www.elsevier.com/locate/ynimg
NeuroImage 31 (2006) 313 – 319

The fusiform face area is tuned for curvilinear patterns with morehigh-contrasted elements in the upper part
Roberto Caldara,a,* Mohamed L. Seghier,b Bruno Rossion,c Francois Lazeyras,bChristoph Michel,d and Claude-Alain Hauert eaCentre for Cognitive Neuroimaging, Department of Psychology, University of Glasgow, 58 Hillhead Street, Glasgow, G12 8QB, UK
Department of Radiology, University Hospital of Geneva, Geneva, Switzerlandc
Unité Cognition et Développement et Laboratoire de Neurophysiologie, University of Louvain, BelgiumdFaculty of Medicine, University of Geneva, SwitzerlandeFaculty of Psychology, University of Geneva, Switzerlandb

Received 22 June 2005; revised 5 December 2005; accepted 7 December 2005
Available online 7 February 2006

The ability to identify conspecifics from the face is of primary interestfor human social behavior. Newborns’ visual preference for schematic
face-like stimuli has been recently related to a sensitivity for visual
patterns with a greater number of elements in the upper compared to
the lower part. At the adult level, neuroimaging studies have identified
a network of cortical areas devoted to the detection and identificationof faces. However, whether and how low-level structural properties offace stimuli contribute to the preferential response to faces in theseareas remain to be clarified. Using functional magnetic resonanceimaging (fMRI), here we investigated whether the adults’ face-sensitive
cortical areas show a preference for top-heavy patterns, similarly to
newborns’ preference. Twelve participants were presented with headshaped and square patterns with either more elements in the upper orthe lower vertical part. In the right fusiform gyrus (Ffusiform facearea_, FFA), an area showing a preference for faces over other visualobject categories, there was a larger activation for curvilinear patterns
with more high-contrast elements in the upper part, even though thesepatterns were not perceived as face stimuli. These findings provide
direct evidence that the FFA is tuned for geometrical properties fitting
best with the structure of faces, a computational mechanism that might
drive the automatic detection of faces in the visual world.
D 2005 Elsevier Inc. All rights reserved.
Keywords: Functional magnetic resonance imaging; Face recognition;
Fusiform gyrus

In human social and biological interactions, it is critical frombirth to identify conspecifics for communication, and the facereadily provides the required non-verbal information. This

* Corresponding author. Fax: +44 141 330 46 06.
E-mail address: r.caldara@psy.gla.ac.uk (R. Caldara).
Available online on ScienceDirect (www.sciencedirect.com).
1053-8119/$ - see front matter D 2005 Elsevier Inc. All rights reserved.
doi:10.1016/j.neuroimage.2005.12.011

particular visual object encapsulates structural rules (e.g., twohorizontal eyes placed above a central nose and mouth). In
humans, it is well established that this configuration spontaneously captures newborns’ attention more than other complex
visual objects (Fantz, 1963). Newborns’ remarkable early visual
preference for schematic face-like patterns has been found withboth moving and static stimuli (Goren et al., 1975; Johnson and
Morton, 1991), as well as realistic faces (e.g., Macchi Cassia etal., 2004), suggesting that humans have a specific and innate
perceptual ability to process faces (Farah et al., 2000). Yet,
recently, such newborns’ visual preferences have been related to alarger sensitivity to visual patterns presenting a greater number ofhigh-contrast elements in the upper compared to the lower verticalpart of the stimulus, rather than to faceness per se (Macchi Cassia
et al., 2004; Turati et al., 2002). This phenomenon has been
observed for schematic head-shaped and square patterns (Turati etal., 2002), as well as realistic faces, presenting more scrambled
high-contrasted elements in the upper part (Macchi Cassia et al.,2004). This non-specific perceptual bias may optimally satisfy theconstraints of newborns’ immature visual system and may
originate from an upper-field advantage in visual sensitivity(Simion et al., 2002).
In human adults, neuroimaging studies have considerablyincreased our knowledge of the functional organization of thehuman visual cortex (for a review, see Grill-Spector and Malach,2004). A network of areas highly responsive to faces has been
identified within the ventral visual pathway (Haxby et al., 2000):
the right middle fusiform gyrus (the so-called Ffusiform face area_(FFA)—Kanwisher et al., 1997) (e.g., Gauthier et al., 2000; GrillSpector et al., 2004; Halgren et al., 1999; Rossion et al., 2000), andin the inferior occipital gyri, the so-called occipital face area (OFA)(e.g., Gauthier et al., 2000). However, little is known about thebasic elements of face stimuli that trigger responses in these areas.
Most importantly, it is unknown whether general properties shared

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R. Caldara et al. / NeuroImage 31 (2006) 313 – 319

elements and a maximum 7 elements (Fig. 1), in order to sample atthe maximum the space covered by all the images (faces andobjects) and consequently extract a general rule rather than anobservation related to a sub-sample of possible patterns. The
number of the elements was greater either in the upper or in thelower half of the stimulus, and the identical set of configurations
was adapted for the use in the different conditions.

Fig. 1. Examples of stimuli used during and after (right—face-like control
stimuli) fMRI scanning.

by face stimuli but also present in non-face patterns, such as a topheavy structure, determine the response properties of these regions.
Here, we used functional magnetic resonance imaging (fMRI)
to investigate whether the very same vertical asymmetry that
determines newborns’ preference is an important property thatplays a role in shaping the response of adult face-sensitive areas in
the occipito-temporal cortex. Since attention modulates activationin the face areas (Wojciulik et al., 1998), we used non-facial stimuli
to prevent the attentional biases that could arise from theperception of artificial faces with misplaced features or a mixtureof face features inserted in non-curvilinear shapes. Indeed, suchattentional biases would be confounded in the brain activations,leading to a difficult interpretation of the results. This experimental
procedure would also have had the disadvantage to explicitly
reveal the nature of the question investigated in our study to theparticipants. Most importantly, the use of non-face stimuli
permitted both to explore the development of the face systemand to investigate whether and how low-level visual structuralproperties shape information processing at the level of high-ordernon-retinotopic visual areas (Levy et al., 2001; Halgren et al.,1999), such as those devoted to face (and object) processing. To
this end, we first functionally identified the brain areas responding
preferentially to faces by comparing the processing of faces andnon-face objects. We then sampled the visual input space bypresenting the participants with twenty high-contrasted curvilinear
and square patterns with either more elements in the upper or in thelower part (see Fig. 1 and Methods). After the scanning session,
participants evaluated the faceness of the presented stimuli, as wellas two schematic face-like control stimuli. We expected that atleast curvilinear patterns with more high-contrast elements in theupper part will enhance the responses of the human adult cortex
coding for faces as their low-level structural properties fit best withthose of human faces.

Methods
Stimuli
Faces and objects
Twenty full front photographs of unknown faces (http://
www.faces.kyb.tuebingen.mpg.de) and 20 pictures of common
objects (object databank at http://www.cog.brown.edu/~tarr/
stimuli.html) were used.
Head-shaped and square patterns
Twenty symmetrical patterns were created for each condition.
We created a set of configurations comprising a minimum of 4

Control stimuli
We created two schematic face-like stimuli (Fig. 1), inscribed inhead-shaped and square patterns, in order to control for theevaluation of the faceness of the stimuli used during the scanningsessions.
Participants
Twelve adult subjects (8 women) with normal vision voluntarily participated in the study. All the participants gave written
informed consent, and the protocol was approved by the ethicalcommittee of the University Hospital of Geneva.
Imaging methods
Experiments were performed on a 1.5 T whole-body system(Philips Medical Systems) using the standard head coil configuration. Acquired multi-slice volume was positioned on sagittal
scout images. A first GRE (gradient-recalled-echo) T1-weighted
sequence (TR (repetition time) = 162 ms; TE (echo time) = 4.47
ms; flip = 80-; FOV (field of view) = 250 mm; matrix = 256 
256; slice thickness = 5 mm) was performed to acquire the same
volume as in the functional session. Anatomical reference images
consisted of a 3D-GRE T1-weighted sequence (TE = 15 ms;
FOV = 250 mm; matrix = 256  256; slice thickness = 1.25 mm).
For the localizer block paradigm and the experiment, functional
imaging consisted of an echo planar imaging (EPI) GRE sequence(TR = 3000 ms; TE = 40 ms; flip = 80-; FOV = 250 mm; matrix =128  128; 30 contiguous 5 mm axial slices). A vacuum cushion(PAR Scientific A/S, Denmark) was used to minimize head
movement.
Tasks
Face and object epochs (18 s; 6 TR) were counterbalanced andseparated by baseline epochs (fixation cross, 9 s; 3 TR). Two runs
were performed, using a Flocalizer_ block design alternating
periods of faces (n = 20) and objects (n = 20) (Kanwisher et al.,1997), with six epochs of each experimental condition (faces orobjects) in each run. In each face and object epoch, 24 stimuli were
presented for 750 ms, without any offset, but a small shift ofposition (20 pixels) between consecutive images. Four of thetwenty four images were repeated twice consecutively, and subjects
were required to detect the immediate repetitions by pressing a
response key (one-back task). When perception of objects is
contrasted to face perception (objects – faces), a robust activity has
been identified in the bilateral parahippocampal gyri (PHG) (e.g.,
Epstein and Kanwisher, 1998; Rossion et al., 2003). We also tookthese brain regions as regions of interest in our experimental
conditions.
Two counterbalanced runs of head-shaped (n = 20) and square
patterns (n = 20) with either more elements in the upper or the
lower vertical part (Fig. 1) followed the localizer scan. In eachR. Caldara et al. / NeuroImage 31 (2006) 313 – 319

epoch, 24 stimuli were presented (18 s; 6 TR) and separated bybaseline epochs (fixation cross, 9 s; 3 TR). Participants had todetect the immediate repetition of the same stimulus (4 out of 24
images) and report their answers with a response key. All theimages were displayed for 600 ms on a black background with aninterleaved fixation cross (150 ms). Participants were required to
fixate this cross during all the experiment.
After the scanning sessions, participants were debriefed. Firstly,
we asked the participants for their general impressions and what
they thought was the aim of the scanning sessions. No feedback on
their answers was provided. Secondly, they were instructed to rate
on a Likert scale of 1 (non face-like) to 8 (face-like) the faceness ofthe patterns and two schematic face-like control stimuli, whichwere not presented during the fMRI scanning sessions. All thesestimuli were randomly presented in sequence on a computer
monitor, and participants were required to express their facenessjudgment by pressing the respective key (1 to 8) of a computer
keyboard. Stimuli were presented without time constraints, and thefollowing stimulus appeared after the response with an interstimulus interval of 2 s. After this final stage, according to our
ethical policy, the real aim of the study was explained in detail tothe participants.
Image and behavioral analyses
Collected data were processed with cross-correlation analysis(Bandettini et al., 1993) after motion correction (Woods et al.,1993) using MEDx software (Sensor Systems, Sterling, VA, USA)(Gold et al., 1998). Data were smoothed spatially by convolution
with a Gaussian kernel (full width half maximum (FWHM) = 6 
6  6 mm3). The cross-correlation, expressed in terms of Z values,
was calculated pixelwise between a delayed boxcar function andthe set of measurements. Individual Z score maps were thennormalized to the Talairach space by applying an affine transformation (Talairach and Tournoux, 1998). Afterwards, the statistical
distribution of the Z values for each subject was calculated, and aprobability value for each Z value was obtained (Moser et al.,1996). Clusters of size of >0.08 cm3 (>10 voxels; voxel size after
normalization was 2  2  2 mm3) showing a statisticallysignificant Z score (typically Z threshold = 3.0, at P < 0.002,
uncorrected) in the faces – objects comparison were considered(Forman et al., 1995). The choice of the cluster size threshold wasbased on the minimal detected size of FFA activation reported in
the initial work of Kanwisher et al. (1997). We then computed
additional post hoc analyses to reveal the influence of theperceived stimuli on the detected MRI signal. For this purpose,clusters activated during the localizer block paradigm wereidentified for each subject (size >0.08 cm3, at P < 0.002,
uncorrected) and used as regions of interest (ROIs). Note thatclusters with signal intensity <50% of the mean intensity of the

315

functional images were ruled out. Principally, six ROIs wereidentified, including the right FFA, the left FFA, the right OFA, theleft OFA, the right PHG and the left PHG (e.g., Rossion et al.,2003). Furthermore, for each defined ROI, a mean timecourse (i.e.
BOLD signal change in percent) was obtained by averaging thetimecourses of individual voxels within the ROI. Then, thedifferent experimental conditions were compared by using 2  2
repeated measure ANOVAs in each individual ROI separately, with
Shape (head-shaped vs. square) and Greater element position(upper vs. lower) as factors. Only effects with a P value < 0.05 areconsidered significant, although they are not necessarily reported
in the presence of a high-order interaction. Planned, pairwise, twotailed t tests were applied at each level of the factors for analyzing
interaction effects.
In order to investigate whether the subjective perceived
faceness of the presented patterns modulated brain activations,when appropriate, we conducted Pearson correlation analyses
across subjects between brain responses averaged across all thevoxels of the ROI and the behavioral rating scores. Facenessratings scores were normalized by rescaling raw rating score values
between 0 (minimum) and 1 (maximum) for each individual. The
normalization process neutralizes subjective differences in facenessrating across the participants, as all participants share values in thesame range. Finally, in the appropriate ROIs, in order to further
investigate whether brain activations and faceness rating scores
were linked, we carried out a 2  2 repeated measure ANOVA with
the BOLD signal for Shape (head-shaped vs. square) and Greater
element position (upper vs. lower) as factors and the normalized
faceness scores as a covariate (Winer et al., 1991).

Results
Behavioral results
No significant differences were observed for the behavioral
performances during fMRI scanning sessions. As expected, the two
schematic face-like control patterns obtained the highest score(M = 0.96 for the head-shaped; M = 0.67 for the square—Likert
scale values normalized from 0 to 1) in the faceness evaluation
performed after the fMRI scanning sessions. Faceness rating scores
were larger for the schematic face-like control stimuli presented
outside scanning as compared to the non-face visual patterns used
for the experimental conditions during fMRI scanning (minimum
significant value, t(11), P < 0.01—see Table 1).
Neuroimaging results
A significant activation for the comparison between faces andobjects was found in all the subjects in two brain areas: the right

Table 1
Mean (TSE) of the normalized faceness ratings (Likert scale: 0 = non-face-like; 1 = face-like) and their respective P values when compared to the control
stimuliHead-shaped

Facenesst(11), Pt(11), P

Square

Upper

Lower

Upper

Lower

0.25 (0.07)<0.001<0.01

0.13 (0.03)<0.001<0.0010.14 (0.06)<0.001<0.0010.01 (0.007)<0.001<0.001

Faceness ratings for the control stimuli
0.96 (0.03)
Head-shaped
0.67 (0.08)
Square316

R. Caldara et al. / NeuroImage 31 (2006) 313 – 319

FFA for faces (rFFA; (x, y, z) = 40, 50, 17) and the bilateralparahippocampal gyri (PHG) for objects (Fig. 2).
Because there was no interaction between the left and rightactivation in the PHG (Talairach coordinates respectively of (x, y,z) = 21, 56, 10; (x, y, z) = 23, 52, 10), their signal was
averaged (Epstein and Kanwisher, 1998). Other brain regions wereactivated for faces, but not in all the subjects. The left FFA (lFFA;(x, y, z) = 38, 52, 17) was activated in ten participants, theright OFA (rOFA; (x, y, z) = 33, 76, 8) in seven and the left
OFA (lOFA; (x, y, z) = 36, 79, 14) in six. In these regions, theonly significant interaction between the Shape and Greater
element position was found in the right FFA (F(1,11) = 7.73,
P = 0.0178) (Fig. 3). Head-shaped patterns with a greater numberof elements in the upper vertical part (mean percentage signal
change = 0.35%) showed larger responses in the rFFA comparedto all other conditions (post hoc t test comparisons, with: headshaped lower, mean percentage signal change = 0.11%, t(11), P <0.001; square upper, mean percentage signal change = 0.12%,
t(11), P < 0.01 and lower, mean percentage signal change =0.14%, t(11), P < 0.001).
There was no correlation between normalized faceness scoresof the head-shaped patterns with a greater number of elements inthe upper vertical part and the rFFA activations (Pearson
correlation; r = 0.38, P = 0.22). However, normalized facenessscores of the head-shaped patterns with a greater number of
elements in the upper part positively correlated with lFFA
activations (Pearson correlation; r = 0.66, P < 0.03).

Fig. 3. Mean BOLD percentage signal change measures across participants,
and their standard errors, related to the processing of the experimental
visual patterns within the functionally defined brain areas. The number ofparticipants that presented significant activations for faces and objects
within their respective selective regions is reported in parenthesis.

In order to investigate whether normalized faceness scores andbrain activations observed in the rFFA are linked, we performed a2  2 repeated measure ANOVA with the BOLD signal for Shape(head-shaped vs. square) and Greater element position (upper vs.
lower) as factors and the normalized faceness scores as a covariate.
As observed for the previous statistical analysis, a significant
interaction was observed between the Shape and Greater element
positions (F(1,11) = 6.356, P = 0.03), head-shaped patterns withmore elements in the upper part showed larger responses in this brain
region (post hoc t test comparisons, with: head-shaped lower t(11),
P < 0.01; square upper t(11), P < 0.05 and lower, t(11), P < 0.01).

Discussion

Fig. 2. Functional regions of interest for faces (FFAs—red) and objects(PHGs—blue) defined by the localizer paradigm. Note, because the groupaverage is reported, the occipital face area activities are not illustrated sinceapproximately only half of the subjects activated these brain regions. (Forinterpretation of the references to colour in this figure legend, the reader isreferred to the web version of this article.)

The role of low-level structural properties in the responses ofthe mature neural adult face and object recognition system wasinvestigated by presenting geometric visual patterns with either
more elements in the upper or the lower visual part. Verbal reports
pointed out that none of the participants noticed that the presented
visual patterns aimed to study face processing, instead, some of theparticipants thought that this experiment was designed to explore
counting mechanisms. More objectively, behavioral results reinforced this view by showing that the patterns used during the fMRI
scanning sessions were not perceived as schematic face-likestimuli. Indeed, normalized faceness ratings scores for theexperimental patterns were significantly lower than those attributed
to schematic (head-shaped and square) face-like control stimuli notpresented during the fMRI experiment. This observation demonstrates that the patterns used in the scanner were clearly dissimilarfrom noticeable face-like shapes.
In order to conceal the aim of our study, we did not measure theactivations for schematic face-like control stimuli. Nevertheless, aprevious study investigated the perception of schematic face-likestimuli by using an experimental design fully comparable with ourprocedure (localizer scan, one-back repetition task and localizationof ROI for faces) and a scanner with a similar magnetic fieldstrength (1.5 T). Tong et al. (2000) found in the rFFA larger

R. Caldara et al. / NeuroImage 31 (2006) 313 – 319

activations for human faces (1.6 mean percentage signal change)
compared to schematic face-like patterns (0.9 mean percentage
signal change). Given this observation, schematic patterns
explained at least more than 50% of the mean percentage signal
change elicited by real faces. In our study, head-shaped patterns
with more elements in the upper part evoked relatively weak
activations (0.35 mean percentage signal change) compared tothose observed for real faces (1.9 mean percentage signal change),
therefore explaining only about 18% of the mean percentage signal
change evoked by the presentation of real faces. Although these areindirect observations, such important differences at the level of therFFA activations strongly suggest that our patterns were notprocessed with the same strength than faces or schematic face-likestimuli. This result resonates with the significant differences
observed behaviorally in terms of faceness rating scores between
the control schematic face-like patterns and the experimental
patterns. Despite these lines of evidence in favor of an explicit nonfacial information processing of the presented patterns, a functionally defined region responding preferentially to faces, the rFFA
(Kanwisher et al., 1997), showed enhanced activations forcurvilinear patterns with more high-contrast elements in the upper
part. Critically, the averaged responses of this brain region were notcorrelated with the normalized subjective larger faceness ratings
observed for the head-shaped with more elements in the upper part.
This finding eliminates the possibility of a straightforward miming
between the similarity to faces and the activations observed in thisarea. However, averaged activations in its homologous region, thelFFA, significantly correlated with these rating scores. It isinteresting to speculate on the nature of this result. lFFA activations
have been found to be positively correlated with the magnitude of
the recognition advantage for same- compared to other-race faces(Golby et al., 2001). Interestingly, despite the presence of thissignificant correlation with the behavior, lFFA responses did notdiscriminate between the experimental conditions tested in the
Golby et al. (2001) study (same- vs. other-race faces), an
observation that is in line with our findings (the lFFA did notshow any sensitivity for the visual patterns). At the functional level,
it has been suggested (Kosslyn et al., 1989) that pathways in the left
hemisphere may drive categorical visual processes that maximize
similarities among exemplars in a category, whereas righthemisphere pathways may mediate coordinate visual processes
that maximize individuation between exemplars in a category. This
position is in line with previous findings showing face featurebased tuning for the lFFA and a whole-based tuning for the rFFA
(Rossion et al., 2000). Thus, lFFA activations might be related to
extract fine-grained processing necessary to detect invariant
information for constructing visual categories (such as the patterns
used in our study). rFFA activations might instead relate toprocesses permitting an optimal identification of the object ofthese categories at the individual level. Future studies are necessary
to clarify the precise nature of such mechanisms in the lFFA andhow the interaction of this region with its homologous part in theright hemisphere affects optimal high-order visual information
processing.
The task performed by the participants outside the scanner was
to explicitly rate the faceness of the presented stimuli. By definition,
the curvilinear patterns with more elements in the upper partpresented in the scanner are those that fit best with this criterion, andthey accordingly received the highest faceness rating scores fromthe participants compared to the other experimental patterns. At thefirst sight, this finding could represent another possible explanation317

of the increase of activation observed in the rFFA. However, if it
was only faceness, rFFA activations would be parametrically andlinearly related to the degree of faceness of the presented stimuli,
but our findings do not support this view. Indeed, for instance, headshaped patterns with more elements in the lower part obtained thesecond score in terms of faceness ratings but evoked the smallest
activations in the rFFA. A comparison between faceness ratings forhead-shaped patterns with more elements in the upper visual partwith those in the lower is not significant, despite that the same
comparison in terms of BOLD signal is highly significant.
Critically, the repeated measure ANOVA with faceness ratingscores as covariate still showed a significant interaction, larger
activations in the rFFA for head-shaped pattern with more elementsin the upper part, demonstrating that there are differences that canbe observed at the level of the BOLD signal which are independentof the normalized faceness scores. Thus, our data support the viewthat BOLD activations are not linearly affected by the degree of
faceness ratings of the stimuli used in our fMRI scanning sessions.
Altogether, our observations favor instead an automatic stimulusdriven discrimination and reveal a precise geometrical tuning in theface recognition system for high-contrasted curvilinear patterns
with more elements in the upper part. Such a visual property might
be relevant for detecting geometrical shapes best fitting with thestructure of faces within the visual world.
Our fMRI results resonate with recent developmental findings
from newborns, which demonstrate a comparable visual preference
for non-face-like (Turati et al., 2002) or face-like (Macchi Cassia etal., 2004) stimuli presenting more elements in the upper part.
Interestingly, contrary to the behavioral observations made in
newborns (Turati et al., 2002), the human adult rFFA was enhanced
to top-heavy patterns only when a curvilinear contour was alsopresent. This suggests that during the life span learning andexperience with faces may have shaped brain responses for patterns
which best fit with face geometry. The curvilinear contour plays amajor role in normal face processing (Kosslyn et al., 1995), andconcentric radial patterns activate the rFFA (Wilkinson et al., 2000).
Our data provide direct evidence for the existence of a curvilinear
vertical asymmetry bias within the mature fine-tuned functional facerecognition system. The brain responses elicited by the perception ofreal faces in the localizer task were larger (1.9 mean percentage
signal change) than those evoked by the visual patterns (0.35 mean
percentage signal change), with identical task demands. Consequently, a small part of the signal encapsulated in the rFFA responses
for faces can be attributed to mechanisms sensitive to the structuralgeometry properties of the visual input itself. It is worth noting,however, that despite our data show that participants did not processthese stimuli as face patterns, it is difficult to unambiguously
conclude on the non-specific nature of such effects. To put it
concretely, is it because our curvilinear non-face patterns with moreelements in the upper part were more similar to faces that we
observed automatic enhanced activations in the rFFA or, conversely,is it because face geometry optimally satisfies the non-specificgeometric tuning of this brain region that this area has developed asensitivity for processing faces? Future studies are necessary to
clarify the causal inferences of such effects.
Among the functionally defined regions for faces and objects,
the rFFA is the unique region showing a modulation of its
responses for the presented visual patterns. This observation
converges with evidence coming with different approaches
revealing a right hemisphere advantage for face processing. A
unilateral lesion of the right hemisphere is sufficient for leading to318

R. Caldara et al. / NeuroImage 31 (2006) 313 – 319

prosopagnosia, the inability to recognize familiar faces (e.g.,
Landis et al., 1986). Moreover, some prosopagnosic patients seemto be disrupted in the presence of the vertical asymmetry as theyuse only the lower part of the face (i.e., the mouth) for face
identification (Caldara et al., 2005). Developmental studies haveshown better face discrimination performances in infants in the left
visual field (de Schonen and Mathivet, 1990) and the necessity of anormal stimulation of the right hemisphere during the early periodof life for developing an efficient face recognition system (LeGrand et al., 2003). Moreover, larger responses for faces in therFFA compared to the lFFA have been found in most fMRI studies(e.g., Grill-Spector et al., 2004; Kanwisher et al., 1997; McCarthy
et al., 1997). We should acknowledge, however, that the procedure
we have applied to identify the critical ROIs (FFAs and PHGs)
diminished the detectability of the OFA regions (we identified therOFA in 7 out of 12 participants and the lOFA in only 6
participants) and consequently decreased the statistical power ofthe performed analyses.
Most interestingly, when faces are presented upside-down, facerecognition is disproportionately impaired compared to object
recognition in similar viewing conditions, leading to the so-called
face inversion effect (for a recent review see Rossion and Gauthier,2002; Yin, 1969). Nevertheless, the vertical asymmetry encapsulated in the face structure is inverted when faces are presented
upside-down, displaying a great number of high-contrasted
elements in the lower part of the stimulus. fMRI studies thatcompared the processing of upright and inverted faces found a
decrease in the activity of the rFFA when faces are inverted (e.g.,
Kanwisher et al., 1998; Yovel and Kanwisher, 2004). In line withthese fMRI findings, we found a decrease of the activity in therFFA when the subjects were presented with patterns with moreelements in the lower visual part, even when those patterns were
inscribed in a curvilinear contour. Thus, one of the factors
explaining the face inversion effect could be related to the basic
structural properties of the stimulus itself, namely, the difference inthe greater number of elements present in the vertical axis, assuggested by recent behavioral findings (Sekuler et al., 2004).
These low-level geometrical quantitative differences might modulate in turn the configural mechanisms involved in subsequent
information processing.
Another consideration regarding the functional role of the rFFA
lies in the increase of activation in this region for non-face visualobjects for which subjects have developed expertise (Gauthier etal., 1999; Tarr and Gauthier, 2000). Our findings suggest that thisprocess may have been facilitated by basic configural properties
shared by the non-face objects used in these studies (e.g., greebles
in Gauthier et al., 1999; cars and birds in Gauthier et al., 2000)
since these visual objects have curvilinear shapes with more
informative elements in the upper part.
Finally, it is interesting to report that a comparable computational rule has been and is still used for creating algorithms in
artificial systems dedicated to the identification and recognition of
faces among visual objects. To achieve this aim, these algorithms
construct curvilinear boundary maps by using the particular spatial
arrangement of eye and mouth positions (e.g., Hsu et al., 2002),
capturing the vertical asymmetry intrinsically present in humanfaces. Here, we revealed the existence and optimality of a similar
computational rule for face processing at the level of the adulthuman brain. Crucially, our data suggest that the neurons of thisbrain region could use this rule to rapidly detect a particulargeometry in the visual world, the human face.

Several million years of natural selection have symmetricallyshaped human faces (Gardner, 1964) with a curvilinear contour andlarger proportion of high-contrasted features in the upper (e.g.,eyes, eyebrows, forehead) compared to the lower part of the face(e.g., mouth, chin). These structural properties embedded in humanfaces fit well with the newborns’ immature visual system andadults’ mature face recognition system, which are both tuned toprocess patterns that have more elements in the upper part. Humanadults required the curvilinear contour for exhibiting a neuralpreference for such asymmetrical vertical bias. Future studies arenecessary to investigate whether this calibration occurs justfollowing the specialization of newborns’ neural pathways
dedicated to face processing, from primitive subcortical circuits
to elaborate cortical representations (de Schonen and Mathivet,
1989; Johnson and Morton, 1991) or occurs ultimately in a mature
face recognition system, working as computational mechanism for
automatic face detection.
Acknowledgments
RC was supported by a post-doctoral fellowship provided by
the Swiss National Science Foundation. BR is supported by the
Belgian National Foundation (FNRS). We are grateful to Fraser
Smith and to two anonymous reviewers for helpful and constructive comments on a previous version of the manuscript.
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