such a correlation and the accuracy gains that can be expected in
a hypothetical real-world task. This simulation is illustrated in Figure 2 and provides an
exemplary guide to the magnitude of the real-world performance gain, which can be
expected for any given level of correlation with an ideally calibrated recruitment measure.
This simulation entailed generating normal bivariate distributions with two dimen-
sions arbitrarily labelled as percentage correct on the ‘recruitment test’ and the ‘real-world
task’, respectively. For simplicity, each variable ranged on a scale from 50% to 100%
representing the full range of performance expected on a two-alternative forced-choice
task (chance-level to perfect accuracy). Operating in simulated conditions, we were able
to optimally calibrate the tests to the scale: Means were centred on the midpoint (75%),
and distribution parameters were set to span the full range of accuracy –a situation that is
unlikely to exist in reality. Using this approach, we simulated 100 recruitment processes
each involving 1000 ‘candidates’, in which the correlation between recruitment test and
real-world accuracy varied randomly. To reiterate, our hypothesized recruitment process
was modelled as a virtual ‘best-case’ scenario –with perfectly calibrated measures, and a
very large sample to select from.
This approach enabled us to plot the expected gains in performance for each level
of correlation, as shown in Figure 2. We computed average real-world performance of
groups containing individuals that scored either >1SD or >2SD on the recruitment test,
with the difference in performance between selected (red, blue lines) and unselected
groups (grey line) showing the estimated beneﬁt of the selection criteria. These
selection criteria were used to reﬂect the strict criteria prescribed in the scientiﬁc
literature (2SD) and the fact that many organizations may opt for a more lenient
criterion so that they could select larger groups of individuals using other selection
measures (e.g., 1SD).
We believe the data shown in Figure 2 are informative for decision makers because
they provide a guide to the real-world beneﬁt that can be expected when the level of
correlation between a selection measure and performance on a real-world task is known.
While the correlation between laboratory-based and real-world measures is often difﬁcult
to estimate, it is important to quantify where possible. Balsdon, Summersby, Kemp, and
White (2018) measured the correlation between the short version of the Cambridge Face
Memory Test (CFMT short; 72 items; Duchaine & Nakayama, 2006), the Glasgow Face
Matching Test (GFMT), and a task designed to simulate passport issuance ofﬁcers’ actual
task (i.e., reviewing passport image arrays to decide whether any of the images matched
the passport applicant). The CFMT short and the GFMT showed correlations with this real-
world task of r=.41 and r=.46, respectively.
Note that other studies have reported
standardized tests such as the CFMT+and GFMT as having substantially less predictive
value for more complex real-world tasks, such as spotting a person in a crowd, a task
mimicking CCTV surveillance (r=.18; Davis et al., 2018), or perpetrator identiﬁcation in
lineups (Ramon, 2018a). Therefore, considering the available data and diverse range of
This may be the case, for example, in situations like border control, which requires processing large volumes of identity matching
decisions, but where recruitment is based on a variety of selection measures that are not only related to face processing ability (e.g.,
experience and character).
Note that most studies of superior face processing abilities have used the more commonly used CFMT+, which comprises 102
items and therefore yields more reliable individual scores.
6Meike Ramon et al.
operational scenarios of SR deployment, a correlation in the range of .4 to .5 would
represent an upper estimate.
What does this mean for the selection of specialist teams based on individual face
processing ability? As shown in Figure 2, for a laboratory-to-world correlation of .5,
selecting individuals scoring more than 2SD above the mean on a laboratory-based
recruitment measure would result in a real-world gain of approximately 12%. In practice,
however, it is likely that selection will be made from small sets of potential recruits and so
it is perhaps more realistic in these cases that less stringent criteria would be used to
recruit high performers. For example, if a recruitment process for passport ofﬁcers
involved testing 100 applicants, a 2SD selection criterion would produce an average of
just two to three successful applicants to choose from. Relaxing selection criteria to 1SD
above the mean, as potentially necessary in practice, leads to an 8% improvement.
representing a 32% reduction in errors (i.e., reduced from 25% to 17%), selection alone
clearly cannot solve the problem of high error rates, but can support the development of
strategies aiming to improve facial identity processing in applied settings.
Figure 2. Monte Carlo simulation to estimate the beneﬁt of recruiting SRs. (1) We simulated 100
normal bivariate distributions representingthe correlation between a recruitment test and a real-world task
for 1,000 ‘candidates’.The level of correlation between accuracy on the recruitment test and thereal-world
task was set randomly for each simulation(.5 in this example). (2) For each of these 100 simulations, three
criteria were applied to recruitment test scores in order to select face processing specialists (no selection,
greater thanone standard deviation above the mean, greaterthan two standard deviations above the mean).
We then calculated the mean accuracy of these groups on the real-world task. (3) Simulation data showing
the mean real-world accuracy of selected groups for all 100 simulations, as a function of the level of
correlation between recruitment test and real-world task. Estimated beneﬁts of selection are signiﬁed by
the difference between regression lines for selected groups (blue, red) and the non-selected group (grey).
The orange shaded area represents the ‘best-case’ correlation between laboratory-based tests and real-
world tasks based on existing estimates (r=.5, see text for details). At this level of correlation, beneﬁtsof
selection are approximately 8% for >1SD and 12% for >2SD selection criteria.
Binomial effect size display analysis (Rosenthal, 2005) can also be used to estimate the effectiveness of recruitment processes.
For example, if one is selecting individuals that perform above average on a recruitment test with a correlation of .5 to the real-
world task, 75% of the recruits will perform above average on the real-world task.
Super-recognizers: From the laboratory to the world and back again 7
Additional gains, however, may be achieved through combination with additional
solutions. For instance, in the context of face matching, comparable gains can be achieved by
aggregating multiple individuals’ responses (i.e., a ‘wisdom of crowds’ approach; Corbett &
Munneke, 2018; Dowsett & Burton, 2015; Jeckeln, Hahn, Noyes, Cavazos, & O’Toole, 2018;
Phillips et al., 2018; White, Burton, Kemp, & Jenkins, 2013; White, Dunn, Schmid, & Kemp,
2015; White, Phillips, Hahn, Hill, & O’Toole, 2015), and these gains are additive with respect to
gains based on recruitment alone (Balsdon et al., 2018). Therefore, the most promising
approach appears to involve a combination of effective, evidence-based solutions to produce
accurate identity processing systems, such as pairing of state-of-the-art algorithms and high-
performing humans (Phillips et al., 2018; Towler, Kemp, & White, 2017).
Of course, the potential beneﬁts of deploying SRs are ultimately determined by the
correlation between the recruitment tests used to select them and the real-w orld tasks they
will be required to perform. As a result, substantial improvement of this correlation is
necessary before selection measures can be used alone to solve the problem of error-prone
face identity processing. Likewise, evaluating whether selection processes improve
operational performance requires linking performance on empirically developed mea-
sures to performance on real-world tasks. At present, this feedback loop simply does not
exist: Specialists are deployed in real-world tasks –sometimes based on laboratory-
developed selection measures –without any ongoing, systematic testing of their
operational efﬁcacy. As we outline in the rest of this article, this is a critical shortfall
because it curtails efforts to develop tests that capture proﬁciencies that are pertinent to
A framework for measuring superior face processing
Face cognition, subprocesses, and experimental assessment
The general process of face cognition, which is presumed to underlie overtly observed
behaviour, includes a number of subprocesses, such as face detection, discrimination,
recognition, and identiﬁcation (for a review, see, e.g., Ramon & Gobbini, 2018).
Developing effective laboratory-based measures of superior face processing that are
relevant for applied settings necessitates appropriate mapping between the cognitive
subprocesses measured in the laboratory and those required in the real world. As
illustrated in Figure 3, this is a challenging goal, because any given real-world task is likely
to rely on different subprocesses.
Generally, researchers design experiments with the aim of investigating one or more
subprocesses. However, the simple one-to-one correspondence between measures and
subprocesses illustrated in Figure 3a rarely exists. On the one hand, various different
experiments can measure the same process (cf. Hildebrandt, Sommer, Herzmann, &
Wilhelm, 2010; Wilhelm et al., 2010) with different levels of efﬁcacy. On the other, as
exempliﬁed in Figure 3b, one experiment can tap into multiple, but not necessarily all
existing subprocesses. In this example, a face recognition experiment involves the ability
to detect the presence of a face, the ability to distinguish between faces, and to recognize
that this person has been seen before. A face identiﬁcation task would involve all of these
subprocesses, as well as the ability to retrieve and provide semantic information ‘This is
Notably, the more subprocesses involved in an experiment, the more difﬁcult it
Note that in some contexts, the term ‘face identiﬁcation’ is used an umbrella term for various tasks that involve processing of
facial identity. Throughout this article, we adopt terminology outlined in a recent paper (Ramon & Gobbini, 2018) in order to
clearly delineate different subprocesses of face cognition.
8Meike Ramon et al.
is to control and determine the contribution of each one. For example, superior
performance in a face identiﬁcation task could be observed because of increased ability in
discerning or recognizing faces, or retrieving semantic information associated with the
majority have identiﬁed SR individuals using laboratory-developed experiments, which
measure one or more aspects of processing facial identity –and tap into these
subprocesses to presumably varying degrees. Careful consideration of the relationship
between subprocesses and utilized measures is particularly critical when creating tests
with the intention of identifying SRs for real-world deployment. Specialists in
operational environments often perform diverse tasks that may include familiar face
recognition, discrimination of unfamiliar faces, and challenging visual search tasks
(Figure 1). Supporting this, a recent study showed that SRs’, facial examiners’, and non-
expert police employees’ performance on laboratory-based tests did not predict real-
life skills required to identify criminals in lineups after viewing CCTV footage of actual
crimes comitted in Switzerland in 2016 (Ramon, 2018a). Because laboratory-based tests
may not be predictive of ecologically meaningfulperformance(seealsoBateet al.,
2018), measures developed for SR identiﬁcation for applied purposes should assess
subprocesses that mirror their respective professional demands.
Inconsistent or inappropriate terminology usage and neglecting procedural differ-
ences further complicates this issue (Ramon, 2018b). To provide a prominent example,
the term ‘holistic processing’ has been widely used in the face processing literature.
Commonly, it is regarded as the mechanism enabling integration of facial information into
a uniﬁed percept (cf., e.g., Rossion, 2009). Different experimental paradigms have been
used to probe this single theoretical construct, including the part-whole advantage
(Tanaka & Farah, 1993), the face inversion effect (Yin, 1969), and the composite face
effect (Young, Hellawell, & Hay, 1987), which can further be implemented in the context
of matching, recognition, or identiﬁcation tasks (see, e.g., Ramon, Busigny, Gosselin, &
Rossion, 2016). If all measures of holistic processing tapped into a single common
mechanism independent of procedural differences, one would expect them to correlate
with one another, as well as with independent measures of face cognition. However,
recent evidence suggests that this is not the case. Rezlescu, Susilo, Wilmer, and Caramazza
(2017) found that holistic processing measures accounted for little to no variance in CFMT
Figure 3. Relationship between cognition and experimental assessment of overt behaviour. (a) A
cognitive process of interest, such as face cognition, can involve different subprocesses, which are ideally
measured in isolation through dedicated experiments designed to this end. (b) More commonly,
experiments designed to measure predominantly one subprocess through observers’ registered
responses (ﬁlled box) also rely upon additional subprocesses (not ﬁlled, thick-lined boxes), but not others
Super-recognizers: From the laboratory to the world and back again 9
performance (face inversion effect; r
=.18; part-whole advantage r
face effect r
=.00), and evidence for correlations between holistic processing measures
was also weak.
Similarly, superior face processing ability does not appear to be a unitary
phenomenon. This is evidenced by the heterogeneous patterns of performance across
tests in studies of SRs described above (see Table 1) and also by studies of individual
differences in face processing more broadly. The proportion of shared variance (r
between face processing tasks is typically in the range of .10 to .25 and appears to
depend on the type of subprocess involved in performing tasks (e.g., Bate et al., 2018;
Burton, White, & McNeill, 2010; Fysh, 2018; McCaffery, Robertson, Young, & Burton,
2018; Verhallen et al., 2017). When considering other abilities that may predict
performance in real-world tasks such as CCTV review and surveillance, this problem is
more acute. For example, the ability to match a person based on body cues does not
appear to correlate with performance on face identity processing tasks (Noyes, Hill, &
O’Toole, 2018), suggesting that face processing tasks are not sufﬁcient to capture
abilities that may be pertinent to operational deployment.
We believe this evidence should compel researchers and practitioners to carefully
consider subprocesses involved in a given task, as well as the use of precise and
appropriate terminology in the context of measuring face processing ability (Ramon,
2018b; Ramon, Sokhn, & Caldara, 2019). The ability of any laboratory-based test to
capture the skill(s) relevant for real-world tasks will be determined by the extent to
which both rely on similar sets of subprocesses (see Figure 3). Moreover, given the
varied applied settings of SR deployment, it is unlikely that any single laboratory-based
test will be sufﬁcient and able to identify SRs. This has immediate implications for
assessment for SR recruitment and for establishing in-depth understanding of their
Bridging the laboratory-world gap to measure ecologically meaningful face processing superiority
To meet the increasing demand for accurate SR identiﬁcation for rea l-world deployment, it
is essential to ensure convergence between hypothesis-driven research and goal-driven
practice. This entails ﬁrst and foremost identifying practitioners’ goals, which typically
exist independently of the theories and models that drive scientiﬁc approaches for
improving understanding of face cognition.
Over many decades, researchers studying professional expertise have addressed this
problem by applying careful analyses of professional tasks. Task-analytic approaches in
professional settings establish a link between a real-world goal, task, or system, and the
cognitive processes that underpin performance (for reviews, see Schraagen, 2006;
Schraagen, Chipman, & Shalin, 2000). These techniques have typically been used by
applied researchers aiming to improve the design of selection, training, or organizational
processes (Schraagen, 2006), and have proven highly beneﬁcial in the development of
selection criteria and performance measures in radiology and general medical practice
(e.g., Corry, 2011; Patterson et al., 2000; Shyu, Burleson, Tallant, Seidenwurm, & Rybicki,
2014). We believe that such practices can serve a similar purpose in the study of superior
face processing, by improving the level of correlation between selection measures and the
real-world task (see Figure 2).
This ﬁrst step –characterizing the real-world tasks –has been bypassed in SR research.
The recruitment of these specialist groups in applied settings has proceeded on the
assumption that the laboratory-based tests –developed by or with scientists –are
10 Meike Ramon et al.
sufﬁcient to select people that will perform well in real-world deployment.
operational tasks (see Figure 2) can involve complex and diverse challenges, which –in
addition to processing of face-related visual information –may also entail the use of
multiple identity cues that are not conﬁned to the face (c.f., Rice, Phillips, Natu, An, &
O’Toole, 2013; Noyes et al., 2018). As a result, systematic analysis of real-world tasks
should be the starting point for development of recruitment and selection tests. This
requires high conceptual precision and adoption of a consistent terminology used to
describe tasks and subprocesses involved in face cognition (Ramon, 2018b; Ramon &
Gobbini, 2018). Finally, this process should not be performed in a theoretical void, but
rather in the light of current scientiﬁc understanding of the face processing system, and
under consideration of interindividual differences and within-subject reliability.
Based on these considerations, we suggest an approach to successful development
and validation of appropriate assessment measures, as schematized in Figure 4. The ﬁrst
step entails effective task analysis. Having identiﬁed the relevant subprocesses,
experiments and performance measures can be developed to evaluate exhibited
behaviour. Careful, direct observation of individual performance (as opposed to, e.g.,
uncontrolled online testing) is particularly relevant during the initial stages of test
development and can provide critical insights regarding the validity of underlying
Figure 4. A framework for practice-oriented development of performance measures. An initial analysis
of the real-world task serves to identify task constraints, practitioners’ goals, and cognitive processes (c.f.
Schraagen, 2006). Researchers can then use this information to derive hypotheses about the cognitive
subprocesses underlying performance and design experiments to test these hypotheses. This leads to the
development of measures, which can be optimized to capture the real-world task through additional task
analyses, and the observed correspondence between accuracy in the measures and performance in on the
real-world task. This process serves to increase the predictive power of tests in terms of predicting
performance in real-world settings.
Indeed, some tests that have been sold to government agencies have not been peer-reviewed and can therefore not be
evaluated (see: https://www.polizei.bayern.de/muenchen/news/presse/aktuell/index.html/281026).
Super-recognizers: From the laboratory to the world and back again 11
assumptions and limitations of the test design.
Were real-world tasks and practition-
ers’ goals translated appropriately into subprocesses and most suitable experiments? Do
the tests developed capture distinct subprocesses that underpin real-world perfor-
mance? Are they internally consistent? Which factors can account for unexpected
observations? Answering these questions in the context of a task-analytic approach can
ensure that experimental tasks are developed appropriately and optimized in alignment
with the real-world tasks.
From the laboratory to the world and back again
In this article, we have identiﬁed three main knowledge gaps in understanding of SRs: (1)
the overlap between laboratory-based tests and real-world performance; (2) the range of
tasks that SRs are expected to perform; and (3) the subprocesses of face cognition
underpinning the real-world tasks and, by extension, novel laboratory assessments of
these tasks. We propose that greater synergy between researchers and practitioners is
necessary in order to address the shortfall in understanding. In this section, we ask how
this should be addressed, describe ongoing efforts to this end. And outline how this can be
improved in the future.
Figure 5 schematizes our proposed knowledge development cycle between the
laboratory and the world. In one direction, knowledge emerges from the laboratory:
Scientists design studies to understand the underlying mechanisms and the boundary
conditions of SRs’ superior performance. This understanding provides the basis for
procedures that can be used by practitioners to, for example, select SRs for real-world
deployment, evaluate the potential beneﬁts of using SRs in their organization, and develop
guidelines for interpreting evidence provided by SRs in court. Critically, knowledge transfer
in the opposite direction –from the world to the laboratory –will advance understanding by
attuning scientiﬁc procedures to real-world constraints, for example through real-world
Figure 5. Continued exchange between scientists and real-world practitioners. This continued
development cycle can serve to improve theoretical knowledge of superior face processing, which can
in turn help to generate improved processes deployed in professional settings.
For example, laboratory-based assessment provides an optimal environment for test development by minimizing confounding
variables, which are impossible to exclude during online testing (e.g., interference or help from others, variations in stimulus
presentation parameters, or technical nuisance factors).
12 Meike Ramon et al.
task analyses, and performance data that evaluate the effectiveness of SR deployment. This
continuous feedback loop stands to beneﬁt both scientists and practitioners alike, enabling
development of better selection measures and improving conceptual and theoretical
This diagram is useful as a high-level outline, but what practical measures can be taken
to facilitate this knowledge cycle? In recent years, three main mechanisms have emerged.
First, scientiﬁc working groups devoted to developing best practice guidelines for face
identiﬁcation have been established, and academics have begun to engage with these
groups (e.g., NIST Face Identiﬁcation Subcommittee).
Second, meetings led by
academics have been jointly attended by psychologists, computer scientists, forensic
scientists, lawyers, police, and employees of various government agencies.
collaborative projects between academics and practitioners have been critical in
transferring the initial laboratory work to applied settings. These include projects aiming
to improve performance in applied settings and to benchmark accuracy of face
identiﬁcation professionals against the members of the public and SRs (e.g., Davis et al.,
2016, 2018; Phillips et al., 2018; Robertson et al., 2016; White et al., 2014; White, Dunn,
et al., 2015; White, Phillips et al., 2015). Collaborations with international police
agencies have also begun to address issues pertaining to SR selection (North Rhine-
Westphalia Police, Germany), prevalence of SRs based on identiﬁcation with real-world
tasks in large-scale professional populations (Berlin Police, Germany),
of SRs and forensic facial examiners in the context of criminal investigation in Switzerland
These developments have served to link the work of researchers and practitioners in a
meaningful way and have fostered a network that can form the basis for future
translational research. While encouraging, it is critical that these initial steps are followed
up by a coordinated approach in the years ahead. Currently, there are very few formal
collaborations between practitioners and academics, despite intense interest in this area
from both groups, which may lead to demand for SRs in applied settings outpacing
scientiﬁc understanding of their abilities. This demand has clearly grown in recent years
and as a consequence professional associations are emerging that offer memberships,
accreditations, and professional opportunities.
In order to facilitate the framework we
have outlined in Figure 5 and increase their credibility, it is imperative that such
organizations allow their means of selection to be scrutinized by the wider scientiﬁc
community, by making their accreditation criteria transparent and publicly available; any
SR-related claims must be rooted in data from peer-reviewed empirical investigations.
Such transparency would facilitate simultaneous progress of scientiﬁc research and
practice in applied settings alike.
A coordinated approach is also necessary in order to establish the potential role of SRs
in the legal system. Although we are not aware of SRs providing expert identiﬁcation
evidence in court, there are reports that they have provided evidence as regular police
witness in the United Kingdom (Edmond & Wortley, 2016; p. 492). Indeed, it has been
suggested that SRs may be an improvement on the current face identiﬁcation experts that
Super-recognizers: From the laboratory to the world and back again 13
are regularly required to provide expert evidence in court (e.g., Edmond & Wortley,
2016). In this context, the question of whether SRs are superior on real-world tasks is
critical to assessing SRs claims of expertise in court. Indeed, a recent study reported that
groups of SRs exhibit performance comparable to groups of professional forensic facial
examiners in same/different face matching of frontal images (Phillips et al., 2018). This
result raises the possibility that SRs could provide evidence in legal trials that is of
comparable quality to that of ofﬁcially trained professionals.
Recent work also indicates that combining the expertise of professionally trained
practitioners and SRs with naturally occurring superior face processingskills could provide
complementary beneﬁts to the accuracy of facial forensic evidence. SRs seem to require
signiﬁcantly less time to achieve performance comparable to that of facial examiners
(Phillips et al., 2018), possibly because they do not rely on a piecemeal processing strategy.
In turn, forensic facial examiners receive substantial training and mentorship in applying a
feature-based approach for facial image comparison (see Facial Identiﬁcation Scientiﬁc
Working Group, 2012), and behavioural tests indicate a greater reliance on analytic,
‘piecemeal’ approaches that differ qualitatively from those of novices and SRs (Towler et al.,
2017; White, Dunn, et al., 2015; White, Phillips et al., 2015). In addition to beneﬁts from
combining these dissociable sources of expertise, incorporating SRs within the framework
of forensic science may alsobring increased legitimacy to the use of SRs in legal practice. For
instance, the high level of transparency in working groups that develop best practice in
training, tools, and procedures used byforensic facial examiners could be a useful model for
developing similar guidelines for testing and deploying SRs.
Conclusions and future directions
In the last decade, research into superior face processing abilities and the deployment of
SRs have emerged and progressed independently. Here, we have identiﬁed problems with
this approach that hinder progress in both areas, and we propose some initial solutions. In
this ﬁnal section, we acknowledge recent work that has begun to address the issues we
have discussed and outline key questions and future challenges.
Recently, a clear focus on developing tests that represent some of the diverse
operational deployments of SRs has emerged. Deviating from early tests designed to
capture broad aspects of face identity processing abilities such as perceptual discrimi-
nation (Burton et al., 2010) and memory (Russell et al., 2009), more recent work has
begun to test SRs on tasks that involve matching image-based facial memories to video
footage (e.g., Bobak, Hancock, et al., 2016; Davis et al., 2018), in-crowd identity search
(e.g., Bate et al., 2018), and face matching (Bobak, Dowsett, & Bate, 2016; Phillips et al.,
2018) in conditions considered more similar to task demands faced in applied settings.
As we have outlined in this article, it is critical to establish the extent to which
performance on laboratory-based tests will likely generalize to real-world tasks. This
aspect has also been proposed as one of seven ‘action points’ for future SR research
(Noyes et al., 2017b). Amongst the important principles identiﬁed to guide future work in
this ﬁeld, the authors suggested that reaching a consensus on a standard approach to
measuring and deﬁning superior face processing abilities is imperative.
From an academic perspective, we agree with this proposal, as it is theoretically
possible to optimize measurements in such a way that they ensure identifying the most apt
14 Meike Ramon et al.
individuals across different subprocesses of face cognition. However, as we have argued
here, even if such a scientiﬁc consensus on ideal experimental assessment were achieved,
this would be unlikely to provide an ‘all-purpose’ set of measures relevant for long-term
real-world deployment. The diverse demands of real-world challenges relating to face
processing make it very unlikely that a standard approach to identifying SRs for applied
settings could be established. Moreover, any such measures would require regular
reviews and updates to continuously match the changing real-life operational demands.
In our opinion, it is therefore necessary for this emerging research ﬁeld to approach the
complex topic that is SR identiﬁcation in a systematic and coordinated fashion. On one
hand, there is the realistic scenario that independent research groups could each develop
their own tests to model a speciﬁc operational task. The danger of such an approach
would be ‘overﬁtting’ tests to speciﬁc tasks, thereby hindering the goal of deﬁning
universal criteria for super face processing abilities. On the other hand, it is clear that the
currently available laboratory-based tests do not adequately capture the diversity of real-
world tasks. We believe that the only solution to this problem is for scientists and
practitioners to reach a consensus on the roles that SRs can be useful for and agree on the
best set of measures to identify the most promising individuals to fulﬁl them.
MR is supported by a Swiss National Science Foundation PRIMA (Promoting Women in
Academia) grant (PR00P1_179872). This work was supported by an Australian Research
Council Linkage Project (LP160101523) and a UNSW Scientia Fellowship to DW. AB is funded
by an EPSRC Programme Grant number (EP/N007743/1). We thank Peter Hancock and Lisa
Stacchi for feedback on an earlier version of the manuscript as Brad Duchaine for providing
examples of test items, and the Landespolizei Schleswig- Holstein for the image depicted in
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Received 19 June 2018; revised version received 1 October 2018
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