rHuman Brain Mapping 30:1615–1625 (2009) r

The Fusiform Face Area Responds Automaticallyto Statistical Regularities Optimalfor Face Categorization
Roberto Caldara,1,2* and Mohamed L. Seghier3
1

Department of Psychology, University of Glasgow, United Kingdom
Centre for Cognitive Neuroimaging, University of Glasgow, United Kingdom
3
Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College of London,
United Kingdom
2

Abstract: Statistical regularities pervade our perceptual world. Assuming that the human brain is
tuned for satisfying the constraints of the visual environment, visual system computations should be
optimized for processing such regularities. A socially relevant and highly recurrent homogenous pattern for which the brain has developed sensitivity is certainly the human face. Yet, for which statisticalregularities the face sensitive regions are tuned for, and to what extent their detection occurs automatically is largely unexplored. Using fMRI we measured activations within the face sensitive areas fornonface symmetrical and asymmetrical curvilinear patterns with either more high-contrast elements inthe upper or in the lower part. Faceness evaluation performed outside of the scanner showed that thesepatterns were not perceived as schematic faces. Noticeably, symmetry violations disrupted perceptionof faceness, despite objective image similarity measures showing high faceness values for those patterns. Among the faces sensitive regions, only the right Fusiform Face Area (FFA) showed sensitivity
to symmetry. This region showed also greater responses to patterns with more elements in the upper
part. Critically, the FFA’s responses were more strongly correlated with the physical objective facenessproperties of the stimuli than the perceived subjective faceness ratings of the observers. These ﬁndings
provide direct evidence that the neural computations of the right FFA are tuned to curvilinear symmetrical patterns with high-contrasted elements in the upper part, which ﬁt best with the physical structure of human faces. Such low-level geometrical regularities might be used by the FFA to automatically
categorize visual shapes as faces. Hum Brain Mapp 30:1615–1625, 2009. V 2008 Wiley-Liss, Inc.
C

Key words: fMRI; face recognition; visual stimuli; extrastriate cortex; functional localization

INTRODUCTION
Contract grant sponsors: Swiss National Science Foundation, Wellcome Trust.
*Correspondence to: R. Caldara, Centre for Cognitive Neuroimaging, Department of Psychology, 58 Hillhead Street, Glasgow, G12
8QB, UK. E-mail: r.caldara@psy.gla.ac.uk
Received for publication 3 September 2007; Revised 13 March
2008; Accepted 19 May 2008
DOI: 10.1002/hbm.20626
Published online 31 July 2008 in Wiley InterScience (www.
interscience.wiley.com).
C 2008 Wiley-Liss, Inc.
V

Our visual world is not randomly organized. Natural
inputs share statistical regularities, and many of them are
actually, or potentially, important for our survival. When
we look around us, we readily recognize an impressively
large number of objects despite substantial variations in
the actual visual input that impinge upon the retina. To
achieve this feat, the visual system must simplify information by detecting regularities and pool the natural range ofvariability into similar categories. A biologically relevant
visual pattern for which the brain has developed sensitiv-

r Caldara and Seghier r

ity is the human face [Haxby et al., 2000]. Despite its large
variability in terms of size, orientation, and luminance, thehuman brain quickly extracts a wealth of nonverbal signals
from faces critical for communication: identity, gender,
expression, and so forth. This highly developed and expert
visual skill relies on a crucial prerequisite at the entry
level: the detection and categorization of visual objects as
faces [Tanaka, 2001]. Human faces are roughly symmetrical objects that share similar salient shapes arranged inﬁxed locations across exemplars (e.g. two eyes above acentral nose and mouth), which results in an intrinsic geometrically top-heavy vertical bias (i.e. more elements in theupper compared to the lower part). Yet whether and how
these geometrical regularities are represented in the cortical system and contribute to its response selectivity forhuman faces remains to be clariﬁed. Moreover, whether
the neural tuning in the face regions would require explicit
use of active mechanisms to achieve such categorization or
not is largely unclear.
Face processing in the human brain is mediated by both
distributed [Haxby et al., 2001] and localized representations [Haxby et al., 2000], with right hemispheric dominance in the bilateral ventral visual pathway [e.g. Caldara
et al., 2006; Kanwisher et al., 1997]. Within this network,
there is a non-retinotopic region that is thought to be critical for efﬁcient face, but not object, processing [GrillSpector et al., 2004], which responds signiﬁcantly more to
faces than any other visual category: the right middle fusiform gyrus, so called the ‘Fusiform Face Area’ (rFFA)[Kanwisher et al., 1997]. Another region posterior to the
FFA––the inferior temporal occipital cortex––also plays acritical role in the extraction of face representations: the socalled Occipital Face Area (OFA) [Gauthier et al., 2000;
Rossion et al., 2003]. Despite the large number of neuroimaging studies in face perception, only a few havedirectly investigated the neural tuning of the face areas.
Levy et al. [2001] have identiﬁed a visual ﬁeld bias in thebrain regions coding for faces and buildings. Face areas
are associated with central-biased representations, compared to periphery-biased representations in the building
areas. This particular tuning in the face system might
relate to the extraction of ﬁne details and global conﬁgurations properties, as the rFFA appears to be equally sensitive to modulations in the spatial frequencies range[Rotshtein et al., 2007]. More recently, neuroimaging studies in humans [Lofﬂer et al., 2005] and macaques [Leopold
et al., 2006] have reported a convergent norm-based coding in the face system, which was expressed by a particular tuning for the average of a subset of faces. Individual
faces were coded as a function of the distance from theaverage face (prototype). The average translates the statistical regularities in faces, as it naturally depicts the invariant information. This norm-based mechanism might represent an economic coding strategy for the face system, as
individual faces would be encoded as a difference fromthe average [Leopold et al., 2006]. However, all these studies do not provide any information regarding a ﬁne-r

grained geometrical tuning in the face system. In an
attempt to clarify this issue, we previously used an
approach inspired by recent ﬁndings obtained in the newborn literature, which showed larger sensitivity to nonface
visual patterns presenting a greater number of high-contrast elements in the upper compared to the lower verticalpart of the stimulus in this population [Turati et al., 2002].
In an fMRI experiment, we presented adult participants
with schematic curvilinear and square patterns with either
more elements in the lower or in the upper part [Caldara
et al., 2006]. The FFA showed sensitivity to curvilinear
patterns with more high-contrasted elements in the upper
part uniquely when they were inscribed in a curvilinear
contour. This neural tuning can be attributed to mechanisms sensitive to the structural geometry of faces andmight relate to an economic strategy used by the neuralface system for categorizing face shapes in the visual
world. However, the visual patterns used in our previous
study [Caldara et al., 2006] were all symmetrical and, consequently, did not allow us to draw any conclusions
regarding this important structural rule.
Symmetrical human faces1 are perceived as sexuallyattractive [Little and Jones, 2003; Mealy et al., 1999; Rhodes
et al., 1998], healthier and more intelligent [Rhodes et al.,
2001; Zebrowitz and Rhodes, 2004], all representing crucial
factors for social and biological interactions. However, theperception of symmetry in humans relies on speciﬁc mechanisms and constraints of the visual system, as, forinstance, symmetry detection of complex radial frequency
shapes differs from those of dot patterns [Wilson and Wilkinson, 2002]. In particular, symmetry detection is betterfor upright compared to inverted faces despite that thesevisual stimuli are matched for their low-level properties[Rhodes et al., 2005], suggesting that high-order mechanisms subtend to this human ability on processing suchstatistical regularity on faces.
A few neuroimaging studies have recently investigated
the role of symmetry in the visual cortex. Symmetry detection for dot patterns involves integration of visual information in extrastriate visual areas with large receptive ﬁelds(i.e. V3A, V4, V7) [Sasaki et al., 2005] and LO [Sasaki et al.,2005; Tyler et al., 2005], suggesting that high-order mechanisms are required for symmetry perception of low-levelstimuli, without the involvement of particular attentional
resources [Tyler et al., 2005]. Recently, Chen et al. [2006]
investigated the role of symmetry for faces by comparing
the activations of the face sensitive regions for front view
upright faces and their phase scrambled version that werevertically symmetric. When these two conditions were contrasted, the only region that was sensitive to symmetry
was the right OFA, but surprisingly not the right FFA(rFFA). Considering the importance of symmetry that isrobustly found for faces in behavioral studies, it is somewhat surprising that this geometrical rule does not affect1

Note, that no human face is perfectly symmetrical.

1616 rr Automatic Tuning in the Fusiform Face Area r

Figure 1.
Left: examples of stimuli usedduring and after the fMRI scanning. Right: the face-like control
stimulus.
the level of activation in the most important face sensitive
area (i.e. the right FFA).
The present study was designed to further investigate
the role of symmetry in the face sensitive areas and its
interaction with the top-heavy vertical bias (upper >lower). More generally, we aimed to improve our understanding of the neural tuning underlying rFFA activations,by using nonface stimuli that optimally modulate rFFA
activations [Caldara et al., 2006]. We used the same methods as our previous functional MRI (fMRI) study [Caldara
et al., 2006]. We sampled the visual input space by presenting the participants with geometrical patterns presenting orthogonal visual properties and tested their tuning in
the occipito-temporal face sensitive regions. After the scanning sessions, participants evaluated the faceness of thepresented stimuli, as well as a schematic face-like stimulus. We also aimed to investigate to what extent the neural
responses of the face sensitive regions were subtended byperceived (subjective) or physical (objective) faceness properties. Thus, we calculated the faceness of each individual
pattern, as measured by an image similarity index withthe schematic face-like control stimulus, and compared therelative contribution of perceived (subjective evaluations)and physical (objective image similarity) faceness indiceswith the brain signals. We expected that at least symmetrical curvilinear patterns with more high-contrast elementsin the upper part will enhance the responses of the humanadult cortex coding for faces, as their low-level structuralproperties are comparable to those of human faces[Caldara et al., 2006].

iii. Twenty symmetrical and asymmetrical head-shaped
patterns with either more elements in the upper orthe lower visual part. To sample at maximum thespace covered by all the images, minimize adaptation effects and consequently extract a general rulerather than an observation related to a sub-sample ofpossible patterns, we created a set of conﬁgurations
comprising a minimum of four elements and a maximum of seven elements.
The number of the elements was greater either inthe upper or in the lower half of the stimulus
and the identical set of conﬁgurations was adapted
for the asymmetrical conditions by misplacing only
one element (black square---see Fig. 1). The center
spatial frequencies [Parish and Sperling, 1991] aresimilar across conditions (mean 6 SD): symmetricalhead-shaped patterns with more elements in theupper part 5 5.91 6 0.44 c/image; symmetricalhead-shaped patterns with more elements in thelower part 5 6.01 6 0.44 c/image; asymmetricalhead-shaped patterns with more elements in theupper part 5 5.96 6 0.43 c/image; and asymmetricalhead-shaped patterns with more elements in thelower part 5 6.01 6 0.43 c/image. Critically, theposition of the squares in the visual patterns weused (see Fig. 1) was different across stimuli (n 520), abolishing potential adaptation effects.
iv. A schematic face-like stimulus (see Fig. 1) inscribedin a head-shaped pattern was created in order tocontrol for the evaluation of the faceness of the stimuli used during the scanning sessions.

METHODS

Participants

Stimuli
Stimuli consisted of:i. Twenty full front photographs of unfamiliar faces(http://faces.kyb.tuebingen.mpg.de).
ii. Twenty pictures of common objects (http://
www.cog.brown.edu/tarr/stimuli.html).

r

Eleven adult participants (seven women, mean age 5
24.5 years, one left handed) with normal vision, voluntarily participated in the study. Six of the participants analyzed here have been included in our previous study[Caldara et al., 2006]. All the participants gave written
informed consent and the protocol was approved by the
ethical committee.

1617 rr Caldara and Seghier r

formally achieved by applying the following formula:Tasks
For all the tasks, a block design that alternated between‘‘control’’ and ‘‘activation’’ sequences was applied. Faceand object epochs (18 s; 6 TR) were counterbalanced andseparated by baseline epochs (ﬁxation cross, 9 s; 3 TR).
Two runs were performed, using a ‘‘localizer’’ block design
alternating periods of faces (n 5 20) and objects (n 5 20)[Kanwisher et al., 1997], with six epochs of each experimental condition (faces or objects) in each run. In eachface and object epoch, 24 stimuli were presented for 750
ms, without any offset, but a small shift of location (20 pixels) between consecutive images [Caldara et al., 2006].
Four of the 24 images were repeated twice consecutively
and participants were required to detect the immediate
repetitions by pressing a response key (one-back task).
For the experimental conditions we used two runs ofsymmetrical and asymmetrical head-shaped (n 5 20) witheither more elements in the upper or the lower verticalpart (see Fig. 1). Three epochs of the four conditions were
pseudorandomly presented in each run. In each epoch 24
stimuli were presented (18 s; 6 TR) and separated by baseline epochs (ﬁxation cross, 9 s; 3 TR). Participants had todetect the immediate repetition of the same stimulus (4out of 24 images) and report their answers with a response
key. All the images were displayed for 600 ms on a black
background with an interleaved ﬁxation cross (150 ms).
Participants were required to ﬁxate on this cross throughout the duration of the experiment. All stimuli were presented to the subjects via a video projector and a system of
mirrors fastened to the head coil. The visual ﬁeld spannedby this set-up was 15 3 118.
After the scanning sessions, participants were debriefed
and required to report their general impressions, and what
they thought was the aim of the scanning sessions. No
feedback was provided. Then they were instructed to rate
on a Likert scale of 1 (nonface-like) to 8 (face-like) the faceness of the patterns and the schematic face-like control
stimulus, which were not presented during the fMRI scanning sessions. All these stimuli were presented in a random sequence on a computer monitor, with the same duration used in the scanner (600 ms) and an inter-stimulusinterval of 2 s. Participants were required to express theirfaceness judgment by pressing the respective key (1–8) of
a computer keyboard. Responses were acquired without
time constraints. After this ﬁnal stage, the real aim of thestudy was explained in detail to the participants in accordance with our ethical policy.
Finally, we also analyzed the objective faceness of theimages used during the fMRI scanning sessions by measuring the similarity between each of the images (20 percondition) and the schematic face-like control stimulus. For
each condition separately, images were recoded as vectors
and a measure of faceness with the schematic face-likestimulus was obtained by computing cosine (cos) similarity values between the each of the stimuli used in theexperiment and the schematic face-like stimulus. This wasrcosðsk fÞ ¼

sk T fjjsk jj jjfjj

where T denotes the vector transpose and k k the norm (i.e.
length or magnitude) of the vector. Formally, the ratio computed in the cos formula corresponds to the cosine angle
between the images (vectors), which has the interesting
property of having a scale invariant value range extending
from 0 to 1 (a formal property that Euclidian distances do
not possess). As the angle between the images (vectors)
shortens, the cosine angle approaches 1, meaning that thetwo images are getting closer and their similarity increases.
On the contrary, a cosine angle near to 0 indicates that theimages (vectors) are orthogonal, uncorrelated (i.e. different).
This formula was iteratively applied to each of the sk stimuli(with k-th extending from 1 to 20 in each condition) and theface-like controls stimulus (f). Finally, the computed cosine
values (20) were compared using an ANOVA with Symmetricity (symmetric vs. asymmetric) and Greater number of
elements’ location (upper vs. lower) as factors.

Imaging Methods
Experiments were performed on a 1.5 T whole-body
MRI system (Philips Medical Systems, Best, Netherlands)
using the standard head coil conﬁguration. Acquired multislice volume was positioned on sagittal scout images. Anatomical reference images consisted of a 3D-GRE T1weighted sequence (TE 5 15 ms; FOV 5 250 mm; matrix
5 256 3 256; slice thickness 5 1.25 mm). For the localizer
block paradigm and the experiment, functional imaging
consisted of an echo planar imaging (EPI) GRE sequence(TR 5 3000 ms; TE 5 40 ms; ﬂip 5 808; FOV 5 250 mm;
matrix 5 128 3 128; 30 contiguous 5-mm axial slices). A
vacuum cushion (PAR Scientiﬁc A/S, Denmark) was used
to minimize head movement. Functional scanning wasalways preceded by 9 s of dummy scans to insure tissue
steady-state magnetization.

Image and Behavioral Analyses
Collected data were processed with cross-correlation
analysis [Bandettini et al., 1993] after motion correction[Woods et al., 1993] and smoothing (full width half maximum (FWHM) 5 6 3 6 3 6 mm3) using MEDx software(Sensor Systems, Sterling, VA) [Gold et al., 1998]. 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 werethen resampled and normalized to the Talairach space[Talairach and Tournoux, 1998]. The normalization procedure used in MEDx consisted of the application of an
afﬁne transformation following identiﬁcation of eight anatomical landmarks (including the anterior and posterior
commissures points). Clusters of size of >0.08 cm3 (>101618 rr Automatic Tuning in the Fusiform Face Area rvoxels; voxel size after normalization was 2 3 2 3 2 mm3)
showing a statistically signiﬁcant Z-score (typically Zthreshold 5 3.0, equivalent to P < 0.002 uncorrected) in
the face-object comparison were considered [Forman et al.,
1995]. We then computed additional post hoc analyses toreveal the inﬂuence of the perceived stimuli on thedetected MRI signal. For this purpose, clusters activatedduring the localizer block paradigm were identiﬁed foreach subject (size >0.08 cm3, at Z-threshold 5 3.0) andused as region of interest (ROI). Here we tested our hypothesis in a handful of ROIs in order to increase the signiﬁcance of our inferences [Saxe et al., 2006]. Principally,
three ROIs were identiﬁed: the right FFA, the left FFA andthe right OFA [for more details see Caldara et al., 2006].
The left OFA was identiﬁed in only four participants,
which decreased its statistical power for testing the effectsof the experimental conditions. For this reason we
excluded this ROI from the analyses.
For each deﬁned ROI a mean timecourse (i.e. BOLD percent signal change) was obtained by averaging the timecourses of individual voxels within the ROI during eachexperimental run. Since not all the participants showed
activation in all the ROIs (which leaded to missing values
across regions), the different experimental conditions wereseparately compared by using 2 3 2 repeated measure
ANOVAs in each individual ROI, with Symmetricity (symmetric vs asymmetric) and Greater number of elements’location (upper vs lower) as factors. To further investigate
whether brain activations and faceness rating scores werelinked, when appropriate we carried out a 2 3 2 repeatedmeasure ANOVA with the BOLD signal for Symmetricity(symmetric vs asymmetric) and Greater number of elements’ location (upper vs lower) as factors and the normalized faceness scores as a covariate [Winer et al., 1991].
Independently for each participant, behavioral facenessrating scores were normalized by rescaling raw ratingscore values between 0 (minimum) and 1 (maximum) for
each individual by using the following formula: (facenessraw rating score 2 minimum faceness raw rating)/(maximum faceness raw rating 2 minimum faceness raw rating). The normalization process neutralizes subjective differences in the rating scores across the participants (since a
judgment of faceness is by deﬁnition not absolute), resulting in all participants sharing values in the same range.
The normalization process also allows the direct comparison of faceness scores within and between subjects.
Finally, we computed within subjects correlation analyses
in order to investigate whether the perceived (subjective) andthe physical (objective) faceness of the presented patterns
were related to brain activations. This analysis was carriedout by completing the following steps. (i) for each participant
we computed a ﬁrst within-subject Person’s correlation
between the proﬁle of activations for all the conditions (mean% signal change averaged across blocs for the symmetricaland asymmetrical head-shaped patterns with more elementsin either the upper or lower part––a vector of length 4) andthe perceived (subjective) faceness rating scores for the re-rspective conditions (a vector of length 4). (ii) We computed asecond within-subject Person’s correlation between subjects’
proﬁle of activations for all the conditions (mean % signal
change averaged across blocs for the symmetrical and asymmetrical head-shaped patterns with more elements in either
the upper or lower part––a vector of length 4) and the physical (objective) normalized faceness scores2 (a vector of length
4). (iii) Since correlation coefﬁcients are not additive, theymust be z-normalized [Chung et al., 2005], before performing
statistical analyses. We thus normalized the obtained correla1þr.
tion coefﬁcient by using Fisher’s transform Z ¼ 0:5  loge 1r(iv) To test whether the observed normalized correlations
between BOLD activations and, respectively, the perceived
and the physical faceness were signiﬁcantly different from 0(null correlation), we used one sample t test across subjects
for each ROI separately. (v) Then, to estimate the averagecorrelation coefﬁcients for each ROI, we applied an inverseof Fisher’s transform on the mean of Z values (Zmean) using
the following formula: raverage 5 tanh(Zmean), in which tanhstands for the hyperbolic tangent. (vi) Finally, to determine
whether the observed correlation coefﬁcients between
BOLD activations and, respectively, the perceived and thephysical faceness were signiﬁcantly different, the obtained Z
values were compared by using separate paired t-test ineach ROI independently.

RESULTS
Visual Image Analyses
The physical image similarity between the stimuli usedduring fMRI scanning sessions and the schematic face-like
control pattern are reported in Figure 2.
The ANOVA revealed a signiﬁcant interaction between
the Symmetricity and Greater number of elements’ location(F(1, 76) 5 7.89, P 5 0.006). As expected, head-shaped visual patterns with more elements in the upper part presented the higher score (cos 5 0.89) in terms of image similarity with the schematic face-like pattern (post-hoc t tests
head-shaped symmetric lower, cos 5 0.85, t(19), P <0.0001; head-shaped asymmetric upper, cos 5 0.88, t(10), P< 0.01; head-shaped asymmetric lower, cos 5 0.87, t(10), P< 0.0001)). We also observed a main effect of the Greaternumber of elements’ location (F (1, 76) 5 15.061, P <0.001), but the Symmetricity factor did not reach signiﬁcance (F(1,76) 5 0.14, P 5 0.7).

Behavioral Results
Participants performed comparably in terms of accuracy
and speed across conditions in the one-back repetition
task. Image repetitions were respectively detected with anaccuracy of 95% (SE 6 0.94%) for symmetrical patterns
with more elements in the upper part, 95.27% (61.08%) for
2

Note that performing the correlation analyses with the raw faceness scores would lead to identical results.

1619 rr Caldara and Seghier r

Figure 2.
Image similarity means and their standard
errors between the stimuli used for thefMRI experimental conditions and the facelike control stimulus (cosine ([0,1]). The average image of the patterns in each respective condition is reported inside the bars.
[Color ﬁgure can be viewed in the online
issue, which is available at www.interscience.
wiley.com.]

Figure 3.
Normalized faceness rating scores, andtheir standard errors, for the fMRI experimental conditions (0: minimum; 1: maximum). Note, the ceiling effect for theface-like control stimulus that was not presented during the fMRI scanning sessions,indicating that the schematic face-like pattern was perceived and rated as the mostface-like stimulus by all the participants.
[Color ﬁgure can be viewed in the online
issue, which is available at www.interscience.
wiley.com.]symmetrical patterns with more elements in the lower
part, 95.36% (61.02%) for asymmetrical patterns withmore elements in the upper part and 95% (61.08%) for
asymmetrical patterns with more elements in the lower
part. Repetition of symmetrical patterns with more elements in the upper part were respectively detected in 494
ms (629 ms), 491 ms (626 ms) for symmetrical patterns
with more elements in the lower part, 475 ms (627 ms) for
asymmetrical patterns with more elements in the upper
part and 505 ms (630 ms) for asymmetrical patterns withmore elements in the lower part. No statistical signiﬁcantdifferences were observed in terms of accuracy or reaction
times.
As expected, faceness rating scores were signiﬁcantly
larger for the schematic face-like control stimulus (1––Likert scale values normalized from 0 to 1) presented outside
the scanner compared to all the visual patterns used during fMRI scanning sessions (Fig. 3–minimum signiﬁcant
P value when compared to any of the experimental conditions: P < 0.0001).

r

Figure 4.
Functional regions of interest for faces identiﬁed by the localizer
paradigm in one participant showing the three ROIs (R/r 5 right;
L/l 5 left). The images are displayed in radiological convention sothat the left direction on the page corresponds to the right direction on the anatomy and vice versa. [Color ﬁgure can be viewed in
the online issue, which is available at www.interscience.wiley.com.]1620 rr Automatic Tuning in the Fusiform Face Area r

This result indicates that the schematic face-like pattern
was perceived and rated as the most face-like stimulus by
all the participants. A repeated measure ANOVA on thefaceness rating scores related to the experimental conditions, revealed a signiﬁcant effect of Symmetricity (F(1, 10)
5 27.53, P < .001) and a marginal effect on the Greaternumber of elements’ location (F(1, 10) 5 4.13, P 5 0.7).
The interaction between these factors was not signiﬁcant(F(1, 10) 5 3.34, P 5 0.1).

Neuroimaging Results
Signiﬁcantly greater activations for faces compared toobjects were found in all subjects in the right FFA (rFFA;(x,y,z) 5 41, 252, 217) (see Fig. 4).
We identiﬁed the other face sensitive regions, but not in
all the subjects. The left FFA (lFFA; (x,y,z) 5 238, 251,216) was activated in nine participants, the right OFA(rOFA; (x,y,z) 5 36, 282, 26) in eight participants.
Repeated measures ANOVA revealed the presence ofadditive effects. Head-shaped patterns with a greater number of elements in the upper vertical part showed larger
responses in the rFFA compared to those with a greaternumber of elements in the lower part (F(1, 10) 5 9.53, P 5
0.011). Symmetrical patterns elicited larger activationsin the rFFA compared to the asymmetrical ones (F(1, 10) 5
5. 3, P 5 0.044) (see Fig. 5). The interaction between Symmetricity (symmetric vs. asymmetric) and Greater numberof elements’ location (upper vs. lower) failed to reach thesigniﬁcance (F(1, 10) 5 0.15, P 5 0.70). But this pattern ofactivations was mostly accounted by the symmetricalhead-shaped patterns with more elements in the upper
part (mean % signal change 5 0.39%), as revealed by thecomparison with the other experimental conditions (posthoc t tests head-shaped symmetric lower, mean signal %change 5 0.15%, t(10), P < 0.0001; head-shaped asymmetric upper, mean % signal change 5 0.25%, t(10), P < 0.05
and lower, mean % signal change 5 0.14%, t(10), P <0.01)). To further investigate the additive effects, a paired
bootstrap analysis with 1,000 permutations was subsequently performed on the differences for each of the factors (Greater number of elements’ location: upper-lower
vs. Symmetricity: symmetric-asymmetric). This analysis
revealed a marginal signiﬁcant effect (P 5 0.07), a greaternumber of elements in the upper part elicited larger activations (mean % signal change 5 0.21%) compared tosymmetry (mean % signal change 5 0.11%).
A supplementary 2 3 2 repeated measures ANOVAs
with the BOLD signal for Symmetricity (symmetric vs.
asymmetric) and Greater number of elements’ location(upper vs. lower) as factors of interest and the normalized
faceness scores as a covariate was performed in order tocontrol for the subjective perception of faceness in thebrain activations. As observed before, we found a mainsigniﬁcant effect for the Greater number of elements’ location (F(1, 10) 5 8.087, P 5 0.019) and for Symmetricityr

TABLE I. Average of the within subjects normalized
correlation coefﬁcients between the individual brain
activations (mean % signal change), and the objective—
physical—faceness (image similarity—cos) and thenormalized subjective faceness—perception—ratings
for all the conditions

Objective FacenessSubjective Faceness

rFFA

lFFA

rOFA

0.71*
0.38*

0.44
0.57

20.35
0.61

The correlation was computed separately in each ROI and transformed into Fisher’s Z values. Correlation signiﬁcantly different
form 0 are reported with an ‘*‘. Note that the absence of correlations signiﬁcantly above 0 in the lFFA and rOFA is due to thehigh variability across subjects observed in these ROIs.

(F(1, 10) 5 9.66, P 5 0.013). The interaction between thesefactors was not signiﬁcant (F(1, 10) 5 0.64, P 5 0.80).
Symmetric patterns also elicited larger activations in therOFA compared to the asymmetrical ones (F(1, 7) 5 5.95,
P 5 0.045). There was also a signiﬁcant effect of theGreater number of elements’ location (F(1, 7) 5 7.93, P 5
0.026); patterns with more elements in the lower partenhanced activation of this brain region. Both effects were
essentially driven by larger activations for the symmetry
condition with more elements in the lower part. The interaction was not signiﬁcant (F(1, 7) 5 2.29, P 5 0.17). However, when the normalized faceness rating scores weretaken into account as a covariate, both effects failed toreach the signiﬁcance (Symmetricity: (F(1, 7) 5 3.7, P 5
0.1; Greater number of elements’ location: (F(1, 7) 5 4.13,
P 5 0.09). No signiﬁcant effects were found in the activations of the left FFA (Greater number of elements’ location(F(1, 8) 5 0.112, P 5 0.74); for Symmetricity (F(1, 8) 5
1.0601, p 5 0.33)and for their interaction (F(1, 8) 5 0.02, P
5 0.89)).
Finally, to investigate whether brain activations wererelated to the physical or the perceived faceness of thestimuli, for each region of interest separately, we computed within-subject Pearson’s correlations between themean percent signal change proﬁle and respectively both,the mean image similarity values (cosine) across all the experimental conditions and the individual normalized faceness scores. The average correlation coefﬁcients arereported in Table I. Average correlations were signiﬁcantly
different from 0 in the rFFA only.
Paired t-test statistical analyses on the Z values correlations observed between the BOLD responses and the physical and the perceived faceness were ﬁnally carried out ineach region separately. The rFFA responses was correlated
more strongly with the physical (r 5 0.71) rather than theperceived (r 5 0.38) faceness of the stimuli (t(10), P 50.01). On the contrary, rOFA activations were more correlated with the perceived (r 5 0.61) rather than the physical(r 5 20.35) faceness of the stimuli (t(7), P 5 0.001). The

1621 rr Caldara and Seghier rsame comparison failed to reach signiﬁcance in the lFFA(t(8), P 5 0.06).

DISCUSSION
We used fMRI to investigate the sensitivity to geometrically structural properties in the brain regions preferentially responding to human faces. Of speciﬁc interest waswhether the neural face system was sensitive to symmetrywhen this visual property was combined with different
curvilinear geometrical conﬁgurations. Unlike previous
studies [e.g. Chen et al., 2006] that confronted participants
with symmetrical zero phase scrambled patterns (not presenting any salient geometrical bias in terms of image contrast) and normal faces, our participants were shown withsymmetrical and asymmetrical curvilinear high-contrasted
patterns with either more elements in the upper or the
lower visual part. This procedure ensured that some critical dimensions modulating the activation of at least therFFA [Caldara et al., 2006], namely a curvilinear top-heavy
vertical bias, were used to optimally investigate the sensitivity for symmetry. Participants were unaware of the aimof the experiment and the behavioral results clearly
showed that the schematic patterns were not perceived as
face-like stimuli [for a detailed discussion on this point see
also Caldara et al., 2006], as conﬁrmed by verbal reports.
Strikingly, behavioral measures revealed a discrepancy
between the perceived (subjective) and the physical (objective) quantiﬁcation of the faceness of the stimuli. A violation of symmetry induced a dramatic disruption of faceness perception in the participants, even though this same
violation does not disrupt the physical faceness of thestimuli, as revealed by a visual image similarity measurewith the schematic face-like pattern. Our neuroimaging
results conﬁrmed the critical role of the right FFA in processing statistical regularities optimal for face categorization. Among face sensitive areas identiﬁed, only the rFFA
shows responses modulated by the geometrical conﬁgurations we manipulated. In line with our previous observations [Caldara et al., 2006], the rFFA was sensitive to agreater number of high contrast elements in the upper
part. Importantly, the present ﬁndings also demonstratethat the rFFA is sensitive to symmetry. Symmetrical patterns elicit larger responses in this area compared to asymmetrical ones.3 Strikingly, however, rFFA activations weremore strongly correlated with the physical faceness of thestimuli, than the perceived faceness, at least as facenessdeﬁned as a simple schematic face-like pattern. It is worth
noting that this pattern of activation cannot be accounted
by an attentional confound, as the task constraints wereunrelated to our questions of interest (one-back repetition
3

Note that the responses elicited in the rFFA by real faces wereabout four times larger than those observed by the visual patterns
we used. This observation also supports the view that these patterns were not perceived as faces.

rtask) and the participants showed similar behavioral performance across conditions. This observation might suggest that the rFFA is an automatic detector of physical patterns sharing objective statistical regularities with thestructure of human faces. However, a potential inﬂuenceof the face context induced by the localizer scans cannot
completely rule out the possibility that participants
extracted and implicitly learned the geometrical rules speciﬁc to faces without awareness. Implicit learning occurs insituations in which participants become sensitive to certain
regularities in the environment (1) in the absence of intention to learn about those regularities (2) in the absence ofawareness that one is learning, and (3) in such a way thatthe resulting knowledge is difﬁcult to express [Cleeremans,
2002].
Human faces can be deﬁned as curvilinear visual objects
presenting a top-heavy vertical bias. Faces encapsulate alarger proportion of salient and high-contrast features in
the upper (eyes, eyebrows, hairs) compared to the lower
part (mouth), which across faces are congruently and bilaterally located along the vertical symmetrical axis (Fig. 6,top), regardless of expression. Symmetry in faces plays acrucial (evolutionary) role for social and biological interactions [Zebrowitz and Rhodes, 2004]. Therefore, it is not
surprising that the current neuroimaging ﬁndings showed
preferential responses to this facial salient cue in the brain
region that is the most sensitive to face processing: therFFA. However, our supplementary analyses using thebootstrap approach revealed a marginal effect in thestrength of the rFFA responses to a top-heavy bias compared to symmetry (the top-heavy patterns electing about
twice of the mean % signal change than the symmetricalones). Since symmetry and the top-heavy bias are both important in face processing (Fig. 6, top) why these properties might not be represented with the same extent within
the neural face system?
Despite our canonical representation of faces being in
the full-front view and the fact that most experiments on
face processing use bilateral symmetrical faces, in everyday life we are continuously confronted with moving facesthat most of the time impinge upon our retina in an asymmetrical conﬁguration along the vertical axis. Thus, theface system might rely more on visual cues extracted fromthe top down bias than symmetry to categorize shapes as
faces. Indeed, the top-heavy bias is invariant, present evenin asymmetrical views (Fig. 6, bottom). In addition, behavioral evidence suggests that information on symmetry
could be even extracted from non symmetrical face views[Troje and Bulthoff, 1998], which might also explain why
the top-down bias showed marginal stronger sensitivity inthe rFFA than symmetry.
Symmetrical and asymmetrical patterns with more elements in the upper part both showed the largest responses
in the rFFA. On average, the patterns presented in these
conditions had a salient contrast in the upper part (see Fig.
2), a region that might be related to the eyes in normal
faces. The eyes are unequivocally the most critical feature

1622 rr Automatic Tuning in the Fusiform Face Area r

Figure 5.
Mean BOLD signal percent
change measures across participants, and their standard errors,
related to the processing of theexperimental visual patterns
within the functionally deﬁned
brain ROIs. The relative numberof participants presenting signiﬁcant activations for faces within
their respective selective regions
is reported in parentheses.
[Color ﬁgure can be viewed in
the online issue, which is availableat www.interscience.wiley.com.]
for face identiﬁcation [Caldara et al., 2005; Ellis et al., 1979;
Gosselin and Schyns, 2001; McKelvie, 1976] and for communicating non verbal information, as they convey emotions, direction of attention and intentions [Baron-Cohen,
1995]. Studies of eye movements have revealed triangularpathway between the eyes and the mouth regions for faceprocessing [Althoff and Cohen, 1999; Yarbus, 1967], with arelatively stronger weight for the eyes. Our data suggest aparticular sensitivity in the rFFA for this ‘‘T’’ shaped contrast, as we observed larger activations in the conditions

Figure 6.
Top-right: Average face calculated across the images of 35
men and 35 women presenting 7
expressions (neutral, fear, anger,
disgust, happy, sad, and surprise––KDEF face database,1998). The composite image
results in a symmetrical face,with a triangular top-heavy bias.
Bottom-right: Composite image
resulting from averaging facecomposites presenting different
views. Note, that now symmetry
disappears, as this property is
not reliably shared across the
exemplars. Critically, however,the ﬁnal face composite stillpresents more elements in theupper part (red dots). [Colorﬁgure can be viewed in the
online issue, which is available atwww.interscience.wiley.com.]r

1623 rr Caldara and Seghier r

presenting this particular geometrical conﬁguration. Thisparticular spatial tuning also provides an alternative explanation to the so-called face inversion effect. The presentation of faces upside-down sharply impairs their recognition compared to objects in similar orientation [for a recent
review see Rossion and Gauthier, 2002; Yin, 1969]. In thisparticular nonecological situation, faces present more elements in the lower compared to upper part, which might
explain the decrease of activations in the brain area processing faces [for a detailed discussion on this point see
also Caldara et al., 2006].
Chen et al.’s study [2006] did not ﬁnd sensitivity forsymmetry in the rFFA, but it did in the rOFA. A possible
explanation for the absence of symmetry sensitivity in therFFA in Chen et al.’s study [2006] could rely on the factthat zero phase symmetrical patterns do not enclose any
salient geometric structure besides symmetry. Therefore,
the contrast is uniformly spread across the image axes anddo not present any bias in the location of high-contrasted
elements (i.e. in the upper/lower part). Consequently, therFFA might not have been sufﬁciently stimulated by thesesuboptimal patterns and might have not reached thethreshold allowing the expression of differential sensitivity. Our previous results [Caldara et al., 2006] clearly
showed that changing one salient dimension of symmetrical visual patterns (i.e. the shape or the location of thegreater number of elements) signiﬁcantly and sharply
increased or decreased the activations in the rFFA. On thecontrary, in line with Chen et al. [2006], we also found an
effect of symmetry in the rOFA. However, this effect was
confounded with the perceived faceness, as showed byboth the correlation analyses and the statistical analyses
taking into account this measure as a covariate. Futurestudies are necessary to clarify the precise nature of themechanisms occurring in the rFFA and rOFA, the dynamics and interaction between face selective regions and theclear role of low-level statistical regularities in face identiﬁcation [Rotshtein et al., 2005], as the current study was
designed to uniquely investigate the modulation of perceptual mechanisms rather than face identiﬁcation.
Newborns tested with similar head-shaped nonface
stimuli [Turati et al., 2002], and also with more realistic
face-like stimuli [Macchi Cassia et al., 2004], showed a visual preference for patterns with more elements in theupper part, but not for symmetry. These observations suggest that a tuning for symmetry is constructed with visualexperience (at least with face-like patterns), which might
explain why the strength for the tuning to this visual property is lower compared to the top-heavy bias. However, it
is worth noting that is difﬁcult to compare newborns’ ﬁndings with the present neuroimaging observations in theadult population. Despite the fact that it has been suggested that the superior colliculus route plays a criticalrole in the newborn preference for face detection [Johnson,
2005], we still lack of direct evidence and it is still
unknown whether the brain regions sensitive to faces inadults are or not involved in the immature newborns’rbrain. Therefore, the present ﬁndings cannot be straightforwardly compared across both populations. Future studies
are necessary to isolate the brain roots of the face preference in the newborn.
To sum up, our observations revealed that the rFFA is
tuned to statistical regularities shared in the intrinsic structure of faces (two horizontal eyes placed above a central
nose and mouth). Symmetry ﬁnely calibrates such neuralcomputational tuning, which occurs automatically in therFFA and as it is related to the objective, physical facenessof the stimuli, and not to the subjective, conscious perception of faceness. This observation indicates that the rFFA
might use this statistical tuning for classifying shapes best
ﬁtting with faces in the visual environment.

ACKNOWLEDGMENTS
The authors express their gratitude to Dr. François Lazeyras (Department Radiology, Geneva University Hospitals) for his help with scanning.

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