NeuroImage 189 (2019) 468–475

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NeuroImage
journal homepage: www.elsevier.com/locate/neuroimage

Reliability of individual differences in neural face identity discrimination
Lisa Stacchi a, *, Joan Liu-Shuang b, Meike Ramon a, 1, Roberto Caldara a, 1ab

Eye and Brain Mapping Laboratory (iBMLab), Department of Psychology, University of Fribourg, Switzerland
Psychological Science Research Institute & Institute of Neuroscience, University of Louvain, Belgium

A R T I C L E I N F O

A B S T R A C T

Keywords:
Individual differences
Face discrimination
Reliability
Viewing positions
FPVS-EEG

Over the past years, much interest has been devoted to understanding how individuals differ in their ability toprocess facial identity. Fast periodic visual stimulation (FPVS) is a promising technique to obtain objective andhighly sensitive neural correlates of face processing across various populations, from infants to neuropsychological patients. Here, we use FPVS to investigate how neural face identity discrimination varies in amplitude andtopography across observers. To ascertain more detailed inter-individual differences, we parametrically manipulated the visual input ﬁxated by observers across ten viewing positions (VPs). Speciﬁcally, we determined theinter-session reliability of VP-dependent neural face discrimination responses, both across and within observers (6month inter-session interval). All observers exhibited idiosyncratic VP-dependent neural response patterns, withreliable individual differences in terms of response amplitude for the majority of VPs. Importantly, the topographical reliability varied across VPs and observers, the majority of which exhibited reliable responses only forspeciﬁc VPs. Crucially, this topographical reliability was positively correlated with the response magnitude over
occipito-temporal regions: observers with stronger responses also displayed more reliable response topographies.
Our data extend previous ﬁndings of idiosyncrasies in visuo-perceptual processing. They highlight the need toconsider intra-individual neural response reliability in order to better understand the functional role(s) and underlying basis of such inter-individual differences.

1. Introduction

behavioral level (e.g., Wilhelm et al., 2010; Herzmann et al., 2010;
Kaltwasser et al., 2014), recent ﬁndings emphasize the importance ofidiosyncratic visual sampling strategies. Speciﬁcally, ﬁxation patterns
exhibited by individual observers suggest reliable preferences in sampling different facial information (Mehoudar et al., 2014; Kanan et al.,2015; Peterson and Eckstein, 2013; see also Walker-Smith et al., 1977).
Efforts have also been made to characterize the neural correlates ofindividual differences in perceptual abilities. For instance, these studies
have shown that subjects' electrophysiological face-sensitive eventrelated potentials (ERPs) differ in terms of both amplitude and latency(Gaspar et al., 2011; Herzmann et al., 2010; Kaltwasser et al., 2014; Daset al., 2010; see also Turano et al., 2016 for sub-group analyses).
Inter-individual variation has also been reported for neural facediscrimination responses obtained using a different approach: fast periodic visual stimulation (FPVS). FPVS relies on the brain's tendency tosynchronize to the frequency of external stimulation. This is exploited toderive a neural measure of a process of interest. The process of facediscrimination has been successfully measured with FPVS in an oddballparadigm, where a sequence of same-identity base stimuli are presented

Faces convey an abundance of information and our ability to process
them efﬁciently is crucial for social interactions. This continues tomotivate a large and growing area of research dedicated to unravelingthe principles governing human face cognition. The majority of studies
have sought to identify commonalities across neurotypical observers.
Consequently, individual differences have long been considered as noise,
the inﬂuence of which should be minimized by averaging. Studies
employing such group statistics across a range of methodologies haveidentiﬁed the eye region as a highly diagnostic source of information for
facial identity processing: it receives the most ﬁxations (Yarbus, 1967;
Henderson et al., 2005) in Western observers (e.g., Blais et al., 2008; for areview see Caldara, 2017), is behaviorally relevant (Schyns et al., 2002),
and elicits stronger neural face-sensitive responses (Nemrodov et al.,2014; de Lissa et al., 2014).
However, an increasing number of studies indicate that groupaverage results are not necessarily representative of the population(Arizpe et al., 2017). Adding to inter-individual variability on the* Corresponding author.Department of Psychology, University of Fribourg, Faucigny 2, 1700, Fribourg, Switzerland.
E-mail address: lisa.stacchi@unifr.ch (L. Stacchi).
1
Both authors contributed equally to this work.
https://doi.org/10.1016/j.neuroimage.2019.01.023
Received 7 August 2018; Received in revised form 19 December 2018; Accepted 9 January 2019
Available online 14 January 20191053-8119/© 2019 Elsevier Inc. All rights reserved.

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NeuroImage 189 (2019) 468–475

clinical purposes, and to establish its functional signiﬁcance in both
healthy and impaired populations (Liu-Shuang et al., 2016; Xu et al.,2017).

at a constant high frequency rate, with periodically intervening oddballstimuliconveyingdifferent
identities(e.g.,
A-A-A-A-A-A-B-A-A-A-A-A-A-C-…). Neural synchronization to the oddball frequency(the frequency of identity change) provides an implicit, objective andhighly sensitive measure of neural face discrimination that has been usedin healthy and neuropsychological cohorts (Liu-Shuang et al., 2016;
Liu-Shuang et al., 2014; Norcia et al., 2015).
Using this paradigm, Xu et al. (Xu et al., 2017) were the ﬁrst to reportpronounced individual differences in terms of both the amplitude andtopography of this neural face discrimination response. Beyond this, theauthors reported small but signiﬁcant correlations with behavioral performance, suggesting that the magnitude of this neural response is atleast partially driven by face identity processing abilities. Although theirﬁndings further emphasize the functional importance of neural idiosyncrasies, two important questions remain unanswered.
The ﬁrst question concerns the impact of facial information ﬁxated.
Normally, in the context of neuroimaging studies, observers are required
to ﬁxate a point on or near the center of the face (e.g., Liu-Shuang et al.,2014; Xu et al., 2017). Standardizing the visual input is a commonplace
procedure in pursuit of comparable stimulus-related responses. The
aforementioned functional individual differences in visual samplingstrategies, however, suggest that the diagnosticity of visual informationdiffers across observers. If such observer-dependent preferences for
speciﬁc facial information or viewing positions (VPs) also existed on theneural level, standardizing the input through enforced ﬁxation on one
only VP would be problematic. Speciﬁcally, recording the response
during ﬁxation of a central VP (e.g., the nose) might accurately represent
observers who prefer encoding face-stimuli at this VP, but may misrepresent those who prefer other facial information (e.g., the left eye). To
clarify this issue, in our FPVS paradigm we presented whole-face stimuli
under enforced ﬁxation to ten different VPs.
Such potential individual differences in VP-preference are directly
related to the second open question: To which degree do idiosyncratic
FPVS face identity discrimination responses reﬂect genuine, intrinsic
inter-individual differences, as opposed to situational noise? One way toassess this is to determine whether idiosyncratic face discrimination responses reoccur systematically over time. Noise is a random factor, which
therefore impacts a measure of interest differently across recordings.
Conversely, intrinsic characteristics of a given subject are more stable(Seghier and Price, 2018). As a consequence, their inﬂuence on a given
response should systematically reoccur over time. Thus, quantifying
test-retest reliability across a range of VPs provides a detailed means toestimate the impact of nuisance factors on the signal(s) of interest.
To this date, the reliability of face discrimination responses obtained
by FPVS has only been investigated across trials acquired within the same
testing session and a single VP (Xu et al., 2017). The authors reported theinter-trial stability of the group amplitude obtained by averaging multiple bilateral electrodes. However, their report provided no information
regarding the topographical reliability. Additionally, the investigatedwithin-session reliability of trials spaced in the scale of minutes does notaddress the issue of reliability across longer temporal intervals.
In the present study, we sought to determine the reliability of the facediscrimination responses (as measured with the FPVS paradigm) across
two acquisition sessions separated by a 6-month interval. Aiming for acomprehensive understanding of individual differences in the facediscrimination response, we computed reliability on three different aspects of the response. First, we addressed the reliability of the neural
information-dependent response patterns observed across VPs. Second,
we determined the reliability of the response amplitude independently
for each VP, to assess the consistency of the response strength of individual observers. Third and ﬁnally, we computed each observer's VPspeciﬁc topographical reliability.
Collectively, our analyses aim to provide a comprehensive characterization of individual differences in neural face discrimination
measured with the FPVS paradigm. We believe that this is a crucial
prerequisite for studies aiming to deploy this paradigm for empirical and2. Methods
2.1. Participants
We tested fourteen adults (seven females, one left-handed; mean age:
24.5  3.8 years) with normal or corrected-to-normal vision and nohistory of psychiatric or neurological disorders. Participants were either
undergraduate students from the University of Fribourg, or acquaintances of the experimenters. All subjects provided written informed
consent and received ﬁnancial compensation for participation. The
experiment was approved by the local ethics committee.
2.2. Stimuli and procedure
Stimulus material and procedures were identical to those previously
reported (Liu-Shuang et al., 2014). Stimuli comprised full-front, colored
images of 50 face identities (25 females) displaying neutral expressions.
Images were cropped to exclude external facial features and wereembedded in a grey background. Stimuli were presented on a VIEWPixx/3D monitor (1920  1080 pixel resolution, 120 Hz refresh rate) andsubtended an average of 11.02 (height) x 8.81 (width) of visual angle ata viewing distance of 70 cm. In each 62s trial, face images were shown
through sinusoidal contrast modulation at a frequency of 6 Hz, withimage size varying randomly between 80 and 120% at each cycle tominimize pixel overlap (visual angle ranged between 8.82 and 13.22
(height) and 7.05–10.57 (width)). A randomly selected face identity(base stimulus) was repeated throughout the sequence, with different
face identities (oddball stimuli) interleaved every 7th face (i.e. 6
Hz/7 ¼ 0.85 Hz; Fig. 1A). Each trial began and ended with 2s during
which maximal image contrast progressively ramped up (fade in) anddown (fade out), respectively. Observers were instructed to ﬁxate a crossin the center of the screen while responding via button press to a brief(200 ms) change in the color of the ﬁxation cross, which occurred
randomly eight times within each trial. This served to ensure maintenance of ﬁxation, and a constant level of attention. Subjects were accurate at this task (0.930.1). Performance of one subject (S14) could not
be computed during the ﬁrst session due to technical issues.
We manipulated the viewing position (VP) by spatially arranging thestimuli in order to have one of ten facial regions behind the central ﬁxation cross (Fig. 1B). Thus, these ten conditions differed in terms of theinformation being ﬁxated by the observers throughout a trial. Note that
VP0 was located slightly below the nasion, at what has been reported tobe the “optimal” ﬁxation position for old/new decisions under highly
controlled viewing conditions at the group level (Hsaio & Cottrell, 2008).
Each observer completed 20 trials (two repetitions per VP condition)
during an initial acquisition session, and 20 trials during a second
acquisition session 6-months later. Generally, each session comprised
two runs of ten trials each. However, subjects could request breaks asrequired.
Both acquisition sessions involved identical stimuli and procedures;
behavior was monitored through a webcam located inside the booth. In
total, we obtained 40 trials (4 repetitions x 10 conditions) for eachobserver.
2.3. EEG acquisition
Electrophysiological data were acquired through the Biosemi ActiView software with a Biosemi Active-Two ampliﬁer system recordingfrom 128 Ag/AgCl active electrodes at a sampling rate of 1024 Hz. Fourelectrodes located at the outer canthi and below the eyes were placed forblink monitoring. In this active electrode system, the quality of electrodecontact with the skin was evaluated by the offset relative to the magni469

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NeuroImage 189 (2019) 468–475

Fig. 1. Schematic illustration of the FPVS paradigm
and the experimental conditions (A). Faces were presented through sinusoidal contrast modulation. Basestimuli displayed the same facial identity and appeared ata frequency of 6 Hz. Different identity (oddball) faces wereinterleaved every 7th stimulus. Displayed here are stimuli
presented at VP2. (B) In each condition faces were aligned
in order to have one of the ten viewing positions (VPs) inthe center of the screen and ﬁxated by the subject (seeright for an example).

following the same logic as the baseline-correction). Harmonics wereconsidered as signiﬁcant until the z-scores of two consecutive harmonics
failed to exceed 1.64 (p .05, one-tailed). Signiﬁcant responses at theoddball frequency (0.85 Hz) and its harmonics reﬂect face identity
discrimination, while responses at the base frequency (6 Hz) represent a
combination of face-related processing and general visual responses tothe stimulus presentation. Based on this threshold the oddball response,
which indexes implicit neural face discrimination, was quantiﬁed bysumming the ﬁrst 9 oddball harmonics (i.e., 0.85–7.71 Hz), excluding the
7th harmonic since it is confounded with the base stimulation frequency
rate. The base response was signiﬁcant until the 9th harmonic (i.e.,
6–54 Hz). However, since we were mainly interested in facial identity
processing, and not the general visual response, we only considered thefundamental base frequency (i.e., 6 Hz) as a sanity-check for our experimental manipulation of ﬁxated visual input.

tude of the feedback loop formed by the CMS-DRL electrodes, which washeld below 25 mV throughout the recording. During data acquisition a
0.16–100 Hz ﬁlter was applied to remove any slow drift over time. Digitaltriggers were sent at the start and end of each stimulation sequence by
the VIEWPixx screen to the acquisition computer.
2.4. Analysis
Preprocessing. EEG data were processed with Letswave 5 (Mouraux
and Iannetti, 2008). Continuous data were ﬁrst digitally bandpass
ﬁltered to exclude frequencies below 0.1 Hz and above 100 Hz (4th order
Butterworth ﬁlter). The signal was then downsampled to 256 Hz andsegmented relative to each condition. For each observer, we extracted
20  66s epochs, which included 2 extra seconds pre- andpost-stimulation. An independent component analysis using a square
mixing matrix algorithm was computed to ﬁlter out noise related toblinks expressed by each subject (one single component was selectedbased on its topography and the correspondence between its temporal
waveform and that of the ocular channels). Data were then visuallyinspected for interpolation of noisy electrodes (max. 5% of all scalp
electrodes per observer were replaced through linear interpolation of the
3 surrounding electrodes). Data were then re-referenced to a common
average reference and cropped to an integer number of oddball's cycles
starting 2 s after stimulation onset and ending 2 s before stimulation
offset (¼ 14932 bins).
Frequency domain analysis. The amplitude of EEG responses in thefrequency–domain was extracted using Matlab's built-in Fast Fourier
Transform (FFT) function. Baseline-correction was applied to all of theresulting amplitude spectra by subtracting from each frequency bin the
average of its surrounding 20 bins (excluding the 2 immediately neighboring bins). For visualization purposes, we also converted the raw
amplitude values into signal-to-noise ratio (SNR) by dividing each frequency bin by the average of its surrounding 20 bins (excluding the 2
immediately neighboring bins). We averaged the resulting signal over 24
occipito-temporal electrodes (A9-A16, A22-A29, B6-B11, D31-D32; cf.,
Fig. 2A) to include channels sensitive to both the general and the facediscrimination response. Since the periodic response to our stimulation is
spread over multiple harmonics (Fig. 2) (i.e. integer multiples of thestimulation frequency), we ﬁrst determined the relevant range of frequency harmonics to take into account, independently for base andoddball frequencies. To this end, we z-scored the signal after averaging
across conditions, subjects, and electrodes (Z-scores were computed

2.5. Statistical analysis
Reliability of individual differences in VP-dependent patterns of response
amplitude at the oddball frequencies. To assess whether the patterns ofresponse amplitude across VPs were stable across the two sessions, we
carried out a regression between the two measures. We averaged trials
from the same session and then regressed the face discrimination responses of session one (AmplitudeSession1) onto those of session two(AmplitudeSession2), using Subject as a random effect to account for individual differences (1). To avoid any a priori assumption regarding the
topography of the effect, we performed the analysis for each scalp electrode independently, applying Holm-Bonferroni-correction to accountfor multiple-comparisons.
AmplitudeSession1~AmplitudeSession2 þ (1jSubject)(1)

Reliability of inter-individual differences in face discrimination response
amplitudes. To investigate whether inter-individual variations in response
amplitude were stable over time, we computed their reliability using
Cronbach's alpha coefﬁcient. This measure, which has been used previously in a similar context (Xu et al., 2017), evaluates the similarity of
responses across measurements. We computed Cronbach's alpha acrossfour trials, for each VP and electrode independently. High reliability
indicates that observers, who for example exhibit strong responses at one
time point, are consistent across different trials and sessions. Conformingwith standard practice, we chose a coefﬁcient threshold of .8, whichindicates that noise biases the responses by 20%, while the remaining
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Fig. 2. Frequency spectra. (A) SNR spectrum averaged across 24 posterior electrodes (right) and all subjects, with all VP conditions overlapping (each dot represents
the response magnitude for a VP condition). Orange peaks indicate the general visual response while blue peaks index the face identity discrimination response. (B)SNR spectrum averaged across the same 24 electrodes in three exemplary subjects for two VPs. Note that an SNR of 1 represents noise level.

occipito-temporal electrodes (Fig. 3). Conditions involving ﬁxations on
the left or right side of the face led to the majority of facial information
being represented on the opposite visual ﬁeld, and a shift of the responseto the contralateral hemisphere. For example, ﬁxation on the left eye(Fig. 3; VP1) led to most facial information being presented in the rightvisual ﬁeld, which elicited strongest responses in the left hemisphere.
Overall, averaging across 15 occipito-temporal electrodes (A10-A15,
A23-A28, B7-B9) led to strongest general visual responses for VP2 (i.e.,
nasion VP; 1.05 μV), followed by VP0 (1.04 μV). Given this study's focus
on the FPVS face identity discrimination response, the general visual responses expressed at the base frequency are not considered for further
analyses.

80% represents the measure of interest (Nunnally, 1978). In addition to
single-electrode analyses, to account for the variability across observers
in the oddball response topography, we pooled 12 bilateral
occipito-temporal electrodes and recomputed the reliability at each VP.
Reliability of individual face discrimination response topographies. To
determine the stability of the scalp distribution of the response over time,
topographical reliability was computed by means of Cronbach's alpha foreach observer, at each VP independently. Signals that were subject toanalysis were taken from posterior electrodes (cf., Fig. 6A), which
included typically responsive channels in the context of neural facediscrimination. Compared to amplitude computation, here we opted toconsider a larger region of interest (ROI) in order to determine whether
initially responsive clusters would be stable or change (expand, shift, or
shrink) over time. High reliability of this posterior ROI would indicatethat the electrodes showing stronger responses and forming responsive
clusters are comparable across time. Conversely, low reliability would
suggest that the response predominantly emerges at different electrodes
at different time-points.
Face discrimination response amplitude and topographical reliability. We
investigated the relationship between the topographical reliability(across 44 individual channels) and magnitude (average of a 12-channel
ROI) of the face discrimination response using Holm-Bonferroni corrected Spearman correlations, independently for each VP.
3. Results
3.1. Base frequency: general visual response
A response at the base frequency (6 Hz) indicates successful synchronization of the visual system to the contrast between backgroundand face-stimuli (Dzhelyova et al, 2016; see Fig. 2). At the group level
this general visual response was expressed predominantly at

Fig. 3. Grand average of the general visual response for the 10 VPs.
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temporal clusters (Fig. 5A). The effect ranged from .39 (A10; p .001) to.52 (A28; p .001).
Reliability of inter-individual differences in face discrimination response
amplitudes. Cronbach's alpha was computed across four trials for each VP
and electrode independently to assess the reliability of inter-individual
differences in amplitude. The results show that the most reliable responses were located at occipito-temporal electrodes. All VPs, with the
exception of VP6 (right cheek), elicited reliable responses at least one
channel (Fig. 5B). Overall, VP0 was the condition with the largest clusterof reliable electrodes. When averaging 12 bilateral occipito-temporalelectrodes, reliability ranged from .89 (VP0) to .56 (VP6) (Fig. 5C).
Contrary to analyses on single electrodes, here only ﬁve of 10 VPs elicitedreliable responses in the selected ROI. Interestingly, these ﬁve VPs all
involved ﬁxation of the midline, or the left side of the face.
Reliability of individual face discrimination response topographies. We
have shown that the response patterns across VPs, and the response
amplitude within each VP were both reliable over a 6-month interval at
the group level. To better understand potential individual differences in
response reliability (i.e., Are all observers equally reliable in their
response topography? Is each subject equally reliable across VPs?), we
computed topographical reliability within each observer and independently for each VP.
Response consistency varied across VPs and observers. Across VPs,
the average reliability pooled across all observers ranged from .62 (VP2;
i.e., nasion) to .34 (VP9; i.e., right corner of the mouth). Across observers,
the averaged alpha values from all VPs ranged from .88 (S10) to .09 (S07)(Fig. 6A). Across VPs and the entire cohort, the majority of observers (10/14) showed a reliable response topography for at least one VP. Two
observers showed high topographical reliability for nearly all VPs (9/10
VPs), while eight exhibited reliable response topographies for only
certain VPs (cf., S10 and S09, see Fig. 6). The remaining observers did notshow any reliable responses between sessions (e.g., S07, see Fig. 6A).
Relationship between FPVS response amplitude and topographical reliability. Visual exploration of our data suggested that subjects with morereliable topographies also exhibited stronger and more typical neuralface discrimination response distributions over bilateral occipitotemporal clusters of electrodes. As illustrated in Fig. 7, correlation analyses showed signiﬁcant positive relationships between the response
amplitude pooled within this region, and the topographical reliability ateach VP. The highest and lowest correlations were found for VP1 (lefteye; r ¼ .92, p .001) and VP6 (right cheek; r ¼ .57, p ¼ .03), respectively.

3.2. Oddball frequency: face discrimination response
Signiﬁcant responses at the oddball frequency (i.e. 0.85 Hz ¼ rate offace identity change) and its harmonics at occipito-temporal electrodes
were found in all participants, and indicate successful neural facediscrimination (Fig. 2B for three exemplary subjects) (Liu-Shuang et al.,2014). Here, we manipulated facial information ﬁxated to determine theeffect on subjects’ neural face discrimination response, both in terms ofamplitude and topography, and compare the inter-, and intra-individual
differences across a time-interval of 6 months to estimate their reliability.
VP-modulation of the face discrimination response. We averaged thebaseline-corrected signal from 4 trials to assess the effect of VP on theamplitude of the face discrimination response. In line with previous work
(Liu-Shuang et al., 2016; Xu et al., 2017), this response was generally
expressed at occipito-temporal electrodes (Fig. 4; Supplementary
Fig. S1). Therefore, we quantiﬁed the neural face discrimination responses within a single bilateral occipito-temporal ROI comprising 12electrodes (D31, D32, A9-A12, B6-B11) across which responses wereaveraged. This was done both at the group level (on the grand-average
signal) and at the individual level.
At the group level, the face discrimination response amplitude wasstrongest for ﬁxation at VP5 (i.e., nose; 1.33 μV), followed by VP1 (i.e.,left eye; 1.32 μV) and VP0 (i.e., nasion; 1.25 μV) (Fig. 4; for group andindividual subject values see Supplementary Table S1). However, at theindividual level, we observed strong inter-individual differences in theobserver-speciﬁc response patterns, which deviated from the group level
observation (Fig. 4; Supplementary Fig. S1). Contrary to the group, VP1
elicited the overall strongest response across observers (i.e., left eye, 4subjects), followed by VP5 (i.e., nose, 3 subjects) (cf. Supplementary
Table S1 for all values).
Mirroring these observations, individual observers’ response topographies differed from the group topography. On average the response wasright-lateralized for the majority of VPs. At the individual level, however,patterns were more heterogeneous and oddball responses could belocated at right-, left-, or bilateral occipito-temporal electrodes (Fig. 4).
Reliability of individual differences in VP-dependent patterns of response
amplitude at the oddball frequencies. To determine whether patterns ofresponse amplitude across VPs observed at the ﬁrst session would be
predictive of those recorded 6 months later, we computed a linear
regression between the two measures independently at each electrode.
This analysis revealed a positive relationship at bilateral occipito-

Fig. 4. Neural face discrimination responses. Face discrimination responses (quantiﬁed as the summed baseline-corrected amplitudes at oddball frequencies) are
shown at the group-level and for ﬁve example subjects across the ten VP conditions. Though globally responses occur consistently over occipito-temporal channels,
variation in response amplitude and topography are visible between and within subjects.
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Fig. 5. Reliability of the VP-patterns and FPVS
response amplitude. (A) Data-driven regression results.
F-values of non-signiﬁcant electrodes were set to 0. VPpatterns were most stable at occipito-temporal electrodes. (B) Reliability of response amplitude across subjects is shown for each condition and each electroderespectively. Alpha values below the .8 threshold were set
to 0 for visualization purposes. (C) Reliability of response
amplitude computed across 12 occipito-temporal electrodes (top) for each VP.

Fig. 6. Topographical reliability of the FPVS response. (A) Individual topographical reliability computed across 44 occipito-temporal electrodes for each subject
and VP. The color scale ranged between alpha ¼ 0 (white) and 1 (black). Asterisks indicate alpha .8. (B) VP-dependent FPVS responses for the most (left) and least(right) reliable subject across all four trials.

4. Discussion

Recently, Xu et al. (2017) reported individual differences in theamplitude EEG face discrimination responses indexed by fast periodic
visual stimulation (FPVS). Similar to numerous neuroimaging (andbehavioral) studies, the authors standardized the visual input by
instructing subjects to ﬁxate on one predeﬁned VP (VP0, i.e. the center ofthe face). In the present study we observed that subjects exhibited idiosyncratic patterns of responses across VPs: while some subjects show
stronger responses when ﬁxating central VPs, others exhibit higher responses to facial information in other regions (e.g., VP1, left eye). Ourﬁndings indicate that observations made based on a single location (i.e.
standardized input) may lead to a misrepresentation of a large proportionof subjects. The importance of these idiosyncrasies is further highlighted
by the fact that the patterns observed at one session could predict thoseexhibited 6 months later. This suggests that the robust “neural preference” for some VPs over others is unlikely to be noise-related. Instead, theobserved reliable VP-dependent responses at occipito-temporal sites arelikely to reﬂect intrinsic characteristics of each individual's face-sensitive
neural networks.
Together, these observations may reconcile previous seemingly
inconsistent ﬁndings regarding the facial features eliciting the strongest
face-sensitive neural response. The majority of studies addressing thisThe present study aimed to provide novel insights into individual
differences in FPVS face identity discrimination recorded by EEG. We
explored inter-individual variability by considering the inﬂuence offacial information being ﬁxated (viewing position; VP), and the consistency of face discrimination responses over time. Our data underline theeffect of VP on responses not only at the group level, but especially at thelevel of individual observers, who exhibited idiosyncratic patterns ofresponses across VPs. Speciﬁcally, for any given VP, we observed individual differences – both in terms of response amplitude, as well as its
topography. Our reliability analysis showed that the VP patterns recorded at the ﬁrst session were largely predictive of those observed at thesecond session, i.e. 6 months later. Additionally, individual differences inthe stability of response amplitude systematically occurred across trials
and sessions for the majority of the VPs at occipito-temporal electrodes.
Concerning topographical reliability, we report a considerable degree ofvariation of the scalp distribution of the neural face discrimination responses across observers. Finally, we observed a positive relationshipbetween the topographical reliability of individual observers’ responses,
and the response amplitude over occipito-temporal electrodes.

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Fig. 7. Scatter plots demonstrating the relationship between individual observers' response amplitude and response reliability. For each VP, the average
amplitude across trials over occipito-temporal channels is plotted against the reliability score across individual trials for all subjects. Stronger face discrimination
responses were more reliable. The dotted line marks the threshold for a response to be considered reliable (alpha ¼ 0.8).

question reported that ﬁxation on the eyes leads to stronger responses
compared to other features (e.g., Nemrodov et al., 2014; de Lissa et al.,2014). However, other studies suggest that ﬁxation on other facial regions evokes comparable or stronger responses (e.g., Zerouali et al.,2013; McPartland et al., 2010). Importantly, these different ﬁndings
were made in the context of group studies, which stressed commonalities(at the expense of individual differences) in neural responses. Here,adopting an individual observer approach, we observed that while many
subjects exhibited stronger responses for the eye-region, others exhibited“neural preferences” for VPs closer to the center of the face (i.e.,
VP2/nasion, VP0/center, VP5/nose). This variation in the populationquestions the existence of a per se optimal position for face processing(see Caldara, 2017), and could account for discrepant ﬁndings reported
in the context of earlier group-studies.
Our data also show that individual differences modulate a given
measure – here the FPVS face discrimination response – on different
levels. In the present context, individual differences could be expressed interms of the neural response amplitude, scalp distribution, or response
reliability. This last parameter is crucial to determine whether anobservation carries potentially meaningful information, as opposed toreﬂecting random noise. Altogether, our results lead to two main
considerations.
First, although individual differences can provide a unique source of
information, not all should be considered equally meaningful. Disregarding whether an individual exhibits reliable responses over time canmask effects in a similar vein as does averaging across observers. In order
to identify genuine variations of interest, reliable responses have to be
separated from unreliable ones, which inherently carry more noise. Thisis important in studies investigating the relationship between multiple
responses. Those obtained from unreliable observers are less likely tocorrelate across measures and will therefore introduce noise, potentiallymasking otherwise stronger relationships. Similarly, quantifying individuals’ reliability would also facilitate determining commonalities
across subjects.

Second, individual differences in reliability also indicate that thisparameter should ideally be assessed for any tested cohort. Moreconcretely, an estimate of good internal consistency reported for ameasure within one group of observers cannot be expected to exist in thecontext of other independent observers. Individual subjects’ reliability isan additional parameter that could be reported along with a given ﬁnding
to help its interpretation and assess the extent to which it can begeneralized. We suggest that – at least in the context of face discrimination measured with FPVS – response amplitude over occipito-temporal
regions can provide an initial estimate of its stability over time.
The relationship we report here suggests that precise and stable topographies may relate to more efﬁcient neural responses. This hypothesis
is in line with recent ﬁndings reporting that the magnitude of face sensitive neural responses were associated with behavioral face recognition(Xu et al., 2017; Elbich and Scherf, 2017). Therefore, functionally
meaningful topographical reliability may represent an additional promising parameter to track e.g. development changes in face processing, or
effects of interventions in clinical populations. A covariance between
neural response reliability and amplitude at the individual level could
also be exploited as an objective index to track potential improvements or
impairments in face processing. Further research is required to understand the cause(s) of differential neural response reliability – functionalor anatomical – as well as the directionality of the observed relationshipbetween reliability and response magnitude.
Acknowledgements
This work was supported in part by an FSR-FNRS postdoctoral grantawarded to JLS [grant no: FC 91608].
Appendix A. Supplementary data
Supplementary data to this article can be found online at https://doi.
org/10.1016/j.neuroimage.2019.01.023.
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L. Stacchi et al.

NeuroImage 189 (2019) 468–475

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