Clinical Neurophysiology 114 (2003) 515–528
www.elsevier.com/locate/Cinph

Face versus non-face object perception and the ‘other-race’ effect:a spatio-temporal event-related potential study
R. Caldaraa,*, G. Thutb, P. Servoirc, C.M. Michelb, P. Boveta, B. RenaultcaFaculty of Psychology and Educational Sciences, University of Geneva, 40 boulevard du Pont d’Arve, 1211 Geneva 4, Switzerlandb
Plurifaculty Program of Cognitive Neuroscience, Department of Neurology, University Hospital of Geneva, Geneva, Switzerlandc
CNRS UPR640 – LENA, Pitié-Salpétrière Hospital, Paris, France
Accepted 25 November 2002

Abstract
Objective: To investigate a modulation of the N170 face-sensitive component related to the perception of other-race (OR) and same-race(SR) faces, as well as differences in face and non-face object processing, by combining different methods of event-related potential (ERP)signal analysis.
Methods: Sixty-two channel ERPs were recorded in 12 Caucasian subjects presented with Caucasian and Asian faces along with non-face
objects. Surface data were submitted to classical waveforms and ERP map topography analysis. Underlying brain sources were estimatedwith two inverse solutions (BESA and LORETA).
Results: The N170 face component was identical for both race faces. This component and its topography revealed a face specific patternregardless of race. However, in this time period OR faces evoked significantly stronger medial occipital activity than SR faces. Moreover, interms of maps, at around 170 ms face-specific activity significantly preceded non-face object activity by 25 ms. These ERP maps were
followed by similar activation patterns across conditions around 190– 300 ms, most likely reflecting the activation of visually derived
semantic information.
Conclusions: The N170 was not sensitive to the race of the faces. However, a possible pre-attentive process associated to the relatively
stronger unfamiliarity for OR faces was found in medial occipital area. Moreover, our data provide further information on the time-course offace and non-face object processing.
q 2002 Elsevier Science Ireland Ltd. All rights reserved.
Keywords: Face processing; Event-related potentials; Temporal segmentation; Source reconstruction

1. Introduction
Recognizing faces is an essential and effortless social
process, which takes only a few hundreds of milliseconds.
At the functional level, it is dissociated from non-face object
recognition, as suggested by several sources of evidence.
For instance, brain-damaged patients with bilateral (Damasio et al., 1982; Gauthier et al., 1999a) or unilateral rightoccipito-temporal lesions (De Renzi, 1986; Landis et al.,1986) have been reported to suffer from prosopagnosia, adeficit that essentially impairs perception and recognition of
faces while sparing object recognition (but see Gauthieret al., 1999a). Patients with a specific deficit for non-face
object recognition and preserved face recognition have also
* Corresponding author. Tel.: þ 41-22-705-9241; fax: þ41-22-705-9229.
E-mail addresses: roberto.caldara@pse.unige.ch (R. Caldara),
bernard.renault@chups.jussieu.fr (B. Renault).

been observed (e.g. Moscovitch et al., 1997), thus providing
a double dissociation between face and object recognition.
The idea that faces are differently processed than non-face
object stimuli has also been suggested by several behavioral
studies. For instance, face recognition is disproportionately
impaired by the inversion of the stimulus as compared to
non-face objects (Yin, 1969; for a recent review see Rossion
and Gauthier, 2002). At the neural level, several methods
have provided evidence for specific brain areas devoted to
face processing. Single-unit recordings in non-humanprimates revealed neurons within the superior temporalsulcus and the inferior temporal cortex that preferentially
respond to human and primate faces (e.g. Perrett et al.,
1982; Desimone, 1991). Recent neuroimaging studies innormal subjects confirmed and refined the role of theoccipito-temporal regions in face and non-face object
processing (e.g. Kanwisher et al., 1997; McCarthy et al.,
1997; Gauthier et al., 1999b, 2000; George et al., 1999).

1388-2457/02/$ - see front matter q 2002 Elsevier Science Ireland Ltd. All rights reserved.
doi:10.1016/S1388-2457(02)00407-8

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These studies suggested that there is at least an area in thefusiform gyrus, the so-called fusiform face area (FFA), thatis highly responsive to faces, although other areas more
activated for faces than objects are usually described in theoccipito-temporal cortex and the superior temporal sulcus(for a review, see Haxby et al., 2000).
Electrophysiological studies have also contributed to theunderstanding of the temporal dynamics of brain activity
during face and non-face object processing. Studies using
intracranial recording techniques in patients undergoing
presurgical epilepsy evaluations reported a face-specific
event-related potential (ERP) component (N200) in thelateral fusiform and inferior temporal gyrus (Allison et al.,1994). More recently, an N200 component for non-face
objects was found from sites that extended from the inferior
lingual gyrus medially to the middle occipital gyrus laterally(Allison et al., 1999; McCarthy et al., 1999; Puce et al.,1999). Moreover, scalp ERP studies on human faceperception have shown a face-sensitive component overthe lateral temporal lobes (N170) (Bentin et al., 1996;
George et al., 1996). Both electrophysiological components
are not affected by face familiarity (Jemel et al., 1999;
McCarthy et al., 1999; Rossion et al., 1999; Bentin andDeouell, 2000), and seem to be related to the ‘structural
encoding stage’ of face processing described in thefunctional architecture of the Bruce and Young (1986)
model (Bentin et al., 1996; Eimer, 1998).
However, previous electrophysiological studies havelargely ignored an important factor in studying theelectrophysiological correlate of face processing: the‘race’ of the faces. In face perception theory, it wasdemonstrated almost a century ago that faces of ones own
race are more accurately recognized than faces of otherraces (Feingold, 1914). This phenomenon, related to the
popular belief that other-race faces ‘all look alike’, has often
been reported as the ‘other-race’ effect (for reviews, see
Shepherd, 1981; Anthony et al., 1992; Meissner andBrigham, 2001). Several empirical studies have confirmed
the robustness of this phenomenon in recognition tasks.
Faces of the same-race (SR) are recognized better and fasterthan other-race (OR) faces. A greater experience with own
race faces has been proposed as an explanation of the otherrace effect (e.g. Brigham and Barkowitz, 1978; Valentine
and Endo, 1992; Fallshore and Schooler, 1995). Nevertheless, frequent contact with OR faces, namely the amountof the encountered OR faces without any need ofrecognizing them at the individual level, does not improverecognition ability for these faces (e.g. Brigham et al., 1982;
Brigham and Malpass, 1985; Platz and Hosch, 1988).
Several experimental and ecological studies suggested thatessentially the quality of the contact, namely the necessity torecognize and discriminate OR faces at individual level, has
an improvement in recognition performances for OR faces(Carroo, 1986; Chiroro and Valentine, 1995). Recently analternative explanation of the other-race effect has been
proposed by authors that have emphasized the role of social

categorization factors in the observation of the other-race
effects (Levin, 1996, 2000). Using visual search tasks, Levin(1996, 2000) has shown that Caucasian-American people
take less time to detect an African-American face (OR face)among Caucasian faces (SR faces), than a Caucasian faceamong African-American faces. To explain these results,
this author suggested that there is a quickly detected racial
marker in OR faces that influence the level of processing for
OR faces. However, the face feature(s) responsible for fasterdetection and classification of OR faces remain unknown(Levin, 1996). Levin (2000) proposed that emphasis on
general social category information specifying race as
visual feature, at the expense of individuating information,change interracial group perception and might be responsible for the other-race effect.
Recently, the neural basis of the perception of other racefaces has been investigated by several neuroimaging
studies (Hart et al., 2000; Phelps et al., 2000; Golby
et al., 2001). In the study by Phelps et al. (2000) two
independent groups of European-American subjects were
exposed with European-American and African-American
pictures and a greater activation of the amygdala was foundfor OR faces, suggesting a role of this structure in thesocial evaluation of racial groups (see also Hart et al.,2000). Golby et al. (2001) investigated the identification
and recognition of different face races by EuropeanAmerican and African-American subjects and showed agreater activation of the FFA for own race faces during an
encoding stage of a face recognition task. Moreover, thedifferential level of activation in the left FFA and the
medial temporal lobe for SR and OR faces was correlatedwith the differential performance in a memory recognition
task performed outside of the scanner. These results havebeen interpreted in the framework of the visual expertise
studies. The primary implication of these results is thatthey showed that the activity of the FFA can be modulatedby the level of expertise for within face category stimuli.
Indeed, previous studies showed a greater activation of the
FFA (and other face sensitive areas) for non-face objects
for which subjects have developed a specific visual
artificial expertise (see Gauthier et al., 1999b, 2000).
However, Golby et al. (2001) suggest that a greaterattention paid for SR faces could also explain a greater
activation of the FFA. The latter interpretation is in linewith the Levin (1996, 2000) explanation of the other-race
effect. Taken together, and independent of the resultinterpretations, all these studies showed that social factors
can influence the perception, the evaluation and therecognition of OR faces, and this can be observed at thebrain level by different patterns of activity.
Given these recent findings, the first objective of thepresent ERP study was to track the temporal course of brain
processing differences between SR and OR faces. To ourknowledge, this question has never been investigated before
in the face and object ERP literature and has even gained
more interest with the recent neuroimaging findings

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described above. Moreover, it has been shown that the facesensitive N170 component can also be modulated by visualexpertise with non-face objects (Tanaka and Curran, 2001;
Rossion et al., 2002). However, the question concerning its
modulation by the visual expertise for within face category
stimuli remains open.
Our first goal was to test whether the large N170
obtained for any kind of face configuration can be
modulated by the interaction between the race of the face
and the race of the observer. Our study will explore theN170 component under the light of the face withincategory expertise. In order to do that, we presented
Caucasian subjects with a classical design previously
used to compare (Caucasian) face and object processing(Bentin et al., 1996), in which we added a set of Asianfaces matched for low level visual properties. Thus,
pictures of Caucasian and Asian faces as well as nonface objects (cars and furniture) were sequentially andrandomly presented to Caucasian subjects. These stimuli
were randomly interspersed with butterfly pictures as
visual target stimuli. Subjects had to detect and mentallycount the occurrence of target stimuli.
The second aim of the present paper was bothmethodological and theoretical. Until now, face andobject processing has been studied in electrophysiologicalstudies by conventional methods of ERP peak identification and measurement. While this method has alreadyprovided important information about the time course of
these processes, it lacks an objective – data-driven –way to define these functional stages of processing, andthus depends highly on a priori hypotheses about the
spatio-temporal areas of interest. For instance, the choiceof the time window to calculate mean amplitudes tends
to be influenced by subjective post hoc considerations(Picton et al., 2000, p. 142). An alternative approach, thespatio-temporal segmentation analysis (Lehmann and
Skrandeis, 1980) is based on multi-channel ERP recordings and analysis of surface potential configurations(taking electrodes of a whole scalp array into account)and has been successfully applied recently to the study of
low-level and high-level visual processes (e.g. Morand
et al., 2000) and other cognitive processes such as mental
imagery (Pegna et al., 1997), motor (Thut et al., 1999,
2000) and language processing (Khateb et al., 1999).
Here we applied this approach for the first time to faceand object processing. It allowed us to submit the surface
potential configurations to spatio-temporal analysis procedures which give access to stable periods of functional
stages of processing (e.g. Michel et al., 1999b), namely
microstates (or segments), for face and non-face objects.
Finally, functional microstates were also submitted to twosource localization algorithms, LORETA (Pascual-Marqui
et al., 1994) and BESA (Scherg and Van Crammon,
1985).

517

2. Methods
2.1. Subjects
Twelve healthy Caucasian volunteers (six males) aged
20 – 30 years (mean age 25.4) participated in this experiment. All the subjects were classified as right-handed asmeasured by the Edinburgh Inventory (Oldfield, 1971), hada right predominant eye and normal or corrected-to-normal
vision. None of the subjects knew more than one Asian
person at the individual level, namely their name and theirassociated semantic information (such as profession), andall were unaware of the purpose of the present study.
2.2. Stimuli
One hundred and sixty different grayscale photographs of
Caucasian (40 male, 40 female) and Asian faces (40 male,
40 female) digitally scanned from a college yearbook wereused. No face had particular distinctive features and malefaces were clean-shaven. All images showed a frontal view,
with eyes aligned on the horizontal midline of the image.
The non-face object stimuli consisted of 240 different
grayscale pictures; evenly distributed in 3 categories(butterflies, cars and furniture). Face stimuli occupied avisual angle of 3:758 £ 4:258 and non-face object stimuli avisual angle of 4:758 £ 4:758.
2.3. Procedure
Subjects were seated in a dimly lit, sound-attenuatedroom. A computer monitor was placed at a distance of 120 cm
from the participants. All stimuli were delivered for 200 ms at
the center of the computer screen, with an inter-stimulusinterval randomly varying between 2500 and 3500 ms.
Stimuli were presented in 5 experimental sessions of 80
images each. In each session an equal number of Asian and
Caucasian faces was presented (n ¼ 16, half male, half
female), randomly alternated with all 3 types of non-face
object stimuli. Participants were engaged in a target detection
task in which the target stimuli were butterflies. They wererequired to mentally count the number of butterflies andreport it at the end of each experimental session. The numberof target stimuli was randomized between sessions.
2.4. ERP recording and averaging
EEG was continuously recorded on line from 62 scalp
sites (Electrocap International montage, Eaton, OH, USA),with an additional row of low temporo-occipital electrodes
TP9/10, P9/10, PO9/10 and O9/10 using a 64-channel
system. The sampling rate was 500 Hz (bandpass 0.16– 160
Hz). All electrodes were referenced against the nose. Eye
movement and eye blink artifacts were monitored using
vertical and horizontal electro-oculogram (EOG) recordings
and corrected off-line by an automatic algorithm. EEG

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epochs extended from 200 ms before to 1200 ms after
stimulus onset. Data were baseline corrected to the first 200
ms of the epoch. Epochs were carefully scanned, and theepochs presenting remaining artifacts were rejected before
averaged to individual evoked potentials. Finally, a lowpass filter of 30 Hz was applied to the averaged data.
2.5. ERP analysis
Data were subjected to two independent analyses
procedures consisting of (1) ERP waveform analysis and(2) analysis of ERP map topography combined with source
localization techniques. For the former approach, wemeasured the face sensitive N170 component. In contrast,in the latter approach time periods of face processing were
defined by applying spatio-temporal segmentation analysis.
Moreover, in order to reveal race-specific brain activation,comparisons between Caucasian and Asian face data were
performed on amplitudes of selected occipito-temporalelectrodes and on three-dimensional (3D)-current density in
occipital regions of interest. The target condition (butterflies) was analyzed for control purposes only.
2.5.1. ERP waveform analysis
Peak amplitudes and peak latencies were computed foreach category over time between 140 and 190 ms poststimulus for each selected occipito-temporal electrode(n ¼ 19) and each condition at a single latency percomponent per subject. Repeated-measures analyses ofvariance (ANOVA) were conducted using GreenhouseGeisser adjusted degrees of freedom. The electrodes ofinterest corresponded to electrodes Pz, P1/2, P5/6, P7/8,
P9/P10, POz, PO3/4, PO7/8, PO9/10, Oz and O1/2. A first
repeated-measures ANOVA was conducted with Condition(Caucasian Faces, Asian Faces, Cars, Furniture) andElectrode Site (Pz-O1/2) as within-subject factors in orderto test face-specific activation within the N170 period.
Scheffé tests were used for post-hoc comparisons between
conditions. A second repeated-measures ANOVA restricted
to the face conditions with Race (Caucasian vs. Asian) andElectrode (Pz-O1/2) as factors was performed in order totest for race-specific activation patterns.
2.5.2. Analysis of map topography: identifying time periods
reflecting condition-specific information processing
2.5.2.1. Reducing real-time ERP map series to time
segments of stable map topography (spatio-temporal ERP
segmentation). For ERP topography analysis, a spatiotemporal segmentation procedure was applied (Lehmann,
1987; Michel et al., 1999b). The procedure aims to define
those intervals where surface ERP map topographies remain
stable for a certain time (segments of stable maps) and thetime points where they change from one stable configuration
into another (segment borders). Such stable segments arecommonly observed in ERP data and presumably reflect

different functional stages of the brain, also referred to as
microstates, activated during information processing (Lehmann and Skrandeis, 1980; Brandeis and Lehmann, 1986;
Michel et al., 1992). The segmentation procedure, driven by
the evolution of the ERP map topographies over time, isapplied to the grand-mean ERP data (across subjects
averages of individual ERPs), rescaled against the averagereference (Lehmann and Skrandeis, 1980). The map strength
is represented by the global field power (GFP) which is thespatial standard deviation of the map’s potential distribution.
The segments and their borders are determined via
calculation of the global map dissimilarity (GMD) between
pairs of successive maps (see Fig. 3). This measure isinversely related to the spatial correlation coefficient
between two maps (Brandeis et al., 1992). Over time, theGMD curve shows low values where maps remain stable andhigh values where maps rapidly change from one topography
configuration into another. GMD minima thus indicate
segment kernels and GMD peaks segment borders. Usually,
low stability of map topography around GMD peaks
coincides with low map strength. After defining segment
borders, ERP maps are averaged within time segments,
resulting in one unique representative map per segment(grand-mean segment maps). Again, grand-mean segment
maps were fitted in the grand-mean ERP data and each time
point was labeled with the segment map it was most highly
correlated with. This fitting procedure was used to smoothand refine the borders of the segments. Finally, a unique
grand-mean segment map was averaged in the newly definedtime-borders (for additional methodological details see also
Khateb et al., 1999).
2.5.2.2. Defining the functional significance of time
segments and their maps. To define the time periods
reflecting specific visual information processing, wesearched for those time segments whose maps only
appear in its own but are absent in the other condition,or vice versa. For this purpose, spatial ERP fitting
procedures were applied to individual data (Pascual-Marqui et al., 1995). For each subject, we calculated thespatial correlation coefficients between a given segment
map and the successive ERP maps of each condition in
the corresponding time intervals. This procedure wasconducted in order to assess how well a given segment
map explains a given condition (goodness of fit ¼ best
explained variance, bev) (Pegna et al., 1997; Khateb
et al., 1999, 2000; Thut et al., 1999, 2000; Morand et al.,
2000; Lantz et al., 2001). Best explained variance wascompared for each segment map between conditions
using t tests. In a first step, comparisons were performed
within the two face and two non-face conditions
separately. We tested whether maps of a given segment
differ within face and non-face object conditions
respectively, by fitting to individual data a given segment
map to the corresponding time segment of its own andthe other condition. In a second step, face and non-face

R. Caldara et al. / Clinical Neurophysiology 114 (2003) 515–528

object maps of a given segment were averaged within
their own category. Those maps of the face conditions
significantly better explaining their own than the nonface object conditions are likely to reflect face-specificprocessing, and vice versa. The remaining maps with nodifferences between the 4 conditions can be interpreted toreflect visual information processing in general.
The ERP map-fitting procedure described above also
provides information about when in time a given segment
map is represented at best (time point of best explained
variance, tpbev). This value was compared between
conditions using t tests for all condition-specific segment
maps in order to reveal differences in timing, i.e. latencies.
2.6. Source localization and analysis of regions of interest(ROIs)
To estimate the brain activity underlying segment map
topography, two inverse solutions were calculated: LORETA (Pascual-Marqui et al., 1994) and BESA (Scherg and519

Van Crammon, 1985). LORETA is a modified weighted
minimal norm solution that searches for the smoothest
distribution by minimizing the norm of the Laplacian of thecurrent vectors. A 3D spherical model was used. LORETA
solutions were calculated within a regular grid of 1152 nodes,
lying within the upper hemisphere of a sphere. Compared todipole solutions, LORETA estimates the underlying generators without any a priori assumption on the number andlocations of the sources. A critical review of this solution isprovided elsewhere (Fuchs et al., 1999; Michel et al., 1999a).
BESA allows the spatio-temporal modeling of multiple
current dipoles over defined intervals. The orientation andthe location of the dipoles were computed by an iterative
least-square fit which minimizes the residual variance (%residual variance). This value indicates the percentage ofdata that cannot be explained by the model (for a detailed
discussion of this method and its limitations see da Silva andSpekreijse, 1991; Scherg and Picton, 1991; Picton et al.,1999). Both inverse solutions were calculated using a
spherical head model. These inverse solutions (LORETA

Fig. 1. ERP waveforms recorded over electrode Pz (upper part) and global field power (map strength) of successive ERP maps (lower part) represented for faces
and non-face objects as well as the control stimuli (butterflies). Note the prominent P300 component observed in the control condition only and its
correspondence with the GFP map strength. Positive values are up.

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and BESA) were applied in the grand-mean average forsegments that appeared to be specific for face or non-face
object processing. The time window used was delimited by
the segmentation analysis. An additional analysis based on
individual segment maps was applied with LORETA only.
Latter maps were calculated by averaging individual ERP
maps within the face-specific time periods as determined by
the grand-mean segmentation. LORETA results obtained
from the Caucasian and Asian segment maps were used for
ROI analysis in the occipito-temporal area in order to explore
race effects regarding brain activation patterns.

3. Results
3.1. Behavioral results
This condition was analyzed for control purposes only.
Both behavioral and ERP data revealed that subjects
attended adequately to the delivered stimuli. The numberof target stimuli was correctly reported in 98% of the trials
and a prominent P300 component was observed for thebutterflies, but not for any other stimulus type (Fig. 1).
3.2. ERP waveform analysis
A 4 £ 19 repeated-measures ANOVA on peaks amplitudes, averaged within the time period of the N170
component, with Condition (Caucasian Faces, Asian
Faces, Cars, Furniture) and Electrode Site as factors
revealed a highly significant main effect for Condition(Fð3; 33Þ ¼ 19:76, P , 0:001) as well as a significant
interaction (Fð54; 594Þ ¼ 15:09, P , 0:001). A partial
repeated-measures ANOVA conducted to identify the originof this interaction was computed for the electrodes P9/10
and P09/10 and revealed an effect of condition(Fð2:12; 23:34Þ ¼ 53:78, P , 0:0000). The ERP waveformsof these 4 electrodes are shown in Fig. 2. They revealed aprominent N170 component in the two face conditions. Thiswas statistically confirmed by a post-hoc Scheffé test
revealing that the face conditions were significantly
different from the non-face object conditions with stronger
negative amplitudes for faces than for non-face objects (seeTable 1). Repeated-measures ANOVA of the N170 peaklatencies show that the small differences between conditions(Caucasian faces ¼ 160:5 ms; Asian faces ¼ 161:1 ms;
Cars ¼ 164:6 ms; Furniture ¼ 165:125 ms) were notsignificant (Fð2:18; 24Þ ¼ 2:48, P ¼ 0:1004).
A 2 £ 19 repeated-measures ANOVA performed on dataof the face conditions only, showed a significant interaction
between the factors Race and Electrode Site(Fð18; 198Þ ¼ 2:68, P ¼ 0:042). A partial repeatedmeasures ANOVA computed for O1/2, Oz electrodes
revealed that Asian faces evoked stronger positive amplitudes than Caucasian (Fð1; 11Þ ¼ 6:10, P ¼ 0:0312). The
waveforms on the Oz electrode are represented in Fig. 2.

Peak latencies on these electrodes for OR faces (158.1 ms)
and SR faces (155.6 ms) were not statistically different(Fð1; 11Þ ¼ 3:92; P ¼ 0:0733).

3.3. ERP analysis of map topography: identifying time
periods reflecting condition-specific information processing
Fig. 3 depicts the global field power (GFP ¼ map
strength) as well as the global map dissimilarity (GMD)
over time for the successive ERP maps of each of the 4conditions (grand-mean ERPs). An ERP map represents thespatial potential configuration (map topography) of all 62
recorded electrodes at a given time point. Spatio-temporalsegmentation of the grand-means using the GMD value as
an indicator for map stability/instability reveals 6 time
periods of stable map topography per condition (labeledwith numbers). These time periods will be referred to as
segments throughout the manuscript. The segment maps’
topographies as well as their onset and offset times areshown in Fig. 4. Visual inspection reveals that map
topography is very similar across conditions for maps
occurring in similar time periods, except for maps 3. Thismap differs between but not within face and non-face object
conditions. This was statistically confirmed by comparing
map topography between conditions (segment-by-segment)using the results of ERP map fitting. Fitting each segment
map of the Caucasian face condition in each subject to its
own and the Asian face data showed that no segment map
explained the Caucasian and the Asian face data significantly differently (see Table 2A, upper part). This suggeststhat the map topography of the two face conditions tends tobe similar. Similarly, no significant differences wereobserved between the two non-face object conditions as
revealed by fitting each ‘Car’ segment map to thecorresponding time segments of the car and the furniture
conditions (see Table 2A, lower part).
In a second step, face and non-face object maps of agiven segment were averaged within their own category.
These maps were then fitted to the individual data, also
averaged within faces and non-face objects, in order toisolate those segment maps which are specific for facestimulation. Table 2B shows that segment maps 3 of the faceconditions better explain their own conditions than the nonface object conditions. This map thus reflects face-specificactivation patterns. Note that the map 3 occurs between 142
and 182 ms post-stimulus partially overlapping the N170
component in time. The ERP map fitting also provided
information about when in time a given segment map wasrepresented at best (time point of best explained variance,
tpbev). This value was compared between face and non-face
object segments 3 revealing that the face segment occurs
significantly earlier (t ¼ -3.625, P ¼ 0:003; mean tpbev:
169.16 ms (faces) vs. 195.33 ms (non-face objects)).

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521

Fig. 2. (Top and middle) Grand-averaged ERP waveforms recorded at lateral temporo-occipital electrodes (P9/10 and PO9/10) in response to Caucasian andAsian faces as well as cars and furniture. (Bottom) Grand-averaged ERP waveforms recorded over central occipital electrode (Oz) in response to Caucasian andAsian faces. Positive values are up.

3.4. Source localization and ROI analysis
Within the time periods identified to reflect conditionspecific activation patterns, two inverse solutions wereapplied: LORETA (Pascual-Marqui et al., 1994) and
BESA (Scherg and Van Crammon, 1985). The source
localization results are shown for the maps of segment 3
in Fig. 5. Visual inspection of the LORETA results

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Table 1
P values of the Scheffé test for the factor Condition (Caucasian vs. Asian vs. Cars vs. Furniture) on peak amplitudes on the electrodes P9/PlO and P09/10 for the
N170 componenta

Caucasian faces
Asian faces
Cars
Furniturea

Caucasian Faces(mean 25.78 mV)

Asian Faces(mean 25.34 mV)

Cars(mean 0.62 mV)

Furniture(mean 0.75 mV)–
0.939,0.001,0.001

0.939–,0.001,0.001,0.001,0.001,0.001,0.001
0.998
–

0.998

Significant effects are highlighted in bold type.

shows that the inverse solution of the face-specificsegment map does qualitatively differ from the solutionof the corresponding non-face object segment map. The
solutions suggest that face-specific activity involves alarger bilateral occipito-temporal network than objectrelated activity. Moreover, Caucasian and Asian maps
differ regarding their maxima with stronger medial
occipital activity for Asian and stronger right occipitotemporal activity for Caucasian faces, although theactivity distribution is similar between the two facestimulus types. To statistically test for Race effects

regarding brain activity, the LORETA algorithm wasapplied to individual ERP maps (Caucasian and Asiandata only) averaged over the face-specific time period as
identified by the spatio-temporal segmentation procedure(segment 3). These results were subjected to ROI
analyses. The ROIs were defined over medial occipitaland bilateral occipito-temporal regions. Mean activity
within the medial occipital ROI was significantly
increased for Asian with respect to Caucasian faces asrevealed by a t test (mean: 46.06 vs. 40.45) (t ¼ 2:68,
P ¼ 0:021). No significant differences were observed forFig. 3. Global field power indicating map strength (GFP, upper panels) as well as global map dissimilarity (GMD, lower panels) shown over time for the 4
grand mean ERPs. The time evolution of the GMD value was used for the spatio-temporal segmentation procedure to define time periods of stable map
topography (low GMD values) and their borders (GMD peaks), the latter marked by vertical lines. Usually, low stability of map topography around GMD peaks
coincides with low GFP values. Six periods of stable maps (segments) were identified per condition, labeled by numbers.

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523

Fig. 4. Segment maps as defined by spatio-temporal segmentation of the grand-mean ERPs. Maps are represented in order of appearance (from left to right) for
each condition separately (Caucasian faces, Asian faces, Cars and Furniture). All maps are rescaled against the average reference. The maps are viewed fromthe top, with the nose up and the left ear left. Their onsets and offsets are given below. Note that maps of overlapping time periods are usually very similar in
topography between conditions, except map 3 which differs between face and non-face object conditions.

the lateral occipito-temporal ROI (mean: 34.3 vs. 32.8)(t ¼ 0:94, P ¼ 0:355).
Finally, BESA obtained a solution with 4 dipoles localized
in the ventral occipito-temporal area for the face condition (%residual variance ¼ 7:39), while two dipoles located in the
medial part of the same area were sufficient to explain the nonface object data (% residual variance ¼ 6:66). Thus, similar to

LORETA, BESA retrieved qualitatively different solutions
for faces than non-face objects.

4. Discussion
The present experimental design incorporated two com-

Table 2t and P values for between-conditions comparisons on best explained variance (bev), performed segment by segment (1–6)aSegments
1

2

3

4

5

6

A
Caucasian Maps in
Caucasian faces vs.
Asian faces cond.

tP

20.2080.839

20.701
0.498

1.203
0.254

20.3090.763

21.506
0.160

0.089
0.931

Car Maps inCars vs.
Furniture cond.

tP

22.0420.066

21.382
0.194

20.8670.405

21.337
0.208

1.634
0.131

1.172
0.266

B
Face Maps inFaces vs.
Non-face object cond.

tP

21.115
0.289

1.625
0.133

3.766
0.003

0.517
0.615

0.286
0.781

1.362
0.201

a
Significant effects are highlighted in bold type. A: Comparisons within face (Caucasian vs. Asian) and non-face object conditions (Cars vs. Furniture). B:
Comparisons between face and non-face object conditions.

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R. Caldara et al. / Clinical Neurophysiology 114 (2003) 515–528

Fig. 5. 3D brain activity estimated for the surface maps n. 3 in face (Caucasian and Asian) and non-face object conditions (Cars and Furniture) using two
inverse solutions (left: LORETA; right: BESA). LORETA: Solutions are represented in 8 transverse slices through the spherical head model with the lowest
slices to the left (nose up, left ear left). BESA: The estimated 3D dipole locations within the head and the time varying activity of each dipole source arereported, along with their spline, dipole and scalp current density maps. For reasons of simplicity, the results of only 2 of the 4 conditions are shown. The
remaining results are comparable to those illustrated here. Note that both LORETA and BESA solutions suggest activation of larger occipito-temporal regionsin face as compared to non-face object conditions. Note also that despite similar source configurations for face conditions, LORETA solutions indicate that thelocation of maximum activity (blue circles) differs between Caucasian and Asian faces.

parisons: (1) perception of face and non-face object stimuliand (2) perception of same-race (SR) versus other-race (OR)faces. The results reported here are derived from an original
combination of different surface ERP analyses and source
localization procedures. Our data demonstrate for the firsttime a difference in electrical brain activity for the perceptionof SR versus OR faces. They also provide evidence fordifferent and common stages in face and non-face object
processing as information processing evolves (not only at thelevel of the N170), and allow us to describe these stages interms of cerebral activation patterns across time. In Fig. 6, we
propose a model that summarizes the time-course of theseprocessing stages. These points are discussed below.
4.1. Face versus non-face object processing and the stagesof information processing
Analysis of the evolution of ERP map topographies
over time suggests that, as visual information processing
unfolds, a face-specific stable map topography occurs
between 140 and 210 ms. Several segments which arecommon to all types of stimuli are observed outside thistime window. The first segment of all conditions is bydefault a noisy segment that follows the onset of thestimuli. Segment 2 peaks in strength at about 110 ms for

all conditions. Undoubtedly, this segment map reflects
the well-known P1 topography, which is associated withearly visual cortical activation (primary visual cortex and
extrastriate areas, e.g. Gomez Gonzalez et al., 1994).
Both segments 1 and 2 are identical across face and nonface object conditions and are followed by the conditionspecific segment 3. This segment overlaps in time theN170 component. It differs in map topography for faceand non-face object conditions. In agreement with thisresult, the waveform analysis on the N170 component
revealed a strong face-specificity for this component.
Furthermore, both inverse solutions that we used,
LORETA and BESA, returned in this time period
different source localization results for the face andnon-face object conditions. The inverse solutions indicate
more distributed occipito-temporal sources for faces than
non-face objects, both in terms of current density(LORETA) and numbers of dipoles (BESA). In addition,
the inverse solutions indicated bilateral sources withmore pronounced activity in the right hemisphere. In thecase of face stimuli, this might reflect a bilateral
activation of the face-sensitive areas, however, with agreater activation of the right than the left side (Dubois
et al., 1999; Ishai et al., 1999; Rossion et al., 2000). As
compared to the face conditions, the sources for non-face

R. Caldara et al. / Clinical Neurophysiology 114 (2003) 515–528

525

Fig. 6. Model of different stages of information processing over time associated with passive viewing offace (Caucasian and Asian faces) and non-face stimuli (carsand furniture) in Caucasian subjects. The ‘butterfly’ stimulus served as target (subjects had to silently count the occurrences of the target stimulus) and was notconsidered in the analysis. Note that the model representation is essentially inferred from the results of the ERP map segmentation analysis (providing clues on
condition-specific stages of information processing). The star indicates the segment in which face processing appears significantly earlier than non-face object
processing.

object stimuli were located more medially, consistently
with recent neuroimaging studies (Epstein and Kanwisher, 1998; Chao et al., 1999; Ishai et al., 1999). In
short, our results confirm that there are early facesensitive processes taking place in occipito-temporal
regions, at least using face passive viewing paradigms.
Interestingly, segment 3 differed not only regarding
electrical activation patterns between face and non-face
object conditions but also regarding timing. As inferredfrom the analysis based on the time point of best
explained variance of map topographies face processing
was about 25 ms faster than non-face object processing,a result that is not found by peak analyses on the N170.
This result may be interpreted as an evidence for aparticular automated expertise for face perception in thetemporal domain. Indeed, face perception may have aunique position in human visual perceptual skills, given
its importance for social communication (see Kanwisher,2000). Alternatively, an equally plausible explanation forthis latency difference could be that the within-category
visual similarity was likely to be substantially greater for
faces than within the two other non-face object
categories (cars, furniture). Future electrophysiologicalstudies should also investigate whether visual expertisewith non-face objects would lead to faster activation of
the occipito-temporal cortex, as suggested by effects ofexpertise found at the level of the N170 (Tanaka andCurran, 2001; Rossion et al., 2002).

With respect to GFP strength, segment 4 is an
important event that peaks at about 230 ms for all
conditions. Its strength is similar to the strength of the
P300 (Fig. 1) and, as previously demonstrated (Wackermann et al., 1993), it reflects a stable ERP topography,that is invariant across conditions in our experiment.
Interestingly, the electrical brain response starts to differ
between the target (‘butterfly’ detection) and non-target
conditions immediately following segment 4 (see Fig. 1).
At the end of segment 4, the GFP waveform splits forthe butterfly condition. This indicates that a butterfly was
perceived and correctly identified as a target immediately
before the end of this segment, implying activation of thecorrectly derived semantic information in this segment’s
time period. This stage of processing is thus probably not
face-specific but related to the dissociation between thetarget item (‘Butterfly’) and the other categories.
Regarding segments 5 and 6, the information provided byour study and available in the literature makes it difficult toprovide any possible explanation of their functionalsignificance.
4.2. Same-Race versus Other-Race face perception
Our data indicate that global correlates (ERP map
topographies) of face processing occurring between 140
and 180 ms post-stimulus tend to be similar for SR and OR
faces. In addition, no differences were observed between

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R. Caldara et al. / Clinical Neurophysiology 114 (2003) 515–528

OR and SR face conditions in the classical N170 facesensitive component located at lateral occipito-temporalelectrode sites (P9/P10 and PO9/PO10). This result would
support the idea that the N170 component is not modulatedby the level of expertise within the class of human faces.
The fact that the FFA activation appears to be sensitive tothe race of the face (Golby et al., 2001) but not the N170 isinteresting. The precise relationship between the FFA
activation and the N170 is unclear at the present state ofknowledge. Previous ERP and neuroimaging studies
reported similar functional modulations for the FFA andthe N170, suggesting that the activation of the FFA
contributes at least in part to the observation of the faceERP component on the scalp (e.g. larger activation for faces
than for objects, small but significant effects of inversion,etc., see Rossion and Gauthier, 2002). Our source
localization data show that the activity reflected by theface sensitive N170 component is related to distributed
occipito-temporal sources and not solely to the activation ofa well-defined small fusiform regions such as the FFA. This
assumption is based on the results of both LORETA and
BESA (see also the discussion below concerning face versus
non-face object processing). In addition, a more detailed
analysis based on the LORETA solution that focused on thebilateral occipito-temporal ROI also failed to discriminate
between SR and OR faces. This result suggests that theincrease of activation for SR faces in the FFA observed in
fMRI (Golby et al., 2001) may take place later than 200 ms.
However, amplitudes over medial occipital electrodes
differed significantly between OR and SR faces. Likewise, ananalysis of inverse solutions (LORETA) applied to this earlystage of face processing confirmed that OR faces evoke more
medial occipital activity than SR faces, although overall
current density patterns were similar. This increase of earlybrain activity for OR faces can be highly surprising at firstglance. However, some findings of previous studies help tointerpret this observation. Two recent fMRI studies foundonly the amygdala to become more activated in response to
OR as compared to SR faces (Hart et al., 2000; Phelps et al.,2000), suggesting a possible role of this structure in theencoding (Hart et al., 2000) and/or an unconscious evaluation
about social groups (Phelps et al., 2000). Given its
distribution on the scalp, the differential activity reported
here for OR and SR is unlikely to arise and be recorded
directly from the amygdala, but could be related to anamygdalar activation of early visual areas during faceprocessing, as described by several studies (Amaral et al.,
1992; Rolls, 1992). Moreover, both the amygdala and theposterior occipital lobe appear to present a higher level ofactivation to unfamiliar faces than familiar faces (Dubois
et al., 1999), and it is conceivable that repeated presentationsof SR faces makes them quickly familiar and leads to adecrease of activation in these regions, while this decrease islikely to be slower for OR faces.
An alternative explanation for the occipito-medialsources predominantly associated with OR face perception

relates to behavioral findings in experiments using the popout effect. Using visual search tasks, Levin (1996, 2000) has
shown that participants detect an OR face faster among SR
faces than vice versa. Faster classification of OR faces has
been interpreted as a pop-out effect for OR faces (Levin,1996). According to Levin (1996), this performance may be
due to the detection of a feature-positive present in OR but
absent in SR faces (even if this face feature(s)-positive
remains unknown). Thus, the greater activity in this area
observed for OR faces may reflect a pre-attentive detection ofa feature-positive. However, there is evidence that suggests
rather a serial search than a pop-out effect for faces and facial
expressions (Nothdurft, 1993). Moreover, the slopes of thereaction times (that increase with the number of targets)
observed in the Levin (1996, 2000) studies also indicate a
serial visual search mechanism, rather than a pop-out effect.
For these reasons, we favor the interpretation that thestronger occipito-medial activation for OR faces reflects thedetection of the relative unfamiliarity of OR faces rather thanrace detection through a face feature-positive.
Finally, although we carefully selected and controlled our
stimuli it may still be that this differential activity between
OR and SR is due to a difference in the stimuli per se (e.g.
low-level visual properties) between Caucasian and Asianface stimuli. Future studies will be necessary to explore thisearly ‘face-race’ effect using, if possible, a fully cross-racial
paradigm with two groups of subjects from different races.
Moreover it will be interesting to use active tasks, that are
possibly more sensitive to an effect of expertise for own racefaces, such as face recognition and classification by race.
4.3. Methodological issues
In the present study, we combined different methods of
ERP analysis involving classical waveform analysis, analysis
based on ERP map topography (spatio-temporal segmentation) and inverse solutions. The first method takes only
selected electrodes into account (that could be referred as alocal approach), while the latter methods are based on multichannel, whole-scalp recordings (as a global approach).
The results presented here turned out to be highly
compatible but also complementary across theseapproaches. The remarkable correspondence between thePOz electrode waveform and the GFP with respect to theP300 component (Fig. 1) is an example of such compatibility. Moreover, both local and global approaches, namely
the inverse solutions, allowed us to identify differences inneuronal responses between OR versus SR faces and todescribe some of the face- and non-face object specificcomponents/segments. Note, however, that although inverse
solutions were able to capture differences between OR and
SR face conditions through ROI analysis procedures, ananalysis of global map topography (spatio-temporal segmentation) failed to reveal any statistically significant
effects. This demonstrates that a small and focal differencein activity may be masked, from a statistical point of view,

R. Caldara et al. / Clinical Neurophysiology 114 (2003) 515–528

when ERP analysis are based on global indices calculated
over a multitude of recorded signals.
Thus, spatio-temporal segmentation analysis allowed us
to obtain some results that would have been difficult to
uncover using classical waveform analysis. Throughsegmentation analysis, we defined time-series of stable
ERP map topographies and their evolution over time. The
time windows defined by the spatio-temporal segmentation
can represent an objective and useful criterion for
determining the time windows used in the classical statistic
waveform analysis. Moreover, identification of sub-stagesof information processing and the assessment of theirrelative timing (showing for example that face processing is
initiated faster than non-face object processing) haveprincipally been made possible by this approach.
Finally, in order to strengthen possible interpretations
regarding source localization, two inverse solution algorithms were applied (LORETA and BESA) and localized
similar generators.

Acknowledgements
R.C. and G.T. were supported by the Plurifaculty
Program of Cognitive Neuroscience, University of Geneva.
The authors wish to thank Denis Brunet, Göran Lantz andLaurent Spinelli of the Functional Brain Mapping Laboratory, University Hospital of Geneva, for their technicalsupport. The present study was performed at the CNRS UPR
640 – LENA. The authors wish also to acknowledge all the
supporting staff of this unit.

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