NEUROREPORTCOGNITIVE NEUROSCIENCE AND NEUROPSYCHOLOGY

Event-related potentials and time course of the‘other-race’ face classi¢cation advantage
Roberto Caldara,CA Bruno Rossion,1 Pierre Bovet and Claude-Alain Hauert
Faculte¤ de Psychologie et des Sciences de l’Education, University of Geneva, 40, bd du Pont d’Arve,1211 Geneva 4, Switzerland; 1Unite¤ de Neurosciences
Cognitive et Laboratoire de Neurophysiologie, Universite¤ de Louvain, Belgium
CA

Corresponding Author: roberto.caldara@pse.unige.ch

Received17 December 2003; accepted 13 January 2004
DOI: 10.1097/01.wnr.0000119729.93564.3e

Other-race faces are less accurately recognized than samerace faces but classi¢ed faster by race. Using event-related
potentials (ERPs), we captured the brain temporal dynamics
of face classi¢cation by race processing performed by 12
Caucasian participants. As expected, participants were fasterto classify by race Asian than Caucasian faces. ERPs results
identi¢ed the occurrence of the other-race face classi¢cation
advantage at around 240 ms, in a stage related to theprocessing of visual information at the semantic level.
The elaboration of individual face structural representation,re£ected in the N170 face-sensitive component, was insu⁄cientto achieve this process. Altogether, these ¢ndings suggestthat the lesser experience of other-race faces engender fewer
semantic representations, which in turn accelerate their speed of
c 2004 Lippincott Williams
processing. NeuroReport 15:905^910 & Wilkins.

Key words: Event-related potentials; Face processing; Other-race e¡ect; N170; Temporal segmentation

INTRODUCTION
Human adults are significantly more accurate at recognizing
individuals of their own race than those of another race (theother-race effect; for a recent review see [1]). More recently,
it has been shown that other-race (OR) faces are fasterclassified by race than same race (SR) faces [2,3]. Twohypotheses of the OR face classification advantage havebeen proposed in the literature. First, this advantage was
explained in a theoretical framework: the face multidimensional space [2]. A small number of OR face exemplars arelocated in a high-density space compared to a large numberof exemplars of SR faces. This particular representation of
OR faces would produce a stronger activation that results ina faster classification. According to an alternative hypothesis, visual information indicating the race of OR faces iscoded as a feature [3] and, as in pop-out studies, results in a
faster detection and classification for those faces. Interestingly, these two hypotheses about the OR face classification
advantage, involve qualitative differences in the stages ofprocessing [2–4].
The present study was designed to explore andclarify this issue by investigating the cerebral dynamics offace classification by race, using event-related potentials(ERPs). Numerous ERP studies of face processing havereported an N170 component located at posterior temporalsites, peaking maximally between 150 and 180 ms following
the presentation of faces and with a lower amplitude fornon-face objects [5–9]. The N170 component is not influenced by face familiarity [8,10,11] (but see [12]) and it has

c Lippincott Williams & Wilkins
0959- 4965 been suggested that it represents a stage of processing
allowing the elaboration of individual face structuralrepresentation [6,8,11]. We recently investigated the otherrace effect using ERPs with a face passive viewing paradigm[7]. We found that the N170 was not sensitive to the race ofthe faces. However, the activity over medial occipitalelectrodes was larger for OR compared to SR faces,
reflecting attentional processes associated to the relatively
stronger unfamiliarity for OR faces rather than race
detection through a face feature-positive [7]. Nevertheless,
it is not yet established whether this activity, as well as theN170 component, can be modulated for the human withinface category class of stimuli when active paradigms
involving the use of racial face-specific diagnostic information are used. Given these observations, the face multidimensional space representation hypothesis [2] would
suggest a differential processing of SR and OR faces taking
place after the construction of an individual face representation, i.e. after the N170. In contrast, the racial-feature coding
hypothesis [3] postulates a faster detection of a racial-featurein the earlier stages of processing, and subsequent changes
of face processing. Here we directly tested these hypotheses
using ERPs while the participants performed a faceclassification by race task. The recorded brain electricalactivity was submitted to classical waveforms analysis
on selected electrodes coupled with recent data-driven
spatiotemporal analysis that use the configuration of
the electric field to define stable periods of functional
processing [7,13–16].

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NEUROREPORTMATERIALS AND METHODS
Subjects: Twelve right-handed Caucasian university students (six female, aged 18–30 years; mean 24.5 years) withnormal or corrected-to-normal vision voluntarily participated in the study, which was conducted with the understanding and consent of each participant. None of theparticipants knew more than one Asian person at theindividual level, namely their name and their associatedsemantic information (such as the age and the profession).
Stimuli and procedure: Participants were seated in a dimly
lit, sound-attenuated electrically shielded room. A computer
monitor was placed at a distance of 120 cm from theparticipants. Stimuli were composed by 288 greyscale
photographs of Caucasian (72 male, 72 female) and Asianfaces (72 male, 72 female) digitally scanned from a college
yearbook. Male faces were clean-shaven and none hadparticular distinctive features. All images showed a frontal
view, with eyes aligned on the horizontal midline of theimage, and occupied a visual angle of 3.75  4.251. Stimuli
were randomly interspersed and sequentially presented for
100 ms at the center of the computer screen, with an interstimulus interval varying at random between 2500 and3500 ms. Randomly, in 29% of the trials (n¼48) and equallyfor both face races, 200 ms after the stimulus offset threequestion marks appeared on the center of the computer
screen. Subjects were required to classify by race thepreviously seen face as quickly as possible by pressing thecorresponding labelled key (Asian or Caucasian) of a twokey bottom box. The position of the labels was counterbalanced across subjects. These verification trials were
excluded for ERPs analysis and were taken into accountonly for the behavioural performance. Two experimental
sessions were conducted with 144 images each, with anequal number of Asian and Caucasian faces (n¼72) and an
equal number of verification trials (n¼24) for each race.
Behavioural analysis: Mean reaction time for each participant was calculated for both face races from the verification trials. These measures were statistically comparedusing a two-tailed paired t-test.
ERP recordings and analysis: EEG was continuously
recorded from 62 channel Ag/Ag Cl electrodes with asampling rate of 500 Hz (band pass 0.15–70 Hz). Sixty ofthese were embedded in an elasticized cap montage (EasyCap, FMS, München, Germany), arranged according theInternational 10-20 system. An electrode was placed on the
tip of the nose and used as a common reference for all thecap electrodes. Ocular artefacts were recorded and monitored by using bipolar electrodes on the outer canthus ofeach eye, and vertical EOG was recorded from bipolarelectrodes placed above and below the dominant eye.
Vertical blinks were corrected off-line by an automaticalgorithm (Scan 4.2, Neuroscan Inc., USA). Electrode
impedances were kept under 5 kO. The recorded signal
was low-pass filtered at 30 Hz and rescaled across theaverage reference. EEG epochs extended from 200 ms before
to 700 ms after stimulus onset and were baseline corrected to
the first 200 ms. Finally, all the epochs were carefully
scanned and remaining artefacts were rejected before
averaged to individual ERPs and grand-means.

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R. CALDARA ETAL.

Data were subjected to two independent analyses
procedures consisting of (1) ERP waveform analysis and(2) analysis of ERP map topography. First, N170 components elicited by Asian and Caucasian faces were comparedby analyzing ERP peak amplitudes and peak latencies at
P7/PO7 and P8/PO8 electrode sites between 140 and 200 ms
post stimulus. A repeated measures ANOVA, using Greenhouse-Geisser adjusted degrees of freedom, was performed
with race of the face (Caucasian vs Asian), electrode site (Pvs PO) and hemisphere (left vs right) as factors in order totest for race-specific activation patterns. In order toinvestigate the other-race effect related to our previous
findings, a repeated measures ANOVA was separately
performed over medial occipital electrodes with the raceof the faces (Caucasian vs Asian) and electrode site (O1, OZ
vs O2) as factors.
Second, grand-mean ERP map series were submitted to a
spatiotemporal segmentation procedure in order to definestable scalp electric field configurations, namely segment
maps, each hypothesized to represent information processing of the brain by functional microstates [15,16]. Usually,
periods of map stability are represented by high values of
the global field power (GFP; the spatial standard deviationof the map’s potential distribution) and low values of the
global map dissimilarity (GMD; the square root of the meanof the squared differences) which is a measure inversely
related to the spatial correlation coefficient between two
successive maps (Fig. 1; for additional methodologicaldetails see [7,15]). Spatiotemporal segment maps and theirtime of occurrence were defined in the grand-mean ERP
map series using a clustering procedure that determines the
optimal number of maps [17]. The specificity of each
topography for a given condition was assessed by calculating, in each individual subject, the spatial correlation
coefficients between a given segment map and the successive ERP maps of both condition in the corresponding timeintervals. This fitting procedure was conducted in order toassess how well a given segment map explains a given
condition (goodness of fit¼best explained variance). The
ERP map fitting procedure described above also provides
information about the time at which a given segment map isbest represented (time point of best explained variance). The
latter index may be considered as a peak value in terms of
the electrical scalp topography distribution and was used toreveal differences in timing. Consequently, best explained
variances and their time points of occurrence werecompared for each segment map between conditions using
two-tailed paired t-tests.

RESULTS
Behavioural results: Asian faces (mean 539 ms) wereclassified faster by race than Caucasian faces (mean
571 ms; t¼3.63, po0.01).
ERPs: Figure 2 shows the time course of face classification
by race processing on the N170 temporal electrode sites (P7/PO7 and P8/PO8) as well as in the medial occipital area (O1,OZ and O2). No significant differences were found between
the N170 evoked by SR and OR faces (F(1,11)¼0.126,
p¼0.728) and there were no significant interactions involving the race of the faces as a factor. The N170 was larger on
the right than the left hemisphere (F(1,11)¼4.99, p¼0.047)

Vol 15 No 5 9 April 2004

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NEUROREPORTBRAIN DYNAMICS OF FACE CLASSIFICATION BY RACE

Caucasian faces

Asian faces

(µv)

5

0

−5
2.5

2

2

1.5
1

1.5
1

0.5

0.5

0
0.4

0
0.4
1

2

3

4

5

6

7

1

3

4

5

6

7

8 segment

100

200

300

400
ms

1

2

3

4

5

0−92

94−152 154−202 204 −286 288 −328 330−396

GMD

0.3

0.2
0.1
0

2

8 segment

0.3
GMD

0

−5
2.5

GFP (µv)

GFP (µv)

(µv)

5

0.2
0.1

0

100

200

300

400

500

600

0

700

0

ms
1

2

3

4

5

6

7

8 segment
+3µv
+1.5
0
−3µv
−1.5

0−72

74−152154−202 204−304 306−336 338−402 404−568

570−700

ms

500
6

600
7

700
8 segment

398−538 540−700msFig. 1. First row: Grand-averaged ERP waveforms of all the electrode channels for Caucasians (left side) and Asian (right side) face conditions. Second
row: The global ¢eld power (GFP) is the spatial standard deviation calculated over the time on the signal recorded on all electrode channels. Third row:
The global map dissimilarity (GMD) is the square root of the mean of the squared di¡erences between two successive maps. Segment periods (1^ 8)de¢ned by the spatiotemporal segmentation procedure are displayed with vertical bars. Usually, low values of the GFP are accompanied by high valuesof the GMD and correspond to segment borders de¢ning periods of map stability. Fourth row: Segment maps as de¢ned by spatiotemporal segmentationof the grand-mean ERPs. Maps are represented in order of appearance (from left to right) for each condition separately. All maps are rescaled against the
average reference. The maps are viewed from the top, with the nose up and the left ear left.Their onsets and o¡sets are given below. Note that maps ofoverlapping time periods are very similar in topography between conditions. Signi¢cant di¡erences based on the time point of best explained varianceanalysis are highlighted in bold.These e¡ects mark an advantage in the time course appearance of Asian compared to Caucasian maps occurring in similar
time periods.

and on the parietal than on the parieto-occipital electrodesite (F(1,11)¼7.81, p¼0.017). Regarding its latency, the N170
peaked earlier on the right compared to the left hemisphere(F(1,11)¼9.55, p¼0.013) and no statistical significant differences were found for the race of the faces. At medial
occipital sites, no statistically significant differences were
found for the race of the faces as a factor (F(1,11)¼0.002,
p¼0.962), as well as in its interaction (F(1.19,13.14)¼1.51,
p¼0.245).
Figure 1 illustrates the grand-mean signal on all of theelectrodes for each condition as well as the GFP (¼map
strength) and the GMD over time. The borders of the eightstable functional microstates, namely segment maps
(labelled with numbers), identified by the spatiotemporalsegmentation analysis and their topography are also
reported. Visual inspection of these topographies suggests
a high similarity in the electrical configuration of bothconditions for the maps occurring in similar time periods.
This observation was statistically confirmed by the fitting
procedure. As shown in Table 1, statistical analysis on thebest explained variance showed that none of the maps’
topography was specific to one condition. However,differences in the time course of such functional stages ofprocessing were revealed by statistical analysis based on thetime point of best explained variance (Table 2). Segments 4(linked to the third peak of the GFP) and 5 occurred fasterfor the Asian rather than the Caucasian condition. Visualinspection of the waveforms revealed that this event isrelated to a component located on the POZ electrode (Fig. 3).
Two separate two-tailed paired t-tests performed on thepeaks’ occurrence of the GFP and the POZ electrode, in thetime window defined by the segmentation analysis,
confirmed this observation. Processing of Asian faces (mean
239 ms) was about 13 ms faster than processing of CaucasianVol 15 No 5 9 April 2004

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NEUROREPORT

R. CALDARA ETAL.

Caucasian faces
Asian faces

P7

P8
4

2

2

0

0
(µv)

(µv)

4

−2

−2

−4

−4

−6

−6
PO8

4

4

2

2

0

0
(µv)

(µv)

PO7

−2

−2

−4

−4
−6

−6
0

100

200

300
O1

400

500

600

700

ms

0

100

200

300

400
ms

OZ

500

600

700

O2

4

(µv)

2
0
−2
−4

0

100 200 300 400 500 600 700 0

100 200 300 400 500 600 700 0

100 200 300 400 500 600 700

ms
Fig. 2. Top and middle: Grand-averaged ERP waveforms recorded at lateral temporo-occipital electrodes (P7/8 and PO7/8) in response to Caucasianand Asian faces. Bottom: Grand-averaged ERP waveforms recorded over central occipital electrodes (O1, OZ and O2). Positive values are up.

Table 1. t and p-values for the comparisons on the best explained variance indexes (%).
Segments

Caucasian faces
Asian faces
t
p

1

2

3

4

5

6

7

8

40.46
44.70
0.496
0.630

86.96
83.14
1.603
0.137

68.96
70.74
0.237
0.817

77.62
74.34
0.860
0.408

53.08
45.06
1.028
0.326

57.59
45.55
1.700
0.117

59.06
55.03
0.640
0.535

46.10
37.51
1.566
0.146

Two-tailed t-tests were performed segment by segment (1^ 8) on the best explained variance for Caucasian and Asian face conditions. No signi¢cant di¡erences were found.

faces (mean¼252 ms; t¼2.51, po0.05) on the GFP, as well ason the POZ electrode (mean Asian 237 ms; mean Caucasian
250 ms; t¼2.21, po0.05). No significant differences were
found for the amplitudes.

908

DISCUSSION
Behavioural results corroborated an OR face classification
advantage [2,3]: Caucasian participants classified Asianfaces by race faster than Caucasian faces. Regarding theVol 15 No 5 9 April 2004

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NEUROREPORTBRAIN DYNAMICS OF FACE CLASSIFICATION BY RACE

Table 2. t and p-values for the comparisons on time points of the best explained variance (ms).
Segment

Caucasian faces
Asian faces
t
p

1

2

3

4

5

6

7

8

78
60
0.996
0.340

126
129
0.568
0.581

177
175
0.328
0.748

246
236
2.671
0.022

326
300
4.563o 0.001370
382
0.845
0.416

468
488
0.770
0.457

616
601
0.704
0.496

Two-tailed t-tests were performed segment by segment (1^ 8) on the best explained variance for Caucasian and Asian face conditions. Signi¢cant e¡ects areindicated in bold.

POZ

Caucasian faces
Asian faces

5
4

(µv)

3
2
1
0
−1
GFP

2.5

GFP (µV)

2

1.5
1

0.5
0
0

100

200

300

400

500

600

700

ms
Fig. 3. Top: Grand-averaged ERP waveforms recorded at the central parieto-occipital electrode (POZ). Bottom: The Global Field Power for the Caucasian and Asian conditions. Note the time course synchronisation between the peaks of the GFP and the waveforms on the POZ electrode.

time course of face classification by race processing, threeimportant results were established.
First, analysis based on segmentation analysis allowed
the identification of differences in the temporal dynamics
of the face classification by race. Asian faces were
processed about 20 ms faster in segments 4 and 5.
This advantage started around 240 ms and was identified
in the time course of the GFP as well as in peak latencyof the component located in the POZ electrode. This
segment, that follows the structural encoding stage of theN170 component [6,8,11], seems to be related to theactivation of the visually derived semantic informationfrom images (faces and non-face objects) [7]. However,contrary to our previous results [7], GFP peaks showedthat Asian faces were faster processed than Caucasian faces.

Of particular interest, in an influential functional modelof face recognition [18], identification of the race of theface, as well as gender and age, occurs at this stage ofprocessing. Regarding the component located at the POZ
electrode, its topography [10] and latency [19] suggests thatit might be related to the P2 or the P250 [20] components,
involved in more integrative stages of face processing [10]and changes in facial configuration [19,20]. However, inregard of all these findings, a clear-cut functional role ofthese components, and their relationship with the semantic
level, can not yet be drawn. In addition, segment topographies were identical across the time course for bothconditions and indicate that face classification by race
processing involves the same functional pathways regardless of race.

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NEUROREPORT
Second, the N170 component was not sensitive to the raceof the faces, even if active paradigms are used. Asian and
Caucasian face features are approximately located in thesame spatial configuration and drive a similar response at
the level of the N170 component. This result reinforces theidea that the N170 component is not modulated by the levelof visual experience within the class of human faces [7] andposit this component as a component that is sensitive tostructural representation of facial configuration [6,8,9,11].
Finally, the previous ERP other-race effect [7], namely astronger medial occipital activity for OR faces, was not
replicated. This result suggests that the previous activity
associated to other-race faces was task-related [7] andreinforces our previous interpretation in terms of attentional
modulations. Altogether, our findings do not support thefeature-positive explanation [3]. This hypothesis assumed afaster detection of the visual cue coding the racial information and predicted a difference in the following stage ofprocessing for OR and SR faces. Our ERP data do notsupport neither the first nor the second of these assumptions. The OR face classification advantage takes place after
the construction of an individual representation reflected by
the N170 component. This findings are rather in favour ofan exemplary density explanation [2] or a more elaborate
processing based on the activation of the visually derived
semantic information [7,18]. In fact, classification of OR
faces might take less processing time in this stage, becausesuch faces evoke lesser semantic representations due to theweaker experience that we have for OR faces.

CONCLUSION
The other-race face classification advantage was identifiedin stages of face processing related on the activation of thevisually derived semantic information. This pattern ofresults is in line with Bruce and Young’s [18] functionalmodel of face recognition as well as a theoretical facemultidimensional space representation proposed by Valentine [2]. Thus, the present ERPs study demonstrates that thesimple extraction and integration of the face features are notsufficient to achieve the face classification by race, a processthat requires semantic mechanisms to be completed.

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