Journal of Vision (2010) 10(1):15, 1–23

http://journalofvision.org/10/1/15/

1Inverting faces elicits sensitivity to race on the N170
component: A cross-cultural study
Luca Vizioli

Department of Psychology and Centre for Cognitive
Neuroimaging (CCNi), University of Glasgow,
United Kingdom

Kay Foreman

Department of Psychology and Centre for Cognitive
Neuroimaging (CCNi), University of Glasgow,
United Kingdom

Guillaume A. Rousselet

Department of Psychology and Centre for Cognitive
Neuroimaging (CCNi), University of Glasgow,
United Kingdom

Roberto Caldara

Department of Psychology and Centre for Cognitive
Neuroimaging (CCNi), University of Glasgow,
United Kingdom

Human beings are natural experts at processing faces, with some notable exceptions. Same-race faces are better
recognized than other-race faces: the so-called other-race effect (ORE). Inverting faces impairs recognition more than for
any other inverted visual object: the so-called face inversion effect (FIE). Interestingly, the FIE is stronger forsame- compared to other-race faces. At the electrophysiological level, inverted faces elicit consistently delayed and often
larger N170 compared to upright faces. However, whether the N170 component is sensitive to race is still a matter of ongoingdebate. Here we investigated the N170 sensitivity to race in the framework of the FIE. We recorded EEG from Western
Caucasian and East Asian observers while presented with Western Caucasian, East Asian and African American faces in
upright and inverted orientations. To control for potential confounds in the EEG signal that might be evoked by the intrinsic
and salient differences in the low-level properties of faces from different races, we normalized their amplitude-spectra,
luminance and contrast. No differences on the N170 were observed for upright faces. Critically, inverted same-race faces
lead to greater recognition impairment and elicited larger N170 amplitudes compared to inverted other-race faces. Our
results indicate a ﬁner-grained neural tuning for same-race faces at early stages of processing in both groups of observers.
Keywords: face recognition, face inversion effect, electrophysiology, culture
Citation: Vizioli, L., Foreman, K., Rousselet, G. A., & Caldara, R. (2010). Inverting faces elicits sensitivity to race on the N170
component: A cross-cultural study. Journal of Vision, 10(1):15, 1–23, http://journalofvision.org/10/1/15/, doi:10.1167/10.1.15.

Introduction
Human beings are natural experts at recognizing faces.
Brain imaging studies have shown that this visualexpertise is implemented by a specialized cortical network
located in the occipital temporal cortex (Haxby, Hoffman,& Gobbini, 2000), optimally tuned to process thisparticular visual category (Caldara & Seghier, 2009;
Caldara et al., 2006). However, such a highly developed
biological skill is markedly impaired in processing two
specific face categories: other-race and inverted faces.
The face recognition impairment observed for otherrace (OR) compared to same-race (SR) faces has long
been reported in the literature (Feingold, 1914), a
phenomenon often referred to as the Other-Race Effect(OREVMalpass & Kravitz, 1969; for a review see
doi: 1 0. 11 67 / 1 0 . 1 . 1 5Downloaded from jov.arvojournals.org on 03/28/2019

Meissner & Brigham, 2001). Although the neural mechanisms at play are yet to be understood, it is widely
accepted that visual expertise plays a crucial role inshaping this recognition deficit. Many studies advocate theexistence of finely tuned mechanisms to process SR faces,
probably developed as a by-product of visual experience(Caldara & Abdi, 2006; Michel, Caldara, & Rossion,
2006; Michel, Rossion, Han, Chung, & Caldara, 2006;
Tanaka, Kiefer, & Bukach, 2004; Walker & Tanaka,
2003), which can plausibly be held accountable for SR
face identification advantage. This identification advantage is paired with faster performance in race categorization tasks for OR faces (Caldara, Rossion, Bovet, &Hauert, 2004; Levin, 1996; Valentine & Endo, 1992). To
account for these findings, Valentine and Endo (1992)
suggested a model in which individual faces are stored
in a multidimensional space as a function of experience.

Received April 6, 2009; published January 29, 2010

ISSN 1534-7362 * ARVO

Journal of Vision (2010) 10(1):15, 1–23

Vizioli, Foreman, Rousselet, & Caldara

Within this space, OR faces are more closely clustered
together than SR faces. These differences in the spatial
distribution of the face exemplars would translate in beresponsible for faster OR race categorization but impaired
identification. A different account was provided by Levin(2000) who suggested that race information is a featurethat is quickly extracted from OR faces at the cost of theinformation required for fine individual discrimination.
Picture plane inversion of faces also affects facerecognition. Inverting faces has been consistently linked
to a significantly greater recognition deficit compared toany other visual category (Yin, 1969): the so-called Face
Inversion Effect (FIE). The FIE is thus regarded as one of
the strongest evidence for specialized face processing.
Although the putative neural mechanisms underlying thisphenomenon have yet to be clarified, some level ofconsensus seems to reign around the idea that visualexpertise may be accountable for the effects of face
inversion (e.g. de Haan, Pascalis, & Johnson, 2002;
Rossion & Gauthier, 2002). One of the main hypotheses
involving qualitative processing differences holds that, asa byproduct of experience, the human brain engages into
distinct mechanisms in processing upright and inverted
faces: holistic mechanisms for upright and featural
mechanisms for inverted faces; or at least that picture
plane inversion significantly impairs holistic processing offaces (Rossion, 2008). One can thus infer that the greaterlevel of expertise with SR faces, which translates in higher
levels of holistic processing (e.g. Michel, Caldara et al.,2006; Michel, Rossion et al., 2006), should elicit astronger FIE compared to OR faces. This prediction is
supported by converging evidence showing that inverting SR
faces leads to a greater impairment in recognition performance compared to OR faces (Buckhout & Regan, 1988;
McKone, Brewer, MacPherson, Rhodes, & Hayward, 2007;
Murray, Rhodes, & Schuchinsky, 2003; Rhodes, Tan,
Brake, & Taylor, 1989). To date, only a single study
reported the opposite pattern of results: Valentine andBruce (1986) found a larger FIE for OR faces compared
to SR faces. However, these authors attempted to equate
performance by modulating the time of exposure for encoding SR and OR faces (of 2 and 5 seconds respectively),
rendering these findings incomparable across groups ofobservers.
Electrophysiological studies in humans have significantly contributed to clarify the time course of visual
processing. With regards to faces, a critical time window
occurs between 130 to 200 ms after stimulus onset, inwhich the early Event-Related Potential (ERP) N170
component takes place. The N170 is a negative deflectionin the ERP signal occurring roughly 170 ms after stimulus
onset, peaking at occipitotemporal sites, which tends tobe larger for faces than any other visual object categories(for a recent review see Rossion & Jacques, 2008). Since
Bentin, Allison, Puce, Perez, and McCarthy (1996) seminal work, the speculative debate on the putative mechanisms underlying the N170 has yet to be resolved. While

Downloaded from jov.arvojournals.org on 03/28/20192

originally this component was believed to reflect the structural encoding of faces prior to individual face recognition(Bentin et al., 1996), recent evidence has shown N170
modulation to identity (Jacques, d’Arripe, & Rossion,
2007; Jacques & Rossion, 2006) and levels of expertise(Rossion & Gauthier, 2002; Tanaka & Curran, 2001),
suggesting that it may reflect more advanced stages offace processing. Earlier studies have identified a positive
component peaking in the time window of the N170
at central and frontal sites: the Vertex Positive Potential(VPPVBotzel & Grusser, 1989; Jeffreys, 1989). Like theN170, the VPP has been reported to respond with larger
amplitude to faces than other visual category, suggestingthat both components reflect the same neural process (Joyce& Rossion, 2005).
Despite the large amount of behavioral findings supporting the ORE, electrophysiological studies have as yet
failed to achieve a degree of consistency in the understanding of this phenomenon. The main ongoing debate
lies in whether the early N170 face-preferential component is sensitive to race. Research has thus far produced
contrasting results. A number of studies have failed toreport sensitivity to race on the N170 (e.g. Caldara et al.,2003, 2004; Tanaka & Pierce, 2009), or on the related
VPP (Ito, Thompson, & Cacioppo, 2004), suggesting thatthe race of the stimuli is not processed until later stages(i.e. roughly 250–300 ms after stimulus onsetVCaldara
et al., 2003, 2004; Tanaka & Pierce, 2009), or that the
N170 is not be sensitive enough to capture race information (Caldara et al., 2003). However, other studies havefound OR faces eliciting larger N170 amplitudes than SR
faces (Stahl, Wiese, & Schweinberger, 2008; Walker,
Silvert, Hewstone, & Nobre, 2008), with two studies
reporting larger N170 amplitude for SR faces (Herrmann
et al., 2007; Ito & Urland, 2005).
The heterogeneity of the N170 results described above
may be explained by considerable methodological differences across studies. Indeed, race modulation of the N170
appears to depend on task demands (Caldara et al., 2003,
2004; Herrmann et al., 2007; Ito & Urland, 2003). Another point of interest is that albeit global low-level visualproperties of face stimuli, such as amplitude spectrum,luminance and contrast, might affect early electrophysiological components such as P1 and N170 (Dakin, Hess,
Ledgeway, & Achtman, 2002; Rousselet, Pernet, Bennett,& Sekuler, 2008), none of the previous studies attempted
to control for or even address all these factors. This is surprising considering the low-level differences that co-vary
with different races of faces. Furthermore, previous studies
have used different types of OR stimuli, including EA, AA
and Hispanic (e.g. Caldara et al., 2003, 2004; Herrmann
et al., 2007; Ito & Urland, 2003, 2005; Tanaka & Pierce,
2009; Walker et al., 2008) faces, leading to contrasting
results. Critically, all the studies that have reported modulation to race on the N170 have only relied on data gatheredfrom a single population: the WC population. This methodological shortcoming undermines generalizations of theJournal of Vision (2010) 10(1):15, 1–23

Vizioli, Foreman, Rousselet, & Caldara

3

Figure 1. Example of the face stimuli used in the experiment. To avoid potential low level confounds on the ERP signal, amplitude
spectrum, luminance and contrast were normalized to all images’ mean.

results, because any effect could be confounded by differences in the face stimuli. A full cross-over interaction
between races of observers and face stimuli is crucial toassess genuine ORE modulations on the ERP signal.
Although behavioral research has extensively investigated the mutual effects of ORE and FIE, to date only
one electrophysiological study has exploited the conjoint
advantages offered by the combinations of these two phenomena (Wiese, Stahl, & Schweinberger, 2009). Wieseet al. (2009) tested the effects of the ORE and FIE on the
N170 using EA and WC faces on WC observers only.
They reported a general amplitude increase for inverted
compared to upright faces and latency delays on the N170
for inverted faces as a function of race. However, Wieseet al. (2009) only tested one group of observers (the WC
population) and did not control for low-level visual properties of the stimulus’ set.
In summary, The FIE is regarded as a well documented
marker of visual expertise for faces (Yin, 1969) both at theelectrophysiological (Bentin et al., 1996; Rossion et al.,1999, 2000) and the behavioral level (e.g. Freire, Lee, &Symons, 2000). Observers respond less accurately andwith longer reaction times when recognizing inverted
faces. Moreover face inversion elicits significantly
delayed N170 (Bentin et al., 1996; Rossion et al., 1999,
2000) and VPP (Jeffreys, 1996), often accompanied byamplitude increases (Itier & Taylor, 2004a, 2004b; Rossion
et al., 2000; Rossion, Joyce, Cottrell, & Tarr, 2003). These
effects have been interpreted as a disruption of holistic
processing for faces (Rossion & Gauthier, 2002).
Here we took advantage of the conjoint effects of the
ORE and FIE to investigate whether the N170 is sensitive

Downloaded from jov.arvojournals.org on 03/28/2019

to race in two groups of observers. We recorded scalp
EEG in WC and EA observers presented with WC, EA
and AA faces in upright and inverted orientations.
Participants were required to perform an orthogonal task,
consisting in the detection of colored faces in the stimulus
sequence. Moreover, to control for potential low-level
confounds in the ERP signals that would relate todifferences in the low-level properties across faces fromdifferent races, we normalized the amplitude-spectrum,luminance and contrast of the entire image set (seeFigure 1). In addition, we used a second “other race”
category of stimuli (i.e., AA faces) as the absence ofdifferences across the two OR faces (i.e. EA and AA for
WC observers and WC and AA for EA observers) would
further strengthen the claim that any observed ERP
modulations are related to the ORE. A separate behavioral
experiment was designed to assess the levels of ORE and
FIE in both groups of observers. Possibly due to higher
levels of expertise, SR faces have been reported to beprocessed more holistically than OR faces. We therefore
hypothesized that SR faces would elicit a larger inversion
effect compared to OR faces. At the electrophysiologicallevel, we predicted FIE modulations as a function of therace of the faces on the face preferential N170 component.
We did not find any significant difference in the uprightconditions. However, our data show sensitivity to race on
the N170 across both groups of observers for inverted
faces, with larger N170 amplitude modulations between
upright and inverted faces (i.e. the magnitude of the FIE)
for SR compared to OR faces. Moreover, although a significant delay for the onset of the N170 was observed forinverted compared to upright faces, we did not observe

Journal of Vision (2010) 10(1):15, 1–23

Vizioli, Foreman, Rousselet, & Caldara

latency differences across races. The electrophysiologicalmodulation was paired with greater recognition impairment for inverted same-race faces compared to other-racefaces.

Methods
Participants
30 subjects (15 East Asians (EA) and 15 Western
Caucasian (WC), with an age range of 19–30 and a meanof 23) took part in the experiment. All the EA participants
were Chinese; they had been in the country for less than
1 month and had previously never lived in a Western
society. All participants provided written informed consent and had normal or corrected-to-normal vision. All
subjects were right handed and 15 (8 EA and 7 WC) werefemale. The experiments received the approval of thelocal ethical committee.

Stimuli
The stimuli consisted of 30 front-view grayscale photographs of WC, EA and African American (AA) faces(5 identities  2 genders  3 races) occupying approximately a visual angle of 3.75-  4.25- (see Figure 1). We
used the WC and EA face database used in previous
studies (Michel, Caldara et al., 2006; Michel, Rossion
et al., 2006). The AA face stimuli were obtained from asubset of identities of the Productive Aging Lab Face
Database (Minear & Park, 2004). The inclusion of asecond “other race” category (i.e. AA faces) represented afurther control on the ERP signal.
Each identity was equally presented in 2 orientations(upright and inverted). All faces were cropped to remove
external features; none had particular distinctive featuresand male faces were clean-shaven. The stimuli were centered in a 5.2-  5.2- background of average luminance(25.4 cd/m2, 23.5-  30.1-). The stimuli were equated interms of spatial frequency content by taking the averageof the amplitude spectra of all 30 stimuli and combiningthat average spectrum with the original phase spectra
to reconstruct each individual stimulus. The RMS (rootmean square) contrast (i.e. the standard deviation of the
pixel intensities) was also kept constant across faces.
Because form information is largely carried by phaserather than amplitude (Rousselet et al., 2008), individual
faces remain easily recognizable after this manipulation,
however still ensuring that any race related differencesin the EEG is not simply a function of differences in therelative visibility of specific frequency components in thestimuli.

Downloaded from jov.arvojournals.org on 03/28/2019

4

Experimental design
EEG study

Participants were seated in a dimly lit, sound-attenuatedelectrically shielded room. Viewing distance was maintained at 80 cm by use of a chinrest. Stimuli were randomly interspersed and sequentially presented for 200 ms(16 frames at 80 Hz) on a Samsung SyncMaster 1100 MB
monitor (resolution 2048  1536), with an inter-stimulusinterval (ISI) randomly varying between 2200 and3200 ms. A black 0.3- fixation cross was presented in
the middle of the screen before stimulus onset andremained on the screen during the whole ISI duration.
Each trial had thus a random duration ranging between
2400 and 3400 ms.
Participants were instructed to perform an orthogonaltask that required pressing the “s” key on the keyboard
every time a green face appeared on the screen and the“k” key for a red face. This orthogonal task was designed
to avoid potential signal modulations due to attentional
confounds linked to the race of the stimuli (Golby,
Gabrieli, Chiao, & Eberhardt, 2001). Red and green faces
appeared in approximately 8% of the trials (i.e. 15 red and
15 green faces). Each face appeared once either as a redor a green stimulus.
The experiment consisted of 5 blocks of 78 trials each(360 trials in total with 60 trials  condition  orientationand 30 red and green faces) and lasted approximately 20–
25 minutes.
Behavioral study

Subsequently to the EEG experiment, to directly assess
the level of ORE and FIE, we recalled the same subjects
asking them to take part in a small behavioral experiment.
9 out of 15 subjects per group participated in thisexperiment; the others failed to reply to our query.
Participants sat in the same dimly lit room with the sameviewing conditions, and were presented with 20 new faces
for each race, one at the time (3s each, 5s ISI). The faces
were obtained from the KDEF (Lundqvist, Flykt, &Öhman, 1998) and AFID (Bang, Kim, & Choi, 2001)
databases. All faces were selected from a set of stimuli
different from the set used in the EEG experiment, andwere also equated for global amplitude spectrum, luminance, and contrast. Stimulus size subtended 3.75- 
4.25- of visual angle. Participants were told explicitly tomemorize the faces. The encoding phase was followed
by a forced-choice old-new recognition task whereby 40
faces (20 old and 20 new) were presented individually.
Participants underwent two blocks of the old/new facerecognition task per race with faces displayed in twoorientations (i.e., upright and inverted). The blocks werecounterbalanced across participants. Faces were blocked
by race during both the encoding and the recognition
phase. The encoding stage was followed by a 1 minuteJournal of Vision (2010) 10(1):15, 1–23

Vizioli, Foreman, Rousselet, & Caldara

pause, after which participants had to indicate whether
each face was old or new by pressing the “s” key on thecomputer keyboard for old faces and the “k” key for new
faces. Each face remained on the computer screen until
the participant’s response, or for a maximum of 200 ms(3s ISI). Participants did not know the ratio of old to newfaces and did not receive any feedback on their responses.
The order and the number of presentation of SR and OR
stimuli were pseudorandom, changing across subjects. As
in previous studies (e.g. Carroo, 1986), dVindices (Swets,
Tanner, & Birdsall, 1961) for WC and EA faces were calculated for each participant to assess accuracy.

EEG recording and analysis
EEG recording

EEG data were acquired by means of a 128-channel
BioSemi Active Two EEG system (BioSemi, Amsterdam,
Netherlands). Electrodes were placed in a nylon cap
according to the 10–5 system (Oostenveld & Praamstra,
2001). Vertical and horizontal electro-oculograms wererecorded by attaching 4 additional electrodes (UltraFlat
Active electrodes, BioSemi) below and at the outer canthi
of both eyes.
Analog signal was digitized at 1024 Hz and band-passfiltered online between 0.1 and 200 Hz. An active electrode (common mode senseVCMS) and a passive electrode (driven right legVDRL) were used to comprise a
feedback loop for amplifier reference, and electrodes
impedances were kept between T20 k4. Subjects were asked
to minimize blinking, head movement, and swallowing.

EEG pre-processing

EEG analysis was performed using EEGLAB (Delorme& Makeig, 2004), Matlab 7.5 (2007b) and BESA 5.2. In
BESA, EEG data were referenced on-line to an averagereference. Noisy electrodes were rejected on a subject-bysubject basis. The signal was low-pass filtered offlineat 40 Hz with a slope of 6 dB. Baseline correction was
performed using 150 ms of pre-stimulus onset. Artifacts
were rejected based on absolute abnormal values larger
than 120 2V. Trials were averaged across an epoch ofj200 ms to +600 ms. Trials including red or green faces
were excluded from the analysis.
Before averaging, single trials were corrected forhorizontal and vertical eye movement and blinking
artifacts by means of PCA. Due to their specific topographical configuration, we first identified these artifacts
on the continuous signal. We then manually selected
portions of the segment showing the topographicalconfiguration of interest. We thus averaged the selected
epochs to create a subject-specific template for thatparticular artifact. The first PCA component (accounting

Downloaded from jov.arvojournals.org on 03/28/2019

5

for 89% to 99%Vmean 92%Vof the variance for individual subjects) was removed.
Across subjects and conditions, the minimum number oftrials accepted after artifact rejection was 252, the maximum 360, and the mean 343. The rest of the analyses
were performed in EEGLAB and Matlab.
Amplitudes and peak latencies were measured at electrodes O1 and O2 for the P1 and P2 components (i.e.
where the amplitude of such components was maximal),and at 9 pairs of occipitotemporal electrodes over the left
and right hemisphere for the N170, and N250. To selectthe electrodes of interest, we performed the mean averageof the ERPs scalp topography at the mean latency of theN170 for upright and inverted stimuli across all the subjects and selected the electrodes with maximal amplitudes(see Figure 1 in the supplementary section). The channels
previously excluded consequently to artifact rejection wereinterpolated using the EEGLAB topoplot function. The
electrodes selected, consistently with the N170 literature(e.g. Bentin et al., 1996; Rossion & Jacques, 2008), were:
P4, P6, PPO6h, P8, P8h, PO8, P10, PO10h, PO10 in theright hemisphere; and P3, P5, PPO5h, P7, P7h, PO7,
P9, PO9h, PO9 in the left hemisphere (see Figure 1 in the
supplementary section).
For each subject, each component amplitude was quantified as the maximal positive (for P1 and P2) or negative(for N170 and N250) voltage measured within a 40 ms
time window centered on the Grand Average ERP latency.

Statistical analyses
In terms of statistical analyses, we employed canonicalmethods (i.e. ANOVA and t-tests) as well as robust
approaches (i.e. bootstrap). Bootstrap has higher statisticalpower and makes fewer assumptions on the distribution of
the data compared to more canonical statistical methods.
Our goal was to address a growingly popular issue that hasoften been overlooked within the electrophysiologicalliterature: the examination of the number of subjects
showing a similar pattern of results (e.g. Rousselet et al.,2008). It is worth noting that most EEG and MEG papers
do not report the number of subjects showing a given
effect, although there is a recent trend in the literaturefavoring this view (e.g. Philiastides & Sajda, 2006; Schyns,
Petro, & Smith, 2007; Smith, Gosselin, & Schyns, 2007).
Ensuring that an effect is observed across all or mostsubjects is essential because in some situations an effectmight be driven by a minority of subjects (for instance theearly P1 difference reported by Rousselet et al., 2008).
This is not to say that such effects are not interesting,rather that they should be interpreted with caution.
A first statistical analysis was performed on the P1
N170, P2 and N250 peak amplitudes and latencies. We
carried out a mixed model repeated measures 5 way

Journal of Vision (2010) 10(1):15, 1–23

Vizioli, Foreman, Rousselet, & Caldara

ANOVAs on the N170 and N250 components. The 5 factors included in the ANOVA were: 2 groups of observers(i.e. WC and EA participants); 3 races of the stimuli (i.e.
stimulus race: WC, AA and EA); 2 orientations in which
the stimuli were presented (i.e. upright and inverted);
2 hemispheres; and the electrodes of interest (as describedabove). Mixed model repeated measures 4 way ANOVAs(group  stimulus race  orientation  hemisphere) werecarried out on P1 and P2 components. These analyses did
not include the electrode factor as only one electrode perhemisphere was considered. A further 5 way ANOVA(group  stimulus race  orientation  electrode hemisphere) was carried out with the latency of the above
mentioned components as the dependent variable.
In light of the results, we then further carried out two
4 ways ANOVA (hemisphere  electrodes  stimulus
race  group) on the N170 peak amplitude, one for the
upright and one for the inverted conditions.
We finally assessed the difference between two conditions across subjects at each electrode independently
using t-tests. The differences assessed were WC vs. AA,
WC vs. EA, and EA vs. AA for WC Observers; and EA
vs. AA, EA vs. WC, and WC vs. AA for EA Observers.
Given the rather heterogeneous, asymmetrical nature of
the distributions of ERP differences (computed both
between upright minus inverted faces per each condition
independentlyVi.e. the magnitude of the FIEVand
between conditions for each orientation independently),
and the dVscores, which were also limited in sample size,
we carried out percentile bootstrap analyses. We sampled
subjects with replacement, averaging the means acrossparticipants independently for each condition, and thencomputing the difference between the means for the two
conditions (for instance inverted WC vs. AA). This
process was repeated 999 times, leading to a distributionof bootstrapped estimates of the mean difference between
two ERP conditions, averaged across subjects. Then the
95% percent confidence interval was computed (alpha =0.05). Finally, the difference between the two sample
means was considered significant if the 95% confidence
interval did not include zero. Note that this bootstrap
technique, relying on an estimation of H1, tends to havemore power than other robust methods like permutation
tests and related bootstrap methods that evaluate the null
hypothesis H0 (Wilcox, 2005).
We then carried out correlation analyses to assess therelationship between the N170 and behavioral magnitudesof the FIE. FIE indexes were calculated at the N170Vby
subtracting the N170 peak amplitude elicited by inverted
to that elicited by upright facesVand behavioralVthe dVscores for upright minus those for inverted facesVlevels.
Pearson correlation coefficients were calculated between
the 2 FIE indexes for each group of observers and for eachrace of the faces. Again, because of the limited sample
size and the asymmetrical nature of the distribution of thedata, bootstrap tests of independence were also carried

Downloaded from jov.arvojournals.org on 03/28/2019

6out. We sampled with replacement pairs of data points(i.e. the N170 and dVFIE indexes), and then computed thePearson correlation between these pairs, across subjects,
for each stimulus race independently. This process was
repeated 599 times, leading to a distribution of bootstrapped Pearson correlation coefficients between the two
FIE indexes. Then, 95% confidence intervals were computed using the special adjustments suggested by Wilcox(2005). Finally, correlations were considered significant
if their 95% confidence intervals did not include zero(alpha = 0.05).
Our second statistical analysis was data driven. Because
we were interested in any significant interaction on theearly cerebral dynamics evoked by faces of different races(across groups of subjects) (i.e. the first 600 ms after
stimulus onset), we carried out an ANOVA independently
at all the electrodes and all the time points. First we
calculated the index of the magnitude of the FIE at theelectrophysiological level by computing the differences
between the amplitude of ERPs elicited by upright minus
those elicited by inverted faces independently for eachstimulus’ race, at all the electrodes and all the time points.
We then carried out a 2 way ANOVA (group  stimulusrace) on these differences across all electrodes and alltime points independently. Post-hoc bootstrap tests were
subsequently carried out between paired conditions bysampling subjects with replacement, as described earlier.
Again 95% percent confidence intervals were computed,
with the difference between the means considered significant if a confidence interval did not include zero.
No correction for multiple comparisons was applied.
However, significant differences were not randomly scattered (as it might be expected with the significant points
being the result of random type I errors) but formed consistent spatiotemporal clusters.

Results
We found four main results. First, both groups ofobservers were significantly less accurate at recognizing
inverted compared to upright same race (SR) faces, butshowed no behavioral inversion effect for other race (OR)and African American (AA) faces. Second, regardless ofthe group of observers, the race of the face stimuli modulated the magnitude of the N170 amplitude face inversion effect (FIE), which was largest for SR faces, with nodifferences between OR and AA stimuli. No race effects
were observed on P1, P2, and N250. Third, no race effects
were observed on the peak amplitude of the N170 for
upright faces in both groups of observers. Finally, thebehavioral FIE positively correlated with the N170 FIE
for SR faces only. We report the analyses of all the
behavioral and ERP results below.

Journal of Vision (2010) 10(1):15, 1–23

Vizioli, Foreman, Rousselet, & Caldara

7

Figure 2. Mean dVscores and standard errors of the old/new face recognition task, for Western Caucasian (green), East Asian (red) and
African American (blue) faces for the upright (U) and inverted (I) orientations.

Behavioral results
We observed significant main effects for orientation(F(1,16) = 216.28; p G .01), with both groups being
significantly more accurate at recognizing upright compared to inverted faces; and race of the stimuli (F(1,16) =39.24; p G .01), with the lowest dVfor AA faces.
As shown in Figure 2, both groups of observers
exhibited a larger FIE (represented by the differences
between dVscores for upright and inverted stimuli) for SR
compared to OR and AA faces. Consistently, the 3-Way
ANOVA (groups  orientations  stimuli) showed significant interactions between Group  Stimuli (F(2,15) =131.03; p G .01) and group  stimuli  orientation(F(2,15) = 16.11; p G .01). Although, within both groups,
all subjects but one consistently displayed larger dV for
upright compared to inverted SR faces only, a paired t-test
contrasting upright and inverted SR faces showed asignificant difference for WC (t(8) = 3.72, p G .01), but
not for EA participants (t(8) = 2.16, p = .06). These low t
values seem to be due to small sample sizes and violationsof homoscedasticity. Bootstrap confidence intervals,
which are less sensitive to these factors, showed thatregardless of the group of observers, participants weresignificantly impaired at recognizing inverted compared to

Downloaded from jov.arvojournals.org on 03/28/2019

upright SR faces (WC: mean difference = .53, bootstrap
CI of the mean difference = [.27, 8], p G .01; EA: 0.29,[.07, .59], p G .01]). No differences between upright andinverted dVscore were observed for OR (WC: t(8) = .47,
p 9 .05; mean difference = 0.12 [j.34, .58], p 9 .05; EA:t(8) = .93, p 9 .05; mean difference = 0.13 [j.13 .39], p 9.05) and AA faces (WC: t(8) = .33, p 9 .05; meandifference = .06 [j.26, 37], p 9 .05; EA: t(8) = .34, p 9.05; mean difference = .09 [j.43, .56], p 9 .05).

EEG results
P1

Table 1 shows P1 results. P1 amplitude was significantly modulated by face orientation (F(1,28) = 30.21;
p G .01VFigure 3). Inverted faces elicited significantly
larger P1 (mean = 3.62 2V; std = 1.63 2V) compared to
upright faces (mean = 2.96 2V; std = 1.43 2V). Importantly, no effect of stimulus race was observed on theamplitude elicited by faces presented in either orientation(groups  stimuli F(2,27) = .56; p 9 .05; groups  stimuli
 orientations F(2,27) = .741; p 9 .05).
P1 elicited by inverted faces (mean lat. = 111 ms; std =7.4 ms) was significantly delayed by 3.4 ms compared toJournal of Vision (2010) 10(1):15, 1–23

GroupsElectrode

Vizioli, Foreman, Rousselet, & Caldara

Orientation

8

Stimuli

Mean
Amplitude

Std.
Deviation

Mean
Latency

Std.
Deviation

Western CaucasianAfrican American
East Asian

3.51
3.30
3.21

1.89
2.01
2.11

108
108
109

9
15
12

Western CaucasianAfrican American
East Asian

4.21
4.03
3.97

2.26
2.12
1.97

115
111
114

11
14
11

Western CaucasianAfrican American
East Asian

4.27
3.87
3.58

4.10
4.07
3.41

109
109
108

13
14
11

Western CaucasianAfrican American
East Asian

4.67
4.51
4.63

4.32
4.08
4.70

110
109
112

14
14
13

Western CaucasianAfrican American
East Asian

2.71
2.69
2.52

1.41
1.16
1.23

104
107
107

11
11
9

Western CaucasianAfrican American
East Asian

3.16
3.00
3.02

1.88
1.25
1.56

110
109
108

9
9
11

Western CaucasianAfrican American
East Asian

2.10
1.80
1.89

1.33
0.88
1.14

108
108
111

11
13
12

Western CaucasianAfrican American
East Asian

2.95
2.36
2.80

1.58
1.32
1.72

113
111
114

11
10
12

Western CaucasianObservers
O2
upright

Inverted

O1
upright

Inverted

East Asian Observers
O2
upright

Inverted

O1
upright

Inverted

Table 1. P1.

that elicited by upright faces (mean lat. = 108 ms; std =7.3 ms; F(1,28) = 31.907; p G 01). The latency of thiscomponent, like its amplitude, was not modulated by stimulus
race in either orientation (group  stimuliVF(1,28) =.35; p 9 05; group  stimuli  orientationsVF(2,27) = 1.42;
p 9 05). No other significant differences were observed.

N170

The mean peak amplitudes, latencies and standard
deviations in each condition are reported in Table 2.

Downloaded from jov.arvojournals.org on 03/28/2019

Figure 4 shows the mean ERPs at the right hemisphere
electrode PO8, where the N170 amplitude was largest(F(8,21) = 4.14, p G .01), and the left hemisphere
electrode PO7, for both stimulus orientations and bothsubject groups. Our main result indicates that stimulus
race systematically modulates the FIE magnitude of theN170 amplitude across groups, which was largest for SR
compared to OR or AA faces (orientation  stimulus groupVF(2,27) = 4.62, p G .05VFigures 4 and 6).
Moreover, inverted faces elicited significantly larger
N170 compared to upright faces regardless of stimulus
race and the group of observers (F(1,28) = 60,41, p G .01).

Journal of Vision (2010) 10(1):15, 1–23

Vizioli, Foreman, Rousselet, & Caldara

9

Figure 3. Grand average ERP waveforms of Western Caucasian and East Asian observers elicited by Upright (red) and Inverted (dottedgreen) faces at two occipital sites showing the largest P1 amplitude (O1, left hemisphere; O2, right hemisphere). The ERPs were obtained
averaging together faces across conditions (i.e. Western Caucasian, East Asian and African American) independently per eachorientation. While P1 was not modulated by the race of the faces in either orientation, a signiﬁcant inversion effect was observed
regardless of the race of the face stimuli, with inverted faces eliciting larger P1 compared to upright faces. No latency differences wereobserved across orientations.

Bonferroni corrected t-tests carried out between the peak
amplitudes elicited by upright and those elicited byinverted faces independently for each stimulus race showthat this effect was significant for all conditions (p G .05).
We also observed a main hemispheres effect, withlarger amplitudes over the right hemisphere (F(1,28) =6.15, p G .05); and a main effect of stimulus race (F(2,27) =6.7, p G .01). Bonferroni corrected paired t-tests showedthat AA faces elicited the smallest amplitude (p G .05),
while no differences were observed between WC andEA faces. The 5 way ANOVA further showed significant
interactions between hemisphere  orientation (F(1,28) =20.04, p G .01), orientation  stimulus (F(2,27) = 6.32,
p G .01); electrode  stimulus (F(16,13) = 4.06, p G .01);

Downloaded from jov.arvojournals.org on 03/28/2019

and hemisphere  stimulus  orientation (F(2,27) = 3.49,
p G .05).
To clarify these results, we carried out two 4 way
ANOVAs on the N170 amplitudes (hemisphere  electrodes 
stimulus  group), one for each orientation. Crucially,
this analysis indicated that for the upright orientation,
stimulus race did not modulate the N170 amplitude(stimuli  groupsVF(2,27) = 1.89, p 9 .05). However,
inverted SR faces elicited the largest amplitude regardlessof the group of observers (stimuli  groupsVF(2,27) =9.15, p G .01). A significant electrode effect indicated thatthe largest N170 was measured at PO8 for both orientations(upright: F(8,21) = 4.29, p G .01; inverted: F(8,21) =34.75, p G .01). ERPs elicited by inverted, but not uprightJournal of Vision (2010) 10(1):15, 1–23

GroupsElectrode

Vizioli, Foreman, Rousselet, & Caldara

Orientation

10

Stimuli

Mean
Amplitude

Std.
Deviation

Mean
Latency

Std.
Deviation

Western CaucasianAfrican American
East Asian

j5.20
j5.37
j5.06

4.77
4.69
4.25

159
159
158

10
14
11

Western CaucasianAfrican American
East Asian

j7.56
j6.40
j6.90

5.20
4.89
5.16

165
164
165

11
15
13

Western CaucasianAfrican American
East Asian

j4.45
j4.21
j4.42

3.81
3.83
3.54

160
162
162

12
14
13

Western CaucasianAfrican American
East Asian

j5.57
j4.81
j5.33

4.12
4.29
4.00

165
164
165

9
11
9

Western CaucasianAfrican American
East Asian

j5.91
j5.73
j5.57

3.41
3.39
2.99

160
161
161

9
9
12

Western CaucasianAfrican American
East Asian

j6.90
j6.61
j7.40

3.53
3.67
3.58

166
167
165

11
11
10

Western CaucasianAfrican American
East Asian

j4.35
j4.30
j3.78

3.45
3.59
3.34

160
161
161

10
9
10

Western CaucasianAfrican American
East Asian

j4.33
j4.34
j4.43

4.40
3.82
4.42

165
164
165

9
10
10

Western CaucasianObservers
PO8
upright

Inverted

PO7
upright

Inverted

East Asian Observers
PO8
upright

Inverted

PO7
upright

Inverted

Table 2. N170.

faces were also found to be significantly larger over theright hemisphere (upright: F(1,28) = 3.51; p 9 .05;
inverted: F(1,28) = 8.72; p G .01). Finally, for inverted,
but not upright, we observed a main effect of stimulus
race (upright: F(2,27) = 2.38, p G .05; inverted: F(2,27) =8.10, p G .01). Bonferroni corrected t-tests showed that
inverted AA faces elicited the smallest N170 amplitude(p G .05). This effect, as shown by the stimuli  hemisphereinteraction (F(2,27) = 5.46, p G .01) was stronger over theright hemisphere.
In light of the ANOVAs’ results, paired sample t-tests
and bootstrap confidence intervals were used to estimate

Downloaded from jov.arvojournals.org on 03/28/2019

effects of stimulus races on the N170 peak amplitude
elicited by inverted faces. No significant amplitude differences were observed (Figures 4 and 5) among stimulus
races over the left hemisphere.
Over the right hemisphere, regardless of the group ofobservers, SR inverted faces elicited the largest amplitude.
For both groups the amplitude differences between SR and
AA inverted faces (at PO8: t(14) = j3.92, p G .01 for WC
Observers, and t(14) = j3.66, p G .01 for EA Observers)
was consistently larger than those between SR and OR (atPO8: t(14) = j3.24, p G .01 for WC Observers and t(14) =j3.81, p G .01 for EA Observers). No significant differ-

Journal of Vision (2010) 10(1):15, 1–23

Vizioli, Foreman, Rousselet, & Caldara

11

Figure 4. Grand average ERP waveforms of Western Caucasian and East Asian observers elicited by Western Caucasian (green), East
Asian (red) and African American (blue) upright and inverted faces, at two occipitotemporal sites showing the largest N170 amplitude(PO7, left hemisphere; PO8, right hemisphere). While no signiﬁcant differences were observed in the upright conditions, inverted faces
consistently elicited larger N170 peaks for Same Race (SR) compared to Other Race (OR) faces on the PO8 electrode, with African
American (AA) faces eliciting the smallest amplitudes.

ences were observed between OR and AA faces (at PO8:t(14) = 1.49, p 9 .05 for WC Observers, and t(14) = 1.61;
p 9 .05 for EA Observers). Bootstrap confidence intervals
showed the same patterns of results (Figure 5 displays
effect size and data distribution at PO8 and PO7).

Downloaded from jov.arvojournals.org on 03/28/2019

The latency of the N170 was significantly delayed by
6 ms for inverted (mean = 166 ms; std = 10 ms) compared
to upright (mean = 160 ms; std = 10 ms) faces (F(1,28) =18.83, p G .01Vsee Figure 6). Importantly, there were no
significant latency differences among the races of stimuli

Journal of Vision (2010) 10(1):15, 1–23

Vizioli, Foreman, Rousselet, & Caldara

12

Figure 5. Bar plots of all paired differences of the N170 peak amplitudes at PO7 and PO8 for both groups of observers for the inverted
condition. Red circles represent individual data points. The error bars denote the 95% bootstrapped (Wilcox, 2005) conﬁdence interval(CI) of these differences. Statistically signiﬁcant differences are conveyed by CI not including zero. Note that amplitudes differences atelectrode PO8 show a consistent pattern of results, with both groups of observers responding to the law: Same-Race vs. African
American 9 Same-Race vs. Other-Race 9 Other-Race vs. African Americans. The difference between Other-Race vs. African Americans
is non-signiﬁcant.

Downloaded from jov.arvojournals.org on 03/28/2019

Journal of Vision (2010) 10(1):15, 1–23

Vizioli, Foreman, Rousselet, & Caldara

13

Figure 6. Grand average ERPs at PO8 and FC2 for the upright and inverted (dotted) conditions, electrodes showing respectively thelargest amplitudes for the N170 and VPP components. Note that the maximal difference between Inverted and Upright faces occurs at
the N170 latency for both the occipitoparietal and frontal site. Both groups of observers showed largest amplitudes for Same-Race 9
Other-Race 9 African Americans. The box plots represent the distribution of individual around the median (red line) of the amplitudedifference between inverted and upright N170s and VPPs; red crosses indicate outliers.

Downloaded from jov.arvojournals.org on 03/28/2019

Journal of Vision (2010) 10(1):15, 1–23

Vizioli, Foreman, Rousselet, & Caldara(F(5,24) = .33, p 9 .05); and no significant interaction
between the race of the stimuli and the groups of observers
in either orientation (stimuli  groupsVF(2,27) = .07;
p 9 .05; stimuli  groups  orientationVF(2,27) = 1.2;
p 9 .05). No other significant latency differences wereobserved on this component.
P2

P2 results are reported in Table 3. The absence ofsignificant groups  stimuli (F(2,27) = .33; p 9 .05) andgroups  stimuli  orientations (F(2,27) = .36; p 9 .05)
interactions on P2 amplitude indicated that stimulus race
did not systematically modulate the amplitude of thiscomponent across groups in either orientation. Moreover,a main effect of stimulus race was observed on amplitude(stimuliVF(2,27) = 4.13; p 9 .05). Pairwise comparisonsrevealed only one significant differences between EA andAA faces, which was true for upright and inverted faces(Bonferroni corrected t-tests, p G .05). In addition, P2
amplitude was modulated by face orientation (F(1,28) =8.48; p G .05VFigure 3). Inverted faces elicited significantly larger amplitudes (mean = 3.03 2V; std = 1.91 2V)
compared to upright faces (mean = 2.74 2V; std = 1.75 2V).
However, this effect was observed only over the left
hemisphere. (orientation  hemisphereVF(1,28) = 5.08;
p G .05).
The latency of P2 elicited by inverted faces (mean =235.5 ms; std = 14.85 ms) was significantly delayed by
5.8 ms compared to that elicited by upright faces (mean =226.5 ms; std = 16.9 ms; F(128) = 22.96; p G .01).
Importantly, the race of the face stimuli did not modulate thelatency of this component in either orientation (stimuli groupsVF(2,27) = .55; p 9 .05; stimuli  groups orientationVF(2,27) = 1.14; p 9 .05). No other significant
differences were observed.
N250

Table 4 shows the N250 results. Because all theelectrodes of interest showed the same pattern of results,
we only report the values for PO8 and PO7, those sites
where the N250 was largest. We observed a main effect ofstimulus race (F(2,27) = 3.87; p G .05) on the N250
amplitude. However, Bonferroni corrected t-tests showed
only a significant amplitude difference between EA andAA faces for the 2 orientations (p G .05). Furthermore,unlike all the previous components, the N250 showed nosignificant amplitude modulations by face orientation(orientationVF(1,28) = .93; p 9 .05). There was also no
significant stimuli  groups (F(2,27) = .09; p 9 .05) andstimuli  groups  orientation (F(2,27) = 1.91; p 9 .05)
interaction.
Similarly to the N250 amplitude, the N250 latency
showed no sensitivity to orientation (F(1,28) = .21; p 9.05) and to race (F(2,27) = 3.23; p 9 .05). Race effects did

Downloaded from jov.arvojournals.org on 03/28/201914

not interact with groups (stimuli  groupsVF(2,27) = .64;
p 9 .05) in either orientation (stimuli  groups orientationVF(2,27) = 1.5; p 9 .05). No other significant
differences were observed for this component.
Component free analyses

Figure 6 shows the N170 and the VPP.
The 2 way ANOVA (group  stimuli) carried out
independently at each electrode and at each time point on
the FIE index revealed significant interactions only in the
N170 time window (p G .05).
The scalp topography in Figure 7 shows the significant
F values (p G .05) for the group  stimuli interaction. SR
faces elicited the largest electrophysiological FIE at the
N170 peak latency, regardless of the group of the observer.
Significant effects were observed over four clusters ofelectrodes: left occipital, right occipital, left frontotemporal and frontal sites. Because all the electrodes within eachcluster showed the same pattern of results, for display
purposes, we only selected the electrodes with the largest
amplitude in each group.
Bootstrap tests revealed a stronger FIE for SR compared
to AA faces in the clusters of electrodes around PO8 andFC2 (Figure 7; PO8: mean difference = .82, CI = [.41, 1.18]
for WC; mean difference = .65 [.10, 1.18] for EA;
FC2: mean difference = j.55 [j.20, j.90] for WC; meandifference = j.20 [j.02, j.45] for EA). Similarly, theFIE was stronger in SR compared to OR faces (PO8: meandifference = .63 [.10, 1.11] for WC; mean difference = .31 [.05, .56] for EA; FC2: mean difference = j.30
[j.10, j.50] for WC; mean difference = j.10 [j.05,
j.30] for EA). No significant differences were observed
between OR and AA faces (PO8: mean difference = .27[j.32, .67] for WC; mean difference = .32 [j.15, .83] for
EA; FC2: mean difference = j.25 [.05, j54] for WC;
mean difference = j.10 [.10, j.28] for EA). Although theleft occipital and frontotemporal clusters showed respectively the same patterns as the right occipital and left
frontal clusters, pairwise comparisons did not reveal any
significant differences (Figure 7).

EEG and behavioral results
As shown in Figure 8, in WC observers there was asignificant correlation between the N170 amplitude andthe behavioral FIE for SR (r(8) = .82, p G .01), but not for
OR (r(8) = .56, p 9 .05) and AA (r(8) = .23, p 9 .05) faces.
Moreover, while the same pattern of results was observedfor EA participants, the correlation detected for SR faces
was non significant (r(8) = .63, p = .06), even though all
subjects but one showed the effect; correlations for AA(r(8) = j.19, p = .62) and OR faces (r(8) = j.50, p 9.17VFigure 8) were also non significant. A more robust
percentile bootstrap test of independence showed significant

Journal of Vision (2010) 10(1):15, 1–23

GroupsElectrode

Vizioli, Foreman, Rousselet, & Caldara

Orientation

15

Stimuli

Mean
Amplitude

Std.
Deviation

Mean
Latency

Std.
Deviation

Western CaucasianAfrican American
East Asian

2.86
2.71
2.87

1.75
1.88
1.75

222
223
224

20
22
22

Western CaucasianAfrican American
East Asian

3.23
3.03
3.15

2.18
2.26
2.09

238
234
229

25
20
18

Western CaucasianAfrican American
East Asian

2.70
2.39
2.47

1.88
1.91
1.68

220
222
228

23
22
32

Western CaucasianAfrican American
East Asian

3.06
2.96
3.32

1.98
1.87
1.94

244
238
229

28
19
17

Western CaucasianAfrican American
East Asian

2.85
2.63
2.93

2.46
2.48
2.25

227
224
233

16
16
19

Western CaucasianAfrican American
East Asian

2.72
2.61
2.93

2.30
2.66
2.29

236
232
236

17
12
19

Western CaucasianAfrican American
East Asian

2.95
2.57
2.95

1.71
1.48
1.63

230
235
228

20
16
16

Western CaucasianAfrican American
East Asian

3.06
3.04
3.19

1.81
1.77
1.78

238
241
233

20
25
20

Western CaucasianObservers
O2
upright

Inverted

O1
upright

Inverted

East Asian Observers
O2
upright

Inverted

O1
upright

Inverted

Table 3. P2.

correlations (p G .01) between behavioral and electrophysiological FIE indexes, for both groups of observers(WC j CI = [0.28, 0.98]; EA j CI = [0.35, 0.94]).

Discussion
We investigated whether the early ERP face-preferential
N170 component (and its positive counterpart, the VPP)

Downloaded from jov.arvojournals.org on 03/28/2019

was modulated by the race and orientation of faces as afunction of the race of the observers.
Consistent with previous behavioral studies (e.g., Blais,Jack, Scheepers, Fiset, & Caldara, 2008; Michel, Rossion
et al., 2006; Rhodes et al., 1989; Tanaka et al., 2004;
Walker & Tanaka, 2003), Western Caucasian (WC) andEast Asian (EA) observers were more accurate atrecognizing same-race (SR) compared to other-race (OR)faces. Importantly, in accordance with past reports(Buckhout & Regan, 1988; McKone et al., 2007; Murray
et al., 2003; Rhodes et al., 1989) face inversion had

Journal of Vision (2010) 10(1):15, 1–23

GroupsElectrode

Vizioli, Foreman, Rousselet, & Caldara

Orientation

16

Stimuli

Mean
Amplitude

Std.
Deviation

Mean
Latency

Std.
Deviation

Western CaucasianAfrican American
East Asian

1.41
1.34
1.61

1.86
1.75
1.78

284
277
282

20
23
18

Western CaucasianAfrican American
East Asian

1.81
1.51
1.67

2.12
2.34
2.11

281
278
285

12
24
15

Western CaucasianAfrican American
East Asian

0.77
0.79
0.65

2.06
2.30
1.77

286
291
288

24
24
22

Western CaucasianAfrican American
East Asian

0.69
0.45
0.88

1.94
2.02
2.29

281
287
285

24
25
23

Western CaucasianAfrican American
East Asian

0.04
j0.10
0.20

1.56
1.49
1.40

280
284
287

15
19
16

Western CaucasianAfrican American
East Asian

j0.11
j0.13
0.04

1.38
1.37
1.44

288
279
287

18
22
15

Western CaucasianAfrican American
East Asian

0.99
0.73
1.02

2.11
2.20
1.57

287
287
284

23
20
21

Western CaucasianAfrican American
East Asian

1.19
1.22
1.41

1.83
1.99
1.88

284
278
289

23
15
15

Western CaucasianObservers
PO8
upright

Inverted

PO7
upright

Inverted

East Asian Observers
PO8
upright

Inverted

PO7
upright

Inverted

Table 4. N250.

greater detrimental effects on the recognition of SR
compared to OR and African-American (AA) faces inboth groups of observers. Moreover, in line with previous
electrophysiological findings (Bentin et al., 1996; Itier &Taylor, 2004a; Jeffreys, 1996; Rossion et al., 1999, 2000;
Rossion & Jacques, 2008), inversion increased anddelayed the N170 and the VPP. Importantly, while therace of the faces did not affect the latencies of thesecomponents in either orientation, we observed a modulation the magnitude of the face inversion effect (FIE) as afunction of race across groups of observersVthis is themain novel finding of our study. Specifically, theDownloaded from jov.arvojournals.org on 03/28/2019

magnitude of the FIE (i.e., the amplitude difference
between upright and inverted faces) was largest for SR,
compared to OR and AA faces, with no differences
between the latter two categories. This effect cannot be
accounted for by low-level visual differences among faces
from different races for three reasons. First, amplitude
spectrum, luminance, and contrast were equated across all
the stimuli; second, we observed a full crossover interaction between groups showing that, regardless of thegroups of observers, SR faces elicited larger FIE compared to OR and AA faces; third, our statistical analyses
showed no significant amplitude differences between theJournal of Vision (2010) 10(1):15, 1–23

Vizioli, Foreman, Rousselet, & Caldara

17

Figure 7. Center: Scalp topography at the N170 latency of signiﬁcant F values for the ANOVA computed on the differences between
Upright minus Inverted faces on all electrodes, across all time points and all conditions. The red clusters highlight signiﬁcant F values
distributions (p G .05). Note that high signiﬁcant F values clustered around the N170 latency at four sites (i.e. left and right occipitoparietal,
frontal and right frontotemporal). Top and bottom: Bar plots reporting the results of the post-hoc tests computed on the pair-wisedifferences (i.e. Same-Race vs. African American, Same-Race vs. Other-Race, Other-Race vs. African Americans) for inverted vs. uprightconditions at the time window where signiﬁcant F values clustered (i.e. N170 window). Within each cluster all the electrodes show asimilar pattern of results. We report here only the electrodes showing the largest differences per cluster. The red dots represent single
data points. The error bars show the 95% bootstrapped (Wilcox, 2005) conﬁdence interval (CI) of these differences. Statistically signiﬁcantdifferences are conveyed by CI not including zero. Both groups of observers on these clusters respond to the law: Same-Race vs. African
American 9 Same-Race vs. Other-Race 9 Other-Race vs. African Americans. The difference between Other-Race vs. African Americans
is always non-signiﬁcant.

Downloaded from jov.arvojournals.org on 03/28/2019

Journal of Vision (2010) 10(1):15, 1–23

Vizioli, Foreman, Rousselet, & Caldara

18

Figure 8. Correlations between the magnitude of the FIE on the N170 (i.e. N170 amplitude elicited by Inverted minus those elicited by
upright faces per each condition) and the magnitude of the FIE on the dVscores. Pearson coefﬁcient, bootstrap conﬁdence interval andrespective p values are reported in the top left corner.

two respective OR categories. These observations
strengthen the claim that the modulations observed on
N170 and VPP components are genuinely related to race.
Interestingly, while both P1 and P2 were sensitive tostimulus orientation, exhibiting larger amplitudes anddelayed latencies for inverted compared to upright faces,
the N250 was not modulated by this factor. Importantly,
the P1, P2 and N250 components were not sensitive torace, as demonstrated by the absence of an interaction
between the race of the faces and the groups of observers.
The electrophysiological results were consistent withthe behavioral data. Both groups of observers showed asignificant positive correlation between the magnitude of
the FIE on the N170 and recognition accuracy only for SR
faces. In line with previous observations (Jacques et al.,2007), these positive correlations indicate that larger N170
amplitudes elicited by inverted faces are associated withlower recognition accuracy.
It is worth noting however that these correlations are
driven by different components of the inversion equation.

Downloaded from jov.arvojournals.org on 03/28/2019

The behavioral FIE is due to the superior recognition of
SR faces in their upright orientation. In contrast, theelectrophysiological FIE is due to the enhanced N170
negativity elicited by SR faces in their inverted orientation. However, in our study the electrophysiological (i.e.,
orthogonal) and the behavioral (i.e., active) findings relied
on different task constraints. Therefore, future studies arenecessary to clarify the very nature of this paradox and
precisely identify the mechanisms underlying the relationship between these measures.
The analyses of the conjoined effects of face inversion
and race on the ERP signal were underlined by arigorous and comprehensive statistical approach. We
carried out both canonical statistical tests (i.e. t-tests) as
well as robust statistics (i.e. bootstrap confidence interval)
on behavioral data. From a methodological point of view,the importance of implementing such a statistical
approach on our data is illustrated by the behavioral
results. Canonical statistical tests (i.e. t-tests) did not
capture significant effects (i.e., p = .06) for the FIE for SR

Journal of Vision (2010) 10(1):15, 1–23

Vizioli, Foreman, Rousselet, & Caldara

faces in EA observers, and the correlation between thisindex with the electrophysiological data. However, as
clearly shown by the standard errors in Figure 2, theabsence of significant effects seems to arise from the large
variance for the inverted orientation (with all participants
but one showing the effects). The power of parametrictests is significantly affected by both the limited sample
size and the asymmetrical distribution of the population.
By using bootstrap confidence intervals, which are lesssensitive to sample size and outliers, both statistics
reached significance.
In the face processing literature, it has been suggested
that the recognition impairment observed for inverted
faces reflects a qualitative switch from holistic to featuralprocessing, or at least an impairment of holistic processing (e.g. Rossion & Gauthier, 2002). Undeniably, due tothe lack of experience with inverted faces, these stimuli
are more difficult to process than those presented in theircanonical (upright) orientation. Concurrently, the amplitude enhancement on the N170 and VPP components
could be related to the relative processing impairment
associated with inverted faces (e.g. Rossion et al., 2000).
It has also been argued that SR faces are processed more
holistically than OR faces (e.g. Michel, Caldara et al.,2006; Michel, Rossion et al., 2006). Thus, if face inversion triggers a switch from holistic to featural processing
mode, it should lead to a greater impairment for the category processed more holistically: SR faces.
Importantly, regardless of these theoretical interpretations, our data show that the relatively larger behavioral
recognition impairment for SR faces due to stimulus
inversion is associated with electrophysiogical modulations, with both measures correlating positively. The
sensitivity of the N170 to race during face inversion
suggests that differences in processing SR and OR faces
begin early, at the perceptual level. These early perceptual
differences fit well with ORE theoretical frameworks. In
the face space model proposed by Valentine and Endo
(1992), which recently found objective support in neuralnetwork simulations (Caldara & Abdi, 2006), faces are
encoded in an arbitrary psychological multidimensional
space. Variations in exemplar density between racesaccount for SR face recognition advantage, because SR
face representations are more widely distributed in theface space than the representations of OR faces. The
physical distance between points, symbolizing psychological representations of individual faces, is a function of theperceived difference between faces, which determines thedensity of the cloud. It would therefore make sense thatdifferences in processing SR and OR faces arise at theperceptual level. Unlike Valentine’s multidimensional
face space model, Levin (1996, 2000) postulates that for
OR faces, race is extracted as a feature at the cost of
individuating information. Therefore, according to Levin,performance for SR and OR faces can be accounted for bydifferences in coding features for faces of different races

Downloaded from jov.arvojournals.org on 03/28/2019

19(Levin, 2000). This account would also be consistent with
our data showing modulations of face processing as afunction of race at early perceptual stages.
In keeping with several previous studies (e.g. Caldara
et al., 2003, 2004; Tanaka & Pierce, 2009), race did notaffect the N170 amplitude for upright faces. However, wefailed to replicate the delayed N170 latency for inverted
OR compared to SR faces (Wiese et al., 2009), as well as
previous modulations of race on the N170 amplitude for
upright faces (Herrmann et al., 2007; Ito & Urland, 2005;
Stahl et al., 2008; Walker et al., 2008). We also failed toreplicate race effects at earlier latencies than that of theN170. Ito and Urland (2003) reported race modulations
between AA and WC faces in WC observers on early ERP
components (i.e. 100 ms after stimulus onset). In theirexperiment, however they used color pictures of faces ofdifferent races that differed markedly in skin color andwere not equated in spatial frequency content. Despite
these physical differences, Ito and Urland (2003) interpreted their findings as evidence for early race categorization. Alternatively, P1 effects might be due todifferences in global low-level visual properties, which
are known to modulate early ERP components (e.g.
Hillyard, Teder-Salejarvi, & Munte, 1998; Johannes,
Munte, Heinze, & Mangun, 1995; Luck, 2005), evenindependently of attention (Hillyard et al., 1998). In ourstudy, we equated some important global low-level visualproperties across stimuli, and we failed to observe any P1
categorical race effect. In line with previous studies (e.g.
Itier & Taylor, 2004a, 2004b), we only observed a generalamplitude and latency increase on the P1 component for
inverted compared to upright faces. Indeed, amplitude
modulations on this component have been associated with
low-level differences in the stimuli (Debruille, Guillem, &Renault, 1998; Halit, de Haan, & Johnson, 2000). In anutshell, despite the fact that controlling for low-levelvisual properties of the stimuli may reduce their ecological validity, our findings suggest that this control
abolishes potentially spurious effects related to salient
differences in faces from difference race. In addition, thesestudies relied on only one group of observers. Importantly,
we show that the investigation of sensitivity to race
requires the use of at least two groups of observers and thepresence of a crossover interaction.
Contrary to previous findings, our data failed to reveal
P2 (Caldara et al., 2004) and N250r (Tanaka & Pierce,2009) race effects. Although controlling for global lowlevel visual properties might have abolished P2 and N250
race effects, it seems more likely that the lack of race
effects on these components in our experiment may result
from differences in task constraints. Indeed, our subjects
were instructed to perform a passive orthogonal task,
whereas Caldara et al. (2004) used a direct racecategorization task with interspersed catch trials, andTanaka and Pierce (2009) employed an active old-new
recognition task, directly tapping into face memory. The

Journal of Vision (2010) 10(1):15, 1–23

Vizioli, Foreman, Rousselet, & Caldara

N250 and the P2 have been related to the active
categorization of visual stimuli (Harel, Ullman, Epshtein,& Bentin, 2007; Latinus & Taylor, 2005; Philiastides,
Ratcliff, & Sajda, 2006; Philiastides & Sajda, 2006), andlong term memory of faces (Schweinberger, Pickering,Jentzsch, Burton, & Kaufmann, 2002). If the task
demands do not entail active categorization of facestimuli, or retrieval of face representations, the effect onthese later components may thus be abolished. Further
research is needed to clarify this issue.
An important question that would require further
investigation is whether the N170 is sensitive only to race
for inverted faces. How can race affect the early electrophysiological dynamics only in a given unfamiliar upsidedown orientation, while producing no effects in its morecanonical upright orientation? This surprising result could
be explained by a ceiling effect. First, humans’ proficiency
in processing upright faces is so high that the ERP signal
might not be sensitive enough to detect any subtle
differences that may exist. However, situations that ismore demanding in terms of visual processingVas it is thecase for inverted facesVmay render such subtleties tobecome more easily identifiable. Second, the tasks andparadigms used so far may be unable to confer enoughpower to measure early amplitude modulations to race.
Our data however do not allow speculation in either
direction. Further research with more sensitive paradigms(e.g., neural adaptation) and task constraints (e.g., face
identification) tapping into the roots of the ORE isnecessary to clarify whether early neural markers ofprocessing upright faces (i.e. the N170) are sensitive torace, or to confirm that race sensitivity occurs uniquely forinverted faces.

Conclusion
Given its actuality in everyday life, the thoroughly
documented ORE remains an interesting topic to bestudied in the field of cognitive and vision science.
Although, behavioral data have shown high levels of
consistency, the neural mechanisms underlying this visual
effect have yet to be fully understood. Electrophysiological studies have reported highly contrasting data. In thepresent study we sought to investigate whether it waspossible to relate modulation of the ERP signal in theearly neural dynamics of face processing to behavioral
differences associated with the ORE. We addressed someof the methodological inconsistencies displayed in previous research by controlling for low level visual properties of our stimuli, which are known to modulate the ERP
signal and by adding a set of other-race faces common
to both group of observers (i.e., AA faces). Since any(significant) effect could be confounded by differences in
the face stimuli, which would critically impact on thegeneralization of the results, we adopted 2 groups ofDownloaded from jov.arvojournals.org on 03/28/2019

20

observers: EA and WC. We observed a full cross-overinteraction between the races of the observers and therace of the faces, which positively correlated with our
behavioral data. These findings indisputably represent a
genuine ORE on the ERP signal. Our results showed thatwhile race did not elicit differences across the whole
ERP for uptight faces, the race of the faces systematicallymodulated the amplitude of the N170 and VPP in the
inverted condition. The neural face system is sensitive torace at early perceptual stages of processing, at least whenfaces are presented upside-down.

Acknowledgments
This project was supported by a British Academy grant(SG-45080) awarded to RC. RC was also supported by
The Economic and Social Research Council and Medical
Research Council (RES-060-25-0010).
We would also like to thank Meike Ramon for
insightful comments on previous versions of this paper.
Commercial relationships: none.
Corresponding authors: Luca Vizioli; Roberto Caldara.
Emails: lucav@psy.gla.ac.uk; r.caldara@psy.gla.ac.uk.
Address: Department of Psychology, University of Glasgow,
58, Hillhead Street, Glasgow, G12 8QB, UK.

References
Bang, S., Kim, D., & Choi, S. (2001). Asian face image
database. In Lab IM, editor. Korea: Postech.
Bentin, S., Allison, T., Puce, A., Perez, E., & McCarthy, G.
(1996). Electrophysiological studies of face perception
in humans. Journal of Cognitive Neuroscience, 8,
551–565. [PubMed] [Article]
Blais, C., Jack, R. E., Scheepers, C., Fiset, D., & Caldara, R.
(2008). Culture shapes how we look at faces. PLoS
ONE, 3, e3022. [PubMed] [Article]
Botzel, K., & Grusser, O. J. (1989). Electric brain
potentials evoked by pictures of faces and non-faces:
A search for “face-specific” EEG-potentials. Experimental Brain Research, 77, 349–360. [PubMed]
Buckhout, R., & Regan, S. G. (1988). Explorations inresearch on the other-race effect in face recognition in
Practical Aspects of Memory: Current Research andIssues. In M. M. Gruneberg, P. E. Morris, & R. N. Sykes(Eds.), Memory in everyday life (vol. 1, pp. 40–46).
Chichester, Sussex, UK: John Wiley and Sons.
Caldara, R., & Abdi, H. (2006). Simulating the ‘otherrace’ effect with autoassociative neural networks:
Further evidence in favor of the face-space model.
Perception, 35, 659–670. [PubMed]Journal of Vision (2010) 10(1):15, 1–23

Vizioli, Foreman, Rousselet, & Caldara

Caldara, R., Rossion, B., Bovet, P., & Hauert, C. A.
(2004). Event-related potentials and time course of
the “other-race” face classification advantage. Neuroreport, 15, 905–910. [PubMed]
Caldara, R., & Seghier, M. L. (2009). The fusiform facearea responds automatically to statistical regularities
optimal for face categorization. Human Brain Mapping, 5, 1615–1625. [PubMed]
Caldara, R., Seghier, M. L., Rossion, B., Lazeyras, F.,
Michel, C., & Hauert, C. A. (2006). The fusiform facearea is tuned for curvilinear patterns with more highcontrasted elements in the upper part. Neuroimage,31, 313–319. [PubMed]
Caldara, R., Thut, G., Servoir, P., Michel, C. M., Bovet, P.,& Renault, B. (2003). Face versus non-face object
perception and the ‘other-race’ effect: A spatiotemporal event-related potential study. Clinical Neurophysiology, 114, 515–528. [PubMed]
Carroo, A. W. (1986). Other race recognition: A comparison of black American and African subjects. Perceptual and Motor Skills, 62, 135–138. [PubMed]
Dakin, S. C., Hess, R. F., Ledgeway, T., & Achtman, R.
L. (2002). What causes non-monotonic tuning offMRI response to noisy images? Current Biology, 12,
476–477. [PubMed]
Debruille, J. B., Guillem, F., & Renault, B. (1998).
ERPs and chronometry of face recognition: Followingup Seeck et al. and George et al. Neuroreport, 9,3349–3353. [PubMed]
de Haan, M., Pascalis, O., & Johnson, M. H. (2002).
Specialization of neural mechanisms underlying facerecognition in human infants. Journal of Cognitive
Neuroscience, 14, 199–209. [PubMed]
Delorme, A., & Makeig, S. (2004). EEGLAB: An open
source toolbox for analysis of single-trial EEG
dynamics including independent component analysis.
Journal of Neuroscience Methods, 134, 9–21.
[PubMed]Feingold, C. A. (1914). The influence of environment on
identification of persons and things. Journal of
Criminal Law and Police Science, 5, 39–51.
Freire, A., Lee, K., & Symons, L. A. (2000). The faceinversion effect as a deficit in the encoding ofconfigural information: Direct evidence. Perception,
29, 159–170. [PubMed]
Golby, A. J., Gabrieli, J. D., Chiao, J. Y., & Eberhardt,
J. L. (2001). Differential responses in the fusiform
region to same-race and other-race faces. Nature
Neuroscience, 4, 845–850. [PubMed]
Halit, H., de Haan, M., & Johnson, M. H. (2000). Modulation of event-related potentials by prototypical andatypical faces. Neuroreport, 11, 1871–1875. [PubMed]

Downloaded from jov.arvojournals.org on 03/28/2019

21

Harel, A., Ullman, S., Epshtein, B., & Bentin, S. (2007).
Mutual information of image fragments predicts
categorization in humans: Electrophysiological andbehavioral evidence. Vision Research, 47, 2010–2020.
[PubMed]Haxby, J. V., Hoffman, E. A., & Gobbini, M. I. (2000).
The distributed human neural system for face perception.
Trends in Cognitive Sciences, 4, 223–233. [PubMed]
Herrmann, M. J., Schreppel, T., Jager, D., Koehler, S.,
Ehlis, A. C., & Fallgatter, A. J. (2007). The otherrace effect for face perception: An event-related
potential study. Journal of Neural Transmission, 114,
951–957. [PubMed]
Hillyard, S. A., Teder-Salejarvi, W. A., & Munte, T. F.
(1998). Temporal dynamics of early perceptual
processing. Current Opinion in Neurobiology, 8,
202–210. [PubMed]
Itier, R. J., & Taylor, M. J. (2004a). Effects of repetition
learning on upright, inverted and contrast-reversed faceprocessing using ERPs. Neuroimage, 21, 1518–1532.
[PubMed]Itier, R. J., & Taylor, M. J. (2004b). N170 or N1?
Spatiotemporal differences between object and faceprocessing using ERPs. Cerebral Cortex, 14, 132–142.
[PubMed]Ito, T. A., Thompson, E., & Cacioppo, J. T. (2004).
Tracking the timecourse of social perception: The
effects of racial cues on event-related brain potentials.
Personality and Social Psychology Bulletin, 30,
1267–1280. [PubMed]
Ito, T. A., & Urland, G. R. (2003). Race and gender on thebrain: Electrocortical measures of attention to therace and gender of multiply categorizable individuals.
Journal of Personality and Social Psychology, 85,
616–626. [PubMed]
Ito, T. A., & Urland, G. R. (2005). The influence ofprocessing objectives on the perception of faces: An
ERP study of race and gender perception. Cognitive,
Affective Behavioral Neuroscience, 5, 21–36.
[PubMed] [Article]
Jacques, C., d’Arripe, O., & Rossion, B. (2007). The timecourse of the inversion effect during individual facediscrimination. Journal of Vision, 7(8):3, 1–9, http://
journalofvision.org/7/8/3/, doi:10.1167/7.8.3.
[PubMed] [Article]
Jacques, C., & Rossion, B. (2006). The speed ofindividual face categorization. Psychological Science,
17, 485–492. [PubMed]
Jeffreys, D. A. (1989). A face-responsive potential
recorded from the human scalp. Experimental Brain
Research, 78, 193–202. [PubMed]Journal of Vision (2010) 10(1):15, 1–23

Vizioli, Foreman, Rousselet, & Caldara

Jeffreys, D. A. (1996). Evoked potential studies of faceand object processing. Visual Cognition, 3, 1–38.
Johannes, S., Munte, T. F., Heinze, H. J., & Mangun, G. R.
(1995). Luminance and spatial attention effects onearly visual processing. Brain Research Cognitive
Brain Research, 2, 189–205. [PubMed]
Joyce, C., & Rossion, B. (2005). The face-sensitive N170
and VPP components manifest the same brain processes: The effect of reference electrode site. Clinical
Neurophysiology, 116, 2613–2631. [PubMed]
Latinus, M., & Taylor, M. J. (2005). Holistic processing offaces: Learning effects with Mooney faces. Journal of
Cognitive Neuroscience, 17, 1316–1327. [PubMed]
Levin, D. T. (1996). Classifying faces by race: The
structure of face categories. Journal of Experimental
Psychology: Learning, Memory, and Cognition, 22,1364–1382.
Levin, D. T. (2000). Race as a visual feature: Using visual
search and perceptual discrimination tasks to understand face categories and the cross-race recognition
deficit. Journal of Experimental Psychology: General,
129, 559–574. [PubMed]
Luck, S. J. (2005). An introduction to event related
potential techniques. Cambridge, Massachusetts
London, England: The MIT Press.
Lundqvist, D., Flykt, A., & Öhman, A. (1998). The
Karolinska directed emotional faces. Stockholm,
Sweden: Karolinska Institute.
Malpass, R. S., & Kravitz, J. (1969). Recognition for facesof own and other race. Journal of Personality andSocial Psychology, 13, 330–334. [PubMed]
McKone, E., Brewer, J. L., MacPherson, S., Rhodes, G.,& Hayward, W. G. (2007). Familiar other-race faces
show normal holistic processing and are robust toperceptual stress. Perception, 36, 224–248. [PubMed]
Meissner, C., & Brigham, J. (2001). Thirty years ofinvestigating the own-race bias in memory for faces:
A meta-analytic review. Psychology, Public Policy,
and Law, 7, 3–35.
Michel, C., Caldara, R., & Rossion, B. (2006). Own-racefaces are perceived more holistically than other-racefaces. Visual Cognition, 14, 55–73.
Michel, C., Rossion, B., Han, J., Chung, C. S., & Caldara, R.
(2006). Holistic processing is finely tuned for faces of
one’s own race. Psychological Science, 17, 608–615.
[PubMed]Minear, M., & Park, D. C. (2004). A lifespan database of
adult facial stimuli. Behavior Research Methods
Instruments and Computers, 36, 630–633. [PubMed]
Murray, J., Rhodes, G., & Schuchinsky, G. (2003). Whenis a face not a face? The effects of misorientation onmechanisms of face perception In M. Peterson &G. Rhodes (Eds.), Perception of faces, objects, andDownloaded from jov.arvojournals.org on 03/28/2019

22

scenes: Analytic and holistic processes (pp. 75–91).
New York: Oxford University Press.
Oostenveld, R., & Praamstra, P. (2001). The five
percent electrode system for high-resolution EEG
and ERP measurements. Clinical Neurophysiology,
112, 713–719. [PubMed]
Philiastides, M. G., Ratcliff, R., & Sajda, P. (2006).
Neural representation of task difficulty and decision
making during perceptual categorization: A timing
diagram. Journal of Neuroscience, 26, 8965–8975.
[PubMed] [Article]
Philiastides, M. G., & Sajda, P. (2006). Temporalcharacterization of the neural correlates of perceptual
decision making in the human brain. Cerebral Cortex,
16, 509–518. [PubMed] [Article]
Rhodes, G., Tan, S., Brake, S., & Taylor, K. (1989).
Expertise and configural coding in face recognition.
British Journal of Psychology, 80, 313–331. [PubMed]
Rossion, B. (2008). Picture-plane inversion leads toqualitative changes of face perception. Acta Psychologica, 128, 274–289. [PubMed]
Rossion, B., Delvenne, J. F., Debatisse, D., Goffaux, V.,
Bruyer, R., Crommelinck, M., et al. (1999). Spatiotemporal localization of the face inversion effect: An
event-related potentials study. Biological Psychology,
50, 173–189. [PubMed]
Rossion, B., & Gauthier, I. (2002). How does the brain
process upright and inverted faces? Behavioral andCognitive Neuroscience Reviews, 1, 63–75. [PubMed]
Rossion, B., Gauthier, I., Tarr, M. J., Despland, P.,
Bruyer, R., Linotte, S., et al. (2000). The N170
occipito-temporal component is delayed andenhanced to inverted faces but not to inverted objects:
An electrophysiological account of face-specificprocesses in the human brain. Neuroreport, 11, 69–74.
[PubMed]Rossion, B., & Jacques, C. (2008). Does physicalinterstimulus variance account for early electrophysiological face sensitive responses in the humanbrain? Ten lessons on the N170. Neuroimage, 39,
1959–1979. [PubMed]
Rossion, B., Joyce, C. A., Cottrell, G. W., & Tarr, M. J.
(2003). Early lateralization and orientation tuning for
face, word, and object processing in the visual cortex.
Neuroimage, 20, 1609–1624. [PubMed]
Rousselet, G. A., Pernet, C. R., Bennett, P. J., & Sekuler,
A. B. (2008). Parametric study of EEG sensitivity tophase noise during face processing. BMC Neuroscience, 9, 98. [PubMed] [Article]
Schweinberger, S. R., Pickering, E. C., Jentzsch, I.,
Burton, A. M., & Kaufmann, J. M. (2002). Eventrelated brain potential evidence for a response of inferior temporal cortex to familiar face repetitions. Brain
Research Cognitive Brain Research, 14, 398–409.
[PubMed]Journal of Vision (2010) 10(1):15, 1–23

Vizioli, Foreman, Rousselet, & Caldara

Schyns, P. G., Petro, L. S., & Smith, M. L. (2007).
Dynamics of visual information integration in thebrain for categorizing facial expressions. Current
Biology, 17, 1580–1585. [PubMed]
Smith, M. L., Gosselin, F., & Schyns, P. G. (2007). Froma face to its category via a few information processing
states in the brain. Neuroimage, 37, 974–984.
[PubMed]Stahl, J., Wiese, H., & Schweinberger, S. R. (2008).
Expertise and own-race bias in face processing: An
event-related potential study. Neuroreport, 19, 583–587.
[PubMed]Swets, J., Tanner, W. P., Jr., & Birdsall, T. G. (1961).
Decision processes in perception. Psychological
Review, 68, 301–340. [PubMed]
Tanaka, J. W., & Curran, T. (2001). A neural basis forexpert object recognition. Psychological Science, 12,
43–47. [PubMed]
Tanaka, J. W., Kiefer, M., & Bukach, C. M. (2004). A
holistic account of the own-race effect in facerecognition: Evidence from a cross-cultural study.
Cognition, 93, B1–B9. [PubMed]
Tanaka, J. W., & Pierce, L. J. (2009). The neural plasticity
of other-race face recognition. Cognitive Affective andBehavioural Neuroscience, 9, 122–131. [PubMed]

Downloaded from jov.arvojournals.org on 03/28/2019

23

Valentine, T., & Bruce, V. (1986). The effect of race,
inversion and encoding activity upon face recognition. Acta Psychologica, 61, 259–273. [PubMed]
Valentine, T., & Endo, M. (1992). Towards an exemplar
model of face processing: The effects of race anddistinctiveness. Quarterly Journal of Experimental
Psychology A, Human Experimental Psychology, 44,
671–703. [PubMed]
Walker, P. M., Silvert, L., Hewstone, M., & Nobre, A. C.
(2008). Social contact and other-race face processingin the human brain. Social Cognitive and Affective
Neuroscience, 3, 16–25. [PubMed] [Article]
Walker, P. M., & Tanaka, J. W. (2003). An encoding
advantage for own-race versus other-race faces.
Perception, 32, 1117–1125. [PubMed]
Wiese, H., Stahl, J., & Schweinberger, S. R. (2009).
Configural processing of other-race faces is delayed but
not decreased. Biological Psychology, 81, 103–109.
[PubMed]Wilcox, R. R. (2005). Introduction to robust estimationand hypothesis testing (2nd ed.). San Diego: Academic Press.
Yin, R. K. (1969). Looking at upside-down faces. Journalof Experimental Psychology: Human Perception and
Performance, 81, 141–145.