RESEARCH ARTICLE

Super-Memorizers Are Not Super-Recognizers
Meike Ramon1*, Sebastien Miellet2, Anna M. Dzieciol3, Boris Nikolai Konrad4,
Martin Dresler4,5☯, Roberto Caldara1☯
1 University of Fribourg, Department of Psychology, Rue P.A. de Faucigny 2, 1700 Fribourg, Switzerland,
2 Bournemouth University, Department of Psychology, Talbot Campus, BH12 5BB, Poole, United Kingdom,
3 Cognitive Neuroscience and Neuropsychiatry Section, UCL Institute of Child Health, 30 Guilford Street,
WC1N 1EH, London, United Kingdom, 4 Radboud University Medical Centre, Donders Institute for Brain,
Cognition and Behaviour, Kapittelweg 29, 6525 EN Nijmegen, The Netherlands, 5 Max Planck Institute of
Psychiatry, Kraepelinstr. 2–10, 80804 Munich, Germany☯ These authors contributed equally to this work.
* meike.ramon@unifr.ch

Abstract

OPEN ACCESS
Citation: Ramon M, Miellet S, Dzieciol AM, Konrad
BN, Dresler M, Caldara R (2016) Super-Memorizers
Are Not Super-Recognizers. PLoS ONE 11(3):e0150972. doi:10.1371/journal.pone.0150972
Editor: Randi Starrfelt, University of Copenhagen,
DENMARK
Received: November 30, 2015

Humans have a natural expertise in recognizing faces. However, the nature of the interaction between this critical visual biological skill and memory is yet unclear. Here, we had theunique opportunity to test two individuals who have had exceptional success in the World
Memory Championships, including several world records in face-name association memory. We designed a range of face processing tasks to determine whether superior/expert
face memory skills are associated with distinctive perceptual strategies for processing
faces. Superior memorizers excelled at tasks involving associative face-name learning.
Nevertheless, they were as impaired as controls in tasks probing the efficiency of the facesystem: face inversion and the other-race effect. Super memorizers did not show increased
hippocampal volumes, and exhibited optimal generic eye movement strategies when they
performed complex multi-item face-name associations. Our data show that the visual computations of the face system are not malleable and are robust to acquired expertise involving extensive training of associative memory.

Accepted: February 22, 2016
Published: March 23, 2016
Copyright: © 2016 Ramon et al. This is an open
access article distributed under the terms of theCreative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any
medium, provided the original author and source arecredited.
Data Availability Statement: Data are uploaded to
Figshare: https://figshare.com/s/
0cd28f111e3c5155372a.
Funding: This work was supported by a Veni grant of
the Netherlands Organisation for Scientific Research(NWO).
Competing Interests: The authors have declaredthat no competing interests exist.

Introduction
Astonishingly high levels of performance may be achieved, seemingly involuntarily, throughrepeated exposure as demonstrated in the area of face processing. Humans are consideredexperts at processing faces [1] as they are discriminated and recognized more efficiently than
non-face objects of comparable complexity and within-category similarity [2, 3]. The comparably larger detriments for (frequently encountered) faces caused by stimulus inversion (i.e., theface inversion effect; [4–6]), and more efficient processing of faces from the same ethnicalbackground (i.e., the other-race effect, ORE; [7–9]) demonstrate the crucial role of experiencefor face processing skills [10–12]. However, early proposals suggested that some degree of
hard-wiring exists for face recognition [13–15], as supported by more recent studies of its heritability. These studies, involving large cohorts of twins [16, 17], indicate a genetic basis for facerecognition, which is a highly specific ability that is uncorrelated with general visual and verbal
recognition performance.

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Alternatively, extremely proficient performance levels can be acquired deliberately throughextensive practice or training. Chess skill, which is attained through extensive, effortful training[18–20], has been associated with differences in perceptual processing [21–25], as well as memory advantages [26]. Mirroring behavioral observations with dog, car, or greeble experts [2, 27–29], recent evidence suggests that face and expert-chess recognition share common processes.
Investigating the composite effect for faces and chess boards in expert, novice and recreational
chess players, Bogan et al. [30] reported that “[c]hess expertise was positively related to thecongruency effect with chess yet negatively related to the congruency effect with faces”. Furthermore, chess ability has been reported to correlate with neural responses in areas recruited
in expert object recognition including the fusiform gyrus [31].
Another domain in which skill has been linked to experience and acquired expertise isexceptional memory. Superior memorizers (SMs) utilize mnemonic strategies, usually involving visualization and mental imagery [32]. It is ‘not known whether recall gurus start out withsuperior memory and attention or gain those skills through practice’ ([33], p.18; see also [34–37]). However, most SMs attribute their superior recall capacities to these highly trained mnemonic techniques [38, 39], which primarily depend on meaningful encoding, mental imagery
and prepared retrieval structures [40, 41].
International Memory Championships offer a competitive platform for SMs to demonstrateand compare their exceptional memory skills. These include a number of disciplines designed
to test superior memory under various conditions, ranging from memorizing card sequences
to abstract forms. One discipline is particularly interesting for the research field of face perception as it probes individuals’ memory skills for stimuli of ‘involuntary’ expertise: associating
names with human faces. In the present study we took advantage of the unique opportunity of
testing two leading SMs (hereafter referred to as SM1 and SM2). Both have demonstrated aparticular proficiency for face-name learning, as evidenced by having repeatedly won therespective discipline at the annual World Memory Championships, and holding world records
in all face-related disciplines in memory sports in recent years (http://www.world-memorystatistics.com/discipline.php?id=NAMES15). Furthermore, for many years both have ranked
among the top ten contestants in the overall World Ranking of memory sports.
Both cases reported here can be considered as exceptional in comparison to normal controls
who are not SMs, as well as representative of the SM population, who achieve astonishing performances through regular training with well-established mnemonic techniques. The SMs
studied here are of particular interest as they are particularly proficient in the discipline of facename learning. In the discipline of non-face picture learning, however, they are not among the
top scorers suggesting that their performance may in fact depend on the material to bememorized.
The basis of exceptional memory, as well as the reciprocity between memory and perception
have been the addressed by numerous previous studies (for a review see [42]; [43–45]). However, to the best of our knowledge it has not been systematically investigated whether superior
memory is associated with differences in visuo-perceptual processing. Therefore, our interestin studying the SMs reported here was twofold.
On the one hand, we sought to determine whether SMs could be considered as ‘Super-recognizers’ [46, 47], i.e., whether or not they present with differential processing and superior
recognition of faces as demonstrated by individuals on the high-performing end of the face
proficiency continuum. Specifically, Super-recognizers (SRs) are identified as such by theirabove normal performance on a variety of different tests. These can involve unfamiliar faceperception or memory (Cambridge Face Perception Test, CFPT; [48]; Cambridge Face Memory Test, CFMT; [49]; see below for more details), as well as identification of famous individuals by pictures taken long before they were publicly known (Before They Were Famous Test,

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Super-Memorizers Are Not Super-Recognizers

BTWF; [46]). Their superior ability at upright matching of facial identity [50–52] gives rise tocomparably larger a face inversion effect [4], a perceptual phenomenon considered to reflect
face-specific processes. In light of these findings, as well as evidence suggesting that visual processing is modulated depending on individuals’ mnemonic goals [53, 54], we investigatedwhether face processing computations that are considered perceptually rooted are malleable
and modulated through explicit training regimes lending to superior memory. Across a rangeof experiments and recording oculomotor behavior we aimed to ascertain whether two SMs
who excel at face related disciplines would exhibit abnormal perceptual face processing as measured by the face inversion and other-race effect.
Furthermore, previous studies indicate that training is associated with structural changes
within specific brain regions [55]. The hippocampus is crucially involved in memory processes[56], specifically the formation of associations between distinct items, i.e, relational binding[57]. It plays a prominent role in face-name learning [58–60] and previous studies havereported learning-related functional [37] and structural differences in the hippocampal regionof individuals with exceptional memory skills (e.g., [61, 62], however see [37]). We therefore
conducted hippocampal volumetric analyses to determine whether extensive training withmnemonic strategies would be associated with increased hippocampal volumes.

Methods
The project was approved by the institutional Review Committee of the University of Glasgow,
Institute of Neuroscience, and the University of Fribourg, Department of Psychology. The procedures were performed in accordance with approved guidelines and regulations; all participants gave written informed consent.

Subjects
Superior memorizers (SMs). SM1 was 28 and 30 years of age at the time of testing. He is aneuroscientist who has been practicing memory sports since 2002. Among other Guinness
world records, he is listed in the 2014 Guinness Book of Records as record holder for memorizing names and faces. In his record performance he memorized 215 German names to the corresponding faces within 15 minutes at the Memoriad in 2015 in Istanbul. He competes in various
other memory disciplines and has been ranked in the top ten of the Memory Sports World
Ranking list for several years. He teaches mnemonic techniques and credits his superior performance on memory tasks solely to these techniques and training.
SM2 was 33 and 35 years of age at the time of testing. He is a lawyer who first came into contact with memory sports in 2005. He was listed as the international runner up in this sport at
the time of testing (currently listed fourth) and has won national and international tournaments. SM2 is memory world record holder in four of the 17 official competition disciplines,
thereby holding the most memory world records. For a long time he also held all world records
related to names and faces. While many of his other memory records–including three Guinness
records–were achieved with memory techniques, according to himself he has always had a very
good natural memory for names and faces even before learning about memory techniques.
Both SMs reported here provided written informed consent (as outlined in the PLOS consent
form) to publish these case details.
Control participants. For behavioral Experiments 1–5, we tested samples of Western
Caucasian university students who received course credits, or were financially compensated forparticipation (see below and Table 1 for demographic details). We did not specifically control
for age and gender, in keeping with previous studies using similar measures of face processing,which have treated ‘young adults’ aged 17–31 years as a uniform population [63–65]. Similarly,

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Super-Memorizers Are Not Super-Recognizers

Table 1. Demographic details for control samples tested for the four tests of face processing andcomparison of the hippocampal volumes.

Hippocampal volumes

n

males

Mean age

Age range

10

10

31±4

25–37

Behavioral experiments
Face-name learning

21

4

21±3

18–32

Same and other-race face-name learning

16

3

21±3

18–32

Other-race effect

21

3

22±4

18–32

Inversion effect

15

4

27±4

22–35

doi:10.1371/journal.pone.0150972.t001

gender differences are (if anything) either minute or absent, and are therefore commonly
neglected in oculomotor studies of face perception [66]. For experiments Experiments 1 and 2,
which involved face-name learning, German-native speakers were tested as controls for thetwo German SMs.
For measurements of hippocampal volumes, structural neuroimaging data were obtained
from age and gender matched members of staff. Oculomotor behavior was acquired from independent control samples for Experiment 1 (n = 15, four males; mean age: 22 years), Experiment
2 (n = 8, one male, mean age: 22 years; one subject’s data for East Asian face-name learningomitted due to technical difficulties during acquisition), and Experiment 4 (n = 15, five males,mean age: 26 years). As there was no indication of differences in oculomotor behavior between
SMs and these controls oculomotor data was not registered for Experiment 5.
The data were collected with the understanding and written consent of each subject; theexperiments conform to The Code of Ethics of the World Medical Association (Declaration of
Helsinki) and comply with the ethical guidelines of the universities where testing took place.
All individuals depicted in the figures exemplifying stimuli used provided written informed
consent (as outlined in the PLOS consent form) to publish images of their likeness.
Individual SM’s behavioral results were compared to those of the respective control groups
according to Crawford and Garthwaite [67]. This procedure has been developed to estimate
the abnormality of individuals’ test scores relative to control samples; it is most useful given
modest sample sizes, but can be used with data from control samples of any size.

Imaging procedures
For the measurement of hippocampal volumes, T1-weighted three-dimensional MPRAGE
images were collected with a 3T Siemens Trim Trio scanner (Siemens Medical, Munich, Germany). 192 1mm-thick sagittal slices were acquired with a field of view of 256 mm2, leading toa voxel size of 1×1×1 mm3. Echo time was 2.52 ms; repetition time, 1.9 ms; flip angle, 9 degrees.
Hippocampal volumes were measured by one of the authors, AD, using BRAINS2 [68] whileblind to subject identity. First, the hippocampus was outlined on every third slice in the sagittal
plane, starting in the most lateral section and continuing medially until it was no longer visible.
Next, markers representing the outlines made on the sagittal sections were displayed in the coronal plane. This approach helped to delineate the hippocampal boundaries on the coronal sections, especially with respect to the dorsal border with the amygdala. The hippocampus wasthen segmented in every coronal section, following a protocol published previously [69]. The
hippocampal volumes were calculated by summing the areas of the coronal outlines. For eachparticipant, a second measurement was obtained by the same rater, and the average was used
for further analysis. Intra-rater reliability of the two measurements was deemed ‘excellent’(intra-class correlation coefficients: left hippocampus, .910; right hippocampus, .952).

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Intracranial volumes were obtained automatically using the ‘New segment’ procedure in SPM8
(http://www.fil.ion.ucl.ac.uk/), running in Matlab (The MathWorks, Inc). To adjust for thebrain size, the hippocampal volumes were then divided by the intracranial volumes. The volumetric analyses involved two-tailed testing to assess any potential differences between SMs andcontrols.

Behavioral testing
We performed five (sets of) experiments to assess SMs’ face memory and perceptual face processing skills. To confirm their superior face-name association learning skills as demonstrated
by their unique track record in memory sports, we first tested face-name learning situations as
they occur in international memory competitions, with numerous uncontrolled stimuli andnames presented simultaneously (Experiment 1; see Results). Next, subjects learned the namesof individually presented same, and other-race faces, using stimuli which were more controlled(available external and non-facial information, low-level visual properties; Experiment 2), to
ensure that their superior performance would also be observed under such experimental conditions. Furthermore, we aimed to determine whether an own-race superiority would beobserved for associative face-name learning, in order to investigate potential recognition andperceptual processing differences with further experiments.
As mentioned, both SMs tested here attribute their superior face-name memory performance to the deliberate training of mnemonic techniques. Therefore, the remaining experiments intended to evaluate this claim using previously reported or classical paradigms toinvestigate recognition and perceptual processing of faces. First, given previous reports of individuals with extraordinary face processing skills, we aimed to determine whether the SMs
would be considered as ‘super-recognizers’ using three previously reported tests of face discrimination and recognition [46, 47]. These tests (Experiment 3; detailed below) are considered
to tap into perception and memory for unfamiliar faces (CFPT, [48]; CFMT, [49]), as well as
identification of famous individuals by pictures taken before they were famous [BTWF; [46]. It
should be noted, however, that merely the CFMT is accepted as a tool for identifying SR as well
as developmental prosopagnosic individuals. The “large variability in control performance on
the CFPT results in a large standard deviation, making significant differences on single-caseanalyses near impossible to achieve” [50]. Additionally, the correlations obtained between
BTWF and CFMT in the only study reporting BTWF control data [46] “suffer from a sampling
error [and therefore] should be seen as tentative”[50].
Finally, two further experiments aimed at tapping into perceptual processing of faces,
involving old/new decisions of same and other race faces (Experiment 4) and a delayed matching task with upright and inverted faces (Experiment 5).
In line with their successful application of memory techniques, we anticipated that SMs
would generally exhibit superior performance for tasks involving learning of face-name associations. On the other hand, in line with our hypothesis that SMs are not Super-recognizers, we
expected them to perform similarly to normal subjects and below the performance of Superrecognizers as described previously. Likewise, we expected to observe an advantage for processing same- over other-race faces, and upright over inverted faces, as exhibited by normal observers. With the exception of Experiment 3, in which previously reported tests that are madeavailable for research purposes [46] were used, stimuli were specifically tailored to meet therequirements of the task as described in the following.
Experiment 1: Face-name association learning with naturalistic images and externalcues. To validate the expected performance differences between SMs and controls, the firstexperiment was designed to approximate the face-name learning task applied in international

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memory competitions. In keeping with this procedure, we selected stimuli that were not controlled with regards to superficial image properties and availability of external/non-facial information. However, in an attempt to minimize floor effects in controls, the face-name pairings
were viewed freely with no time constraints (participants’ learning times were recorded).
Ninety-six full frontal color photographs were selected from those made available by various
Olympic teams’ websites. These were cropped to the same size and randomly assigned German
first and last names (face-name associations identical for all participants). Four different ‘grids’of faces with their respective names were created, encompassing 1395 x 1048 pixels (150 dpi).
Each grid depicted 12 females and 12 males. Subjects viewed each of the four grids freely withno time constraints (random order of presentation). After all grids had been learned once, thiswas repeated a further two times, i.e., each grid was learned three times in total. Stimulus presentation and response registration was controlled using Matlab (The MathWorks, Inc.) and
the Psychophysics Toolbox extensions [70, 71]. Upon completion of the last learning session,and after a two minute delay, participants received four sheets depicting the learned grids,
albeit without names. They had unlimited time to write down as many first and/or last names
as they could recollect. Subjects received .5 points for each name recalled correctly (first orlast); the maximum score that could be achieved was thus 96 points.
Experiment 2: Western Caucasian (WC) and Eastern Asian (EA) face-name learning.
The second experiment was designed to test whether SMs would show an advantage for processing own over other-race faces in the realm of an explicit memory task, i.e., not in the commonly used old/new recognition tasks (see below; [72]). Contrary to Experiment 1, here more
controlled stimuli were used (for examples see Results), which were further presented individually. Two sets of stimuli (same and other race, i.e., WC and EA) were created from a larger,
continuously expanding database of 3D face models developed at the Institute of Neuroscienceand School of Psychology at University of Glasgow, UK. Stimuli from this database have beenused to address a range of different research questions and are readily perceived as WC or EA,
respectively (see e.g., [73–75]). Each stimulus set consisted of images of 50 identities (25females), which were full-frontal color images rendered from the identities’ original 3D facemodels using 3D Studio Max. The images were cropped of external features and placed on agrey background canvas encompassing 600 x 800 pixels. All faces were normalized for feature
location, as well as luminance and contrast; all faces were randomly assigned entirely new German first and last names (identical across subjects). Participants completed face-name association learning tasks for WC and EA faces, respectively, on two separate days (order randomized;
intervals between testing identical for controls and SMs). Stimulus-name pairs were presented
without time constraints, but subjects were informed that they would see each pair only once;
subsequent trials were initiated by subjects pressing the space bar. After all 50 pairs were presented the previously learned images were presented at random without names while theexperimenter recorded subjects’ verbal responses. Stimulus presentation and response timeregistration was controlled using Matlab (The MathWorks, Inc.) and the Psychophysics
Toolbox extensions [70, 71]. Subjects received .5 points for each name recalled correctly (firstor last); the maximum score that could be achieved for both Western Caucasian and East
Asian face-name learning was 50 points.
Experiment 3: Tests of superior face recognition. To determine whether our SMs could
be considered Super-recognizers we employed three tests that have been utilized in healthy andabnormal populations to assess individuals’ face processing skills: the (long version of the)Cambridge Face Memory Test (CFMT) and Cambridge Face Perception Test (CFPT) [47–49],
and the Before They Were Famous Test (BTWF) [46], which are available free of charge whenused for research purposes. The CFMT involves learning of six unfamiliar male faces fromthree different views, the recognition of which is then tested in a three-alternative forced-choice

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task with increasing difficulty as the test proceeds. This test has been used in more recent studies to identify both SR and developmental prosopagnosics [50–52]. The CFPT is a test of facematching based on perceived similarity, for both upright and inverted faces. Subjects are
required to ‘sort a set of six frontal views of faces by similarity to a target face shown from athree-quarter view’ [46]. The frontal view face stimuli were created by morphing the target facewith six other faces, in varying proportion to create stimuli varying in similarity to the target
face. Finally, in the BTWF test observers are presented 56 photographs of famous individuals,
depicting said individuals before they were famous (often as children), which they are required
to identify by name or other uniquely identifying personal information. As noted above,
although the most reliable and commonly accepted means of identifying SRs remains the
CFMT [50], we tested SM1 and SM2 using the CFPT and BTWF.
Experiment 4: Other-race effect (ORE). The fourth experiment has been reported previously as a sensitive measure for demonstrating the well-established ORE in the realm of an old/new decision task [72]. In this setting the ORE manifests as subjects’ comparably higher proficiency at recognizing faces of their own (as compared to another) race (as tested with twogroups of observers and stimuli, respectively). Thus, this experiment was applied to ascertain
whether SMs present with an ORE, as robustly observed in observers who do not practice mnemonic techniques (see Results for examples of stimuli; a detailed description of the stimuli andexperimental procedure is provided elsewhere [72]). Subjects were presented four learning
blocks, each of which was followed by a recognition block. Each block consisted of images offaces of same-race (WC faces, taken from [76]), or other-race individuals (EA faces, taken from[77]); the same procedure as follows was adopted independent of stimulus race. Fourteen identities were presented in each learning block. Subsequent recognition blocks involved presentation of 28 identities (14 old/new). Importantly, the identities learned were depicted using
different images than those used for recall in the recognition blocks (the same identity displayed different facial expressions; for examples see Results) to circumvent participants
employing image-based learning strategies. Subjects were required to distinguish previously
learned (‘old’) from new identities. Stimulus presentation and response registration was controlled using Matlab (The MathWorks, Inc.) and the Psychophysics Toolbox extensions[70, 71].
Experiment 5: Face Inversion Effect (FIE). This final experiment involved a perceptual
matching task devoid of an explicit long-term memory component. It was designed to assess
whether SMs would show a decrease in face matching performance that is attributed to deficient perceptual encoding of stimuli when presented in an uncommon orientation. This phenomenon—referred to as the face inversion effect [4]—is reduced or absent in developmental
and acquired prosopagnosia [47, 78, 79], and its magnitude correlates with processing of facial
identity [46]. Thus, here we aimed to determine whether SMs would exhibit a larger FIE as
compared to controls. The stimuli were taken from the stimulus set created elsewhere [80];these consisted of images of 20 females converted to greyscale, cropped of external features andplaced on a 242 x 342 pixel canvas with grey background. Additionally, for each image, a noisemask with the same power spectrum as the original image was created (for examples see
Results). Subjects performed a delayed match-to-sample, two-alternative forced-choice task,
implemented with an experimental procedure similar to that employed previously [46, 81]. On
each trial a target stimulus was presented centrally for 150 ms; after a 500 ms blank, the target’s
mask was presented for 200 ms (target faces and mask encompassed ~7 and 8 degrees of visual
angle in height). After a 1000 ms blank two juxtaposed probes (~9 degrees of visual angle inheight) were presented for 500 ms. Subjects responses were recorded 2500 ms from the probes’
onset; trials were separated by a 1000 ms inter-trial interval. The experiment comprised 760 trials (20 identities, paired with each other one; each combination presented twice to control for

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response side), and was completed twice by each subject, on two separate days. For these testing
sessions, participants received different instructions while matching the identical stimuli(order randomized across subjects): irrespective of the stimulus orientation (which varied randomly across trials), they were told to utilize either information located in the lower visual
field, or the eyes. This was done because after initial testing of SM1, he commented that irrespective of stimulus orientation he had focused on the bottom of stimuli presented. SM2 on theother hand reported having focused on the eyes across orientations. For this reason, on the following testing day, we requested both to utilize the respective other strategy and adopted thesame procedure for control subjects (interval between testing identical for controls and SMs).
Stimulus presentation and response registration were controlled using Eprime. Contrary to
Experiments 2 and 4, which involved stimulus presentation blocked by condition (i.e., race),
here orientation varied randomly on a trial-by-trial basis. Therefore, per behavioral measure,analyses were carried out on the difference between upright and inverted face processing
observed for SMs as compared to control subjects.

Eye tracking
For Experiments 1, 2 and 4 oculomotor data was acquired in independent groups of controls
and SMs. Across experiments, saccades and fixations were determined using a custom algorithm using the same filter parameters as the EyeLink software (saccade velocity threshold = 30°/sec; saccade acceleration threshold = 4000°/sec2) and merging fixations close
spatially and temporally (<20ms, < .3°).
Eye movement patterns were compared at a general level between SMs and controls. For
single faces studies (i.e., Experiments 2, 4), the patterns were also compared to prototypical patterns corresponding to local vs. global information sampling strategies. Finally, the number offixation clusters was considered a proxy of the number of key regions attended to.
Fixation distribution maps, weighted by fixation duration, were extracted individually foreach observer. The fixation maps of each of the SMs were correlated with those of each of thecontrols. Similarly, the fixation maps of the controls were correlated with those of all the othercontrols. Since correlation coefficients are not additive, they must be z-normalized [82] before
performing statistical analyses. We thus normalized the obtained correlation coefficients by
j. We then applied a bootstrapping procedureusing Fisher’s transform Z ¼ 0:5  loge j 1þr
1r(10000 resamples) on the obtained Fisher transformed correlation coefﬁcients in order toobtain the distributions of ﬁxation pattern correlations between on the one hand each SM compared to the controls, and on the other hand each control compared to the other controls.
We also correlated the SMs’ fixation patterns to simulated prototypical patterns corresponding to global vs. local information sampling strategies (fixations on the center of the face,
vs. on the eyes and/or mouth, respectively; [83]). The prototypical patterns were created bygenerating 2000 normally distributed random fixations (.5 degree of visual angle as standard
deviation) around either the eyes and mouth for the local prototype, or around the center ofthe face for the global prototype.
Finally, the number of significant fixation clusters (p < .05) for each observer was determined with the iMap toolbox (version 2.1; [84]). iMap identifies fixation clusters in the stimulus space using a robust statistical approach correcting for multiple comparisons, by applying a
one-tailed Pixel test [85]. iMap2.1 does not take into account variability across observers [86],which is a valid approach here given the fact that we are considering the number of fixation
clusters at an individual level. In experiment 1, the number of significant clusters was determined for each grid stimulus independently as there was no reason to expect similar patterns
between grids. The number of significant clusters was then averaged across grids.

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Fig 1. Hippocampus volumetry in SMs and controls. a. ICV-corrected hippocampal volumes of SMs and controls. Z-scores were calculated based onmeans and standard deviations of 10 control participants. b. Hippocampi of SM1 and SM2 (red) and one representative control subject (green) displayed ona coronal slice, as well as in 3D.
doi:10.1371/journal.pone.0150972.g001

Results
Hippocampal volumes
The hippocampal volumes of all participants are shown in Fig 1. Mean and unilateral intracranial volume (ICV) corrected hippocampal volumes of SM2 were significantly below those ofcontrol participants (mean, t(9) = -4.24, p = .002; left t(9) = -5.39, p < .001; right t(9) = -3.09,
p = .013, one sample t-tests, two-tailed). Hippocampal volumes of SM1 did not differ from thecontrols’ (mean, t(9) = -1.55, ns; left, t(9) = -1.71, ns; right, t(9) = -1.34, ns; one sample t-tests,
two-tailed). Thus, our results clearly do not show a positive association between hippocampal
volume and exceptional memory skills for face-name associations.

Behavioral testingExperiment 1: Face-name association learning with naturalistic stimuli. Table 2 and Fig
2 summarize the results observed for SMs and control subjects. Of the 96 possible points, SM1
and SM2 scored 92 and 96, respectively, which was significantly better than controls (t(19) =2.01, p = .03, and t(19) = 2.16, p = .02). This superior performance cannot be attributed to differences in learning times, as for each learning phase, SMs’ learning times did not differ

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Super-Memorizers Are Not Super-Recognizers

Table 2. Results for Experiment 1: Face-name association learning with naturalistic stimuli.
Learning times
Score

1st phase

2nd phase

3rd phase

Total

Controls

37.5±26.5260±149120±59102±54161±87

SM1

92

174

136

116

142

SM2

96

246

161

210

206

doi:10.1371/journal.pone.0150972.t002

Fig 2. Individual subjects’ results for Experiment 1: Face-name association learning with naturalistic stimuli. Subjects’ learning times (left) andoverall recognition scores (right; subjects received .5 points per correctly recalled name). Individual controls are depicted in yellow to red colors, in order torelate their learning times to the respective scores obtained; SMs’ performance is displayed as black (SM1) and blue (SM2) lines and circles, respectively.
doi:10.1371/journal.pone.0150972.g002

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significantly from the time spent by controls (SM1: t(19) = -.54; t(19) = .27, t(19) = .25; SM2:t(19) = -.09; t(19) = .25; t(19) = 1.95, ns per learning phase and SM subject). Note that controls’
behavior was characterized by a large degree of variability, both in terms of learning times andrecall performance.
Analyses of oculomotor behavior revealed highly variable fixation patterns for SM vs. controls, or controls vs. controls. SMs’ fixation patterns were not less correlated with the controls’
than the controls between themselves (SM1 vs. controls: .34±.06; SM2 vs. controls: .27±.06;
controls vs. controls: .19±.02). However, SMs exhibited a larger average number of fixation
clusters than controls (SM1: 20; SM2: 19.5; controls’ range: 11–18), indicating that SMs
attended to significantly more faces than the controls.
Experiment 2: Western Caucasian and Eastern Asian face-name learning. Table 3 andFig 3B summarize the results observed for SMs and control subjects. Statistical tests were performed to test the hypothesis that own-race face processing would be more efficient (i.e., higher
recognition scores, shorter learning times). Controls’ scores (i.e. number of correctly recalled
names) were non-significantly higher for WC as compared to EA faces, t(15) = 1.65, p = .06.
This should be attributed to the floor effect observed for WC face stimuli in over half of thesubjects. Contrariwise, subjects who achieved at least 8 points for WC faces exhibited inferior
performance for EA faces, as well as shorter learning and/or recognition times for own-racecompared to other race faces. Controls’ learning and correct recognition times were not shorter
for WC as compared to EA faces (t(15) = .19, and t(12) = -1.26, ns). Both SMs correctly named
significantly more WC than EA face stimuli (SM1: t(98) = 2.12, p = .02; SM2: t(98) = 2.18, p =.02). Neither of the SMs’ learning times were longer for EA as compared to WC faces (SM1: t(98) = -2.30, p = .99; SM2: t(98) = -1.16, p = .88); the same held for their recognition times(SM1: t(43) = -.56, p = .71; SM2: t(50) = -1.02, p = .84). Thus both SMs showed a clear advantage for learning and recalling face-name associations of same, as compared to other-race faces.
The lack of such a difference on the group level for controls should be attributed to over half ofthe controls exhibiting floor effects.
With regards to oculomotor behavior, SMs’ fixation patterns were not less correlated withthe controls’ than the controls between themselves. This held for both learning of WC facename associations (SM1 vs. controls: .77±.06; SM2 vs. controls: .88±.11; controls vs. controls:.67±.03), as well as EA ones (SM1 vs. controls: .73±.07; SM2 vs. controls: .90±.09; controls vs.
controls: .69±.06). The number of fixation clusters exhibited across control subjects ranged
between 1 and 4 for both WC and EA faces, within which SMs’ fell (see S1 Table). The individual fixation maps further confirm the absence of differences between controls’ and SMs’ fixation patterns (see S1 Fig). Controls displayed individual variability in idiosyncratic samplingstrategies, from global information sampling strategies characterized by fixations on the centerof the face, to local strategies typified by fixations on the eyes and mouth ([87]; see S1 Table).
Although less obvious for EA as compared to WC faces, SM1 showed a tendency to adopt a
local, rather than a global face information sampling strategy, while SM2 seemed to adopt a
more global strategy.
Table 3. Results for Experiment 2: Western Caucasian and Eastern Asian face-name learning.
Western Caucasian
Score

Learning time

Controls

6.9±9.4

SM1

22.5

SM2

30

East Asian
Recall time

Score

Learning time

Recall time

28±23

15±6

3.7±3.1

28±19

18±8

17±6

23±22

13.5

15±6

20±13

38±19

24±22

19.5

33±21

19±11

doi:10.1371/journal.pone.0150972.t003

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Fig 3. a. Examples of stimuli and b. results for Experiment 2: Western Caucasian and Eastern Asian face-name learning. Individual subjects’ performance(number of correctly recalled first and/or last names) plotted as circles on top of the group (bars represent standard deviations); dark circles demonstrateSMs’ performance (SM1: black; SM2: blue).
doi:10.1371/journal.pone.0150972.g003

Experiment 3: Tests of superior face recognition. For the long version of the CFMT SM1
and SM2 achieved 62 and 67 correct, respectively. These scores are well below the previously
used cut-off of (90/102; i.e., two standard deviations above the control average; [46, 47, 50–52]). On the BWTF task SM1 and SM2 recognized 3 and 19 individuals, respectively. For the
CFPT, the number of errors for upright vs. inverted face matching exhibited by SM1 was 46 vs.
54, while SM2 made 18 vs. 50 mistakes. The SMs’ scores are either within the normal range(SM1) or the high end (SM2) of previously reported controls (n = 26, mean upright error ± SD:
35.4±12.9; [47]).
Experiment 4: Other-race effect (ORE). Table 4 and Fig 4 summarize the results observedfor SMs and control subjects. Controls’ RTs did not differ as a function of stimulus race,t(20) = -1.43, p = .17, but their d’ scores were significantly higher for WC as compared to EA
stimuli, t(20) = 6.53, p < .001. Although SM1’s RTs were significantly longer than those of controls for WC stimuli, t(19) = 2.55, p = .02, the difference in RTs for WC and EA stimuli was ofcomparable magnitude as the non-significant RT difference (see above) observed for controls,t(19) = -1.75, p = .10. Comparing SM1’s RTs across conditions revealed no significant

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Table 4. Results for Experiment 4: Other-race effect.
d’

RT

WC

EA

ðWCEAÞ
ðWCþEAÞ

WC

EA

ðWCEAÞ
ðWCþEAÞ

Controls

1.56±.53

1.00±.47.24±.181.74±.70

1.91±.76-.04±.13

SM1

1.53

1.04

.19

3.57

2.87

.11

SM2

1.71

1.24

.16

2.71

1.72

.23

doi:10.1371/journal.pone.0150972.t004

difference, t(38) = 1.12, p = .27. SM2’s RTs did not differ significantly from those exhibited bycontrols, irrespective of race of stimuli (WC: t(19) = 1.35, p = .19; EA: t(19) = -.24, ns). Most
importantly the difference in d’ exhibited by both SMs was comparable to the d’ difference
observed for control subjects (SM1: t(19) = -.38, ns; SM2: t(19) = .23, ns).
Paralleling the findings of Experiment 2, SMs’ fixation patterns were not less correlated withthe controls’ than the controls between themselves (SM1 vs. controls: .77±.07; SM2 vs. controls:.72±.05; controls vs. controls: .64±.03). As for Experiment 2, controls’ strategies varied in theextent to which their information sampling style could be described as local, as opposed to
global (see S1 Table), with SM1 and SM2 exhibiting a more local and global information sampling strategy, respectively (see also S2 Fig).
Experiment 5: Face inversion effect. Table 5 and Fig 5 summarize the results observed for
SMs and control subjects. Irrespective of region attended (eyes vs. bottom part of the face), as
well as behavioral measure considered, performance detriments related to stimulus inversion
did not differ between SMs and control subjects. For matching of the eye region, SM1’s andSM2’s inversion effects did not differ significantly from those observed in controls (accuracy:t(13) = .18, and t(13) = -.12, ns; RTs: t(13) = .64, and t(13) = .23, ns). The same was observed
for matching of bottom face parts, where both SMs’ inversion effects did not differ significantly
from those exhibited by controls’ (accuracy: (t(13) = -1.57, p = .07, and t(13) = -1.05, p = .16;
RTs: (t(13) = -.30, t(13) = .72, both ns).

General Discussion
In the present study we tested face processing and memory of two superior memorizers (SMs),
who regularly excel in international memory competitions and have particularly demonstrated
unique performances in the discipline of face memory, as compared to learning of non-facevisual material. Although here we tested only two single cases, the SMs reported here credit
their memory skills to deliberate training in mnemonic strategies employed by SMs in general,and share several world records in face-name association memory. Several studies have indeed
demonstrated that mnemonic strategies can considerably improve face-name association
learning [88–90]. We sought to determine whether superior face-name association learning
abilities would be observed alongside differences in perceptual strategies of face processing.
This is of particular interest given recent findings suggesting that individuals’ mnemonic goals
modulate visual processing [53, 54]. Furthermore, differences in perceptual processing andmemory advantages have been reported for other fields of expertise, e.g. expert chess players[21–26]. Previous investigations involving SMs have classically focused on identifying intellectual, anatomical and functional correlates of superior memory (e.g., [37, 39]). Our study provides novel insights in the field, by showing that superior memory does not rely on
fundamentally different visual strategies for faces.
Our results demonstrate that neither of the SMs tested here can be considered a ‘Super-recognizer’ [46, 47, 50–52]. Relative to control subjects, SMs’ superior memory performance for

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Fig 4. Examples of stimuli and results for Experiment 4: Other-race effect (ORE). a. Examples of stimuli presented. During encoding subjects were
presented images of WC and EA identities, which were presented depicting a different facial expression during recognition. b. Controls’ mean d’ (barsrepresent standard errors) per race with SMs’ scores (SM1: black; SM2: blue) superimposed. c. Individual subjects’ d’ advantage for recognition of sameover other-race faces (ranked normalized difference scores). All subjects, including SMs showed superior performance for old/new decisions of own-racefaces.
doi:10.1371/journal.pone.0150972.g004

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Table 5. Results for Experiment 5: Face inversion effect.
uprightðuprightinvertedÞ
ðuprightþinvertedÞ

inverted

Eyes matching

accuracyRTaccuracyRTaccuracy

RT

Controls

92.63±4.47591±9684.58±4.75

653±119.0456±.0200.0481±.0339

SM1

87.11

596

78.42

689

.0525

.0721

SM2

84.21

511

76.58

584

.0475

.0665

Controls

91.10±3.74

597±105

83.50±5.84642±129.0443±.0241.0342±.0302

SM1

86.05

743

84.74

775

.0077

.0208

SM2

96.32

765

92.89

842

.0181

.0480

Bottom matchingdoi:10.1371/journal.pone.0150972.t005

name-face associations was observed in the absence of significantly increased hippocampi or fixation pattern differences during tasks of face perception. Finally, extensive and efficient usage of
mnemonic techniques did not abolish commonly reported perceptual impairments of face processing (other-race effect, inversion effect). This contrasts recent evidence suggesting that holistic
face processing may be modulated by cognitive training in individuals with developmental, but
not acquired prosopagnosia [91, 92]. We observed that SMs’ eye movements differed from thoseof controls only in the context of a task involving memorization of face-name association for
multiple, simultaneously presented items. Together, these findings lend support to the idea thatface processing abilities are at least to a certain extent hard-wired and robust to training.

Neurofunctional relationship between memory and perception
Several studies have reported neuroanatomical changes induced by cognitive training (for areview see e.g., [55]), including those observed in the hippocampi of London taxi drivers [61].

Fig 5. Experimental design and results for Experiment 5: Face inversion effect (FIE). a. Examples ofstimuli presented on a given trial (see Methods). b. Controls’ average decrease in accuracy related toinversion as a function of face part attended. Individual subjects’ accuracy and RT advantage for upright over
inverted face matching when attending c. the eyes and d. the mouth. Note that SMs’ performance decreases(SM1: black; SM2: blue) were not significantly different from those observed for controls.
doi:10.1371/journal.pone.0150972.g005
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The hippocampus is crucially involved in memory processes [56] and particularly face-namelearning [58–60]. Various studies indicate that it is critical for domain-general relational binding (i.e., the formation of associations among distinct items; for a review see [57]), and abnormal hippocampal development is associated with face learning impairments in cases ofdevelopmental amnesia [93]. Therefore, we sought to determine whether the SMs tested here
would present with increased hippocampal volumes.
Supporting previous studies which reported no differences in hippocampal volume in SMs[37], and despite our SMs reporting excessive training in a spatial learning strategy (i.e., themethod of loci), they did not present with increased overall hippocampal volumes. Note that
Maguire et al. [61, 62] reported differential effects of navigational expertise on subsections of
the hippocampus in London taxi drivers. Further studies with larger number of SMs are
required to address the question whether memory skill is associated with systematic differencesin sub-regions of the hippocampus.
Our data offer the view that rather than coarse structural differences in the hippocampus,
superior memorizers and controls will likely differ on the functional level of distributed networks, involving prefrontal and medial temporal lobe structures, which operate in concert withthose involved in visual processing [37, 94, 95]. Recent evidence suggests that neural signaturesof vividness and memorability can be observed within structures that receive extensive inputs
from prefrontal regions. These include face and scene-preferential high-level perceptual
regions, as well as medial temporal lobe structures, i.e., the perirhinal cortex and hippocampus[96] and the amygdala [97, 98]. We suggest that such effects of vividness and memorability
may be directly related to one of the main principles involved in skilled memory: meaningful
encoding [40]. However, future functional neuroimaging investigations directly addressing thisquestion are necessary to confirm this hypothesis in our exceptional SMs.

Associative learning in superior memorizers vs. controls
Two experiments were conducted that involved learning of face-name associations. The first was
designed to closely parallel the conditions during international memory competitions, in whicharrays of faces with external information and names were presented. Here, as predicted, both SMs
excelled, achieving significantly higher scores (but similar learning times) compared to controls.
Analyses of fixation patterns generally revealed a large degree of variability, which is attributed tothe complexity of the stimuli depicted (arrays of 24 faces), and parallels the variability observed for
subjects’ recognition performance. SMs’ fixation patterns did not correlate less with those observed
for controls, as compared to when controls’ patterns were correlated. However, both SMs attended
to significantly more faces than any of the controls, most probably indicating the use of an optimal
non-face specific visual scanning strategy. This is in line with their superior recall performance, andis interpreted as controls fixating fewer faces once their memory capacity was reached.
Based on their own reports and post-experiment debriefing, SMs treated faces as tokens inExperiment 1, using other, non-facial information to elaborate during encoding. Indeed previous research indicates that deeper processing of faces, i.e. when more detailed semantic information is associated with faces during formation of associations, can enhance recognition
performance [87, 98–100]. Given the small effect sizes obtained under the use of a confidence
measure, some authors have expressed the need for additional studies using recognition tasks
to strengthen their ‘preliminary indication of a top-down effect during face learning’ [100]. We
believe that SMs’ superior face recall provides strong support for the notion that top-down
effects (e.g., via meaningful and elaborate encoding) are beneficial for subsequent recall. However, future studies using a similar experimental design are required to determine whether theunderlying mechanism of this advantage is specific to faces (see below).

PLOS ONE | DOI:10.1371/journal.pone.0150972 March 23, 201616 / 24Super-Memorizers Are Not Super-Recognizers

One could however argue that SMs’ performance in Experiment 1 and the face-name discipline in international championships do not call upon face processing per se, but rather pictorial memory and name recall. However, the observation that both SMs excel in particular in
the face-name, but not non-face image learning discipline of the World Memory Championships is not compatible with this idea. Nevertheless, the remaining experiments were designed
to expand the initial observations made in Experiment 1, using more controlled stimulus material as used for tasks designed in the domain of face processing (exclusion of non-facial information, equalization of low-level image properties, individual item presentation).
Experiment 2 involved learning of individual face-name associations for same-race (WC)and other-race (EA) faces, all learned with national names. Here, again, in the absence of differences in encoding times, SMs achieved higher scores than control subjects. Importantly, however, all subjects who were not at floor showed comparably superior performance for learningand recalling same-, as compared to other-race face-name associations. These latter results,
obtained in the absence of differences in fixation patterns during learning and recall, provide
an indication that perceptual face processing does not differ in SMs tested here.
Interestingly, despite SMs outperforming controls in both experiments, controls’ performance was characterized by a high degree of variability. Notably, some controls were able toachieve relatively high scores—especially those who took more time during learning phases.
This emphasizes the motivational component underlying superior memory skills as achieved
through mnemonic techniques, which can generally improve memory. Motivation, exposureand ability to select and organize information can lead to impressive knowledge displayed byexperts across various fields [42]. However, it also calls into question the claim that there areno ‘naturals’ [101]. For instance, Wilding and Valentine [32] reported a case of naturally (i.e.,
untrained) exceptional memory, whose performance at face-name learning even exceeded
those of SMs. Further work is required to determine the nature of untrained superior memory.
One possibility is that individuals with natural superior face memory have generally superior
face processing abilities, i.e., are Super-recognizers.

Super face memorizers are not super face recognizers
Perceptual aspects such as distinctiveness of face stimuli or vividness of face imagery have been
demonstrated to strongly influence face-name memory [102–105], suggesting that superior
memory for face-name associations might rely on exceptional perceptual processing of face stimuli. In experiment 3, both SMs completed three tests, for which data from so-called ‘Superrecognizers’ have been reported [46, 47, 50–52]. These involved two tasks of face recognition(CFMT, BTWF test) and one test of face perception (CFPT). SM1’s scores across all tests were
within the range of previously reported normal face processing abilities. SM2 achieved normalscores on the CFMT, the test most commonly used for identification of SRs and developmental
prosopagnosics [46, 47, 50–52]. For the CFPT and BTWF test, SM2 achieved somewhat higherscores, which were, however, in the high end of the data previously reported for normal controls [46, 47]. Thus, based on these previously utilized tests of superior face recognition, theSMs tested here cannot be considered as Super-recognizers.

Normal perceptual face processing in superior memorizers
Experiments 4 and 5 were conducted to further verify the absence of perceptual processing differences in SMs. Importantly, these experiments did not require learning of face-name associations, but instead involved old-new recognition of same and other-race faces, and delayed
matching of upright and inverted faces. In Experiment 4 both SMs showed an advantage fordiscriminating between previously seen and unseen same- over other-race faces. Importantly,

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as in Experiment 2, the magnitude of this so-called other-race effect did not differ from thatobserved in controls, and all observers showed comparable patterns of fixations. Finally, theresults of Experiment 5, which revealed inversion–related performance detriments of comparable magnitude for normal observers and SMs, provided further support of the idea that perceptual face processing is uninfluenced by memory techniques. An open question, however, iswhether perceptual face processing as tested here is robust to feedback training reported to
impact face matching performance [106, 107].

Conclusion
Our data shows that SMs’ superior performance in face-name association is not accompanied
by superior/expert visual skills. It also indicates that SMs excel at face (and other visual) memory tasks because of deliberate training in mnemonic strategies, rather than a pre-existing or
acquired propensity in processing facial information or a reorganization of the computational
processes of the face system. In other words, exceptional memorizers are made, not born [96]and do not possess exceptional visual abilities. In fact, despite extensive training lending totheir ranking as leading competitors in the face memory discipline of the World Memory
Championships, the SMs tested here did not differ from control subjects in tests of perceptual
face processing or face recognition and cannot be considered ‘Super-recognizers’ [46]. However, they did show superior performance when tested with the experimental designs used during international competitions, which involved the memorization of the simultaneouspresentation of many (uncontrolled) faces and names. Future studies are required to clarify thenature of this advantage, including its neuro-functional basis. Furthermore, it would be interesting to investigate in detail SMs who, despite extensive training, are particularly challenged
by face memory disciplines, as well as testing Super-recognizers with the experiments reported
here. Lastly, it would also be of interest to determine whether, during acquisition of mnemonic
techniques, their learning curves for faces (as compared to other visual stimuli) would differ
qualitatively from those of normal controls given their presumed higher efficiency at perceptual face processing and face memory. This would indicate whether stimulus-specific training
effects can be found provided superior pre-training proficiency at handling certain types ofvisual information.
Taken together, our results support the idea that certain aspects of face processing are unaffected by explicit training with mnemonic techniques as employed by SMs. These rely mostly
on perceptual mechanisms which are possibly solely modulated through repeated, real-life
experience [108–111]. Although our findings are confined within the domain of face processing, they are relevant for theories of memory and perception as they indicate that exceptional
memory can be observed in the absence of exceptional visual processing abilities. The neuralcomputations performed by the visual system might be robust and impermeable to the computations performed by the mnemonic system.

Supporting Information
S1 Fig. Individual fixation maps for Experiment 2—Western Caucasian and Eastern Asianface-name learning. Note that SMs’ fixation patterns are in the range of those observed forcontrols. For example SM1’s pattern with WC faces is very similar to that of Control 1 (topleft).
(PDF)
S2 Fig. Individual fixation maps for Experiment 4—Other-race effect (ORE). Note thatSMs’ fixation patterns are in the range of those of the controls. For example SM2’s pattern with

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WC faces is very similar to that of Control 8 (second row, forth column).
(PDF)
S1 Table. Results of the cluster analyses for Experiments 2 and 3. The ZR scores correspondto the Z Fisher’s tranformed correlations between the individual fixation maps and the prototypical local and global models (see Methods).
(DOCX)

Acknowledgments
The authors thank Valerie Schmitz and Natalie Becker for help during stimulus preparationand data acquisition, Prof. David Gadian for guidance regarding hippocampal volume measurements, and two anonymous reviewers for their helpful comments. This work was supported by a Veni grant of the Netherlands Organisation for Scientific Research (NWO).

Author Contributions
Conceived and designed the experiments: MR RC MD. Performed the experiments: MR. Analyzed the data: MR RC SM AD. Contributed reagents/materials/analysis tools: MR RC SM AD.
Wrote the paper: MR RC MD SM AD BK.

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