Peter de Lissa,

Pauline Schaller,

and Roberto Caldara

Eye and Brain Mapping Laboratory (iBMLab), Department of Psychology, University of Fribourg, Fribourg 1700, Switzerland
Correspondence should be addressed to Peter de Lissa, Eye and Brain Mapping Laboratory (iBMLab), Department of Psychology, University of Fribourg, Rue
P.-A.-de-Faucigny 2, Fribourg 1700, Switzerland. E-mail: peter.delissa@unifr.ch; Roberto Caldara. roberto.caldara@unifr.ch

Abstract
When asked to categorize faces according to ‘race’, people typically categorize other-race faces faster than faces belonging to theirown race. This ‘Other Race Categorization Advantage’ is thought to reflect enhanced sensitivity to early visual signals characteristicof other-race faces, and can manifest within 200 ms of face presentation. However, recent research has highlighted the importance of
signal intensity in this effect, where visual-degradation of the face images significantly enhances the effect and exposes a behaviouralthreshold at very low levels of visual quality where other-race visual signals are able to be perceived while same-race signals are not. The
current study investigated the effect of signal intensity in race categorization processes in the brain through electroencephalography
and in accuracy/reaction times. While replicating the previously observed enhancement of the other-race categorization advantage,
we also found enhanced sensitivity to other-race faces in early P1 peaks, as well as later N170 and N250 peaks. These effects, however,related to the varying levels of signal intensity in the face stimuli, suggesting that race categorization may involve different types ofperceptual and neural processes rather than one discrete process. The speed at which race is perceived depends on the intensity of theface signal.

Keywords: face processing; race; other-race face categorization advantage; N170; P1

Introduction
The social intra- and interpersonal consequences of the perception of human race are often deep and wide-reaching. However,
the early low-level perceptual dimensions that lead to perception of race and the plethora of social consequences are, however,
not yet as clarified as it should be, given the range of the effects
that can stem from this fast visual categorization. The salienceof race in the early perceptual processes has become clear, withbehavioural tasks revealing a clear bias towards the recognitionof ‘other-race’ visual signals in faces, leading to faster categorization of other-race faces when discriminating race (Valentine
and Endo, 1992; Levin, 1996, 2000; Caldara et al., 2004; Zhao andBentin, 2008, 2011; Ge et al., 2009; Feng et al., 2011). Termed the
Other Race Categorization Advantage (ORCA), such an enhanced
sensitivity has the potential to bias visual attention when people
are confronted with competing face stimuli where faster perception of race in other-race faces may lead to enhanced attention
and focus in various contexts involving saccadic selection of facetargets (de Lissa et al., 2021). However, a recent study has shownthat not only can enhanced sensitivity to other-race faces leadto reaction time (RT) differences when categorizing race, butalso differences in the fundamental ability to perceive and categorize race at all. When images of same- and other-race faces
are systematically degraded in visual quality, there is a distinct

threshold difference where same-race faces are no longer reliably able to be categorized in terms of race, while other-racefaces are (de Lissa et al., 2022). Such an effect was interpreted torelate to a differential sensitivity to the visual patterns characteristic of different race faces, where an overall speed difference
is apparent in typical, full-structure face images. However, when
the intensity of the visual signals of the faces is systematicallydecreased, it leads to a point where the visual signals characterizing same-race faces are not reliably perceived while those forother-race faces are. Such an effect has the potential for profoundsocial effects when visual quality is markedly reduced, such as in
low-resolution photo/video or in live situations where low ambient light makes visual perception more difficult. However, thefinding also provides an opportunity to probe further the neuralactivations relating to race categorization.
Event-related-potential (ERP) studies investigating race modulation of neural responses have found mixed effects in the earlyvisual P1 and N170 peaks. While the P1 peak is generally associated with low-level stimulus attributes (Regan, 1989), a number ofstudies have found race modulation of this early brain potential,
although with mixed results. While some have found larger activations for own-race faces (Caharel et al., 2011; Herzmann, 2016;
Giménez-Fernández et al., 2020), others have reported the opposite effect of larger P1 peaks to other-race faces (Hahn et al., 2012;© The Author(s) 2024. Published by Oxford University Press.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which
permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

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Social Cognitive and Affective Neuroscience, 2024, Vol. 19, No. 1
in de Lissa et al. (2022) where participants were unable to reliably categorize race when presented with significantly degradedsame-race faces, and yet they retained the ability to perceive racesignals in other race faces. At this critical point, we might findclear neural expressions of the sensitivity to other race faces thatis often found in purely behavioural paradigms. The principal ofvisually degrading stimuli has previously been used to observe
how face perception and sensitivity to face stimuli manifests
neurally as face structure is lost within phase-scrambled backgrounds. By parametrically reducing the visual information contained in face images in a face discrimination task, Rousselet et al.
(2008) found that the neural responses between 100 and 200 ms(encompassing the P1 and N170 responses to faces) decreased systematically until the structure was completely degraded leaving
no face-sensitive neural response at all. Accordingly, we should
expect a similar reduction in N170 response to face structure in arace-in-noise paradigm while preserving race categorization ability. By reducing the other encoding processes leading to N170
activity, we should hope to find a clearer contribution of the visual
race signal processes. Further, the results of de Lissa et al. (2022)
suggest that the ORCA itself is enhanced as face structure isdegraded, leading to a maximally enhanced sensitivity to otherrace faces at 25–30% structure level (phase-coherence level). We
may thus be able to view the neural activity relating to racecategorization processes more clearly when the behavioural difference is at its peak. Finally, and perhaps most critically, the onset
of other-race sensitivity in both Swiss and Japanese observers wasobserved at 20% structure level (de Lissa et al., 2022), where participants were able to reliably categorize the other-race faces by
race whereas the same-race faces were still categorized at chancelevel. Neural responses at this behavioural threshold may provide
insight into the presence vs absence of race perception—when
visual signal is reduced to a critical threshold, the visual signalsof other-race faces remain salient enough for race categorization to be reliably performed while same-race face signals are
no longer perceived. Therefore, in the current study, we sought
to implement the race-in-noise paradigm to determine whether
the behavioural threshold at which race processing differences
first manifest also leads to early N170 differences indexing theheightened sensitivity to other-race face signals.

Methods
The Human Ethics Committee at the University of Fribourg
approved the methods and procedure used in this study. All participants provided written informed consent in accordance with
the Declaration of Helsinki. All data used in statistical analyses and represented in figures are available online on the OpenScience Framework repository at https://osf.io/sx6b2.

Participants
A total of 38 participants (West European) were recruited fromthe student pool at the University of Fribourg. While de Lissa et al.
(2022) used a cross-cultural design with both Swiss and Japaneseparticipants, the current study utilized only a sample of Swiss participants to focus directly on the clear enhancement of the ORCA
in this cultural group.
One participant dataset was rejected due to severe electroencephalography (EEG) artefacts due to movement, and one dataset
was rejected due to improper engagement with the behaviouraltask (chance performance through all coherence levels). A totalof 36 participant datasets were thus statistically analysed in
the study (Mage = 23.7 years, SDage = 4.5, 35 right-handed, 33

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Wang et al., 2020), with others reporting no P1 modulation of raceat all (Senholzi and Ito, 2013; Vizioli et al., 2010a,b, see Serafini and
Pesciarelli, 2023 for review.) In a mixed result, He et al. (2009) founda modulation of P1 by race, with Black faces eliciting larger peaks
than White faces (own race in the participant sample), whichwere in turn higher than Asian faces. The authors noted that theeffect may have arisen from low-level visual property differences
between the face stimuli such as skin tone (which were not balanced), but also speculated that there may exist an interaction
between different facial features of various races and the deployment of visual attention. A clarification of the contribution of race
processing to the P1 would move towards determining the timingof race perception in stimuli that are balanced for low-level visualproperties, particularly in light of the behavioural results indicating that race categorization can occur in as little as 200 ms after
face presentation (de Lissa et al., 2021).
Similar to the conflicting pattern of P1 results, studies investigating race-modulation in early N170 responses have foundmixed results, with a number of studies finding no effect for
upright faces (Caldara et al., 2003, 2004; Lv et al., 2015; Proverbio and De Gabriele, 2019; Sun et al., 2014; Tanaka and Pierce,
2009; Vizioli et al., 2010a,b; Wiese et al., 2009), others finding anenhancement of N170 amplitude to other-race faces (Walker et al.,2008; Stahl et al., 2010; Caharel et al., 2011; Ofan et al., 2011; Wieseet al., 2014; Anzures and Mildort, 2021) and another finding the
opposite pattern (Ito and Urland, 2005; Vizioli et al., 2010a). Senholzi and Ito (2013) provided clear evidence that the N170 effectis related to the task instructions, where race categorization led
to enhanced N170 peaks for same-race faces while face recognition led to enhanced N170s to other-race faces. However, Wiese(2013) found the opposite pattern where other-race faces elicitedlarger N170 peaks in a race categorization task. Accordingly, it isdifficult to confidently state whether the N170 is specifically modulated by race, or if it is then how. Part of the issue might relate
to how race processing itself contributes to the N170 peak, relative to the other structural-encoding processes taking place at thistime (Bentin et al., 1996; Rossion et al., 1999; Eimer, 2000). A relatively small, nuanced activation difference due to race perception
might be overwhelmed in size by other co-occurring processes
contributing to N170 activity, and consequently may not manifest
as clearly or as reliably by contrast.
More robust effects have been observed in the later N250
peaks, with larger activations found for other race faces (Sunet al., 2014; Wiese et al., 2014; Herzmann, 2016), interpreted
as reflecting greater resources required for individuating thesefaces relative to same-race faces due to differences in experience (Herzmann, 2016) and differences in face-related semantic
activation (Caldara et al., 2004). However, in light of evidence
from saccadic-response paradigms suggesting that race categorization behavioural responses can reliably occur prior to theN250 time-range (de Lissa et al., 2021), it is possible that earlier
perceptual processes (P1 and N170) give rise to the later ones. The
lack of distinct race modulation patterns in these early neural
responses therefore might suggest a poor signal-to-noise ratio forsuch signals within the backdrop of other neural processes in ERP
recordings.
However, the race-in-noise paradigm may provide a usefulapproach to address this potential issue. By presenting faces
which have been systematically phase-scrambled to decrease theamount of visual information contained in face images, we cansee not only the enhanced behavioural sensitivity to other racefaces but potentially also any neural differences associated withthis race perception. Specifically, a critical threshold was observed

P. de Lissa et al.

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female). All participants reported having normal or corrected-tonormal vision, and were given research participation credit for
their respective studies. The sample size in the current study
was informed by the effect sizes from EEG studies investigatingother-race categorization and identity effects in terms of neural
responses (Senholzi and Ito, 2013; Wiese, 2013), with calculations based on the smallest effect size (0.19) at alpha = 0.05 andpower = 0.8. These calculations suggested a sample size of N = >35
to be a conservative approach.

Stimuli and procedure
The stimuli consisted of 20 grayscale face images of 10 European and 10 Asian identities (equal numbers of male and female).
The images were neutral expression portrait photographs of Belgian (West European—WE) and Chinese (East Asian—EA) students
aged from 18 to 25 years, utilized in a previous study investigating the impact of the systematic removal of visual information on
race categorization judgements (de Lissa et al., 2022), as well asother studies investigating race perception (Michel et al., 2006a,
2006b; de Lissa et al., 2021). The faces were cropped to exclude
ears and hair, and were matched for amplitude spectra, luminance and contrast using the SHINE toolbox (Willenbockel et al.,2010). Systematic degradation of the structure of the face images
was achieved through a phase-scrambling technique which randomizes the location of all spatial frequencies in the images while
preserving amplitude spectra across orientations and spatial frequencies (Honey et al., 2008). In order to adapt the array of stimuli
from the preceding behavioural study (de Lissa et al., 2022) toan ERP paradigm utilizing more trials per condition, a subset ofstimuli were used to test critical levels of coherence from 10%(where participants had been unable to reliably categorize race)
through to 60% coherence (where participants exhibited an accuracy ceiling), in 10% coherence steps. An additional 100% stimulus
coherence condition was added to provide a baseline from which
to view face and race processing when stimulus quality was notdegraded (see Figure 1).
Participants were presented with WE and EA faces possessing systematically varied amounts of phase coherence, and were
instructed to categorize the race of the faces through a keyboard. The instructions noted that participants might find someconditions impossible to categorize, in which case they could
make a random guess to avoid pressing the same response button. The experimental stimuli (11.6˚ × 11.6˚ in viewing angle) were
presented on a 24-inch VIEWPixx/3D monitor (1920 × 1080 pixels, 120 Hz refresh rate) at a distance of 70 cm, and presented

through Experiment Builder (v1.10.1630) software. Trials commenced with a blank screen presented from between 2100 and2500 ms. A central fixation cross was then presented for 500 ms,
followed by a 200 ms blank screen before the target face stimuli were presented for 200 ms. The target faces were replacedwith a black screen until the participant pressed a key. There
were 840 trials in total, with 60 trials for each of the 2 stimulus
race types (EA and WE) and 7 coherence conditions (10%, 20%,
30%, 40%, 50%, 60% and 100%). Breaks were implemented every
60 trials.

EEG recording
EEG was recorded with a Biosemi Active-Two amplifier systemusing 128 Ag/AgCl electrodes sampling at 1024 Hz. EEG data
were processed offline though EEGLAB (v2021.1) running on MATLAB 2021a. Bandpass filtering (0.1–30 Hz) was performed before
epochs were formed (−100 to 1000 ms), followed by baselinecorrection to the average voltage from −100 to 0 ms. Eye-blink
artefact correction was achieved through Independent Component Analysis (ICA, Delorme and Makeig, 2004). Bad EEG channels were excluded from ICA analysis, and interpolated subsequently. The EEG epochs were then re-referenced using acommon-average reference, and epochs were rejected if voltages
exceeded ±100 μV within the first 500 ms after stimulus presentation. ERP averages were formed for each condition and for eachparticipant.

Data processing & analysis
Behavioural data
Participant datasets were assessed for inclusion/exclusion before
statistical analysis. Trials with behavioural response times shorter
than 100 ms and longer than 2000 ms were excluded from thedatasets. Participants with lower than 75% overall accuracy in the
full-structure 100% coherence conditions were rejected. This process rejected participants who were not engaged in the task andmade single-key responses. These inclusion criteria excluded 2
datasets from the 38 participants, leaving 36 behavioural and EEG
datasets for analyses. The behavioural study on which the current
study was based utilized an additional rejection criteria whereparticipants exhibiting higher than 75% accuracy in a 0% coherence condition were excluded from analysis (de Lissa et al., 2022).
We did not utilize this additional criteria in the lowest coherence
level (10%) in the current study as the preceding study had alreadyestablished that the 20% coherence condition was the behaviouralthreshold for participants’ ability to categorize EA faces, and the

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Fig. 1. Examples of EA and WE face stimuli used in the study, ranging from 10% structural coherence through to 60%, and at 100% full coherence. The
WE and EA were equated for low-level visual qualities.

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Social Cognitive and Affective Neuroscience, 2024, Vol. 19, No. 1

bias to make an EA or WE response when guessing is conceptuallyunrelated to this threshold.
Condition means were calculated for each participant for accuracy (correct responses divided by accepted trials) and RT (ofcorrect trials). Accuracy and RT data were separately subjectedto Bayes Factor analyses for an effect of stimulus race at eachof the seven coherence levels. Bayes Factor thresholds representing sufficient evidence to denote the presence of an effectin the accuracy and RT data (difference in means) were taken
as BF10 = >3, and at BF10 ≤ 0.33 for evidence supporting the null
hypothesis. Evidence for the null hypothesis is presented in the
figures containing Bayes Factor analyses but is not discussed forbrevity.

EEG data
After the EEG pre-processing steps, the participant ERP averages
for the 100% and 10% coherence levels were compared to confirm
the spatial location of face-sensitivity in our electrode montage
and using a common-average reference, which corresponded toleft and right occipito-temporal scalp regions (see Figure 2A) overthe early N170 and later N250 time ranges corresponding to scalp
regions common to N170 activations found in previous studies(Joyce and Rossion, 2005). Two occipito-temporal electrode clusters of eight sensors each were selected to represent the left andright hemisphere face-sensitive scalp areas encircling the maximal N170 peak electrodes (Rousselet et al., 2004; Kuefner et al.,2010) for statistical analyses (see Figure 2B, noted in red). Bayes
Factor analyses were performed on the average voltages in theseleft and right hemisphere clusters, and topographic maps of bothvoltage differences and BF10 evidence levels were computed toprovide further insight into the specific location of effects foundin the cluster analyses.
Comparisons were performed for each time sample (0–420 ms)
in the left and right hemisphere clusters. The time range of theanalyses was selected to avoid motor-preparation activity contaminating the EEG and confounding the interpretation of ERP
data. To lessen the likelihood of false-positives, the evidenceboundary for reporting sufficient evidence to denote an effectof a difference between conditions was set at BF10 = >10, corresponding to strong evidence (Schönbrodt and Wagenmakers,
2018; Stefan et al., 2019). Evidence in favour of the null hypothesis(comparable means) remained at BF10 ≤ 0.33. Statistical analyses
were conducted in RStudio (2021.09.2) using the BayesFactor package (0.9.12–4.3, Morey et al., 2018). Finally, scalp maps of Bayes

Factor values are presented to depict the intensity of evidence ateach electrode to complement the left and right mean electrodecluster analyses (Huber-Huber et al., 2019).

Results
ORCA—RT and accuracy
The analyses of race categorization RTs showed that at 10% coherence there was no conclusive evidence of difference or similarity
between mean EA (843 ms) and WE (823 ms) faces (BF10 = 0.429).
At 20% coherence level, an extreme evidence for an ORCA effectwas observed (BF10 = 1.14 × 103 ), with an average RT of 784 ms for
EA faces while average WE face RT remained high at 838 ms. The
RT ORCA peaked at 30% coherence level (BF10 = 1.58 × 106 ) andcontinued in the 40% (BF10 = 2.15 × 103 ), 50% (BF10 = 7.15 × 104 ) and
60% (BF10 = 435.9) coherence levels. At 100% coherence, not only
was evidence of a difference not observed, but there was moderate evidence that the EA and WE face categorization RTs were thesame (BF10 = 0.204).
The analyses of race categorization accuracy revealed evidence
that WE faces were more accurately categorized than EA faces at
10% (BF10 = 291.8). At 20%, this difference reversed, with EA faces(82.6%) being more accurately categorized than WE faces (70.4%;
BF10 = 625.2). At 30% coherence, evidence of an advantage for EA
faces peaked (96.8% vs 84.7%; BF10 = 5.84 × 103 ), which was also
observed but to a much smaller degree at 40% coherence (96.8% vs91.4%; BF10 = 13.6). At 50% coherence, there was no clear evidenceof difference or similarity in accuracy between the EA (97.1%) andWE (94.6%) faces. At 60% coherence, there was substantial evidence that the EA and WE faces were categorized at the sameaccuracy (96.5% vs 95.8%; BF10 = 0.179). At 100%, there was again
no evidence for similarity or difference between the EA and WE
faces (96.5% vs 97.8%; BF10 = 0.372).
The evidence of an advantage for WE faces at 10% coherence (57.8%) appears to come at the cost of accuracy for EA
faces (44.2%), with overall accuracy at this level of coherence being 50.1% (see Figure 3). A d’ analysis of accuracy
at 10% of the WE condition (which incorporates incorrect EA
responses as WE false alarms) suggested very low discriminability (mean d’ = 0.034) and a bias towards WE responses(mean C = 0.18). Coupled with the lack of RT effect in the
10% coherence condition, it is likely that this accuracy effectwas due to a bias of the participants to respond ‘WE’ when
guessing.

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Fig. 2. A topographical comparison of the effect of face sensitivity (all 100% faces minus all 10% faces) confirmed bilateral occipito-temporal scalp
locations as the areas of maximal difference from an early N170 period through to an early N250 period. Accordingly, two bilateral electrode clusters(B) were chosen for Bayes Factor analyses in the seven coherence levels (10–60%, and 100%).

P. de Lissa et al.

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ORCA—EEG
At 10% coherence, no evidence of voltage differences was apparent, with left and right activation being mostly comparable(Figure 4).

BF10 = 757.5, max BF10 = 6911.9 at 309 ms] and right [BF10 > 10 from
260 to 316 ms, mean BF10 = 47.7, max BF10 = 114.4 at 303 ms] hemispheres. Only the strongest evidence in the left and right hemispheres is reported; however, there were additional periods ofdifference in the N250 range (see Figure 6).

20% coherence

40% coherence

At the 20% coherence level (the level at which both race categorization accuracy and RT effects emerged for the EA faces), an
enhanced activation for EA faces was observed in the late N170
time range (see Figure 5) in both the left [BF10 > 10 from 161 to
170 ms, mean BF10 = 16, max BF10 = 18 at 166 ms] and the right[BF10 > 10 from 170 to 183 ms, mean BF10 = 17, max BF10 = 22.5 at
177 ms] hemispheres.

At 40% coherence, the enhanced N250 to EA faces were observedin both left [BF10 > 10 from 295 to 343 ms, mean BF10 = 128.9,
max BF10 = 357.8 at 317 ms] and right [BF10 > 10 from 276 to
319 ms, mean BF10 = 64, max BF10 = 158 at 287 ms] hemispheres(see Figure 7).

10% coherence

30% coherence
At 30% coherence, the N250 enhancement for EA faces continued
to emerge strongly in both left [BF10 > 10 from 262 to 328 ms, mean

50% coherence
At 50% coherence, the earlier differences were no longer apparent (see Figure 8); rather, the later N250 enhancement for EA
faces endured in the right hemisphere (RH) [BF10 > 10 from 269 to
284 ms, mean BF10 = 24.9, max BF10 = 36.7 at 276 ms].

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Fig. 3. The reaction time profiles indicate an enhanced other-race categorization advantage arising at 20% coherence and continuing through to 60%.
Accuracy similarly showed an other-race advantage at low coherence levels, excepting for the 10% condition. Shaded areas represent bootstrapped
95% confidence intervals.

6Social Cognitive and Affective Neuroscience, 2024, Vol. 19, No. 1

Fig. 5. At the 20% coherence level, EA faces produced more negative voltages in the late N170 time ranges in both the left and right electrode clusters.
Scalp voltage topographies (top) reflect EA minus WE voltages, while the scalp Bayes Factor topographies (bottom) reflect the regions where evidenceof a difference (BF10 > 10, also reflected in the shaded periods in the waveforms) or of comparability (BF10 < 0.33) were observed.

60% coherence
The comparison at 60% coherence showed a brief enhancementof the early P1 to EA faces in the right hemisphere [BF10 > 10 from90 to 97 ms, mean BF10 = 14.5, max BF10 = 17.8 at 94 ms]. EA faces
also elicited greater RH N250 activation [BF10 > 10 from 291 to
293 ms, mean BF10 = 10.6, max BF10 = 10.8 at 292 ms] in the righthemisphere, albeit for only a very short duration (see Figure 9).

100% coherence
The comparison of the EA and WE faces at 100% (full structure)
coherence revealed an early enhanced P1 peak in the left hemisphere (LH) cluster for the EA faces [BF10 > 10 from 80 to 97, mean
BF10 = 58.3, max BF10 = 106.6 at 88 ms] (see Figure 10). The N170
voltage in the RH however was larger for WE faces compared to EA[BF10 > 10 from 146 to 149 ms, mean BF10 = 10.8, max BF10 = 11.4 at
147 ms]. Similarly, the WE faces elicited greater LH voltage in the

N250 time range [BF10 > 10 from 333 to 347 ms, mean BF10 = 32.6,
max BF10 = 50.3 at 340 ms].

Discussion
The overall pattern of the ERP results suggests that there aredifferent stages or types of sensitivity to visual race signals
depending on the quality of the visual stimulus, ranging fromvery early P1 and N170 effects through to later N250 effects
which are more commonly found for recognition/individuation
processes. Behaviourally, we observed that systematic reduction
in image phase coherence enhanced the categorization advantage for other-race faces relative to same race faces in both RT
and accuracy, peaking at 30% phase coherence. This pattern replicates the behavioural results of de Lissa et al. (2022), although thecurrent study utilized a subset of coherence steps to make thetesting time in the ERP paradigm more feasible. An accuracy effectDownloaded from https://academic.oup.com/scan/article/19/1/nsad076/7475779 by University of Fribourg - Bibliothèque cantonale et universitaire user on 19 January 2024

Fig. 4. At 10% coherence levels, the EA and WE faces did not elicit any difference in neural response in the left or right electrode clusters. No scalp
voltage topographies of voltage differences are illustrated as no Bayes Factor evidence (BF10 > 10) was found at this level of visual degradation.

P. de Lissa et al.

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Fig. 7. The enhanced N250 voltages continued in the 40% coherence level in both left and right hemispheres.

was, however, observed in the 10% phase coherence conditionin the current study, contrasting with the earlier findings whereaccuracy for both same- and other-race faces remained essentially at chance levels from 0% through to 15% coherence levels.
However, as noted in section ‘ORCA—RT and accuracy’, the combination of the average response accuracy being approximately
50% chance level, the d’ analysis result and the absence of a RT
effect at the 10% coherence level suggests that there was a bias torespond ‘WE’ when guessing. As the response keys were counterbalanced, this is unlikely to be due to hand-dominance effects.
It is reasonable to conjecture that such a pattern might emerge
when participants do exhibit greater accuracy in one condition
compared to another, so that when there is ambiguity, they may
be more likely to choose the race they are less confident about,which in this case would be the WE condition. However, clarification of this particular effect might best be addressed by amending
the paradigm to include a third ‘unsure’ response key in futurestudies.

Turning to the ERP analyses, the 10% coherence condition did
not show any observable evidence of a stimulus race processing difference, which corresponds to the lack of RT effects andthe average accuracy at chance level. The emergence of bilateral occipito-temporal effects at the 20% coherence level corresponded with clear accuracy and RT effects, reflecting enhanced
sensitivity to other-race visual signals. The timing of these differences in the waveforms suggests they involve the later N170
activations rather than the peaks themselves. While approaching
the P200 in time, which has been found to index distance-tonorm in terms of face typicality (Schweinberger and Neumann,
2016; Wuttke and Schweinberger, 2019) and sensitivity to otherrace faces in face recognition (Anzures and Mildort, 2021) andrepetition suppression (Wang and Han, 2021) paradigms, it is perhaps more likely that the extent of the phase-scrambling to a 20%
coherence level created a very difficult condition for the visualsystem to interpret (see Figure 5 for an example of the stimuli
at this coherence level), which may introduce variability in the

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Fig. 6. At 30% coherence, the enhanced amplitude to EA faces shifted later in time, corresponding with an N250 time range, manifesting in both theleft and right hemisphere.

8Social Cognitive and Affective Neuroscience, 2024, Vol. 19, No. 1

Fig. 9. At 60% coherence, the later N250 effect (EA > WE) barely reached the BF10 evidence threshold in the right hemisphere, while an earlier effect
was also observed where EA faces elicited larger P1 amplitudes than the WE faces.

latency of the N170 akin to face inversion (see Rossion and Gauthier, 2002 for a review) or misaligning face-halves (Jacques andRossion, 2010). Further, previous reported N170 repetition suppression effects in race processing implicate an early stage ofperceptual encoding whereby other-race faces are not encoded for
identity as efficiently compared to same-race faces (Vizioli et al.,2010a,b). The late N170 effects in our study may index a similar
process of encoding including identity, aided through the perception of other-race visual signals in comparison to the less salient
same-race signals.
At 30% coherence level, the late-N170 effect was no longer
observable; instead, a very distinct occipito-temporal effect in the
N250 time range clearly manifested in both hemispheres, which
was also observed at the 40% coherence level, and at 50% and 60%
although only in the right hemisphere. The transition from a lateN170 effect to an N250 is quite interesting and invites speculationthat the transition is rather categorical than gradual. Specifically,
the N250 within the context of race is theorized to reflect thegreater effort required when individuating other-race faces (Herzmann, 2016). The transition from an N170 effect to an N250 effectmay reflect the greater amount of visual information available inthe 30+% coherence levels, whereas the 20% coherence level may
be sufficiently degraded to remove the ability to perceive characteristic individuating features while retaining the visual signals
necessary for race categorization. The influence of individuation
processes on the N250 has been observed in a series of ERP studies focusing on race, where other-race faces have been found toelicit greater negative activation than same-race faces (Sun et al.,2014; Wiese et al., 2014; Herzmann, 2016). Caldara et al. (2004)
interpreted the later activation differences relating to race processing as reflecting the interplay between race perception anddifferences in the degree of semantic representations engenderedby same- and other-race faces. Viewed within the framework offace and race perception advanced by Valentine and Endo (1992),
the greater experience with same race faces produces a broader
and more elaborated face-space representation area compared

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Fig. 8. While an N250 EA effect was still apparent in the right hemisphere, it was shorter and not observed in the left hemisphere.

P. de Lissa et al.

9with other-race faces, which are clustered more tightly due tothe increased salience and thus apparent similarity of the visualsignals characteristic of different races. The enhanced N250 activation observed in our study in the higher coherence levels (aswell as in preceding studies utilizing full-structure faces) may
thus reflect the individuating differences inherent when encountering same- and other-race faces. However, the earlier N170
effect at 20% would appear more likely to reflect a lower-level perception that ignites individuation. In other words, the perception
and categorization of race does not intrinsically require individuation processes, but rather relies on the perception of visual signals
preceding this processing stage which may also modulate later
processes. In terms of timing, this interpretation is in line withthe behavioural results of de Lissa et al. (2021) which used peripheral presentations of same- and other-race faces, finding reliable
race categorization to take place within 200 ms of presentation.
Such remarkable speeds implicate very early stages of face processing. Indeed, at 60% and 100% coherence levels, a very earlyP1 effect was observed, with higher amplitudes for other- thansame-race faces (Figures 9 and 10). While the P1 is an inconsistent index of race processing (see Serafini and Pesciarelli, 2023 forreview), some studies have found an enhancement for other-racefaces (Anzures and Mildort, 2021), and have been interpreted asreflecting differences in how people process configural information in same- and other-race faces (Hahn et al., 2012; Wang et al.,2020). The theoretical framework for such an effect was advancedby Zhao and Bentin (2011) as being due to enhanced use of configural information in individuation processes in same- relative
to other-race faces, while not a process specifically necessary forrace-categorization processes. It is of particular interest to thecurrent study that these effects only manifested in response tovisually clear and structurally intact faces, and were eroded asphase-coherence was further degraded.
In fact, the sequence of effects observed in the current study
implicates a series of processes that are affected as visual quality is degraded. At full and partial phase coherence levels, thedifferences are very early P1, and not so substantial in voltage
magnitude. As image quality is lost, a later N250 effect emerges,
corresponding to when individuation processes would typicallybe expected to occur. And finally, a behavioural and ERP threshold

is observed where enhanced sensitivity to other-race faces is ata threshold and late N170 effects emerge. These results suggestthat there may be varying visual cues for race at varying levelsof visual quality. However, it is important to note that the natureof these visual cues is not clarified in the current study. The specific differences in physiognomy that people rely on to categorize
race might interact with visual quality to produce an asymmetry
in how different race faces are impacted by visual degradation.
Such a consideration may move closer to understanding how thespecifics of visual cues used in race categorization may change
according to the different race faces presented. In line with thisconcept, further investigation should be directed at how othervisual cues might be compromised through visual stimulus degradation, such as dominance, aggressiveness, etc. We did not control
for these dimensions in our stimuli set; however, the possibility
for these to interact with perception of race at varying levels ofvisual degradation is an important thing to consider, given our
results. For example, visually degrading faces through Gaussian
filtering has been observed to lead to enhanced ratings of attractiveness (Sadr and Krowicki, 2019), which has also been linked torace and health perception using morphed ‘mixed race’ composites (Rhodes et al., 2005). Given that the current behavioural andneural results point to enhancements in sensitivities to other racefaces as a function of how much visual information is containedin the face images, the race-in-noise paradigm seems well-suited
in investigating the relationships these other factors might havewith race perception in visually degraded stimuli, as well asfactors that are independent.

Conclusions
These results may have strong implications for how stimulus
quality—the available information—may impact various stagesof processing, from early face categorization through to individuation. Race can be extracted at different latencies in the timecourse and ERP components at the expense of the granularityof categorization, from a very early gross estimation (i.e. P1) tomore elaborate (i.e. N170) and rich representations (i.e. N250).
As such, the categorical perception of race cannot be considered
as one unique process, but rather may be performed at various

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Fig. 10. EA faces elicited a larger P1 in the left hemisphere, whereas WE elicited larger N170 and N250 time range activations in the right and left
hemispheres, respectively.

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Social Cognitive and Affective Neuroscience, 2024, Vol. 19, No. 1

Data availability
All data used in statistical analyses and represented in figures
are available online on the Open Science Framework repository
at https://osf.io/sx6b2.

Funding
This work was supported with funding from the Swiss National
Science Foundation awarded to R.C. (100019_189018).

Conflict of interest
The authors declared that they had no conflict of interest withrespect to their authorship or the publication of this article.

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Received: 13 June 2023; Revised: 17 October 2023; Accepted: 12 December 2023
© The Author(s) 2024. Published by Oxford University Press.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which
permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

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Social Cognitive and Affective Neuroscience, 2024, 19(1), 11–11
DOI: https://doi.org/10.1093/scan/nsad076
Advance Access Publication Date: 15 December 2023
Original Research – Neuroscience