© 2000 Nature America Inc. • http://neurosci.nature.com

articlesExpertise for cars and birds recruits
brain areas involved in facerecognition
Isabel Gauthier1,2, Pawel Skudlarski2, John C. Gore2 and Adam W. Anderson2
1 Present Address: Department of Psychology, Vanderbilt University, Wilson Hall, Nashville, Tennessee 37240, USA
2 Department of Diagnostic Radiology, Yale University Medical School, Fitkin Basement, 333 Cedar Street, New Haven, Connecticut 06510, USA© 2000 Nature America Inc. • http://neurosci.nature.com

Correspondence should be addressed to I.G. (isabel.gauthier@vanderbilt.edu)

Expertise with unfamiliar objects (‘greebles’) recruits face-selective areas in the fusiform gyrus (FFA)
and occipital lobe (OFA). Here we extend this finding to other homogeneous categories. Bird andcar experts were tested with functional magnetic resonance imaging during tasks with faces,familiar objects, cars and birds. Homogeneous categories activated the FFA more than familiar
objects. Moreover, the right FFA and OFA showed significant expertise effects. An independent
behavioral test of expertise predicted relative activation in the right FFA for birds versus cars withineach group. The results suggest that level of categorization and expertise, rather than superficial
properties of objects, determine the specialization of the FFA.

Face and object recognition differ in at least two ways. First, faces
are recognized at a more specific level of categorization (for example, ‘Adam’) than most objects (for example, ‘chair’ or ‘car’). Second, although we are experts with faces, we have much lessexperience discriminating among members of other categories.
Level of categorization and expertise are relevant even for unfamiliar faces and objects. A person passed on the street may be
encoded at the individual level and recognized the next day,whereas a mug may be replaced by another mug without ournoticing. Processing biases for different categories depend on our
experience with levels of categorization and our expertise in
extracting diagnostic features1.
Viewing faces activates a small extrastriate region called thefusiform face area (FFA)2–10. Neuropsychological studies suggestthat the brain areas responsible for face and object processing
can be dissociated11–14. According to one view, extrastriate cortex contains a map of visual features15–16, suggesting that the same
region should not be recruited for processing different object categories when the relevant features differ.
On the other hand, prosopagnosia is often associated with
deficits discriminating among nonface objects within categories.
For example, a bird watcher became unable to identify birds17,
whereas another patient could no longer identify car makes18.
Thus, one hypothesis holds that prosopagnosia is a deficit in
evoking a specific context from a stimulus belonging to a classof visually similar objects19. At least some prosopagnosic patients
have difficulty with classes in which objects are both visually andsemantically homogeneous20,21. Evidence from brain-lesion studies is still under debate13,22; however, additional data from brain
imaging may help resolve these questions.
Several lines of research converge to suggest that level of categorization and expertise account for a large part of the activationdifference between faces and objects. First, behavioral effects23–25
once thought unique to faces have been obtained with objects,
often with expert subjects26–29. Second, nonface objects elicit morenature neuroscience • volume 3 no 2 • february 2000

activation in the FFA when matched to specific labels as comparedto more categorical ones (for example, ‘ketchup bottle’ versus ‘bottle’)3,30. Third, expertise with animal-like unfamiliar objects (‘greebles’) recruits the right FFA4. However, it remains unclear whether
expertise with any homogeneous category is capable of recruiting
the neural substrate of face recognition.
This experiment had three purposes. First, we tested whether
long-term expertise with birds and cars would recruit face-selective areas. Second, the interaction between level of categorization and expertise was investigated. Third, we tested how these
two factors depend on attention to stimulus identity. The FFA
typically activates more for faces than objects, even during passive
viewing7. This suggests that faces are processed automatically at
the subordinate level. Here we asked whether this is also true forother expertise domains.

RESULTSWe tested 11 car experts and 8 bird experts with many years ofexperience recognizing car models or bird species (Table 1). The
right and left FFA and right occipital face area (OFA) were
defined in passive-viewing localizer scans (see Methods). The
OFA is also face selective31 and active in greeble experts4. A right
FFA was found in all subjects (median size, 6 voxels), a left FFA
was found in 13 subjects (4 bird experts, 9 car experts; mediansize, 5 voxels) and a right OFA in 15 subjects (7 bird experts, 8
car experts; median size, 7 voxels).
Subjects also underwent identity and location scans. Stimulus
presentation was identical in both conditions, and subjects detected immediate (1-back) repetitions in either the identity of the picture or its location while ignoring the other dimension. Blocks of
16 grayscale faces, objects, cars or birds shown sequentially werealternated with periods of fixation (Fig. 1). Pilot experiments indicated that the absence of color cues did not eliminate the advantageof experts over novices. Behavioral data in the scanner was available
for 16 of the 19 subjects. Performance was better in the identity191© 2000 Nature America Inc. • http://neurosci.nature.com

articles© 2000 Nature America Inc. • http://neurosci.nature.comaLocationb

Locationor identity

IdentityTime

Fig. 1. Examples of stimuli and tasks for the fMRI protocol. (a) Images(256 × 256 pixels in size, 256 grays) from each of 4 categories(Caucasian faces without hair, passerine birds from New England, carmodels for the years 1995 and 1998 and various familiar objects) wereused in the fMRI study. (b) Example of stimulus presentation during thefMRI runs. Subjects made 1-back repetition judgments regarding either
location or identity (an identity repeat would show identical images,
although sometimes in different locations—see Methods for details).

Inspection of Fig. 2 suggests baseline differences between categories and between groups. First, responses to birds were larger thanto cars in FFAs of both hemispheres (right, F1,17 = 11.13, p < 0.05;
left, F1,11 = 5.47, p < 0.05). Again, although animal faces may activate this area more than objects10, here we found no difference
between cars and birds in novices. Bird experts showed more activation for any object category than car experts, although not significantly in any ROI. Given these baseline trends, it was crucial tomeasure the effect of expertise by comparing activation for cars andbirds in the two groups. The predicted expertise effect was significant (a group × category interaction) in the right FFA (Figs. 2 and 3;
F1,17 = 19.22, p < 0.0005) and in the right OFA (F1,13 = 4.86, p < 0.05).
There was no expertise effect in the left FFA (F < 1).
One important question is whether this expertise effect arises from the same area as face expertise. To test this, we used a setof criteria (see Methods) based on a definition of face cells in neurophysiology32. This defines a smaller FFA than any other definition to our knowledge (also eliminating the majority of OFA andleft FFA ROIs, which were not analyzed further). We call this ROI
the center of the FFA (median = 3 voxels), in which each voxel ishighly face selective. Even in this ROI, both the level of categorization effect in novices (F1,17 = 6.37, p < 0.02) and the expertiseeffect were present (F1,17 = 10.25, p < 0.006; Fig. 3). These effects
also held when analyzed in a subset of subjects whose FFA could
be defined using described criteria6,10 (see supplemental material
at http://neurosci.nature.com/web_specials/). To assess the magnitude of the expertise effect, we plotted the main effects and interaction separately for the center of the right FFA33 (Fig. 4). The
statistically significant expertise effect contributed a difference ofabout 0.4% signal change between groups, whereas the group andcategory main effects contributed about 0.3% and 0.1% signal
change, respectively, and were not significant (F < 1 for both).
Corresponding values in the larger right FFA ROI were 0.2, 0.1
and 0.3, respectively. The expertise effect alone accounted for 32%
of the difference between faces and objects in the right FFA definedat t = 2 and for 36% in the center of the right FFA.
We measured the center of mass of the signal change for activated voxels for birds, cars and faces (relative to objects). Thiswas done in a ROI of 25 × 25 voxels (each 1.3 mm by 1.7 mm,
y × x over 3 slices in Talairach space, centered on the right andleft FFA from the localizer). The only significant differences forcars or birds relative to faces were obtained in novice subjects

than the location runs (identity performance ± s.e., 89.4 ± 2.1;
location, 86.0 ± 2.3; F1,14 = 9.98, p < 0.01) and this effect was larger for birds and objects than for cars and faces (task × categoryinteraction, F1,14 = 5.86, p < 0.01). These categories varied more inshape, making location judgments more difficult.
The percent signal changes in the three regions ofinterest (ROIs) were assessed using a fixation baseline. Table 1. Subject information and behavioral results.
First, we describe all significant effects pooled acrossBird experts
task, coming back to this factor later. The level of cat- Mean age ± s.e.
34.4 ± 2.0
egorization effect was measured by comparing activa- Mean years experience ± se
18 ± 3.3
tion in novices to cars or birds versus objects. The
effect of level of categorization was significant in theright FFA (F1,17 = 14.36, p < 0.02) and in the left FFA Behavioral data during fMRI
(F1,11 = 8.76, p = 0.02.). This effect was marginal in the (% correct identity ± s.e.; location ± s.e.)
86 ± 3; 81 ± 4
right OFA (F1,13 = 3.67, p < 0.08). The interaction Objects
85 ± 3; 82 ± 3
between level and group was significant in both the Faces
84 ± 3; 81 ± 3right FFA (F 1,17 = 6.61, p < 0.02) and the left FFA Cars
(F1,11 = 6.47, p < 0.05). Post-hoc tests (p < 0.05) indi- Birds
87 ± 3; 81 ± 4cated that the level effect was only significant for car
experts viewing birds. It may be tempting to believe Behavioral data pre-test (d′ ± s.e.)that birds activate the FFA because of their faces10. Birds upright2.53 ± 0.10
However, the difference between birds and cars for
Cars upright1.41 ± 0.12
novices was not significant in either area (p > 0.5 forBirds inverted
2.23 ± 0.20
both), and the group effect arises from a difference inCarsinverted
0.84± 0.13
activity for common objects (larger in birders).
192

Car experts
31 ± 2.5
20.6 ± 3.8

93 ± 3; 88 ± 3
92 ± 3; 91 ± 3
92 ± 2; 91 ± 2
92 ± 3; 89 ± 3

1.06 ± 0.07
2.42 ± 0.14
1.01 ± 0.09
1.58 ± 0.20

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articles

3.125 × 7 mm3) window in Fig. 5.
Experts (and to some extent birdcar experts
ers viewing cars) showed a distribution of activation for birds andCenter of right FFA
Right FFA
cars that was relatively limited to
the localizer peak of the FFA. The
mean percent signal change in thecenter voxel was compared to thatin the 8 voxels surrounding it, andto the surrounding ‘outside’ 16
voxels. An ANOVA (3 regions × 3
categories × 2 groups) revealed amain effect of region (F2,22 = 7.18,
p < 0.004) with more activity inthe center than in both outerregions. Because of greater activObjects
Cars
Birds
Faces
Objects
Cars
Birds
Faces
ity for faces than birds and cars
only in the center, the category ×
region interaction was marginal
Left FFA
Right OFA
(F4,44 = 2.18, p < 0.09), consistent
with our other analyses. Crucially, there was no significant difference among the three categories
in activity for each of the two surrounding regions, suggesting that
activation is as focused for objects
as for faces.
As a more direct way of assessing the expertise effect, we measured the correlation between
behavioral performance outside
the scanner with the signal
Objects
Cars
Birds
Faces
Objects
Cars
Birds
Faces
change in the three ROIs during
Fig. 2. Mean percent signal change for each object category in the two expert groups in three face-specific ROIs the location and identity tasks. In
and in the center of the right FFA. The average percent signal increase from fixation for each object category in thethe behavioral test, subjects
different ROIs was averaged across subjects in each ROI for each expert group. Error bars indicate standard errorof the mean. The Talairach coordinates for the center of each ROI ± standard error were right FFA, x = 38 ± 2, judged whether sequentially prey = –50 ± 1, z = –7 ± 1; left FFA, x = –38 ± 2, y = –56 ± 4, z = –6 ± 2; right OFA, x = 40 ± 2, y = –75 ± 3, z = –3 ± 1. sented pairs of birds and cars
(upright or inverted) belonged tothe same species or car model.
The expertise effect was significant (group × category interaction; Table 1), bird experts being(see Table 2). Center of mass for expert categories was indistinmore sensitive for birds than cars and vice versa for car experts
guishable from that obtained for faces (even using a lenient sta(F1,17 = 59.40, p < 0.0001). The effect of orientation and interactistical test). We also compared the activation distribution in theright FFA for the three categories by averaging nonspatiallytion of category with orientation were significant, with the inversmoothed individual maps, centered on the most face-selective
sion effect stronger for cars than for birds (F1,17 = 14.27, p <voxel in the localizer. This is shown in a 5 × 5 voxel (each 3.125 ×
0.002). Both groups were poorer with inverted than upright cars,
Mean % signal change

Mean % signal change

Mean % signal change

Mean % signal change

Faces – objects

Car expert

Fig. 3. The right FFA shows an expertiseeffect for birds and cars. One axial obliqueslice through the FFA for one expert for eachcategory shows the t-maps obtained whencomparing the activation for faces, cars andbirds with the activation elicited by objects
during the location 1-back runs. The voxels
marked by white crosses indicate the right
FFA and OFA as defined in the passive viewing
runs for these two subjects. (In this car
expert, the OFA was actually in the slice
immediately below and is shown on the same
slice as the FFA only to illustrate its in-planelocation.) Note that the center of the right
FFA may be slightly different depending on thetask (here passive viewing versus 1-back location) and that its size varies between subjects.

Bird expert© 2000 Nature America Inc. • http://neurosci.nature.com

bird experts

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Cars – objects

Birds – objects

t = 4.5

t=1193© 2000 Nature America Inc. • http://neurosci.nature.com

articles© 2000 Nature America Inc. • http://neurosci.nature.com

194

Percent signal change

Percent signal change

bird experts
whereas the inversion effect for birds only
approached statistical significance in birdcar experts
Main effects
Interactioners (p = 0.068).
A group analysis was performed on
the fMRI data during all 1-back tasks
with cars and birds (see Methods). Thisless precise analysis allowed us to seekother regions showing an expertise effectregardless of the category, beyond theones we could define functionally. This
showed an area in right ventral temporal
cortex that was more activated in experts
than novices (Fig. 6). In addition to thisstream of activation, going from the right
OFA toward the right FFA, a bilateralregion in the parahippocampal gyrus was
Cars
Birds
Cars
Birds
also more active in experts. This area
overlaps with the parahippocampal place Fig. 4. Main effects with grand mean and interaction partialed out and interaction effect with thearea (PPA)34, functionally defined as the grand mean and main effects partialed out, in the center of the right FFA. Each observed condition
region responding more to scenes than mean can be reconstructed by adding the value for the main effects and the interaction effect to theobjects. (It also responds more to objects grand mean (in this case, 0.845).
than faces.) Further work will be required
to identify the role of this area in perceptual expertise. The only region more activated in novices than experts was a small bilateral area of thesmall (1-back identity versus passive viewing; but see ref. 36). We
lateral occipital gyrus, superior to the OFA. This area has been
also found no effect of task on the advantage of faces over objects infound to activate more for letter strings than faces35, and its selecthe right FFA (p > 0.28), nor on the expertise effect. Expertise may
influence how objects are automatically processed, an idea that wetive activation for novices could reflect a switch from a featuralcome back to in our discussion.
to a more configural strategy.
In each ROI, we correlated the percent signal change for birds
minus cars with relative expertise, the difference in sensitivity
DISCUSSION(d′) for upright birds minus upright cars. As the 2 groups comPrevious studies suggest that level of categorization and expertisebined would produce a bimodal distribution, the correlation coefcontribute to the specialization of the FFA. The present results show
ficients were calculated for each group separately using our largest
how their contributions add up to account for a considerable parthomogeneous sample (the 12 subjects scanned with axial slices.)of the difference typically found between objects and faces.
For both groups, relative expertise was positively correlated with
In our experiment, experts would know more names for the
relative percent signal change for birds versus cars in the rightbirds or cars than novices would. However, naming is not likely
FFA and only for the location task (car experts, r = 0.75; bird
to account for the effects in the FFA because unfamiliar faces
experts, r = 0.82; p < 0.05 for both; Fig. 7).
activated this area the most, whereas common objects that are
We also considered task effects beyond that found in the correeasily named elicited the least activation. In addition, expertise
lation analyses. The only ROI showing a significant influence ofeffects for novel objects can be obtained in the FFA for unfatask was the right FFA, where this factor interacted with level of catmiliar exemplars of a trained category4.
egorization (F1,17 = 6.58, p < 0.02): the subordinate-level advantage
Why would faces recruit the FFA more than expert recognition of objects? There are many possibilities. First, the FFA
was larger when novices attended to the identity than to the location
may be dedicated to face recognition (innately or through expeof the stimuli. In prior studies6,10, the effect of task in the FFA wasrience), although it may mediate the processing of
Table 2. Center of mass coordinates in the middle temporal lobe forother objects to some extent. At the least, our study
category-selective areas, given in Talairach coordinates.
demonstrates that an innate bias is unnecessary forobjects to recruit this area with expertise. Second,
Left hemisphere
Right hemisphere
we cannot claim to have equated objects with faces
x
y
z
x
y
z
on level of categorization and expertise22. The faces
Bird experts
may constitute a more visually homogeneous set
Faces
–31.3
–49.8
–7.6
40.8
–48.2
–8.5
than our bird or car images. Faces are recognizedCars–29.3*–47.3*
–7.8
39.9
–47.5
–9.0
as individual exemplars, whereas even experts
Birds
–30.8
–49.6
–7.9
40.3
–48.1
–8.3
mainly recognize cars and birds at themodel/species level. Although our subjects had
Car experts
years of experience with cars or birds, they still had
been practicing face recognition for many more
Faces
–29.0
–48.7
–9.1
38.6
–48.1
–9.1
years. Thus, face recognition being in a sense ‘more
Cars
–28.9
–49.2
–8.1
38.3
–47.2
–8.5
subordinate’ and relying on ‘greater expertise’ may
Birds–30.5*
–49.8
–7.0
41.2*
–47.8
–8.9
be what make it seem ‘special’, leaving little contri*Value significantly different from the coordinate for faces in the same expert group accordbution for a component of object category per se.
ing to a least significant difference test; p < 0.05.
Additionally, categorization level and expertise may

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articlesMean % signal change
0

Birds – objects

1.8

Cars – objects

Bird experts

Faces – objects

0.9

be only two of several factors that
determine the specialization of thisarea. (Other factors may include symmetry, properties of associated semantic knowledge, number of exemplars,value to the perceiver37.)
The effect obtained in the right
OFA suggests that expertise may be
responsible for specialization of a largepart of the face recognition system (atleast in the right hemisphere). In theleft FFA, we found an effect of level ofcategorization, with no detectable contribution of expertise. Whereas subordinate-level processing
may recruit both hemispheres, here visual expert recognitionof homogeneous categories seems to be mainly a right hemisphere process.
Our most striking result may be a very strong correlation
between a behavioral test of object expertise and the relative activation in the right FFA for birds and cars. It is remarkable thatthe expertise of a subject was so accurately predicted from theactivation in a small part (six voxels) of the brain, especially as
the behavioral and fMRI experiments shared neither a common
task nor stimulus set. In addition, this analysis suggests that activation of the right FFA was more directly correlated with expert
performance than the right OFA.
Car experts© 2000 Nature America Inc. • http://neurosci.nature.com

Fig. 5. Spatial distribution of percent signal
change for faces, birds and cars (relative to an
objects baseline) in a 5 × 5 voxel window inthe right FFA, centered on the most strongly
activated voxel in the localizer. Dashed lines
indicate the three regions within which activation was averaged for analyses. Note thatthe highest activation during experimental
runs for faces may not be identical to thehighest peak in the localizer (consider car
experts in the faces – objects condition).

There seems to be an important interaction between automaticity of processing at the subordinate level and expertise. It isargued that the preference of the FFA for faces does not depend on
the task6,10 (but see ref. 35), a claim also supported by our results.
However, we found an interaction between level of categorizationand task for novices, indicating that, for most people, simply seeing
an object among similar exemplars may not prompt complete subordinate-level processing. Automatic subordinate-level processing
for experts could also explain a surprising finding: the correlation
between behavior and activation in the right FFA was significant
only when subjects attended to the location of the objects. During
the identity task, subjects had to perform subordinate-level recognition with both categories, regardless of expertise. Novices may

Fig. 6. Expertise effect in the temporal cortex. The t-maps
for all subjects with axial slices (14) were transformed into acommon standard space. Voxels showing a significant expertise effect across subjects (p < 0.01) are displayed on thetransformed anatomical images for slices 2 to 5 for a single
subject. The red to yellow voxels were more active forexperts than novices across the identity and location tasks,
whereas the blue voxels were more active for novices. The
right hemisphere is shown on the left. The FFA is typicallyfound in slice three.
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articlestral pathway, because it suggests that responses of neurons in
extrastriate cortex may not be organized according to the visual features that they detect15,16; rather, their functional organization may depend on the different processes important for
object recognition. For instance, some areas may be more suited for featural processing, whereas other areas may supportconfigural and holistic processing, hallmarks of subordinatelevel expertise. Our results suggest that expert subordinate-level
recognition for any category may be mediated in the same
regions, either by virtue of activating common cells or throughselectively activating different populations that are intermingled. Other techniques, such as single-cell recording, will benecessary to distinguish between these two alternatives.

Relative % signal change

car advantage

bird advantage

Location task

car experts, r = 0.75*
bird experts, r = 0.82*

METHODS

bird advantage

Stimuli. One hundred and seventy six images each of passerine birds andcars were obtained from public sources on the world-wide web. Images
were converted to 8-bit grayscale 256 × 256-pixel format, and objects
were isolated and placed on a 50% gray background. Objects were selected to be familiar to our expert population. For each category, 112 images
were used in the behavioral test, whereas the remaining 64 objects fromeach category were used for experimental scans. Faces without hair(n = 64, scanned in a 3-D laser scanner, courtesy of Niko Troje and Heinrich Bülthoff, Max Planck Institute, Tübingen, Germany) and 64 imagesof non-living familiar objects were prepared in the same way as the cars
and birds and also used in experimental scans. Localizer scans used 90
grayscale photographs of faces and 90 pictures of familiar objects.

car experts, r = 0.10
bird experts, r = 0.004
Relative % signal change

bird advantage

Identity task

car advantage© 2000 Nature America Inc. • http://neurosci.nature.com

Relative expertise (d′)
car advantage

Relative expertise (d′)
car advantage

bird advantage

Fig. 7. Relationship between a behavioral measure of expertise andactivation in the right FFA. Relative expertise is the sensitivity (d′) for
bird minus car matching. The dashed and full lines respectively indicate
the best linear fits for car and bird experts. Significant correlation coefficients are marked with an asterisk (p < 0.05).

then use a featural strategy, whereas experts may use a more configural strategy26–28. Perhaps only configural processing is a goodpredictor of behavioral expertise. In contrast, during the location
task novices may not access the subordinate level, whereas experts
did so automatically.
Birds and cars differ in many aspects. (Birds are small animals with moveable parts, covered with feathers that have specific markings; cars are large man-made objects made of metal
and typically uniform in texture.) Combined with a previous
study showing an expertise effect in the FFA with ‘greebles’4,
our results suggest very few constraints on the structure of theobjects for which expertise can recruit this small area. This isimportant for any theory of visual representation in the ven196

Subjects. Subjects, all male, included 11 car experts and 8 bird experts.
Informed consent was obtained from each subject, and the study wasapproved by the Human Investigation Committee at the School of Medicine, Yale University. Eight subjects were left handed. Handedness did
not correlate with any effect reported here.

Behavioral task. Each subject performed 10 blocks of 56 sequential
matching trials, alternating between blocks showing birds or cars. There
were four conditions (upright and inverted cars and birds). Each trial
showed two images from the same category and orientation. Upright andinverted trials were randomly intermixed. On each trial, a fixation crossappeared for 500 ms, followed by stimulus 1 for 1000 ms and a patternmask for 500 ms before stimulus 2 appeared and remained on the screen
until a response was made. Subjects judged whether the two images
showed birds from the same species or whether cars were from the samemodel but different years (mostly 1995 versus 1998). No difference wasfound in mean sensitivity between categories for novices. However,
responses to cars were slower than responses to birds for all subjects (RTsfor hits with cars, 1138 ms; birds, 1046 ms; p < 0.05, suggesting that thecars were more difficult).
fMRI task. Experimental scans consisted of three runs of a one-back location task alternated with three runs of a one-back identity task. The only
difference between identity and location runs was instructions to detect
immediate repetitions in either location or identity (Fig. 1). Each run
lasted 5 min, 36 s and consisted of 16 epochs (16 s each) with 5 fixation
periods (16 s each) interleaved at regular intervals. During each epoch, 16
objects appeared, each shown for 725 ms followed by a 275 ms blank.
Objects (each 12° × 12°) appeared in one of 8 locations within an overall
area subtending 18° × 18° of visual angle. The order of the four categories
was counterbalanced across runs.
ROI selection. Regions of interests were functionally defined using two
localizer scans, which included 16 epochs (16 s each) of passive viewing offaces or common objects centered on the screen (25 pictures per epoch).
Each run began with 16 s of fixation, and an 8-s fixation period was included after every 2 passive viewing epochs. The right and left FFA and right
OFA were defined as contiguous voxels activated at an arbitrary threshold of
t = 2 in the middle fusiform gyri (c–d, F-G, 9-10 in Talairach space), andthe same threshold was applied in the right ventral occipital lobe (c-d, H-I,
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articles© 2000 Nature America Inc. • http://neurosci.nature.com9-10) for the right OFA . The raw data were noisier than when using a higher field scanner and a surface coil6,10 so the same level of significance for
ROI definitions could not be applied. However, the magnitude and spatial
extent of the effect for a given functional area should be similar regardless ofstatistical power, and we used a criterion leading to ROIs comparable insize to those in published studies6,10 . Our FFAs show at least twice as muchpercent signal change for faces as for objects (each compared to fixation.) To
eliminate the influence of less face-selective voxels, we defined the ‘centerof the right FFA’ using criteria more stringent than in any published study.
The only voxels selected were found within the cluster of contiguous voxels
selected using the less stringent criterion, showed twofold greater percentage signal change for faces as objects (compared with fixation) and, in eachsubject, did not have less than half the percentage signal change for faces ofthe voxel showing the maximum signal change for faces.
fMRI imaging parameters and analyses. Most (16) subjects were scannedat the Yale School of Medicine on a 1.5 T GE Signa scanner equipped withresonant gradients (Advanced NMR, Wilmington, Massachusetts) using
echo-planar imaging (gradient echo single shot sequence, 168 images per
slice, FOV = 40 × 20 cm, matrix = 128 × 64, NEX = 1, TR = 2000 ms,
TE = 60 ms, flip angle = 60 ms). Six contiguous 7-mm-thick axial-obliqueslices aligned along the longitudinal extent of the fusiform gyrus covered
most of the temporal lobe. Some subjects (two car experts and one bird
expert) were scanned using coronal-oblique slices. Three more subjects(two car experts and 1 bird expert, I. G. et al., Soc. Neurosci. Abstr. 25,
212.9, 1999) were scanned using coronal slices on the 3 T GE scanner at
the MGH-NMR Center in Charlestown, Massachusetts. In this case, acustom bilateral surface coil was used to collect 168 images per slice in 12
near-coronal slices, 6 mm-thick. The imaging parameters were TR = 2000
ms, TE = 70 ms, flip angle = 90°, 180 degrees and offset = 25 ms.
Before statistical analysis, images from the 1.5 T scanner were motion
corrected for three translation directions and the three possible rotations
using SPM-96 software (Wellcome Department of Cognitive Neurology, London, UK). On the 3 T scanner, a bite bar was used to minimize
head motion. Maps of t-values and percent signal change, both corrected for a linear drift in the signal38, were created. Maps were spatiallysmoothed using a Gaussian filter with a full-width half-maximum valueof two voxels, except for analyses in the center of the right FFA, where
regions of interests were very small and no smoothing was performed.
In group composite maps, the percent signal change relative to fixation
baseline for both birds and cars was multiplied with contrast weights foreach subject (1 and –1 for bird experts and –1 and 1 for car experts). Under
the null hypothesis of no expertise effect, the expected value for this contrast
was equal to zero. We used a randomization test to asses the statistical significance of percent signal changes. A population distribution for each voxel
was generated by calculating randomized mean values (1000 times) of thecontrast in which randomly chosen subsets of half the subjects got reversed
weights. The observed contrast, calculated without sign reversal, was assigned
a p value or proportion of times that the observed contrast was more extreme
than the randomized contrast). To show the anatomy clearly, the p values
were overlaid on the normalized anatomical images for a single subject(threshold at p < 0.01; Fig. 6).
Note: Additional analysis can be found on the Nature Neuroscience web site(http://neurosci.nature.com/web_specials/).

ACKNOWLEDGEMENTS
We thank Nancy Kanwisher and René Marois for discussions and Jill Moylan,
Terry Hickey and Hedy Sarofin for technical assistance. This work was supported
by NINDS grant NS33332 to J.C.G. and NIMH grant 56037 to N. Kanwisher.
I.G. was supported by NSERC.

RECEIVED 11 AUGUST; ACCEPTED 7 DECEMBER 1999
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