Journal of Vision (2022) 22(13):9, 1–16

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Stimulus size modulates idiosyncratic neural face identity
discrimination
Lisa Stacchi

Eye and Brain Mapping Laboratory (iBMLab),
Department of Psychology, University of Fribourg,
Fribourg, Switzerland

Roberto Caldara

Eye and Brain Mapping Laboratory (iBMLab),
Department of Psychology, University of Fribourg,
Fribourg, Switzerland

Humans show individual diﬀerences in neural facial
identity discrimination (FID) responses across viewing
positions. Critically, these variations have been shown tobe reliable over time and to directly relate to observers’
idiosyncratic preferences in facial information sampling.
This functional signature in facial identity processing
might relate to observer-speciﬁc diagnostic information
processing. Although these individual diﬀerences are avaluable source of information for interpreting data,
they can also be diﬃcult to isolate when it is not possible
to test many conditions. To address this potential issue,
we explored whether reducing stimulus size would help
decrease these interindividual variations in neural FID.
We manipulated the size of face stimuli (covering 3°, 5°,6.7°, 8.5°, and 12° of visual angle), as well as the ﬁxation
location (left eye, right eye, below the nasion, nose, andmouth) while recording electrophysiological responses.
Same identity faces were presented with a basefrequency of 6 Hz. Diﬀerent identity faces wereperiodically inserted within this sequence to trigger an
objective index of neural FID. Our data show robust andconsistent individual diﬀerences in neural face identity
discrimination across viewing positions for all face sizes.
Nevertheless, FID was optimal for a larger number ofobservers when faces subtended 6.7° of visual angle andﬁxation was below the nasion. This condition is the mostsuited to reduce natural interindividual variations inneural FID patterns, deﬁning an important benchmark tomeasure neural FID when it is not possible to assess andcontrol for observers’ idiosyncrasies.

Introduction
An increasingly large body of work is providing
evidence of functionally meaningful individual
differences in face processing. Eye movement studies
have reported significant differences across culturesduring face recognition (e.g., Western Caucasians vs.

East Asians; e.g., Blais et al., 2008; Kelly et al., 2010;
Miellet et al., 2012; for a review, see Caldara, 2017).
Notably, variations have also been reported between
individuals from the same cultural background during
face identification, suggesting that observers haveidiosyncratic visual sampling strategies that are not well
represented by group averages (Mehoudar et al., 2014;
Walker-Smith et al., 1977). These idiosyncrasies havebeen shown to be reliable over a period of 18 months(Mehoudar et al., 2014), across settings (i.e., lab vs.
real world; Peterson et al., 2016), and to be sufficiently
distinct to allow algorithms to identify an observer fromtheir scanning paths (Kanan et al., 2015). Crucially,such individual differences in sampling strategies do
not appear to correlate with behavioral performancein identity-related tests. That is, individuals exhibiting
different fixation biases can attain similar performancelevels (Arizpe et al., 2017, Blais et al., 2008). However,forcing observers to fixate away from their naturally
preferred viewing position (VP) is detrimental totheir face identification performance (Peterson &Eckstein, 2013), suggesting that these idiosyncrasies arefunctionally meaningful.
Recently, individual differences in face discrimination
have also been documented at the neural levelby means of the fast periodic visual stimulation(FPVS) electrophysiological approach (Stacchi,
Liu-Shuang, et al., 2019). FPVS consists inpresenting faces of varying identities embedded
at periodic intervals in a stream of same-identity
faces displayed at a particular base frequency (e.g.,
AAAAAABAAAAAACAAAAAAD . . .). A neural
response at the same frequency of identity changes
provides an index of the ability of the neural system todiscriminate between identities (Liu-Shuang et al., 2014;
Rossion et al., 2020). Importantly, studies have shownthat the amplitude of the response triggered by FPVS
is positively correlated with behavioral performance

Citation: Stacchi, L., & Caldara, R. (2022). Stimulus size modulates idiosyncratic neural face identity discrimination. Journal of
Vision, 22(13):9, 1–16, https://doi.org/10.1167/jov.22.13.9.
https://doi.org/10.1167/jov.22.13.9

Received February 6, 2022; published December 29, 2022

This work is licensed under a Creative Commons Attribution 4.0 International License.
Downloaded from jov.arvojournals.org on 01/05/2023

ISSN 1534-7362 Copyright 2022 The Authors

Journal of Vision (2022) 22(13):9, 1–16

Stacchi & Caldara

for face recognition tests (Cambridge Face Memory
Test, Xu et al., 2017; Benton Face Facial Recognition
Test, Dzhelyova et al., 2020) and a FPVS-like identity
discrimination task (Retter et al., 2021). These results
support the notion that the strength of the neural FID
as recorded using the FPVS–electroencephalogram(EEG) approach is a useful tool to index face identity
processing.
Recent findings also show that presenting faces atdiverse facial viewing positions (e.g., left eye, right eye,etc.) leads to significant variations across participantsin terms of neural amplitude patterns and topographies(Stacchi, Liu-Shuang, et al., 2019; see Xu et al.,2017, for similar individual differences during fixationof a central viewing position [VP] just below thenasion). VP-dependent neural responses differed acrossindividuals, with some participants exhibiting larger
responses while fixating, for example, the left eye andothers during fixation of the nose (Stacchi, Ramon,
et al., 2019). Interestingly, the more a face region was
fixated by an observer during natural viewing, themore likely this region was to trigger a strong neuralfacial identity discrimination (FID) response in the
EEG-FPVS. Furthermore, these robust idiosyncrasies
across VPs were reliable over a period of 6 months(Stacchi, Ramon, et al., 2019; see Dzhelyova et al.,2019, for 2-month reliability of the FPVS response at
the central VP). Altogether, these findings suggest that
individual differences recorded through EEG-FPVS canreflect genuine and functionally meaningful variations
in face discrimination.
These reliable and meaningful variations provide
a unique tool for researchers to investigate therelationship between different processes and how
the visual system can flexibly adapt to individual’s
idiosyncrasies to achieve the same goal of facial identity
processing (Stacchi, Liu-Shuang, et al., 2019; Stacchi,
Ramon, 2019, for a discussion). These findings areconceptually in stark contrast with the traditional
experimental methodology of enforcing fixation ona predefined viewing position that is the same acrossall subjects. This approach stems from the need tostandardize the visual input; if the fixation locationis the same, each visual system will receive the same
information, and therefore it should be possible tocompare their response. This might be true under someresearch questions, but it is not a cure-all. For example,
it would have severe implications if a researcher was toassess neural FID at a standardized location and use
this to measure a representative index of individuals’
face system abilities. Within this context, the visual
input would be the same, but how well a given system
could operate on such information would not be agiven, and it would result in some systems facing greater
challenges than others because they were submittedwith a potentially nonoptimal input. The consequence
would be that a subset of the tested observers could
exhibit a neural response that could be categorized as
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weak but that would not be a realistic index of theirneural FID. This misrepresentation could in in turnlead to misleading and noisy data interpretation.
To circumvent this issue, researchers should ideallytest each subject across multiple viewing positions,which would, however, considerably increase testing
duration. This is not always a realistic goal, neitherwith traditional event-related potentials (ERPs) nor
with the FPVS paradigm, which requires significantly
less time than the former to extract high signal-to-noise
ratio (SNR) responses. For instance, young or clinicalpopulations might be unable to undergo long testing
sessions. Additionally, the need to test multiple viewing
positions would result in less time available to test otherexperimental manipulations.
Alternatively, researchers could also first record theoculomotor behavior of observers to establish theirpreferred viewing position and only subsequently
proceed to the EEG recording. However, this approach
would require at least two sessions and two different
techniques, engendering heavy experimental constraints.
To overcome these issues, the current study aimed
to determine if it is possible to reduce the extent of
individual differences across VPs as expressed duringneural face identity discrimination. Essentially, altering
the viewing position of a face induces a change in
the information input to the visual system. Morespecifically, in terms of information intake, the faceregion that is fixated is sampled at high resolution as the
fovea is the portion of the retina with maximal visualacuity, while the facial information surrounding thepoint of foveation is sampled at a lower resolution. Thisis highly relevant when stimuli are relatively large andonly a small portion of a face can be sampled within
the fovea and a single fixation. In our previous studies,
we used relatively large face stimuli (i.e., ∼11° ofvertical visual angle; Stacchi, Liu-Shuang, et al., 2019;
Stacchi, Ramon, et al., 2019). We thus expect that whenstimuli are smaller and most of the facial features canbe sampled within one fixation, the viewing position
will play a minor role in information gathering. If thisprediction is correct, then adjusting stimulus size could
help to reduce individual differences in neural responses
for FID across VPs and therefore make participants
more comparable within the group.
To this aim, we thus recorded EEG signals ofobservers while face images were presented throughfast periodic visual stimulation to trigger neural FID.
Experimental conditions varied along two dimensions,each with five levels: viewing position (left eye, right eye,nose, mouth, and just below the nasion) and size (3°, 5°,6.7°, 8.5°, and 12° of vertical visual angle). We expect
stimulus size to impact both the response amplitude,
independently of VPs, and response patterns across
VPs. First, we expect identity processing to be morenegatively impacted for small faces than for larger ones
(Yang et al., 2014). This might occur because whenstimuli are too small, it might become more difficult

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to rapidly and efficiently extract the fine-grained
information necessary to successfully discriminate
between face identities. This should lead to decreased
and nonsignificant FID responses. To probe thishypothesis, our analysis will first focus on determining
the minimum size at which we can reliably obtain valid
responses. Only once those conditions are identified will
we assess the impact of VP on response patterns.
Then, for the significant neural FID responses, wehypothesize that for smaller face sizes, the point offixation will no longer be relevant because almost allof the information could be sampled by fixating any
facial region. This would result in participants nolonger showing preferences for specific VPs. Therefore,
in this scenario, the arbitrary choice of a fixed VP
when measuring neural FID would not be problematic.
Alternatively, it is possible that as stimuli become
smaller, observers’ VP-related biases will not necessarily
be reduced but will nonetheless converge toward thesame region. When the whole face can be sampledthrough one fixation only, observers could benefit
more from a central viewing position (i.e., just below
the nasion; see Blais et al., 2008; Miellet et al., 2013),
which allows for the simultaneous sampling of a larger
portion of the face foveally.
Regardless of these hypotheses, identifying anexperimental condition that systematically triggers
significant neural FID and simultaneously reduces
individual variations in response patterns across VPs
would represent a significant methodological advance.
It would also increase the already impressive efficiencyof the FPVS technique in establishing a representativeindex of neural FID in only a few minutes.

Materials and methods
Participants
We tested a sample of 30 young Caucasian adults,
mainly undergraduate students at the University of
Fribourg (7 males, 2 left-handed, mean age: 22.4
± 2.5, range: 18–30). Participants had normal orcorrected-to-normal vision, and none had reported
to have a history of psychiatric or neurologicaldisorders. They all provided written consent prior to
the experiment. The study was approved by the local
ethics committee and conformed to the Declaration of
Helsinki.

Procedure
Stimuli and procedure
Stimuli consisted in images of 50 face identities (25females), all displaying a neutral expression. These

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stimuli have been used in previous EEG-FPVS studies(e.g., Liu-Shuang et al., 2014; Stacchi, Liu-Shuang,
et al., 2019; Stacchi, Ramon, et al., 2019; first presented
in a study by Laguesse et al., 2012). Faces were all
full-front, colored, cropped to remove external facial
features, and embedded in a gray background. Stimuli
were presented on a VIEWPixx/3D monitor (1920 ×
1080-pixel resolution, 120 Hz refresh rate) by means of
MATLAB 2016B (PsychToolbox and a custom graphics
toolbox). Within each 62-s trial, a stream of faces waspresented through sinusoidal contrast modulation ata base frequency of 6 Hz; hence, each stimulus lasted
0.166 ms. This frequency was used because it has beenshown to be the most optimal to trigger neural FID in
the context of an FPVS paradigm (Alonso-Prieto et al.,2013; Retter et al., 2021).
Each sequence consisted in one randomly selectedbase face that was repeated throughout the whole
trial and different randomly selected oddball identities(different from the base identity) interleaved periodicallyevery seventh base face (i.e., 6 Hz/7 = 0.85716 Hz)(Figure 1A). Within each trial, face size randomly
varied between 90% and 110% in order to minimize
pixel overlap. This 20% variation has been shown to be
sufficient to reduce low-level adaptation (Dzhelyova &Rossion, 2014).
In order to maintain attention and fixation,
participants were instructed to monitor a fixation crosspresented in the middle of the screen and overlapping
the images stream. The cross changed color randomly
8 to 10 times each trial, and subjects had to report by
button press the occurrence of such change. At thistask, participants performed nearly at ceiling (M =0.92, SD = 0.12).
Trials varied along two dimensions: stimulus size
and VP. Size was parametrically modulated across five
steps (3°, 5°, 6.7°, 8.5°, and 12° of vertical visual angle).
These sizes were chosen in order to not overlap despite
the within-trial 20% variation and were obtained byrescaling the stimuli. We varied the VP by arranging
faces so that one out of five facial regions were alignedwith the center of the screen and hence the fixation
cross. VP consisted of left and right eyes (VP1 and
VP3, respectively), nose (VP5), mouth (VP8), and aregion slightly below the nasion (VP0; Figure 1B and
Figure 2). The last VP was selected to include thefixation location typically tested with this paradigm
while the remaining VPs were selected to induce fixationon the main facial features.
Each combination of size and VP was presented
twice, once with male faces and once with female
faces, resulting in a total of 50 trials (5 sizes × 5 VPs
× 2 repetitions). Trials with evident motion-related
noise contamination were repeated at the end of theexperiment (M = 3). Including numerous breaks
taken to ensure subjects’ attentive state, testing lasted
approximately 1.5 h.

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Figure 1. Schematic illustration of the paradigm and the experimental conditions. (A) Faces were presented using fast periodic visual
stimulation and a sinusoidal contrast. (B) Conditions varied in terms of stimulus size (top row) and viewing position (bottom row). The
face images depicted in this ﬁgure were created by the authors, were not used in the study, and are for illustration purposes only.
Permission to use these identities has been granted to the authors.

Figure 2. Schematic illustration of a 2° foveal visual ﬁeld depending on stimulus size and VP. To allow for an easier comparison of the
foveal coverage across conditions, the foveal visual ﬁeld was resized accordingly. The face images depicted in this ﬁgure were createdby the authors, were not used in the study, and are for illustration purposes only. Permission to use this identity has been granted tothe authors.

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EEG acquisition
EEG was acquired by means of BioSemi(Amsterdam, Netherlands) ActiView software with a
Biosemi Active-Two amplifier system and 128 Ag-AgCl
Active electrodes. Offset was lowered and maintained
below 25 mV relative to the common mode sense (CMS)
and driven right leg (DRL) by slightly abrading thescalp and adding saline gel. The signal was digitalized ata sampling rate of 1024 Hz and online bandpass filtered
between 0.1 and 100 Hz to remove slow drifts over time.
Digital triggers were sent by means of a VPixx
Technologies (Saint-Bruno, Canada) screen.

EEG preprocessing and statisticalanalysis
Preprocessing
Preprocessing was carried out in Letswave 5(Mouraux & Iannetti, 2008), an open-source toolbox
running in MATLAB 2016b.
First, the EEG signal was filtered with a bandpass
fourth-order zero-phase Butterworth filter between 0.1
and 100 Hz and downsampled to 256 Hz. Subsequently,
data were segmented in epochs starting 2 s before
stimulation onset until 2 s after stimulation offset.
Independent component analysis was computed using
a square mixing matrix algorithm, and the component
that more strongly related to eye blinks was removed
in each subject (components were identified throughtheir topography and the correspondence between
their time course and that of frontal electrodes locatedabove the eyes). Data were then visually inspected
and cleaned using trial substitution and channelinterpolation. First, bad trials flagged during dataacquisition and therefore repeated at the end of theexperiment were compared with their repetition, andthe one containing more noise (larger fluctuation ornoise over a larger number of channel) was removed.
Subsequently, noisy channels were visually identified
and replaced through linear interpolation of theirthree closest neighboring electrodes (max. 5% of
all scalp electrodes were interpolated per observer).
Data were then re-referenced to the average of
all electrodes and additionally cropped between
2 s after and before stimulation onset and offset,
respectively. This resulted in segments of approximately
58.33 s (= 14,933 bins). This specific length wasselected as it corresponds to an integer number ofoddball cycle, which will allow for precise extractionof frequencies of interest. Finally, epochs wereaveraged together within condition for each subject
separately.

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Frequency domain analysis
Frequency spectrum was extracted from averaged
epochs by means of fast Fourier transform (FFT),
MATLAB’s built-in function.
Signiﬁcant harmonics
In order to assess the number of base and oddballharmonics to include in the response quantification,
data were averaged across subjects and 26 occipital
temporal electrodes (selected based on previous studies
and visualization of the current data set throughconditions and subjects; A9–A16, A22–A29, B6–B12,
D30–D32). The grand-average signal was z-scored(20 surrounding frequencies, excluding immediately
adjacent and two most extreme bins [minimum andmaximum]), and responses to base and oddball
fundamentals and harmonics were isolated. Harmonics
were retained for further analysis if their z-scoreexceeded the 1.64 (p < 0.05, one-tailed) thresholdfor more than half of the conditions. This led to four
significant harmonics for the base frequency (from 6
to 24 Hz) and 10 harmonics for the oddball frequency(from 0.8572 to 8.572 Hz), excluding the seventh as itcorresponds to the fundamental of the base. However,as the 10th harmonic frequency falls within the alpharange, we only retained harmonics up to, and including,the 9th (i.e., 7.715 Hz).
Response quantiﬁcation
In order to quantify base and oddball responses,
FFT signals were first baseline corrected by subtractingfrom each frequency the average of 20 surrounding
frequencies (excluding immediately adjacent andtwo most extreme bins [minimum and maximum]).
Subsequently, baseline-subtracted harmonics weresummed together.

Relationship between base and oddball
responses
Visual inspection of the data suggested that both thebase and the oddball response amplitude increased atlarger sizes. We performed within-subject Spearman
correlation to explore whether the two responses
covaried. We applied Holm–Bonferroni correction tocontrol for multiple comparisons.

Eﬀect of stimulus size on response amplitude
To evaluate the impact of stimulus size on boththe base and oddball responses amplitude, 26

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occipitotemporal electrodes were pooled together (i.e.,
A9–A16, A22–A29, B6–B12, D30–D32). Subsequently,
size effect was evaluated for each VP separately using
a linear mixed model. To account for interindividual
variations, the variable subject was added to the model,
which can be summarized as follows:
Amplitude ∼ Size + (1|Subjects) .

(1)

Post hoc contrasts between each size were corrected
for multiple comparisons using Tukey’s honestly
significant difference (HSD) test.

6

Outlier detection
Within this article, we defined a preferential response
as a significantly stronger, or outlier, response for a
VP compared to responses to other VPs. Therefore,
outlier detection was performed on each subject
and size independently by estimating the deviationof each VP-related response through the medianabsolute deviation (i.e., MAD). Therefore, each
response was converted into its deviation (MADscore ) as
follows:
MADscore = xi − M j /MAD,(2)

Signiﬁcance and outlier detection
The impact of size and viewing position variation
was evaluated based on two criteria. The first is response
significance. The main goal was to determine whether a
response can be reliably obtained from a large portionof the subjects independently of condition, or on thecontrary, some parameters are suboptimal for such
purpose.
Second, we aimed to determine the extent andhomogeneity of VP-dependent preference across sizes.
Specifically, our goal was to assess whether at somesizes, the neural bias would either decrease or converge
across subjects toward the same VP.
Response signiﬁcance detection
We first determined response significance at theindividual level. We reasoned that if a size would
systematically fail to elicit a significant response, it
could be excluded from further statistical analysis on
VP-related preference, hence reducing data dimension
and number of comparisons.
Therefore, for each type of response (baseand oddball), each subject, and each condition
independently, FFT epochs were cropped into
segments composed of group-significant harmonics(first to fourth for base response and first to ninth
harmonics, excluding the seventh for the oddball)and 24 surrounding bins. These smaller epochs werethen summed together before pooling 26 occipital
temporal electrodes together. Finally, the z-score of thefrequency of interest was computed by means of thesame parameters used at the group level.
Responses above 1.64 (p < 0.05) were consideredsignificant. In order to later compute proportions,
responses were then relabeled as 1 if they weresignificant and as 0 otherwise. However, the focus
of subsequent analyses was more on the absenceof a significant response. Therefore, to facilitate
visualization, proportion, and proportion difference
estimation of nonsignificant responses, binomial
values were switched, with 1 indexing a nonsignificant
response and 0 a significant one.

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where
MAD = c ∗ median(|xi − M j )|),
where xi is the ith item of the data series, Mj is themedian of the data series, and c = 1/0.75 quantile ofthe data series.
Any response with a deviation exceeding 2.5 wasconsidered an outlier (Leys et al., 2013). As for
significance detection, responses’ deviation score wasrelabeled as 1 if the response was considered an outlier
and 0 otherwise. Importantly, subjects might exhibit
more than one outlier response. To avoid considering
each subject multiple times, for statistical comparisonof the total number of subjects exhibiting outlier
responses, each participant was counted not more thanonce.
Complete information regarding the number ofsubjects showing outlier or nonsignificant responses
across VPs and size was then summarized separately.
Changes in proportion in terms of nonsignificant
or outlier responses were evaluated by means of the
McNemar mid-p test, which is well suited to compare
dependent samples of medium or small sizes (Fagerland
et al., 2013). Multiple comparisons were controlledusing Holm–Bonferroni correction. Moreover, 95%
confidence intervals of proportions and proportion
differences were estimated by means of 5,000 bootstrap
samples.
Finally, after identifying the condition(s) most
optimal in terms of significant responses and outlier
convergence, we explored the impact of these conditions
on subjects for whom it did not trigger extremely
large responses. Our aim was to identify not only thecondition most optimal for the majority of subjects
but also the one that was the least suboptimal (i.e.,
triggering weaker within-subject responses comparedto other conditions). To do this, for each size ofinterest, we explored the response triggered by the“optimal” VP in terms of significance and how it ranked
with respect to responses evoked by other viewing
positions.

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Results
Base response
The neural response at the general frequency wascomputed to ensure the proper synchronization of thevisual system to the visual stimulation.
The results of the linear mixed model evaluating
the effect of size on the response amplitude show thatsize has a significant effect on response amplitude at
all VPs: VP0, F(4, 136) = 56.33, p < 0.0001; VP1, F(4,136) = 30.11, p < 0.0001; VP3, F(4, 136) = 33.84, p <0.0001; VP5, F(4, 136) = 9.96, p < 0.0001; and VP8,
F(4, 136) = 13.42, p < 0.0001. Post hoc comparisons
showed that the base response grows significantly as
stimuli’s size increments (Figure 3A). Topographyshows a medial-occipital distribution for conditions
where fixations were directed toward the center of theface. When fixations were enforced on the left andright eyes, we found an additional activity in ipsilateral
occipital regions, which increases with stimuli’s size
(Figure 3A).
At the individual level, the base response wassignificantly stronger than surrounding noise in all 25
conditions for all 35 subjects except for 1 condition (i.e.,
3°-VP0) for S16.

Oddball responseGroup level
The oddball response was used as an index of neural
FID. The results from the linear mixed model revealed
a main effect of stimulus size on response amplitude,
independently of the VP considered: VP0, F(4, 136)
=14.21, p < 0.0001; VP1, F(4, 136) =13.77, p < 0.0001;
VP3, F(4, 136) = 9.40, p < 0.0001; VP5, F(4, 136)
= 14.83, p < 0.0001; and VP8, F(4, 136) = 11.64, p< 0.0001. Post hoc contrast between sizes showedthat response amplitude increases significantly withstimuli’s size. However, it plateaus at size 6.7° or 8.5°
depending on the VP considered, in which case a further
size increment does not lead to a significantly larger
response. Figure 3B summarizes the group responses aswell as the results of the contrasts extracted from thelinear mixed model.
Individual level
Base and oddball relationship: Holm–Bonferroni
corrected Spearman correlations between oddball andbase response resulted in only one subject showing asignificant relationship between responses at the baseand oddball frequencies (S1, r = .68, p < 0.001; see
Figure 4 for all subjects).
Response significance: The significance of the response
was severely modulated by the stimulus size. Figures 5

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and 8 highlight the number of subjects who failed
to show a significant response according to size and
VP. As it becomes apparent, the smallest size thatwas tested was associated with the largest numberof nonsignificant (NS) responses. Importantly, mostsubjects (i.e., 27/35) exhibited nonsignificant responses
for some viewing positions but significant neural FID
for other VPs (Figures 6 and 8, Supplementary Table
S1). To ensure that significance of those responses was
not due to a subtle difference, significant responses
were also evaluated against a more severe threshold of
p = 0.01. This showed that a pattern of significant (atp = 0.01) and nonsignificant (at p = 0.05) persisted in
25 subjects (Supplementary Table S1 for z-score of all
subjects in all conditions).
Collapsing all VPs together within each size showsthat at the smallest stimulus size (3°), 85.7% (95%
confidence interval [CI]: 74.4, 97.1) of subjects exhibitedat least one nonsignificant response. When considering
the next size (5°), the total number of NS subjects
decreased by 9 (25.7%; 95% CI: 9, 42.5; mid p =0.0032), leaving only 60% (95% CI: 43.4, 76.6) ofsubjects with at least one nonsignificant response. A
similar significant decrease (8 subjects, 22.9%; 95% CI:
8.7, 37.1; mid p = 0.0018) was observed between sizes
5° and 6.7°, where less than half of the subjects (37.1%;
95% CI: 20.8, 53.5) exhibited a nonsignificant response(Table 1, Figures 5 and 8). As the size kept increasing,
the number of nonsignificant subjects stabilized, andthe remaining differences were no longer significant (seeFigure 6 for all differences).
Outlier VP-dependent responses: Because the two
smallest sizes (i.e., 3° and 5°) triggered a nonsignificant
response in more than half of the subjects, we decided
to not investigate these conditions beyond descriptive
statistics and to mainly focus on the remaining sizes.
As shown in Figure 7A, size 8.7° led to more thanhalf of the subjects to show at least one outlier response(51.4%; 95% CI: 33.5, 69.3), while in size 6.7° and 12°,
this occurred in less than a third of the participants(Table 2). Additionally, the paired difference of eightsubjects (22.9%; 95% CI: −1.6, 47.3) between size 8.5°
and 12° was significant (mid p = 0.039), as well as thepaired difference of eight subjects (22.9%; 95% CI:
2.7, 43.1) between size 6.7° and 8.5° (mid p = 0.0245)(Table 2).
The outlier distribution across VPs shows that VP0
led to a larger number of outliers in size 6.7° and 12°,
while this was obtained by VP3 in size 8.5°. Importantly,
VP0 in size 6.7° was the outlier response for 70% of thesubjects showing an extreme response. On the other
hand, VP0 included only 50% of subjects in size 12°
and VP3 55.6% of subjects in size 8.5° (Figure 7B).
Finally, at size 6.7°, 8.5°, and 12°, fixation on VP0,
VP3, and VP0, respectively, led to a nonsignificant
response in five, four, and four subjects (Figure 5).
Excluding those, the same viewing positions also

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Figure 3. Grand-average base and oddball responses for all sizes and viewing positions. Responses at the base (A) and oddball (B)frequencies are visualized as the average across subjects and as the average over occipitotemporal electrodes (bars) or at eachelectrode (topographies). Error bars represent the standard error. *p < 0.05. **p < 0.01. ***p < 0.001.

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9

Figure 4. Relationship between base and oddball responses. Scatterplot of the base (y-axis) and oddball (x-axis) responses for all
condition for each subject separately. Correlation coeﬃcient and p value are reported for every observer.

Figure 5. Distribution of nonsigniﬁcant (NS) responses acrosssize and VP in terms of number of subjects. The same subject
could have more than one NS condition and therefore thecumulative sum within one size or VP can exceed the total
number of subjects (N = 35).

triggered a response amplitude below the median (i.e.,
MADscore < 0; with respect to other viewing positions
within the same size) in five, five, and six additional
subjects (Table 3, Figure 8).

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Figure 6. Number of subjects showing a nonsigniﬁcant response
according to only stimulus size. Stack bars illustrate the exact
number of VPs that were NS for each subject. For example,
green bars represent the number of subjects with three
nonsigniﬁcant responses in each size. *Signiﬁcant diﬀerences
between the number of subjects at mid p < 0.05.

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Figure 7. Distribution of outlier response. (A) Number of subjects showing at least one outlier response. *Signiﬁcant diﬀerence in total
number of subjects at mid p < 0.05. (B) Distribution of subjects exhibiting outlier responses across VPs and sizes. The y-axis
represents the number of subjects showing an outlier response for a given VP over the absolute number of subjects showing at least
one outlier response at a given size. Please note that a subject could exhibit more than one outlier response per size, and as such,
numbers do not perfectly add up in A and could lead to percentages more than 100%.

Proportion (%)
3°
85.7 (74.4, 97.1)

5°
60.0 (43.4, 76.6)

6.7°
37.1 (20.8, 53.5)

8.5°
40.0 (23.7, 56.3)

Proportion diﬀerence between groups (%)
3°–5°5°–6.7°25.7 (9.0, 42.5)
22.9 (8.7, 37.1)

6.7°–8.5°
2.9 (−5.6, 11.6)

8.5°–12°8.6 (−2.8, 19.9)

12°
31.4 (16.1, 46.8)

Table 1. Proportion and proportion diﬀerence across conditions of number of subjects exhibiting a non-signiﬁcant neural FID
response. Note. Number in parentheses are 95% conﬁdence interval estimated as ± 2 standard errors.

Proportion (%)
3°
25.7 (10.9, 40.5)

5°
28.6 (13.1, 44.0)

Proportion diﬀerence between groups (%)
3°–5°5°–6.7°2.9 (−17.0, 22.7)
0 (−19.4, 19.4)

6.7°
28.6 (13.5, 43.6)

8.5°
51.4 (33.5, 69.3)

6.7°–8.5°−22.9 (−2.7, −43.1)

8.5°–12°22.9 (−1.6, 47.3)

12°
28.6 (13.6, 43.5)

Table 2. Proportion and proportion diﬀerences between conditions of number of subjects exhibiting outlier responses. Note.
Numbers in parentheses are 95% conﬁdence interval estimated as ± 2 standard errors.

Discussion
This study aimed to assess the impact of stimulus
size on rapid neural FID response as a function of
the fixated face region (VP). We tested participants
with fast periodic visual stimulation in which a streamof same-identity faces was presented at a generalbase frequency of 6 Hz, with different-identity faces
periodically interleaved every seventh image (i.e.,
0.85 Hz) in the sequence. Across conditions, stimuli

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varied in their size and in the VP that participants wererequired to fixate.

The impact of face size on neural facediscrimination responses
Our data show that stimulus size modulates theamplitude of neural FID: The smaller the stimuli, theweaker the response. Interestingly, while amplitude
appears to linearly increase across stimulus sizes at theJournal of Vision (2022) 22(13):9, 1–16

Stacchi & Caldara

MAD > 2.5

MAD > 0MAD = 0MAD < 0

3°
VP0
VP1
VP3
VP5
VP8

1
2
2
3
1

10
7
13
11
11

1
4
5
4
8

5
4
1
5
5

5°
VP0
VP1
VP3
VP5
VP8

4
2
2
3
1

11
13
13
14
2

4
5
8
6
4

5
6
4
4
16

6.7°
VP0
VP1
VP3
VP5
VP8

7
1
3
0
1

14
16
6
11
10

4
4
9
7
5

5
8
9
12
15

8.5°
VP0
VP1
VP3
VP5
VP8

3
2
10
7
0

11
8
9
11
7

4
6
7
9
7

12
13
5
5
14

12°
VP0
VP1
VP3
VP5
VP8

5
1
2
3
1

14
8
11
15
9

6
8
3
8
8

6
15
16
4
12

Table 3. Number of subjects exhibiting a MAD score above,
equal to, or below diﬀerent thresholds for each VPs and Sizes.
Note. Subjects were included in these categories only if they
exhibited a signiﬁcant neural FID.

base response, the oddball response seems to benefit
from stimulus size increments only until 6.7° to 8.5° ofvisual angle but does not show significant amplitude
increases for 12°. This observation could be explainedby the modulations of the visual processes involvedin the extraction of information used during FID.
Specifically, identity processing involves both a holisticcoarse percept of faces and a finer-grained analysis
of information that are idiosyncratic to each face(e.g., Rossion, 2008; Rossion et al., 2011). At small
sizes (e.g., 3° of visual angle), the rapid extractionof detailed idiosyncratic information might be moredifficult, especially given that stimuli remained onscreen for only 166 ms, allowing for only one fixation at
a predetermined location. As stimulus size grows andvisibility increases, this analysis of fine components andtheir integration into a whole might become easier.

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The pattern of neural response amplitude as afunction of size mimics the pattern of face recognition
behavioral performance reported by Yang et al.
(2014), in which stimulus size was also parametricallymanipulated. In this later study, performance improved
from 1° to 7° of horizontal visual angle, reaching aplateau thereafter. Yang et al. (2014) suggested thatthe improvement might relate to the involvement ofholistic processing occurring for faces larger than 6°of horizontal visual angle. However, the question of
why such an increase levels off remains unanswered.
In opposition to Yang et al. (2014), McKone(2009) reported that holistic processing during facecategorization is impoverished for stimuli larger thanapproximately 6.5° of vertical visual angle. However,one must emphasize that McKone (2009) used different
stimuli (i.e., Mooney faces) and a different task (i.e.,face categorization/detection). As such McKone’s(2009) observations and theoretical implications might
not necessarily apply to face identity discrimination.
However, the mechanisms behind these observations
remain open for discussion.
While the cutoff for optimal holistic processing might
not be the same across different face subprocesses,
it would be interesting to investigate whether, as for
categorization, also in identity processing there is asize limit beyond which holistic processing would
be impaired. Regardless of this theoretical debate,further studies are required to explore the question ofwhether neural FID decreases for stimuli larger than
12° of vertical visual angle (which relates to the plateau
observed here).

The impact of face size on VP-dependent neural
FID
The main goal of the current study was to determine
whether reducing face size would reduce neural
response variation across viewing positions and/or
across observers. We addressed these hypotheses
in two steps. First, we assessed the significanceof neural responses, so as to exclude from further
analysis conditions that were less likely to trigger
significant neural FID responses. At the individuallevel, we observed a greater number of nonsignificant
responses for smaller sizes compared to larger stimuli.
Additionally, analysis revealed that the numberof subjects showing nonsignificant FID responses
significantly decreased between 3° and 6.7° of visual
angle but did not change substantially between the threelargest sizes. Based on these results, we did not further
investigate 3° and 5° sizes, as they failed to trigger asystematic neural FID.
Analysis of the data from the three remaining sizes(6.7°, 8.5°, and 12° of visual angle) revealed that the
8.5° size triggered extremely high responses (outlier

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Figure 8. Summary of each subject’s MAD score and response signiﬁcance. If a response z-score was below 1.64 (p < 0.05), it wascolor coded as dark gray. Signiﬁcant responses were color coded according to their MAD score. Orange indicates a MAD score < 0,yellow indicates a MAD score = 0, light green indicates a MAD score > 0, and light blue indicates a MAD score > 2.5 (outlier).

responses/visual sampling preference) in more subjects
than for sizes 6.7° and 12° of visual angle, respectively.
Importantly, compared to size 12°, size 6.7° led to agreater agreement between observers. Specifically, atsize 6.7°, more than half of those exhibiting an outlier
response did so when fixating the central viewing
position (VP0), and in general, this viewing position
triggered an above-the-median response in 21 out of 35
participants and a nonsignificant response in only 5 ofthem. Finally, fixation below the nasion still triggered
an above-the-median response in two out of three

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observers when considering only subjects showing astrong positive bias for a different viewing position.
Altogether, these observations indicate that the 6.7°
size, which is the smallest among those systematicallyleading to significant neural FID (i.e., 6.7°, 8.5°, and12°), reduced individual differences in neural bias
the most overall. In fact, a large portion of subjects
showed a convergence on VP0 in terms of either
outlier responses or above-the-median response. Thisfinding is in line with our expectation that although
VP-dependent biases do not appear to decrease with

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increasingly smaller stimulus sizes, they appear toconverge toward the same central viewing position(VP0). At this size, it is likely that both coarse- as wellas fine-grained mechanisms can be used to process
information gathered from a single fixation in order toreach optimal neural FID. Our data are in line with aprevious report by Peterson and Eckstein (2013), who
implemented a foveated ideal observer to determine
the viewing position maximizing the sampling of
information relevant in an identity task. They foundthat the most effective strategy is to direct the first
fixation just below the eyes. For midrange-size stimuli,
this location might be strategic as it enables perceiving
faces as a unit and at the same time sampling facial
regions such as the eyes with relatively high resolution(fixation on VP0 at 6.7° of visual angle places the eyes
just outside foveal vision; Figure 2).

Individual diﬀerences when viewing small facesizes
In order to identify an experimental condition
that reduces the influence of viewing position onneural FID, we applied different criteria. The first
aimed to filter the conditions in terms of robustnessof the evoked FID neural responses. Consequently,
we discarded the two smallest sizes (3° and 5°), as
they led to many nonsignificant responses acrossconditions. Importantly, while these two conditions
were no longer interesting with respect to our original
goal, the pattern of responses recorded in these twocases become relevant from a theoretical perspective
and is worth discussing. We found that even at thesmallest visual angle (3°), most of our observers still
exhibited responses indicative of both optimal andsuboptimal viewing conditions. In other words, morethan three-quarters of our participants exhibited
nonsignificant neural FID responses for some VPs(despite showing a significant response to the basestimulation) but at the same time highly significant
responses when fixation was constrained to other VPs.
These observations might indicate that fixation of
specific viewing positions can provide an advantage forneural FID even under challenging conditions, such as
when fine-grained information might be more difficult
to extract. However, future studies are necessary
to clarify what drives these individual differences.
For example, a question that cannot be addressed
within our current methodology is the contributionof different facial information to the recorded neural
response. Although fixation was constrained to a
specific viewing position, observers still had access to
information from the whole face through parafoveal
and peripheral vision. As such, and because there is nota straightforward relationship between the information
fixated and its use in natural vision (e.g., Caldara

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et al., 2010), our data cannot disentangle how thesedifferent samplings might have been used to achieve
neural FID. By addressing this issue, we could also shedlight on the possible strategies for changes across sizes
and therefore on potential within-subject difference
in VP preferences across scales. Observers could findit more advantageous to sample faces from different
locations, since across different face sizes, more or lessinformation can be gathered foveally. For example, onvery large images, parafoveal information might not
be as useful, and it might be more efficient to locate
specific features within the foveal visual field rather thanattempting to sample the whole face in one glance by
fixating the center of the face.
In line with this reasoning, it would also be important
to determine whether these preferences persist for evensmaller stimuli (provided that they are able to triggera significant neural FID). At our smallest size, thefoveal visual field systematically overlapped across VPs;
nonetheless, stimuli were still too large to allow for an
identical foveal input across fixations.
Additionally, in the current study, we did not control
for eye movements. That is, subjects had to simply
perform an orthogonal task consisting of reporting
color changes in the fixation cross. The main goalof the current study was to provide insight to futurestudies on which experimental manipulation would
be the most suited for the largest portion of thetested population. Therefore, we reasoned that for ourdata to be meaningful in such a context, our settings
should have matched those that are commonly used in
FPVS-EEG studies, which do not typically monitor eye
movements (e.g., Liu-Shuang et al., 2014; Retter et al.,2021). Subsequent experiments that specifically focus
on information contribution to neural FID should
implement a stricter control of eye movements toensure the precise fixation at a specific viewing position
for the whole duration of the visual stimulation.
Despite these limitations, our findings build on
previous reports of individual differences and highlightonce again both their complexity and the experimenter’sdifficulties to control for them. This prompts numerous
questions, such as what drives idiosyncrasies, or how
do they develop? If studies focusing on cross-culturaldifferences suggest that individual differences insampling strategies might be determined by theenvironment (Blais et al. 2008; Caldara, 2017), then theyshould also investigate the contribution of the physicalproperties of the visual system, such as potential
individual differences in contrast sensitivity across thevisual field. If the most advantageous portion of thevisual field for face processing varies across observers,
then, as already suggested by Peterson and Eckstein(2013), it is reasonable to expect that this might shapesampling strategies to optimize information intake.
Finally, our results support the possibility that theface system shares similar face-processing mechanisms

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across observers but does not rely on a universal setof representations to achieve face identification. In
fact, the face system is tuned to neural and visual
idiosyncrasies (see Stacchi, Liu-Shuang, et al., 2019;
Stacchi, Ramon, et al., 2019). These observations
might have profound implications for models of
information selection and representation, as thesame computation should be able to reach effective
face recognition with a different set of inputs (i.e.,
idiosyncratic preferred visual features/representations).
This is a challenge that computational modelingstudies of face recognition should take over in the
future. Future models should not only take into
consideration the notion that different observers might
prefer different facial information to process facial
identity (Stacchi, Liu-Shuang, et al., 2019; Stacchi,
Ramon, et al., 2019) but also that this information
might depend on the image’s scale. Stimulus size should
be carefully considered in the models, by adapting
visual properties of interest as a function of the scale.
The present data, along with the growing literature on
individual differences (Arizpe et al., 2017; Mehoudar
et al., 2014; Peterson & Eckstein, 2013), make itless likely that a model with fixed rules discarding
fixation location and stimulus size could provide arepresentative description of the human face system.
On the contrary, our results strongly support the ideathat any computational model of face processing should
implement flexible rules (see Caldara, 2017), which
would allow solving the same task by the use of different
strategies.

Should we standardize the viewing position?
The present study aims to provide a guideline tofuture studies on which viewing position is the least
detrimental to the fewest subjects. Our data show
that, among the conditions that we tested, fixation
just below the nasion on stimuli subtending 6.7° ofvertical visual angle is the one that best meets thesecriteria.
However, it is important to acknowledge that our data
do not support the common practice of standardizing
stimulus size and certainly not the viewing position.
Our results highlight the complexity of abolishing
individual differences and VP-related preferences,
as these persisted across all stimulus sizes, and they
reinforce the idea that standardizing the viewing
position will result in observers’ misrepresentations.
Nevertheless, we are fully aware that testing
numerous conditions to ensure the inclusion of the
optimal viewing position for each observer is notrealistic, and experimenters must compromise on thismatter. For this reason, we urge researchers to carefully
consider the existence of visual idiosyncrasies forface processing when necessary—for example, when

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14

attempting to establish a relationship between neural
FID and a behavioral measure, as observers would beforced to fixate a predefined location during neuralassessment but might be allowed to sample information
using their preferred strategy in the behavioral task.
This contrast could lead to a mismatch in task difficulty
and potentially return a weaker link between measures.
Another scenario where VP standardization might
be counterproductive is when investigating neural
FID at the single subject level and using this measure
as an index to categorize observers. In this context,
experimenters should be aware that a weak response
might be caused by suboptimal testing conditions, andthey should complement their findings with further
assessments.

Conclusions
Our data show that FID neural responses increase
with stimulus size, reaching a plateau from 6.7° to
8.5° of visual angle onward. Individual differences
across VPs were present for all stimulus sizes, evenfor the smallest. However, importantly, when faces
are centered below the nasion and subtend 6.7° ofvisual angle, these individual differences across VPs
are significantly reduced. This stimulus size should
be used conventionally to reduce the impact ofpotential individual differences in neural FID response
patterns, as it decreases the likelihood of obtaining
and interpreting nonrepresentative measures at thesingle-subject level. Altogether, these findings highlightthe need to rigorously control for face stimulus sizeand viewing position, while positing a benchmark
to reduce FID idiosyncratic responses. Overall, they
prompt the necessity for further studies to elucidate themechanisms at the root of individual differences during
face processing.
Keywords: idiosyncrasies, face discrimination, stimulus
size, viewing positions, EEG-FPVS

Acknowledgments
LS was partly supported by a Swiss National Science
Foundation grant awarded to RC (10001C_201145).
Commercial relationships: none.
Corresponding authors: Lisa Stacchi and Roberto
Caldara.
Emails: lisa.stacchi@unifr.ch androberto.caldara@unifr.ch.
Address: Department of Psychology, University of
Fribourg, Fribourg, Switzerland.

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