ArticlePDF Available

Super-Recognizers: From the lab to the world and back again

Authors:

Abstract and Figures

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 CCTV, 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 lab-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 to address.
Content may be subject to copyright.
British Journal of Psychology (2019)
©2019 The Authors. British Journal of Psychology published by
John Wiley &Sons Ltd on behalf of British Psychological Society
www.wileyonlinelibrary.com
Super-recognizers: From the lab to the world and
back again
Meike Ramon
1
, Anna K. Bobak
2
* and David White
3
1
Applied Face Cognition Lab, University of Fribourg, Switzerland
2
Psychology, Faculty of Natural Sciences, University of Stirling, UK
3
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: annakbobak@gmail.com).
All authors contributed equally to this work.
DOI:10.1111/bjop.12368
1
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.
1
They have
been reported to have assisted investigations of several high-profile cases, for example,
Alice Gross’s murder in the United Kingdom,
2
the recent Novichok poisonings in
Salisbury (UK),
3
and the mass assaults on women in Cologne (Germany) on New Year’s
Eve 2015.
4
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).
1
https://www.ksta.de/koeln/koeln-archiv/-ich-schlage-jeden-computer–wie-ein-super-recogniser-der-koelner-polizei-arbeitet-
28162628.
2
https://www.newstatesman.com/politics/uk/2016/08/super-recognisers-scotland-yard.
3
https://www.telegraph.co.uk/news/2018/08/28/police-use-super-recognisers-hunt-salisbury-poisoners/.
4
https://www.landtag.nrw.de/portal/WWW/dokumentenarchiv/Dokument/MMD16-14450.pdf.
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
seen’
5
and assure their staff’s high ability through ‘vigorous and continued training’.
6
Another professional association
7
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 517% 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
5
http://superrecognisersinternational.com/agency (Date accessed: 31/11/2018).
6
https://www.linkedin.com/pulse/afr-human-verification-centres-kenneth-long-fsra-qii/?published=t (Date accessed: 31/11/2018).
7
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
IQ
Unfamiliar
identity
learning /
recognition
Unfamiliar
identity
matching
Famous face
identification
Holistic
processing
Object
processing
Emotion
processing
Other non-
identity related
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+,
recognition
from moving
footage
1-in-10 test X X X X X X
Bobak et al., 2016b X CFMT+CFPT, MFMT,
GFMT
XX X XX X
Bobak et al., 2016c WTAR,
WASI
CFMT+CFPT,
SMT-faces
X CFPT IE,
CFE,SMT-IE
CCMT, SMT-
hands and
houses
X X GBI
Bobak et al., 2016d X CFMT+CFPT X X X X X Self-report,
SIAS, STAI-T,
Davis et al., 2016 X CFMT+,
Old/New
UFMT
1-in-10 test,
GFMT
FFRT X Object
Memory
Test-flowers
XX X
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,
SMT-faces
X CFPT-IE
SMT-IE
CCMT,
SMT-hands
and houses
Ekman 60,
RMITE
Age (PFPB),
Gender
(PFPB)
Eye-tracking
BORB
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,
CBT
Phillips et al., 2018 X X Matching of
image pairs
XX X XX X
Belanova et al., 2018 X CFMT+, AFRT,
IFRT
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
NEO PI-R
TM
; 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
clinical condition
8
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
8
https://www.nhs.uk/conditions/face-blindness/.
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).
9
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.
10
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
9
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).
10
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.
11
Albeit
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.
11
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
real-world performance.
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
Meike’.
12
Notably, the more subprocesses involved in an experiment, the more difficult it
12
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
face.
AmongstthelimitednumberofSRstudiespublishedtodate(seeTable1),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
(unconnected box).
Super-recognizers: From the laboratory to the world and back again 9
performance (face inversion effect; r
2
=.18; part-whole advantage r
2
=.06; composite
face effect r
2
=.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
2
)
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
underlying abilities.
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.
13
However,
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.
13
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.
14
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.
14
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
understanding.
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).
15
Second, meetings led by
academics have been jointly attended by psychologists, computer scientists, forensic
scientists, lawyers, police, and employees of various government agencies.
16,17
Third,
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),
18
and deployment
of SRs and forensic facial examiners in the context of criminal investigation in Switzerland
(Ramon, 2018a).
19
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.
20
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
15
https://www.nist.gov/topics/forensic-science/facial-identification-subcommittee.
16
http://forensic.psy.unsw.edu.au/ufig.html.
17
https://www.nist.gov/news-events/events/2018/11/international-face-performance-conference-ifpc-2018.
18
https://www.radioeins.de/programm/sendungen/die_schoene_woche/_/lka-berlin-sucht-den–super-recognizer-.html.
19
https://www.moz.de/nachrichten/brandenburg/artikel-ansicht/dg/0/1/1679849/.
20
https://www.associationofsuperrecognisers.org.
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.
21
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
21
https://www.nist.gov/topics/forensic-science/facial-identification-subcommittee.
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.
Acknowledgements
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
Figure 1.
References
Balsdon, T., Summersby, S., Kemp, R. I., & White, D. (2018). Improving face identification with
specialist teams. Cognitive Research: Principles and Implications,3(1), 25. https://doi.org/10.
1186/s41235-018-0114-7
Baron-Cohen, S., Wheelwright, S., Hill, J., Raste, Y., & Plumb, I. (2001). The “reading the mind in the
eyes” test revised version: A study with normal adults, and adults with Asperger syndrome or
high-functioning autism. Journal of Child Psychology and Psychiatry,42(2), 241251.
https://doi.org/10.1111/1469-7610.00715
Bate, S., Frowd, C., Bennetts, R., Hasshim, N., Murray, E., Bobak, A. K., ... Richards, S. (2018).
Applied screening tests for the detection of superior face recognition. Cognitive Research:
Principles and Implications,3(1), 22. https://doi.org/10.1186/s41235-018-0116-5
Belanova, E., Davis, J. P., & Thompson, T. (2018). Cognitive and neural markers of super-recognisers’
face processing superiority and enhanced cross-age effect. Cortex,108,92111. https://doi.org/
10.1016/j.cortex.2018.07.008
Bennetts, R. J., Mole, J., & Bate, S. (2017). Super-recognition in development: A case study of an
adolescent with extraordinary face recognition skills. Cognitive Neuropsychology,34, 357
376. https://doi.org/10.1080/02643294.2017.1402755
Bobak, A. K., Bennetts, R. J., Parris, B. A., Jansari, A., & Bate, S. (2016). An in-depth cognitive
examination of individuals with superior face recognition skills. Cortex,82,4862. https://doi.
org/10.1016/j.cortex.2016.05.003
Super-recognizers: From the laboratory to the world and back again 15
Bobak, A. K., Dowsett, A. J., & Bate, S. (2016). Solving the border control problem: Evidence of
enhanced face matching in individuals with extraordinary face recognition skills. PLoS ONE,
11(2), e0148148. https://doi.org/10.1371/journal.pone.0148148
Bobak, A. K., Hancock, P. J., & Bate, S. (2016). Super-recognisers in action: Evidence from face-
matching and face memory tasks. Applied Cognitive Psychology,30(1), 8191. https://doi.org/
10.1002/acp.3170
Bobak, A. K., Pampoulov, P., & Bate, S. (2016). Detecting superior face recognition skills in a large
sample of young British adults. Frontiers in Psychology,7, 1378. https://doi.org/10.3389/fpsyg.
2016.01378
Bobak, A. K., Parris, B. A., Gregory, N. J., Bennetts, R. J., & Bate, S. (2017). Eye-movement strategies in
developmental prosopagnosia and “super” face recognition. The Quarterly Journal of
Experimental Psychology,70(2), 201217. https://doi.org/10.1080/17470218.2016.1161059
Bowles, D. C., McKone, E., Dawel, A., Duchaine, B., Palermo, R., Schmalzl, L., ...Yovel, G. (2009).
Diagnosing prosopagnosia: Effects of ageing, sex, and participantstimulus ethnic match on the
Cambridge Face Memory Test and Cambridge Face Perception Test. Cognitive Neuropsychology,
26(5), 423455. https://doi.org/10.1080/02643290903343149
Bruce, V., Bindemann, M., & Lander, K. (2018). Individual differences in face perception and person
recognition. Cognitive Research: Principles and Implications,3(18). https://doi.org/10.1186/
s41235-018-0109-4
Bruce, V., Henderson, Z., Greenwood, K., Hancock, P. J., Burton, A. M., & Miller, P. (1999).
Verification of face identities from images captured on video. Journal of Experimental
Psychology: Applied,5(4), 339360. http://psycnet.apa.org/doi/10.1037/1076-898X.5.4.339
Burton, A. M., White, D., & McNeill, A. (2010). The Glasgow face matching test. Behavior Research
Methods,42(1), 286291. https://doi.org/10.3758/BRM.42.1.286
Corbett, J. E., & Munneke, J. (2018). “It’s Not a Tumor”: A framework for capitalizing on individual
diversity to boost target detection. Psychological Science,29, 16921705. https://doi.org/10.
1177%2f0956797618784887
Corry, C. A. (2011). The future of recruitment and selection in radiology. Is there a role for
assessment of basic visuospatial skills?. Clinical Radiology,66(5), 481483. https://doi.org/10.
1016/j.crad.2010.12.003
Davis, J. P., Forrest, C., Treml, F., & Jansari, A. (2018). Identification from CCTV: Assessing police
super-recogniser ability to spot faces in a crowd and susceptibility to change blindness. Applied
Cognitive Psychology,32, 337353. https://doi.org/10.1002/acp.3405tps
Davis, J. P., Lander, K., Evans, R., & Jansari, A. (2016). Investigating predictors of superior face
recognition ability in police super-recognisers. Applied Cognitive Psychology,30, 827840.
https://doi.org/10.1002/acp.3260
Dennett, H. W., McKone, E., Tavashmi, R., Hall, A., Pidcock, M., Edwards, M., & Duchaine, B.
(2012). The Cambridge Car Memory Test: A task matched in format to the Cambridge Face
Memory Test, with norms, reliability, sex differences, dissociations from face memory, and
expertise effects. Behavior Research Methods,44, 587605. https://doi.org/10.3758/s13428-
011-0160-2
Dowsett, A. J., & Burton, A. M. (2015). Unfamiliar face matching: Pairs out-perform individuals and
provide a route to training. British Journal of Psychology,106(3), 433445. https://doi.org/10.
1111/bjop.12103
Duchaine, B., Germine, L., & Nakayama, K. (2007). Family resemblance: Ten family members with
prosopagnosia and within-class object agnosia. Cognitive Neuropsychology,24(4), 419430.
https://doi.org/10.1080/02643290701380491
Duchaine, B., & Nakayama, K. (2006). The Cambridge Face Memory Test: Results for neurologically
intact individuals and an investigation of its validity using inverted face stimuli and
prosopagnosic participants. Neuropsychologia,44(4), 576585. https://doi.org/10.1016/j.ne
uropsychologia.2005.07.001
16 Meike Ramon et al.
Edmond, G., & Wortley, N. (2016). Interpreting image evidence: Facial mapping, police familiars and
super-recognisers in England and Australia. Journal of International and Comparative Law,
3(2), 150.
Facial Identification Scientific Working Group (2012). Guidelines and recommendations for facial
comparison training to competency. Retrieved from www.fiswg.org/document/
Fysh, M. C. (2018). Individual differences in the detection, matching and memory of
faces. Cognitive Research: Principles and Implications,3(1), 20. https://doi.org/10.
1186/s41235-018-0111-x
Geskin, J., & Behrmann, M. (2017). Congenital prosopagnosia without object agnosia? A literature review
Cognitive Neuropsychology,35(12), 454. https://doi.org/10.1080/02643294.2017.1392295
Goldberg, L. R. (1999). Abroad-bandwidth, public domain, personality inventory measuring the lover-
levels facets of several five-factor models. In I. Mervielde, I. Deary,F. De Fruyt & F. Ostendorf(Eds.),
Personality psychologyin Europe (Vol. 7, pp. 728). Tilburg, The Netherlands: Tilburg University
Press.
Hancock, P. J. B., Bruce, V., & Burton, A. M. (2001). Recognition of unfamiliar faces. Trends in
Cognitive Sciences,4, 330337. https://doi.org/10.1016/S1364-6613(00)01519-9
Hart, S. G., & Staveland, L. E. (1988). Development of the NASA-TLX (Task Load Index): Results of
empirical and theoretical research. In P. A. Hancock & N. Meshkati (Eds.), Human mental
workload (pp. 139183). Amsterdam, the Netherlands: Elsevier.
Hildebrandt, A., Sommer, W., Herzmann, G., & Wilhelm, O. (2010). Structural invariance and age-
related performance differences in face cognition. Psychology and Aging,25, 794810. http://
psycnet.apa.org/doi/10.1037/a0019774
Holdnack, H. A. (2001). Wechsler test of adult reading. San Antonio, TX: WTAR Psychological
Corporation. https://doi.org/10.1007/978-1-4419-1698-3
Jeckeln, G., Hahn, C. A., Noyes, E., Cavazos, J. G., & O’Toole, A. J. (2018). Wisdom of the social versus
non-social crowd in face identification. British Journal of Psychology,109, 724735. https://
doi.org/10.1111/bjop.12291
Jenkins, R., White, D., Van Montfort, X., & Burton, A. M. (2011). Variability in photos of the same
face. Cognition,121(3), 313323. https://doi.org/10.1016/j.cognition.2011.08.001
Lander, K., Bruce, V., & Bindemann, M. (2018). Use-inspired basic research on individual differences
in face identification: Implications for criminal investigation and security. Cognitive Research:
Principles and Implications,3(1), 26. https://doi.org/10.1186/s41235-018-0115-6
Lander, K., Bruce, V., & Hill, H. (2001). Evaluating the effectiveness of pixelation and blurring on
masking the identity of familiar faces. Applied Cognitive Psychology,15(1), 101116. https://
doi.org/10.1002/1099-0720(200101/02)
Ledford, H. (2008). Translational research: The full cycle. Nature News,453, 843845. https://doi.
org/10.1038/453843a
Mattick, R. P., & Clarke, J. C. (1998). Development and validation of measures of social phobia
scrutiny fear and social interaction anxiety. Behaviour Research and Therapy,36(4), 455470.
https://doi.org/10.1016/S0005-7967(97)10031-6
McCaffery, J. M., Robertson, D. J., Young, A. W., & Burton, A. M. (2018). Individual differences in face
identity processing. Cognitive Research: Principles and Implications,3(1), 21. https://doi.org/
10.1186/s41235-018-0112-9
Navon, D. (1977). Forest before trees: The precedence of global features in visual perception.
Cognitive Psychology,9(3), 353383. https://doi.org/10.1016/0010-0285(77)90012-3
Noyes, E., Hill, M. Q., & O’Toole, A. J. (2018). Face recognition ability does not predict person
identification performance: Using individual data in the interpretation of group results. Cognitive
Research: Principles and Implications,3(1), 23. https://doi.org/10.1186/s41235-018-0117-4
Noyes, E., Phillips, P. J., & O’Toole, A. J. (2017a). Face recognition assessments used in the study of
super-recognisers. arXiv preprint arXiv:1705.04739.
Noyes, E., Phillips, P. J., & O’Toole, A. J. (2017b). What is a super-recogniser? In M. Bindemann & A.
M. Megreya (Eds.), Face processing: Systems, disorders and cultural differences (pp. 173201).
New York, NY: Nova Science Publishers Inc.
Super-recognizers: From the laboratory to the world and back again 17
Patterson, F., Ferguson, E., Lane, P., Farrell, K., Martlew, J., & Wells, A. (2000). A competency model
for general practice: Implications for selection, training, and development. British Journal of
General Practice,50, 188193.
Papesh, M. H. (2018). Photo ID verification remains challenging despite years of practice. Cognitive
Research: Principles and Implications,3(1), 19.
Phillips, P. J., Yates, A. N., Hu, Y., Hahn, C. A., Noyes, E., Jackson, K., Cavazos, J. G., ...O’Toole, A. J.
(2018). Face recognition accuracy in forensic examiners, super-recognisers and algorithms.
Proceedings of the National Academy of Sciences,115, 61716176. https://doi.org/10.1073/
pnas.1721355115
Ramon, M. (2018a, June). Super-Recognizers in law enforcement hype or hope? Invited
symposium talk presented at the 29th International Congress of Applied Psychology (ICAP),
Psychology: Connecting Science to Solutions, Montreal, Canada.
Ramon, M. (2018b). The power of howlessons learned from neuropsychology and face
processing. Cognitive Neuropsychology,35(12), 8386. https://doi.org/10.1080/02643294.
2017.1414777
Ramon, M., & Bobak, A. K. (2017). Super-Recognizer. Gehirn & Geist. Spektrum der Wissenschaft.
Ramon, M., Busigny, T., Gosselin, F., & Rossion, B. (2016). All new kids on the block? Impaired holistic
processing of personally familiar faces in a kindergarten teacher with acquired prosopagnosia.
Visual Cognition,24,321355. https://doi.org/10.1080/13506285.2016.1273985
Ramon, M., & Gobbini, M. I. (2018). Familiarity matters: A review on prioritized processing of
personally familiar faces. Visual Cognition,26, 179195. https://doi.org/10.1080/13506285.
2017.1405134
Ramon, M., Sokhn, N., & Caldara, R. (2019). Decisional space modulates visual categorization
Evidence from saccadic reaction times. Cognition,186,4249. https://doi.org/10.1016/
j.cognition.2019.01.019
Rezlescu, C., Susilo, T., Wilmer, J. B., & Caramazza, A. (2017). The inversion, part-whole, and
composite effects reflect distinct perceptual mechanisms with varied relationships to face
recognition. Journal of Experimental Psychology: Human Perception and Performance,
43(12), 19611973. http://psycnet.apa.org/doi/10.1037/xhp0000400
Rice, A., Phillips, P. J., Natu, V., An, X., & O’Toole, A. J. (2013). Unaware person recognition from the
body when face identification fails. Psychological Science,24, 22352243. https://doi.org/10.
1177%2f0956797613492986
Robbins, R., & McKone, E. (2007). No face-like processing for objects-of-expertise in three
behavioural tasks. Cognition,103(1), 3479. https://doi.org/10.1016/j.cognition.2006.02.008
Robertson, D. J., Jenkins, R., & Burton, A. M. (2017). Face detection dissociates from face identification.
Visual Cognition,25(78), 740748. https://doi.org/10.1080/13506285.2017.1327465
Robertson, D. J., Noyes, E., Dowsett, A. J., Jenkins, R., & Burton, A. M. (2016). Face recognition by
metropolitan police super-recognisers. PLoS ONE,11(2), e0150036. https://doi.org/10.1371/
journal.pone.0150036
Rosenthal, R. (2005). Binomial effect size display. Wiley StatsRef: Statistics Reference Online.
Rossion, B. (2009). Distinguishing the cause and consequence of face inversion: The perceptual field
hypothesis. Acta Psychologica,132(3), 300312. https://doi.org/10.1016/j.actpsy.2009.08.002
Russell, R., Chatterjee, G., & Nakayama, K. (2012). Developmental prosopagnosia and super-
recognition: No special role for surface reflectance processing. Neuropsychologia,50(2), 334
340. https://doi.org/10.1016/j.neuropsychologia.2011.12.004
Russell, R., Duchaine, B., & Nakayama, K. (2009). Super-recognizers: People with extraordinary face
recognition ability. Psychonomic Bulletin and Review,16(2), 252257. https://doi.org/10.
3758/PBR.16.2.252
Schraagen, J. M. (2006). Task analysis. In The Cambridge handbook of expertise and expert
performance (pp. 185201). Cambridge, UK: Cambridge University Press.
Schraagen, J. M., Chipman, S. F., & Shalin, V. L. (Eds.). (2000). Cognitive task analysis. London, UK:
Psychology Press.
18 Meike Ramon et al.
Shakeshaft, N. G., & Plomin, R. (2015). Genetic specificity of face recognition. Proceedings of the
National Academy of Sciences,112, 1288712892. https://doi.org/10.1073/pnas.1421881112
Shyu, J. Y. L., Burleson, J., Tallant, C., Seidenwurm, D. J., & Rybicki, F. J. (2014). Performance
measures in radiology. Journal of the American College of Radiology,11(5), 456463. https://
doi.org/10.1016/j.jacr.2013.11.019
Spielberger, C. D., Gorsuch, R. L., Lushene, R. D., Vagg, P. R., & Jacobs, G. A. (1983). Manual for the
State-Trait Anxiety Inventory (STAI). Palo Alto, CA: Consulting Psychologists Press.
Tanaka, J. W., & Farah, M. J. (1993). Parts and wholes in face recognition. Quarterly Journal of
Experimental Psychology,46(2), 225245. https://doi.org/10.1080/14640749308401045
Thomas, A. L., Lawler, K., Olson, I. R., & Aguirre, G. K. (2008). The Philadelphia face perception
battery. Archives of Clinical Neuropsychology,23, 175187. https://doi.org/10.1016/j.acn.
2007.10.003
Towler, A., Kemp, R. I., & White, D. (2017). Unfamiliar face matching systems in applied settings. In
Face processing: systems, disorders and cultural differences. New York: Nova Science
Publishing, Inc.
Verhallen, R. J., Bosten, J. M., Goodbourn, P. T., Lawrance-Owen, A. J., Bargary, G., & Mollon, J. D.
(2017). General and specific factors in the processing of faces. Vision Research,141, 217227.
https://doi.org/10.1016/j.visres.2016.12.014
Wechsler, D. (1999). Wechsler abbreviated scale of intelligence-second edition (WASI-II). San
Antonio, TX: NCS Pearson. https://doi.org/10.1177/0734282912467756
White, D., Burton, A. M., Kemp, R. I., & Jenkins, R. (2013). Crowd effects in unfamiliar face
matching. Applied Cognitive Psychology,27(6), 769777.
White, D., Dunn, J. D., Schmid, A. C., & Kemp, R. I. (2015). Error rates in users of automatic face
recognition software. PLoS ONE,10(10), e0139827. https://doi.org/10.1371/journal.pone.
0139827
White, D., Kemp, R. I., Jenkins, R., Matheson, M., & Burton, A. M. (2014). Passport officers’ errors in
face matching. PLoS ONE,9(8), e103510. https://doi.org/10.1371/journal.pone.0103510
White, D., Phillips, P. J., Hahn, C. A., Hill, M., & O’Toole, A. J. (2015). Perceptual expertise in forensic
facial image comparison. Proceedings of the Royal Society B,282, 20151292. https://doi.org/
10.1098/rspb.2015.1292
Wilhelm, O., Herzmann, G., Kunina, O., Danthiir, V., Schacht, A., & Sommer, W. (2010). Individual
differences in perceiving and recognizing facesOne element of social cognition. Journal of
Personality and Social Psychology,99, 530548. https://doi.org/10.1037/a0019972
Wilmer, J. B., Germine, L., Chabris, C. F., Chatterjee, G., Williams, M., Loken, E., ... Duchaine, B.
(2010). Human face recognition ability is specific and highly heritable. Proceedings of the
National Academy of sciences,107, 52385241. https://doi.org/10.1073/pnas.0913053107
Wirth, B. E., & Carbon, C. C. (2017). An easy game for frauds? Effects of professional experience and
time pressure on passport-matching performance. Journal of Experimental Psychology:
Applied,23(2), 138157. https://doi.org/10.1037/xap0000114
Yin, R. K. (1969). Looking at upside-down faces. Journal of Experimental Psychology,81(1), 141
145. http://psycnet.apa.org/doi/10.1037/h0027474
Young, A. W., Hellawell, D., & Hay, D. C. (1987). Configurational information in face perception.
Perception,16(6), 747759. https://doi.org/10.1068%2fp160747n
Young, A. W., Perrett, D. I., Calder, A. J., Sprengelmeyer, R., & Ekman, P. (2002). Facial expressions
of emotion: Stimuli and tests (FEEST). Bury St. Edmunds, UK: Thames Valley Test Company. Ref
Type: Computer Program.
Received 19 June 2018; revised version received 1 October 2018
Super-recognizers: From the laboratory to the world and back again 19
... The present study investigated visual saliency in a unique observer cohort: Super-Recognizers (SRs), individuals with superior skills for processing facial identity information (Ramon, 2021;Ramon et al., 2019;Russell, Bobak, & White, 2009). To date, only one study has compared the gaze behavior of SRs to that of controls and reported reduced visual attention toward bodies and an increased dwell time on inner facial features, especially the nose (Bobak, Parris, Gregory, Bennetts, & Bate, 2016). ...
... Previous studies revealed that neurotypical observers exhibit reliable idiosyncratic gaze biases toward specific semantic stimulus categories and that their tendency to immediately fixate a face within a scene is correlated with face recognition ability (de Haas et al., 2019). Extending this work, here we examined visual saliency in Super-Recognizers (SRs), individuals with exceptional face identity processing skills (Ramon, 2021;Ramon et al., 2019;Russell et al., 2009). To this end we registered oculomotor behavior of 10 SRs and 43 controls. ...
... Notably, although most patients with ASD or DP show atypical gaze behavior toward faces, the extent can vary idiosyncratically across individuals (Pantelis & Kennedy, 2017). Here, we show that the superior face identity processing skills of SRs (Ramon, 2021;Ramon et al., 2019;Russell et al., 2009) can go along with enhanced salience for faces. However, we also observe large individual differences in gaze biases among SRs. ...
Article
Full-text available
Neurotypical observers show large and reliable individual differences in gaze behavior along several semantic object dimensions. Individual gaze behavior towards faces has been linked to face identity processing, including that of neurotypical observers. Here, we investigated potential gaze biases in Super-Recognizers (SRs) - individuals with exceptional face identity processing skills. 10 SRs, identified with a novel conservative diagnostic framework, and 43 controls freely viewed 700 complex scenes, depicting more than 5000 objects. First, we tested whether SRs vs. controls differ in fixation biases along four semantic dimensions: Faces, Text, objects being Touched and Bodies. Second, we tested potential group differences in fixation biases towards eyes and mouths. Finally, we tested whether SRs show less intra- and inter-individual variability with regard to their preferred vertical fixation position in faces. SRs showed a stronger gaze bias towards Faces and away from Text and Touched objects, starting from the first fixation onwards. Further, SRs spent a significantly smaller proportion of first fixations and dwell time towards faces on Mouths but did not differ in dwell time or first fixations devoted to eyes. Face fixation of SRs also fell significantly closer to the theoretical optimal fixation point for identification, just below the eyes. Our findings suggest that reliable superiority for face identity processing is accompanied by early fixation biases towards faces and preferred saccadic landing positions close to the theoretical optimum for face identification. We discuss future directions to investigate the functional basis of individual fixation behavior and face identity processing ability.
... Indeed, research has shown that unfamiliar face matching skills present substantial individual differences across observers, with some individuals performing at chance levels while others performing at ceiling levels (Bruce et al., 2018;Burton et al., 2010;Estudillo & Bindemann, 2014;McCaffery et al., 2018). Thus, this account highlights the importance of using objective face identification tasks during personnel selection for those applied settings whereby the identification of others is demanded (Bobak et al., 2016;Estudillo, 2021;Fysh et al., 2020;Ramon et al., 2019;Robertson et al., 2016). In addition, according to the data limit account, a large variance of errors in face matching can be explained by the properties of the face stimuli (Estudillo & Bindemann, 2014;Fysh & Bindemann, 2017). ...
... For example, some research has shown that participants were better at matching faces when both faces of the pairs were wearing glasses or were not wearing glasses, compared to a condition in which only one of the faces was wearing glasses (Kramer & Ritchie, 2016), and this effect seems to be driven by match trials (Graham & Ritchie, 2019). A more recent study found that hue congruency (i.e., both faces in color, in grayscale or mixed) does not affect face matching performance, but observers were more prone to make match responses in the incongruent condition compared to the congruent conditions (Bobak et al., 2019). More relevant to our purpose, a recent study comparing matching performance when only one or both faces of a pair had a face mask did not find any advantage of the two-mask condition compared to the one-mask condition (Carragher & Hancock, 2020). ...
... Although previous research has shown congruency effects in unfamiliar face matching (Bobak et al., 2019;Graham & Ritchie, 2019;Kramer & Ritchie, 2016), this congruency effect has not been recently replicated with face masks (Carragher & Hancock, 2020). In the current study, we have reported better performance in the congruent conditions (i.e., full-view and two-mask conditions) compared to the incongruent condition (i.e., one-mask condition). ...
Article
Full-text available
Although the positive effects of congruency between stimuli are well replicated in face memory paradigms, mixed findings have been found in face matching. Due to the current COVID-19 pandemic, face masks are now very common during daily life outdoor activities. Thus, the present study aims to further explore congruency effects in matching faces partially occluded by surgical masks. Observers performed a face matching task consisting of pairs of faces presented in full view (i.e., full-view condition), pairs of faces in which only one of the faces had a mask (i.e., one-mask condition), and pairs of faces in which both faces had a mask (i.e., two-mask condition). Although face masks disrupted performance in identity match and identity mismatch trials, in match trials, we found better performance in the two-mask condition compared to the one-mask condition. This finding highlights the importance of congruency between stimuli on face matching when telling faces together.
... Face perception and face recognition are important components in social and forensic settings (Bruce & Young, 2012). Face memory and face matching abilities focus on face recognition research (Bate et al., 2018;Bobak, Hancock, et al., 2016;Ramon et al., 2019) and face processing research (Verhallen et al., 2017). The Cambridge Face Memory Test Long Form (CFMT+) measuring short-term face memory (Russell et al., 2009) and the Glasgow Face Matching Test Short Form (GFMT-S) measuring face matching (Burton et al., 2010) are frequently used in the laboratory and online studies ( Table 1 in Ramon et al., 2019). ...
... Face memory and face matching abilities focus on face recognition research (Bate et al., 2018;Bobak, Hancock, et al., 2016;Ramon et al., 2019) and face processing research (Verhallen et al., 2017). The Cambridge Face Memory Test Long Form (CFMT+) measuring short-term face memory (Russell et al., 2009) and the Glasgow Face Matching Test Short Form (GFMT-S) measuring face matching (Burton et al., 2010) are frequently used in the laboratory and online studies ( Table 1 in Ramon et al., 2019). Currently, there are only a few reports of psychometric properties for the CFMT+ and the GFMT-S (e.g., Cronbach's α in Petersen & Leue, 2021;Verhallen et al., 2017). ...
... The magnitude of the reliability coefficients of the CFMT+ and the GFMT-S is important for applied and assessment settings like the personnel selection of police officers (cf. Ramon et al., 2019). The lower the reliability coefficients are, the less accurately tests measure a person's true score (American Educational Research Association, 2014). ...
Article
The Cambridge Face Memory Test Long (CFMT+) and the Glasgow Face Matching Test Short (GFMT-S) are frequently used tests in face recognition research. No test-retest results in conjunction with internal consistency, mean inter-item correlations (MICs), and pre-post mean differences have been reported. The internal consistency and the MICs provide insights into the homogeneity of items. In an online study (N = 72), we investigated the test-retest reliability, Cronbach's α, split-half reliability, MICs, and retest mean differences for the CFMT+ and the GFMT-S. The CFMT+ showed satisfactory reliability coefficients above .88, whereas the coefficients of the GFMT-S were mainly dissatisfactory and below .75. We argue that task characteristics like heterogeneous stimulus material might lower MICs, response behavior might enhance reliability, and practice effects might increase the means of the CFMT+ in repeated measurements. Therefore, an integrative evaluation of different psychometric parameters helps explaining variations of reliability in face recognition tests.
... One of the most carefully validated tests is the Cambridge Face Memory Test (CFMT), which measures a person's ability to accurately recognize briefly presented, unfamiliar faces (Duchaine & Nakayama, 2006). High CFMT scores are associated with better performance in many real-world situations, such as cross-race face recognition in social situations (McKone et al., 2021), identity verification (e.g., border patrol), and identification in CCTV footage (Bobak et al., 2016;Ramon et al., 2019;Verhallen et al., 2017), or even following the plot for each character in the television show Game of Thrones (Grabman & Dodson, 2020). ...
Article
Research on face perception has revealed highly specialized visual mechanisms such as configural processing, and provided markers of interindividual differences –including disease risks and alterations– in visuo-perceptual abilities that traffic in social cognition. Is face perception unique in degree or kind of mechanisms, and in its relevance for social cognition? Combining functional MRI and behavioral methods, we address the processing of an uncharted class of socially relevant stimuli: minimal social scenes involving configurations of two bodies spatially close and face-to-face as if interacting (hereafter, facing dyads). We report category-specific activity for facing (vs. non-facing) dyads in visual cortex. That activity shows face-like signatures of configural processing –i.e., stronger response to facing (vs. non-facing) dyads, and greater susceptibility to stimulus inversion for facing (vs. non-facing) dyads–, and is predicted by performance-based measures of configural processing in visual perception of body dyads. Moreover, we observe that the individual performance in body-dyad perception is reliable, stable-over-time and correlated with the individual social sensitivity, coarsely captured by the Autism-Spectrum Quotient. Further analyses clarify the relationship between single-body and body-dyad perception. We propose that facing dyads are processed through highly specialized mechanisms –and brain areas–, analogously to other biologically and socially relevant stimuli such as faces. Like face perception, facing-dyad perception can reveal basic (visual) processes that lay the foundations for understanding others, their relationships and interactions.
Article
Following traumatic brain injury in adulthood, Pierrette Sapey (PS) became suddenly unable to recognize the identity of people from their faces. Thanks to her remarkable recovery of general brain function, liveliness, and willingness to be tested, PS's case of prosopagnosia has been extensively studied for more than 20 years. This investigation includes hundreds of hours of behavioral data collection that provide information about the nature of human face identity recognition (FIR). Here a theory-driven extensive review of behavioral and eye movement recording studies performed with PS is presented (part I). The specificity of PS's recognition disorder to the category of faces, i.e., with preserved visual object (identity) recognition, is emphasized, arguing that isolating this impairment is necessary to define prosopagnosia, offering a unique window to understand the nature of human FIR. Studies performed with both unfamiliar and experimentally or naturally familiar faces show that PS, while being able to perceive both detailed diagnostic facial parts and a coarse global facial shape, can no longer build a relatively fine-grained holistic visual representation of a face, preventing its efficient individuation. Her mandatory part-by-part analytic behavior during FIR causes increased difficulties at extracting diagnostic cues from the crowded eye region of the face, but also from relative distances between facial parts and from 3D shape more than from surface cues. PS's impairment is interpreted here for the first time in terms of defective (access to) cortical memories of faces following brain damage, causing her impaired holistic perception of face individuality. Implications for revising standard neurofunctional models of human face recognition and evaluation of this function in neurotypical individuals are derived.
Article
Face perception is crucial to social interactions, yet people vary in how easily they can recognize their friends, verify an identification document or notice someone’s smile. There are widespread differences in people’s ability to recognize faces, and research has particularly focused on exceptionally good or poor recognition performance. In this Review, we synthesize the literature on individual differences in face processing across various tasks including identification and estimates of emotional state and social attributes. The individual differences approach has considerable untapped potential for theoretical progress in understanding the perceptual and cognitive organization of face processing. This approach also has practical consequences — for example, in determining who is best suited to check passports. We also discuss the underlying structural and anatomical predictors of face perception ability. Furthermore, we highlight problems of measurement that pose challenges for the effective study of individual differences. Finally, we note that research in individual differences rarely addresses perception of familiar faces. Despite people’s everyday experience of being ‘good’ or ‘bad’ with faces, a theory of how people recognize their friends remains elusive. The ability to recognize identity, emotion and other attributes from faces varies across individuals. In this Review, White and Burton synthesize research on individual differences in face processing and the implications of variability in face processing ability for theory and applied settings.
Thesis
Die Fähigkeit, Gesichter zu erkennen und Personen korrekt zu identifizieren, erstreckt sich von unter- bis überdurchschnittlichen Leistungen. Personen mit überdurchschnittlichen Gesichtserkennungsfähigkeiten werden „Super-Recognizer“ genannt und durch ihre Leistungen in Gesichtserkennungstests identifiziert. Im Fokus dieser Dissertation stehen die Untersuchung von Faktoren, die die individuelle Gesichtserkennungsleistung modulieren, und die Überprüfung der psychometrischen Qualität von zwei Gesichtserkennungstests. Die Untersuchung von Einflussfaktoren trägt dazu bei, das Verständnis über beteiligte kognitive Prozesse zu erweitern und andererseits können praktische Implikationen für die Polizeiarbeit abgeleitet werden. Als Gesichtserkennungstests wurden in dieser Arbeit der „Cambridge Face Memory Test Long“ (CFMT+; Russell et al., 2009) und der „Glasgow Face Matching Test Short“ (GFMT‑S; Burton et al., 2010) gewählt. Trotz der Einsatzhäufigkeit und ‑dauer finden sich keine bis wenige psychometrische Kennwerte in der Literatur. Insbesondere bei der Klassifizierung von Leistungsunterschieden müssen Testwerte jedoch eine reliable und valide Aussagekraft haben, um diagnostische Fehlentscheidungen zu vermeiden. In der vorliegenden Arbeit werden die theoretischen Grundlagen und drei empirische Studien vorgestellt, deren englischsprachige Publikationsartikel ebenfalls in der Arbeit zu finden sind. Im letzten Kapitel werden die Ergebnisse der drei Studien im Forschungskontext kritisch diskutiert. Zusammenfassend sprechen die Ergebnisse der Studien für eine zufriedenstellende psychometrische Qualität des CFMT+. Der GFMT‑S scheint dagegen nicht zur Klassifizierung von Leistungsunterschieden geeignet zu sein. Abschließend werden die Limitationen, die Implikationen und der weitere Forschungsbedarf beschrieben. Arbeit kann abgerufen werden unter: https://macau.uni-kiel.de/receive/macau_mods_00002459
Article
Accurate face identity processing (FIP) is a critical component of security professions. Unfortunately, however, rapid face matching as required in real-life situations such as passport controls cannot be improved via training. While here accuracy is a high priority, it is neither the only, nor most important performance-measure. Officers must process high-throughput information as efficiently as possible – accurately and rapidly. In scenarios with grave public safety implications, however, efficiency is not sufficient. Suspect surveillance and mass-data analysis in criminal investigations also demand processing ample sensitive material consistently over extended periods. Police agencies have sought to optimize operations through personnel selection targeting FIP abilities. Yet to date, the lab-based tests researchers have proffered neither reflect officers’ specific tasks, nor the efficiency and consistency critical to accomplishing them. Therefore, we aimed to benchmark the three most challenging FIP tests available against two work-samples — tasks developed in consultation with police practitioners to measure specific, situationally critical performance. We solicited participation from 390 police officers from Regional Police and Criminal Investigation Departments, yielding a representative sample of 114 participating Protection Police Officers, Mass Data Analysts, and Search Unit Members who regularly employ FIP skills in their work. Data-driven analyses of officers’ FIP abilities revealed that work-sample efficiency and consistency represented most relevant dimensions of variation, and accounted for lab-test performance. Furthermore, performance on either work-sample was better predicted by performance on the other, than by lab-based test scores. This demonstrates the limitations of lab-based tests for applied settings, and stresses the need for predicting police officers’ FIP abilities through contextually and practically relevant performance measures.
Article
Full-text available
Super-recognisers inhabit the extreme high end of an adult face processing ability spectrum in the population. While almost all research in this area has evaluated those with poor or mid-range abilities, evaluating whether super-recognisers' superiority generates distinct electrophysiological brain activity, and transcends to different age group faces (i.e., children's) is important for enhancing theoretical understanding of normal and impaired face processing. It may also be crucial for policing, as super-recognisers may be deployed to operations involving child identification and protection. In Experiment 1, super-recognisers (n = 315) outperformed controls (n = 499) at adult and infant face recognition, while also displaying larger cross-age effects. These findings were replicated in Experiment 2 (super-recognisers, n = 19; controls, n = 28), although one SR with frequent infant exposure showed no cross-age effect. Compared to controls, super-recognisers also generated significantly greater electrophysiological activity in event-related potentials associated with pictorial processing (P1) and explicit recognition (P600). Experiment 3, employing an upright and inverted sequential matching design found super-recognisers (n = 24) outperformed controls (n = 20) at adult and infant face matching, but showed no upright cross-age matching effects. Instead, they displayed larger inversion effects, and cross-age inversion effects, implicating the role of holistic processing in their perceptual superiority. Larger cross-age effects in recognition, but not matching suggests that super-recognisers' adult face recognition is partly driven by experience. However, their enhanced infant face recognition suggest super-recognisers' superiority is also experience-independent, results that have implications for policing and for models of face recognition.
Article
Full-text available
Background Matching unfamiliar faces to photographic identification (ID) documents occurs across many domains, including financial transactions (e.g., mortgage documents), controlling the purchase of age-restricted goods (e.g., alcohol sales), and airport security. Laboratory research has repeatedly documented the fallibility of this process in novice observers, but little research has assessed individual differences based on occupational expertise (cf. White et al., PLoS One 9:e103510, 2014; White et al., Proceedings of the Royal Society B 282(1814):20151292, 2015). In the present study, over 800 professional notaries (who routinely verify identity prior to witnessing signatures on legal documents), 70 bank tellers, and 35 undergraduate students completed an online unfamiliar face-matching test. In this test, observers made match/nonmatch decisions to 30 face ID pairs (half of which were matches), with no time constraints and no trial-by-trial feedback. Results Results showed that all groups performed similarly, although age was negatively correlated with accuracy. Critically, weekly and yearly experience with unfamiliar face matching did not impact performance. Conclusions These results suggest that accumulated occupational experience has no bearing on unfamiliar face ID abilities and that cognitive declines associated with aging also manifest in unfamiliar face matching.
Article
Full-text available
This journal is dedicated to “use-inspired basic research” where a problem in the world shapes the hypotheses for study in the laboratory. This review considers the role of individual variation in face identification and the challenges and opportunities this presents in security and criminal investigations. We show how theoretical work conducted on individual variation in face identification has, in part, been stimulated by situations presented in the real world. In turn, we review the contribution of theoretical work on individual variation in face processing and how this may help shape the practical identification of faces in applied situations. We consider two cases in detail. The first case is that of security officers; gatekeepers who use facial ID to grant entry or deny access. One applied example, where much research has been conducted, is passport control officers who are asked to match a person in front of them to a photograph shown on their ID. What happens if they are poor at making such face matching decisions and can they be trained to improve their performance? Second, we outline the case of “super-recognisers”, people who are excellent at face recognition. Here it is interesting to consider whether these individuals can be strategically allocated to security and criminal roles, to maximise the identification of suspects. We conclude that individual differences are one of the largest documented sources of error in face matching and face recognition but more work is needed to account for these differences within theoretical models of face processing.
Article
Full-text available
People vary in their ability to identify faces, and this variability is relatively stable across repeated testing. This suggests that recruiting high performers can improve identity verification accuracy in applied settings. Here, we report the first systematic study to evaluate real-world benefits of selecting high performers based on performance in standardized face identification tests. We simulated a recruitment process for a specialist team tasked with detecting fraudulent passport applications. University students (n = 114) completed a battery of screening tests followed by a real-world face identification task that is performed routinely when issuing identity documents. Consistent with previous work, individual differences in the real-world task were relatively stable across repeated tests taken 1 week apart (r = 0.6), and accuracy scores on screening tests and the real-world task were moderately correlated. Nevertheless, performance gains achieved by selecting groups based on screening tests were surprisingly small, leading to a 7% improvement in accuracy. Statistically aggregating decisions across individuals—using a ‘wisdom of crowds’ approach—led to more substantial gains than selection alone. Finally, controlling for individual accuracy of team members, the performance of a team in one test predicted their performance in a subsequent test, suggesting that a ‘good team’ is not only defined by the individual accuracy of team members. Overall, these results underline the need to use a combination of approaches to improve face identification performance in professional settings. Electronic supplementary material The online version of this article (10.1186/s41235-018-0114-7) contains supplementary material, which is available to authorized users.
Article
Full-text available
In recent years there has been growing interest in the identification of people with superior face recognition skills, for both theoretical and applied investigations. These individuals have mostly been identified via their performance on a single attempt at a tightly controlled test of face memory—the long form of the Cambridge Face Memory Test (CFMT+). The consistency of their skills over a range of tests, particularly those replicating more applied policing scenarios, has yet to be examined systematically. The current investigation screened 200 people who believed they have superior face recognition skills, using the CFMT+ and three new, more applied tests (measuring face memory, face matching and composite-face identification in a crowd). Of the sample, 59.5% showed at least some consistency in superior face recognition performance, although only five individuals outperformed controls on overall indices of target-present and target-absent trials. Only one participant outperformed controls on the Crowds test, suggesting that some applied face recognition tasks require very specific skills. In conclusion, future screening protocols need to be suitably thorough to test for consistency in performance, and to allow different types of superior performer to be detected from the outset. Screening for optimal performers may sometimes need to directly replicate the task in question, taking into account target-present and target-absent performance. Self-selection alone is not a reliable means of identifying those at the top end of the face recognition spectrum. Electronic supplementary material The online version of this article (10.1186/s41235-018-0116-5) contains supplementary material, which is available to authorized users.
Article
Full-text available
We investigated the relationships between individual differences in different aspects of face-identity processing, using the Glasgow Face Matching Test (GFMT) as a measure of unfamiliar face perception, the Cambridge Face Memory Test (CFMT) as a measure of new face learning, and the Before They Were Famous task (BTWF) as a measure of familiar face recognition. These measures were integrated into two separate studies examining the relationship between face processing and other tasks. For Study 1 we gathered participants’ subjective ratings of their own face perception abilities. In Study 2 we used additional measures of perceptual and cognitive abilities, and personality factors to place individual differences in a broader context. Performance was significantly correlated across the three face-identity tasks in both studies, suggesting some degree of commonality of underlying mechanisms. For Study 1 the participants’ self-ratings correlated poorly with performance, reaching significance only for judgements of familiar face recognition. In Study 2 there were few associations between face tasks and other measures, with task-level influences seeming to account for the small number of associations present. In general, face tasks correlated with each other, but did not show an overall relation with other perceptual, cognitive or personality tests. Our findings are consistent with the existence of a general face-perception factor, able to account for around 25% of the variance in scores. However, other relatively task-specific influences are also clearly operating. Electronic supplementary material The online version of this article (10.1186/s41235-018-0112-9) contains supplementary material, which is available to authorized users.
Article
Full-text available
There are large individual differences in people's face recognition ability. These individual differences provide an opportunity to recruit the best face-recognisers into jobs that require accurate person identification, through the implementation of ability-screening tasks. To date, screening has focused exclusively on face recognition ability; however real-world identifications can involve the use of other person-recognition cues. Here we incorporate body and biological motion recognition as relevant skills for person identification. We test whether performance on a standardised face-matching task (the Glasgow Face Matching Test) predicts performance on three other identity-matching tasks, based on faces, bodies, and biological motion. We examine the results from group versus individual analyses. We found stark differences between the conclusions one would make from group analyses versus analyses that retain information about individual differences. Specifically, tests of correlation and analysis of variance suggested that face recognition ability was related to performance for all person identification tasks. These analyses were strikingly inconsistent with the individual differences data, which suggested that the screening task was related only to performance on the face task. This study highlights the importance of individual data in the interpretation of results of person identification ability. Screening tasks are now being implemented in the recruitment of candidates for jobs that involve person identification. To date, such screening has focussed exclusively on face-recognition ability. However, real-world identifications often involve the use of cues from the face, the body, and biological motion. We test whether screening on face-recognition ability predicts person-identification performance more broadly. This research has important implications for the applied field of person recognition.
Article
Full-text available
Previous research has explored relationships between individual performance in the detection, matching and memory of faces, but under limiting conditions. The current study sought to extend previous findings with a different measure of face detection, and a more challenging face matching task, in combination with an established test of face memory. Experiment 1 tested face detection ability under conditions designed to maximise individual differences in accuracy but did not find evidence for relationships between measures. In addition, in Experiments 2 and 3, which utilised response times as the primary performance measure for face detection, but accuracy for face matching and face memory, no correlations were observed between performance on face detection and the other tasks. However, there was a correlation between accuracy in face matching and face memory, consistent with other research. Together, these experiments provide further evidence for a dissociation between face detection, and face matching and face memory, but suggest that these latter tasks share some common mechanisms.
Article
Manual and saccadic reaction times (SRTs) have been used to determine the minimum time required for different types of visual categorizations. Such studies have demonstrated extremely rapid detection of faces within natural scenes, whereas increasingly complex decisions (i.e. levels of processing) require longer processing times. We reasoned that visual categorization speed is not only dependent on the processing level, but is further affected by decisional space constraints. In the context of two different tasks, observers performed choice saccades towards female (gender categorization) or personally familiar (familiarity categorization) faces. Additionally, familiarity categorizations were completed with stimulus sets that differed in the number of individuals presented (3 vs. 7 identities) to investigate the effect of decisional space constraints. We observe an inverse relationship between visual categorization proficiency and decisional space. Observers were most accurate for categorization of gender, which could be achieved in as little as 140ms. Categorization of highly predictable targets was more error-prone and required an additional ~100ms processing time. Our findings add to increasing evidence that pre-activation of identity-information can modulate early visual processing in a top-down manner. They also emphasize the importance of considering procedural aspects as well as terminology when aiming to characterize cognitive processes.
Article
Even experts routinely miss infrequent targets, such as weapons in baggage scans or tumors in mammograms, because the visual system is not equipped to notice the unusual. To date, limited progress has been made toward improving human factors that mediate such critical diagnostic tasks. Here, we present a novel framework for pairing individuals’ estimates to increase target detection. Using a wisdom-of-crowds approach that capitalizes on the visual system’s ability to efficiently combine information, we demonstrated how averaging two noninteracting individuals’ continuous estimates of whether a briefly presented image contained a prespecified target can significantly boost detection across a range of tasks. Furthermore, we showed how pairing individuals’ estimates to maximize decorrelated patterns of performance in one task can optimize performance on a separate task. These results make significant advances toward combating severe deficits in target detection using straightforward applications for maximizing performance within limited pools of observers.