Revisiting the Role of the Fusiform Face Area in Expertise
Merim Bilalić1,2
Abstract
■ The fusiform face area (FFA) is considered to be a highly
specialized brain module because of its central importance
for face perception. However, many researchers claim that
the FFA is a general visual expertise module that distinguishes
between individual examples within a single category. Here, I
circumvent the shortcomings of some previous studies on the
FFA controversy by using chess stimuli, which do not visually
resemble faces, together with more sensitive methods of anal-
ysis such as multivariate pattern analysis. I also extend the pre-
vious research by presenting chess positions, complex scenes
with multiple objects, and their interrelations to chess experts
and novices as well as isolated chess objects. The first experi-
ment demonstrates that chess expertise modulated the FFA
activation when chess positions were presented. In contrast,
single chess objects did not produce different activation pat-
terns among experts and novices even when the multivariate
pattern analysis was used. The second experiment focused on
the single chess objects and featured an explicit task of iden-
tifying the chess objects but failed to demonstrate expertise
effects in the FFA. The experiments provide support for the gen-
eral expertise view of the FFA function but also extend the scope
of our understanding about the function of the FFA. The FFA
does not merely distinguish between different exemplars within
the same category of stimuli. More likely, it parses complex
multiobject stimuli that contain numerous functional and spa-
tial relations. ■
INTRODUCTION
The fusiform face area (FFA), a region in the inferotem-
poral cortex, plays an important role in face perception
(Duchaine & Yovel, 2015; Kanwisher, McDermott, & Chun,
1997). The exact function of this brain region is however
debated. On one side of the debate, there is the view that
the FFA is exclusively a face-specific brain module. This
view reflects the immense importance of face perception
in our lives (Kanwisher & Yovel, 2006) and builds on the
evolutionary assumption that a brain area can evolve to re-
flect exclusively the importance of a single stimulus cate-
gory (Fodor, 1983). However, faces are not only crucial in
our lives but also constitute one of the most often encoun-
tered, and consequently most often practiced, categories.
This fact has led to the view that the FFA is in fact a general
visual expertise module, not necessarily specific to faces
(Gauthier, Skudlarski, Gore, & Anderson, 2000; Gauthier,
Tarr, Anderson, Skudlarski, & Gore, 1999). According to
this expertise hypothesis, the FFA is responsible for visual
individuation, that is, the ability to differentiate between
different objects within any single category of stimuli. Here,
I use the game of chess as a model for visual expertise to
demonstrate that the FFA is indeed a general expertise
module but that its function may extend beyond individu-
ation and encompass parsing relations between elements
of a complex stimulus.
There is no denying the importance of the FFA in face
perception. Damage to and around the FFA results in the
1Tübingen University, 2University of Klagenfurt
© 2016 Massachusetts Institute of Technology
inability to perceive faces (Barton, 2008; Mayer & Rossion,
2007; Barton, Press, Keenan, & O’Connor, 2002), and
faces commonly activate the FFA twice as much as any
other stimuli (Kanwisher & Yovel, 2006). However, the
heightened response to faces in the FFA could also be
a consequence of our extensive experience and expertise
in dealing with faces. This is the essence of the expertise
hypothesis. One way to investigate this possibility is to
compare the FFA response in people who have experience
with certain nonface stimuli with the response in people
who have less experience with the particular stimulus cat-
egory. The expertise hypothesis has been tested with a
number of nonface stimuli, ranging from birds (Gauthier
et al., 2000), to cars (McGugin, Van Gulick, Tamber-
Rosenau, Ross, & Gauthier, 2015; McGugin, Newton, Gore,
& Gauthier, 2014; Gilaie-Dotan, Harel, Bentin, Kanai, &
Rees, 2012; McGugin, Gatenby, Gore, & Gauthier, 2012;
Xu, 2005; Grill-Spector, Knouf, & Kanwisher, 2004;
Gauthier et al., 2000), butterflies (Rhodes, Byatt, Michie,
& Puce, 2004), Pokémon characters ( James & James,
2013), and novel object types (Gauthier et al., 1999). The
results have been mixed, and their interpretation has been
the focus of an extensive debate (Op de Beeck & Baker,
2010; Bukach, Gauthier, & Tarr, 2006; Kanwisher & Yovel,
2006). A factor that further complicates the interpretation
is the visual similarity of the investigated stimuli with faces:
Cars, birds, and even butterflies have face-like features
(Kanwisher & Yovel, 2006).
An obvious way around the resemblance problem is
to find stimuli that do not look like faces. Radiological
Journal of Cognitive Neuroscience 28:9, pp. 1345–1357
doi:10.1162/jocn_a_00974
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stimuli, such as thorax x-rays, would fit the not-face-like
description. Harley and colleagues (2009) demonstrated
that expert and novice radiologists have similar activation
levels when quickly examining x-rays for abnormalities.
The experts’ behavioral performance, however, was reli-
ably correlated with the activation in the FFA, whereas
the FFA activation in novices was not predictive of how
well they identified abnormalities within the x-rays. To-
gether with my colleagues (Bilalić, Grottenthaler, Nägele,
& Lindig, 2016), I have recently shown that there were
indeed no differences in the FFA activation between ex-
perienced radiologists and medical students during the
perception of radiological stimuli when the classical uni-
variate analysis was performed. However, when we em-
ployed the more sensitive multivariate pattern analysis
(MVPA), the FFA could differentiate between radiological
images and other neutral stimuli in radiologists, but the
FFA in medical students was still not sensitive enough to
differentiate between the stimuli within and outside
specialization.
Here, I employ the MVPA on an expertise domain
where the stimuli do not resemble faces—the game of
chess. When looking at the chessboard, one processes
both individual objects (the chess pieces) and the chess
positions composed of these individual objects. Chess is
particularly suitable for testing FFA’s expertise hypothesis
not only because hardly anyone would mistake either
chess positions or chess objects for faces but also because
it enables us to further extend the expertise hypothesis.
Individual chess objects can, however, be differentiated,
as there are six different chess object categories (see
Figure 1: king, queen, bishop, knight, rook, and pawn).
Because of accumulated domain-specific knowledge, ex-
pert chess players are quicker than their less-skilled peers
at individualizing chess objects as well as retrieving their
function (Bilalić, Kiesel, Pohl, Erb, & Grodd, 2011; Kiesel,
Kunde, Pohl, Berner, & Hoffmann, 2009; Saariluoma,
1995). The real chess expertise, however, lies in using
knowledge to quickly assess the gist of chess positions
(Bilalić, Turella, Campitelli, Erb, & Grodd, 2012; Bilalić,
Langner, Erb, & Grodd, 2010; Gobet & Simon, 1996).
Unlike novices, experts do not perceive numerous chess
objects as individual objects but rather as meaningful units
of objects and their relations called chunks (Chase &
Simon, 1973) and templates (Gobet & Simon, 1996). Given
that they have previously acquired and stored numerous
chunks and templates, experts can quickly perceive them
in new chess positions. In this way, the previously ac-
quired knowledge enables experts to quickly orient them-
selves when confronted by unfamiliar chess positions
often consisting of over 20 individual objects (Bilalić,
McLeod, & Gobet, 2009).
These aspects make chess an ideal domain for investi-
gation of the FFA expertise hypothesis. Not only can we
check whether the FFA is sensitive to chess expertise in
the individuation process by presenting single chess ob-
jects, but also we can investigate whether perception of
chess positions, considered to be the essence of chess
expertise, also modulates the FFA. This would enable
us to differentiate between two versions of the expertise
hypothesis: one that involves recognition of individual
members within a category (individuation) and another
that involves perceptual processes that integrate multiple
features and the relations between them. The perceptual
process of automatically parsing complex multiobject en-
vironments, which is a characteristic of chess experts,
bears a similarity to that found in face perception. Both
processes are automatic, quick, and efficient in binding
individual features into meaningful units. Indeed, our
previous study (Bilalić, Langner, Ulrich, & Grodd, 2011)
indicated that the FFA is expertise modulated when chess
positions were presented. Although some studies con-
firmed the FFA’s involvement in the perception of chess
positions (Righi, Tarr, & Kingon, 2013), other studies
Figure 1. Stimuli. (A)
Experiment 1 presented chess
positions, chess objects, faces,
rooms, and tools to participants
who needed to identify direct
repetition of two stimuli (1-back
task). (B) Experiment 2 used
explicit individuation task
asking participants what was
the presented chess object
(identity), if there was check
(check), and what is the
geometrical shape present
(control). The red color
indicates the nature of the task
and was not presented to the
participants.
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Journal of Cognitive Neuroscience
Volume 28, Number 9
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could find no differences between experts and novices
when they checked their FFA responses to chess posi-
tions (Bartlett, Boggan, & Krawczyk, 2013; Krawczyk,
Boggan, McClelland, & Bartlett, 2011).
Here, I extend the previous research by employing
both the classical univariate fMRI analysis and the MVPA
to see whether the FFA is indeed sensitive to chess ex-
pertise. I also introduce a novel manipulation, as I pres-
ent not only chess positions, as was done in previous
studies (Bilalić, Langner, et al., 2011), but also individual
chess objects. The first experiment, in which participants
passively observed the chess stimuli, demonstrates FFA
modulation with respect to chess positions and an ab-
sence of modulation with respect to chess objects. In
the second experiment, the FFA could not distinguish re-
sponses between chess experts and novices when they
were explicitly involved in an individuation task of indi-
vidual chess objects, even when the more sensitive tech-
nique MVPA was employed.
METHODS
Participants
In the first experiment, there were 16 chess experts (M =
25.9, SE = 1.6) and 19 chess novices (M = 29, SE = 1.1).
The second experiment involved 12 experts (M = 27.2,
SE = 1.9) and 13 novices (M = 28.8, SE = 1.2). Chess skill
is measured on an interval scale called Elo (Elo, 1978)
with a theoretical mean of 1500 and a standard deviation
of 200. The players above 2000 rating points are consid-
ered experts, whereas the very best players in the world,
grandmasters, have a rating around 2500. The experts in
the first experiment had an average rating of 2061 Elo
(SE = 133), whereas the experts in the second experiment
had an average of 2140 Elo (SE = 119). Novice players were
competent hobby players who played chess occasionally
and would beat beginners without any problems. Although
novices were not rated, because they did not play chess
regularly (and not in chess clubs and tournaments), it is ob-
vious that their chess skills are vastly inferior to experts.
The experts were recruited in local chess clubs through
the author’s personal contacts. The novices were recruited
via university’s electronic mailing lists as well as announce-
ments on the blackboard list. The experts were paid A30
and novices were paid A15 per hour for their participation
in the experiments. All participants were right-handed.
Four of the experts and one of the novices participated
in other chess-related experiments (see Bilalić et al.,
2012; Bilalić et al., 2010; Bilalić, Kiesel, et al., 2011; Bilalić,
Langner, et al., 2011). One expert and two novices were fe-
male in the first and second experiments. There were six
experts and eight novices who participated in both exper-
iments (in different sessions conducted on different days).
Written informed consent was obtained in line with the
study protocol as approved by the ethics committee of
Tübingen University.
Design and Procedure
Experiment 1 directly tested responses to individual
chess objects and chess positions. The participants had
to indicate if the current stimulus was the same as the
previous one (1-back task). There were five classes of
stimuli: faces, chess positions, chess objects, rooms,
and tools. The face stimuli were black and white pictures
of students not previously used in the localizer task (Leube,
Erb, Grodd, Bartels, & Kircher, 2001). The chess stimuli
were full-board positions taken from a database of four mil-
lion chess games (ChessBase Mega Base 2007; ChessBase
GmbH, Hamburg, Germany; www.chessbase.com) and
individual chess objects taken from the same graphical
software (chess positions were made out of individual
pieces). Rooms were interior pictures taken from the Inter-
net. Tools depicted single isolated everyday objects with a
clear-cut function (e.g., hammer, screwdriver). The pic-
tures of tools were taken from Brodeur, Dionne-Dostie,
Montreuil, and Lepage (2010).
In Experiment 1, I presented face or chess stimuli (always
upright) in blocks of five stimuli (Figure 1B). A single stim-
ulus lasted for 2.75 sec and was followed by a mask. A base-
line (gray screen with a center cross) was presented at the
beginning, after each block, and at the end of the experi-
ment for 14 sec. All four conditions were presented in each
of the three runs four times (12 blocks of each condition in
all runs). The physical dimensions of each stimulus were
336 × 336 mm.
Experiment 2 tested explicitly for individuation and
featured four tasks. In the check task, participants indi-
cated if the white king was attacked (i.e., placed in check)
by the only black piece present. There were four different
stimuli with two pieces on a 3 × 3 miniature chessboard
(see Figure 1B). The white king was always on the first
square of the upper left corner, whereas the identity of
the other piece (knight or rook) and its location (center
of the lower row or the end of the upper row) were varied.
In the identity task, participants were presented with
the same stimuli as in the check task, but this time, they
were asked to identify the black piece presented (the
white king was also presented to keep the two chess
tasks visually identical). In the nonchess control task,
chess pieces had been replaced by gray-colored geomet-
rical shapes (a circle for the king; a diamond and square
for the knight and rook, respectively). As in the two chess
tasks, the identity (diamond or square) and position
(center of the lower row or the end of the upper row)
of the target stimulus were varied, and participants were
asked to indicate its shape. The second control task re-
quired participants to take into account not only the
shape of the geometrical figures but also whether they
were on the white or black square. This control task will
not be presented here because its results are essentially
identical to those of the first control task. A part of the
experiment was reported elsewhere (Bilalić, Kiesel,
et al., 2011). The experiment reported here includes
Bilalić
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additional participants (four experts and five novices),
and the individual ROI and MVPA analyses presented
here are completely new and have not been reported
elsewhere. The experiment employed a block design.
There were four runs and 12 blocks in each run (four
blocks for each condition in a single run). The runs were
block-randomized and counterbalanced across partici-
pants. The experiment started with an empty 3 × 3 board
(baseline) for 13.5 sec and was followed by a written in-
struction for 3 sec indicating the task type (check, identity,
or control). After the instruction, there was a gap (an empty
3 × 3 board) with a black center cross appearing on it after
one second. The cross lasted for 0.5 sec and was used to
warn participants of the upcoming stimulus. The stimulus
lasted for 2 sec, after which the gap and cross were re-
peated as a warning for the next stimulus. There were four
trials (stimuli) in a block, and after each block, the baseline
was presented. The stimuli in the second experiment
were 126 × 126 mm for the whole stimulus.
Localizer Task
The face recognition paradigm was a localizer task used
to isolate individual FFAs by having participants passively
watch pictures of faces and objects. The pictures of
faces were taken from students of Tübingen Univer-
sity (see Leube et al., 2001) and were not later used in
Experiment 1. The stimuli in the localizer were the same
as in the first experiment: 336 × 336 mm for the whole
stimulus.
In all experiments, the stimuli were projected onto a
screen above the head of the participant via a video pro-
jector in the adjacent room. The setup resulted in a visual
field of 14.6° for the whole scene (face or chess board).
Participants saw the stimuli through a mirror mounted on
the head coil and indicated their decision by pressing
one of two buttons of an MRI-compatible response de-
vice held in their right hand (the left button was for
“yes”; and the right button, for “no”).
MRI Acquisition
A 3-T scanner (Siemens Trio; Siemens, Erlangen, Germany)
with a 12-channel head coil was used to acquire all neuro-
imaging data. The measurement covered the whole brain
using a standard EPI sequence with the following param-
eters: repetition time = 2.5 sec, field of view = 192 ×
192, echo time = 35 msec, matrix size = 64 × 64, 36
slices with thickness of 3.2 mm + 0.8 mm gap resulting
in voxels with a resolution of 3 × 3 × 4 mm3. The ana-
tomical images covering the whole brain with 176 sagittal
slices were obtained after the functional runs employing
a magnetization prepared rapid gradient echo sequence
with a voxel resolution of 1 × 1 × 1 mm3 (repetition
time = 2.3 sec, inversion time = 1.1 sec, echo time =
2.92 msec).
Univariate fMRI Data Analysis
I used the SPM software package (SPM8; Wellcome De-
partment of Imaging Neuroscience, London, UK; www.
fil.ion.ucl.ac.uk/spm) for all fMRI analysis. The prepro-
cessing involved spatial realignment to the mean image
including unwarping and coregistration of the mean EPI
to the anatomical image for each participant separately.
The images were neither normalized nor smoothed be-
cause we wanted to employ the unstandardized individ-
ual data in the univariate pattern analysis and MVPA later.
In all experiments and the localizer, the blocks of stimuli
were modeled explicitly in a general linear model (GLM)
together with an implicitly modeled baseline (the model-
ing of the hemodynamic activation relied on a canonical
response function, autocorrelation corrected with a first-
order autoregressive model, and a 128 high-pass filter).
The movement parameters were also added in the GLM
to account for the variance introduced through head
movements. The mean percent signal change for each
task for each participant individually was extracted from
all the voxels within the selected region of the ROI using
Marsbar SPM Toolbox (Brett, Anton, Valabregue, &
Poline, 2002).
The goal of the study was to check whether experts
and novices differ in the percent signal change in the
FFA and posterior STS (see below) when presented
with different stimuli. That is why I compared them di-
rectly on each of the five categories (chess positions,
chess objects, faces, rooms, and tools) using a t test. In
each instance, I corrected for multiple comparisons using
the Bonferroni correction. In the first experiment, the
significance level was p < .01 because dividing .05 by
5 (the number of multiple comparisons) gives the thresh-
old of .01. In the second experiment, the employed
threshold was p < .017 because there were three com-
parisons (check, identity, and control—dividing .05 by
3 gives .017).
MVPA
The MVPA analyses were performed using the Decoding
Toolbox (Hebart, Görgen, & Haynes, 2014). The toolbox
uses the support vector machine (SVM) method of MVPA
to ascertain whether the defined ROIs can distinguish be-
tween different stimuli among chess experts and novices.
All comparisons were binary SVM classifications and fo-
cused on the comparisons between chess stimuli on
the one hand and the neutral stimuli on the other. In
the first experiment, chess positions were compared with
rooms and with tools. Chess objects were also compared
with the same neutral categories (rooms and tools), but I
also compared chess positions and chess objects. In the
second experiment, I compared the individuation task
with the control task and the check task with the control
task as well as the individuation task with the check task.
For all classifications, I used a linear SVM with standard
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Journal of Cognitive Neuroscience
Volume 28, Number 9
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cost parameter, c = 1, as implemented in the LIBSVM 3.0
library (Chang & Lin, 2011). The classification was based
on the β values previously obtained by the GLM and all
voxels within an ROI. A leave-one-trial-out method (e.g.,
Sterzer, Haynes, & Rees, 2008) where the data set was
divided into (1) a training set of N pattern vectors (vector
length = number of voxels) and (2) a test set of two pat-
tern vectors, one from each stimulus type, was em-
ployed. The β is then scaled in all training sets (0–1) as
well as in test sets to ensure that one does not duplicate
the univariate analysis. I trained iteratively the SVM clas-
sifier on the training data sets (N) and tested on an inde-
pendent test data set, not used in the training. These
training and testing procedures were repeated 100 times.
The percentage of successful categorization of test items
based on the previous independent training data was ob-
tained for each comparison and for each participant. At
the group level, I tested with one-sample, one-sided
t tests to find out whether the average classification accu-
racy among the participants for the binary comparison in
question was significantly greater than the chance level
(50%). Because there were five binary comparisons in
the first experiment, I adjusted the significance level from
p = .05 to p = .01 (.05 / 5 = .01). In the second exper-
iment, we had three binary comparisons and have ad-
justed the significance level to p = .017 (.05 / 3 = .017).
The comparisons between groups were performed using
two-sided t tests, but here, I used the same adjusted sig-
nificance levels (.01 for the first experiment and .017 for
the second experiment).
(M = 386 mm3, SE = 56 mm3) from that in novices (M =
348 mm3, SE = 42 mm3) in the first experiment (t(33) =
0.55, p = .59). The difference in the size of the FFA was
also not significant between the groups in the second ex-
periments (experts: M = 434 mm3, SE = 58 mm3; nov-
ices: M = 403 mm3, SE = 50 mm3; t(23) = 0.42, p =
.68). Adding the size of the FFA as a covariate in the anal-
yses reported in the Results section did not change the
pattern of the results presented in the main text.
In addition to the right FFA, I also identified another
face area, the pSTS (Campanella & Belin, 2007). In all par-
ticipants, only voxels that were significantly more active
when viewing faces than objects at p < .0001 in the loca-
lizer task were included. Two of the experts and two of
the novices in the first experiments had their pSTS iden-
tified using a more liberal significance threshold of p <
.001. In the second experiment, in all participants, the
pSTS could be identified using the p < .0001 threshold.
The volume of the pSTS in the first experiment in experts
(M = 337 mm3, SE = 57 mm3) was no bigger than that in
novices (M = 311 mm3, SE = 40 mm3; t(33) = 0.55, p =
.27), and this was also the case in the second experiment
(experts: M = 373 mm3, SE = 71 mm3; novices: M =
442 mm3, SE = 53 mm3; t(23) = 0.35, p = .73). Again,
adding the size of the ROIs in the analysis produced
essentially the same pattern of results.
RESULTS
Experiment 1: Perception of Chess Positions and
Chess Objects
Cross-categorization MVPA
Behavioral Results
I performed a stronger test for shared processes in pro-
cessing faces and chess stimuli in the FFA. I first trained
the binary classifier on all possible faces-versus-rooms
comparisons and tested on completely different stimuli–
chess positions versus rooms as well as chess objects ver-
sus rooms. If face and chess perception share similar
processes and play a role in the FFA’s functioning, then
the FFA should be sensitive even if the learned patterns
are tested on different comparisons involving face and
chess stimuli. The same procedure was performed on
the second neutral stimuli—tools.
Localizer Analysis
The right FFA was identified in each participant as the ac-
tivated area in the right lateral part of the mid-fusiform
gyrus when I subtracted activation while passively watch-
ing faces from that while passively watching objects. In
most participants, I was able to apply a stringent criterion
including only voxels significant at the p < .0001 level. In
two experts and three novices in the first experiment and
in one expert and one novice in the second experiment, I
used a less stringent threshold ( p < .001) to identify the
right FFA. The size of the FFA was no different in experts
Although the task was relatively simple, experts were still
0 =
better at detecting repetitions of chess positions (d
0 = 2.84, SE = 0.04;
3.02, SE = 0.05) than novices (d
t(33) = 2.84, p = .008). The same trend was found in
0 =
spotting the repetitions of chess objects (experts: d
0 = 2.91, SE = 0.06), but the
3.04, SE = 0.04; novices: d
difference did not reach statistical significance (t(33) =
1.73, p = .09). There were no differences between the
two groups when they had to indicate the repetitions
0 =
of faces (experts: d
2.99, SE = 0.09; t(33) = 0.68, p = .50), rooms (experts:
0 = 2.97, SE = 0.08;
0 = 3.01, SE = 0.06; novices: d
d
0 = 2.94,
t(33) = 0.39, p = .70), and tools (experts: d
0 = 3.02, SE = 0.04; t(33) = 0.94,
SE = 0.08; novices: d
p = .36).
0 = 2.91, SE = 0.07; novices: d
fMRI Univariate Analysis
Not only were the experts better at spotting the repeti-
tions of chess positions, but their FFA also responded
more (M = 1.20, SE = 0.10) than the FFA in novices
(M = 0.65, SE = 0.11) when the chess positions were
presented (t(33) = 3.59, p = .001). On the other hand,
there were no differences in the FFA activation between
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experts (M = 0.65, SE = 0.09) and novices (M = 0.60,
SE = 0.12) when observing the chess objects (t(33) =
0.33, p = .74). The chess positions elicited in general
more FFA activation than chess objects (t(34) = 3.47,
p = .001). The differences were only confined to chess
stimuli because when they were presented with faces, ex-
perts (M = 1.77, SE = 0.16) and novices (M = 2.03, SE =
0.12) did not differ in their FFA response (t(33) = 1.27,
p = .21). The same lack of expertise modulation in the
FFA was found with the two neutral categories, rooms
(experts: M = 0.75, SE = 0.11; novices: M = 0.52, SE =
0.14; t(33) = 1.19, p = .24) and tools (experts: M = 0.82,
SE = 0.09; novices: M = 0.77, SE = 0.11; t(33) = 0.27,
p = .70).
In contrast to the FFA, the other face-related areas,
pSTS, did not display the same pattern of results. There
were no significant differences between experts (M =
0.30, SE = 0.21) and novices (M = 0.14, SE = 0.17) in
the pSTS activation when chess positions were observed
(t(33) = 0.61, p = .55). The same lack of skill differences
Figure 2. MVPA results in
Experiment 1. (A) The
success rate for the FFA in
differentiating between chess
stimuli (positions and objects)
and other neutral stimuli
(rooms and tools) in experts
and novices. (B) The success
rate for the pSTS in
differentiating between chess
stimuli and other neutral stimuli
in experts and novices. The
dotted line represents 50%
success rate—chance level.
Error bars indicate SEM.
*p < .01 (adjusted for multiple
comparisons).
was observed for chess objects (experts: M = 0.28, SE =
0.18; novices: M = 0.12, SE = 0.13; t(33) = 0.70, p = .49).
The difference between chess positions and chess ob-
jects was also not found in pSTS (t(34) = 0.21, p =
.84). There were no differences between experts (M =
0.73, SE = 0.18) and novices (M = 0.94, SE = 0.18) when
they observed faces (t(34) = 0.80, p = .43). The other
two neutral stimulus categories also produced no skill
differences in pSTS (for rooms, experts: M = 0.18,
SE = 0.18; novices: M = 0.01, SE = 0.16; t(33) = 0.72,
p = .47; for tools, experts: M = 0.12, SE = 0.17; novices:
M = 0.16, SE = 0.12; t(33) = 0.22, p = .82).
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fMRI MVPA
The univariate fMRI analysis showed that the FFA is mod-
ulated by expertise as well as the kind of chess stim-
uli, chess positions, or chess objects. As can be seen in
Figure 2A, the MVPA confirms that the FFA in experts
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1350
Journal of Cognitive Neuroscience
Volume 28, Number 9
can reliably differentiate between chess positions and
other neutral categories: rooms (t(15) = 7.4, p < .001)
and tools (t(15) = 7.8, p < .001). Experts’ FFA also
reliably differentiated between chess objects and the other
two neutral categories: rooms (t(15) = 3.5, p = .002) and
tools (t(15) = 3.3, p = .002). The FFA in novices, on the
other hand, could not match the success rate of classifica-
tion in experts’ FFA. The FFA in novices could reliably dis-
tinguish between chess positions and rooms (t(18) = 3.2,
p = .003), but in all other comparisons, the FFA could not
reliably differentiate between chess and neutral categories
when the significance level was adjusted for multiple com-
parisons: chess object versus room (t(18) = 1.97, p = .03),
chess positions versus tool (t(18) = 2.18, p = .021), and
chess object versus tool (t(18) = 0.24, p = .039).
Experts’ FFA was significantly better than that of novices
at differentiating chess positions from rooms (t(33) =
2.89, p = .007) and tools (t(33) = 4.01, p < .001). How-
ever, the differences in the FFA sensitivity between ex-
perts and novices did not reliably differentiate the
chess objects from rooms (t(33) = 0.99, p = .33) and
tools (t(33) = 2.32, p = .026).
One way to check the sensitivity of the FFA to exper-
tise is to compare chess positions and chess objects di-
rectly. Experts’ FFA could differentiate between chess
positions and chess objects (t(15) = 7.7, p < .001), but
the FFA of novices could not reliably tease apart the two
chess stimuli (t(18) = 1.88, p = .038). The differences in
the success rate between the FFA of experts and novices
in recognizing chess positions and objects were also sig-
nificant (t(33) = 2.86, p = .004).
The other face area, pSTS, displayed a different pat-
tern of results, as seen in Figure 2B. Although there
was a tendency in experts for the pSTS to be able to re-
liably distinguish between chess and neutral stimuli, one
of the binary comparisons approaches the corrected
significance level of p < .01 (chess position vs. room:
t(15) = 1.76, p = .048; chess position vs. tool: t(15) =
1.69, p = .09; chess object vs. room: t(15) = 2.12, p =
.025; chess object vs. tool: t(15) = 1.91, p = .038; chess
position vs. chess object: t(15) = 0.77, p = .23). The
same tendency was noticeable in novices, but again,
none of the comparisons survived the set significance
level (chess position vs. room: t(18) = 1.34, p = .09;
chess position vs. tool: t(18) = 1.52, p = .07; chess ob-
ject vs. room: t(18) = 1.91, p = .035; chess object vs.
tool: t(18) = 1.84, p = .041; chess position vs. chess ob-
ject: t(18) = 1.09, p = .15). There were also no differ-
ences between experts and novices when it came to
the ability of their pSTS to differentiate between chess
and neutral stimuli (chess position vs. room: t(33) =
0.48, p = .67; chess position vs. tool: t(33) = 0.16, p =
.87; chess object vs. room: t(33) = 0.18, p = .86; chess
object vs. tool: t(33) = 0.64, p = .52). Finally, the experts’
FFA did not differentiate more reliably between chess po-
sition and chess object than the pSTS of novices (t(33) =
0.10, p = .91).
Cross-categorization MVPA
In the next step, I went further and asked if the differ-
ences between faces and other neutral stimuli (rooms
and tools) can be used to distinguish between chess
stimuli and neutral stimuli. In this cross-categorization
procedure, I first trained the FFA of experts and novices
to distinguish between faces, on the one hand, and
rooms and tools, on the other (see Figure 3A). Then,
the obtained activation pattern in the FFA was used to
distinguish between chess stimuli, on the hand, and the
neutral stimuli, on the other. In other words, I checked
whether the faces and chess stimuli share similar under-
lying processes that may help the FFA to differentiate
both stimuli from the neutral stimuli using the same un-
derlying activity within the FFA.
Figure 3B shows that none of the cross-categorization
procedures in the FFA were significant. Only the FFA of
experts in the comparison between faces and rooms and
its implementation on chess positions versus rooms ap-
proached significance level (t(15) = 1.84, p = .043). All
other cross-categorizations were not significant (face
vs. room to chess positions vs. room: t(15) = 0.64, p =
.26 for novices; face vs. tool to chess positions vs. tool:
t(15) = 0.71, p = .25 for experts and t(18) = 0.38, p =
.35 for novices; face vs. room to chess object vs. room:
t(15) = 1.13, p = .14 for experts and t(18) = 0.79, p =
.22 for novices; face vs. tool to chess object vs. tool:
t(15) = 0.12, p = .45 for experts and t(18) = 0.04, p =
.47 for novices). When we compared the success of
experts’ and novices’ FFA on the cross-categorization,
there was again marginally significant differences for
the comparison face versus room to chess positions ver-
sus room (t(33) = 2.06, p = .048). None of the other
cross-categorization comparisons were different between
experts and novices (face vs. tool to chess positions vs. tool:
t(33) = 0.26, p = .79; face vs. room to chess object vs.
room: t(33) = 0.05, p = .96; face vs. tool to chess object vs.
tool: t(33) = 0.12, p = .90).
Figure 3C shows that the control face ROI, the pSTS,
was not successful in the cross-categorization procedure.
None of the procedures reached the significance levels
adjusted for multiple comparisons (face vs. room to
chess positions vs. room: t(15) = 1.21, p = .12 for ex-
perts and t(15) = 1.0, p = .16 for novices; face vs. tool
to chess positions vs. tool: t(15) = 1.24, p = .11 for ex-
perts and t(18) = 0.78, p = .22 for novices; face vs. room
to chess object vs. room: t(15) = 1.28, p = .11 for experts
and t(18) = 0.68, p = .25 for novices; face vs. tool to
chess object vs. tool: t(15) = 1.54, p = .08 for experts
and t(18) = 0.26, p = .40 for novices), and there were
no differences between pSTS of experts and novices (face
vs. room to chess positions vs. room: t(33) = 0.10, p =
.91; face vs. tool to chess positions vs. tool: t(33) = 0.48,
p = .64; face vs. room to chess object vs. room: t(33) =
0.37, p = .71; face vs. tool to chess object vs. tool: t(33) =
1.08, p = .28).
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Figure 3. MVPA cross-categorization results in Experiment 1. (A) Illustration of the cross-categorization procedure. Instead of training and testing on
the same categories (but different instances of the same categories), it was trained on one type of category and tested on a different type of category.
I trained first the classification algorithm on the binary comparison of faces and rooms. Then, the learned patterns were tested on the binary
comparison involving a new category—chess positions. The same procedure was done for the tools (instead of rooms—second column), and then
the same was repeated for chess objects instead of the chess positions (third and fourth columns). (B) Classification accuracy for the cross-
categorization procedure presented as percentage of correctly classified instances of the binary comparisons with rooms and tools for the FFA in
experts and novices. (C) Classification accuracy for the cross-categorization procedure presented as percentage of correctly classified instances of the
binary comparisons with rooms and tools for the pSTS. The dotted line represents 50% success rate—chance level. Error bars indicate SEM. #p < .05
(*p < .012 adjusted for multiple comparisons).
Experiment 2: Individuating Chess Objects and
Their Interrelations
I have established that the FFA’s response to chess stim-
uli, in particular chess positions, is modulated by chess
expertise. However, I could not find evidence, even with
the use of the sensitive MVPA, that isolated chess ob-
jects, the building blocks of chess positions, were differ-
ently represented in chess experts’ FFA than in chess
novices’ FFA. In the second experiment, I sought to
shed further light on the relationship between the FFA
and expertise. I was interested in the role of the FFA in
the individuation of isolated chess objects and their re-
lations to other chess objects. The second experiment is
identical to the commonly employed individuation para-
digm in other domains (e.g., Xu, 2005; Rhodes et al.,
2004; Gauthier et al., 1999, 2000). The experiment goes
one step further and also examines recognition of
domain-inherent relations between those objects. I pre-
sented two chess objects (a king plus a variable chess
object) on a reduced 3 × 3 square chessboard. Partici-
pants indicated either whether the two presented chess
objects were in a check relation (“check task”) or whether
the variable piece was a bishop or a knight (“identity
task”). In the nonchess control task, the identity task
was repeated with two geometrical shapes instead of
chess pieces (see Figure 1B).
Behavioral Results
Experts were faster in the chess tasks than novices, even
when they only had to individualize the chess object
(identity, experts: M = 0.52 sec, SE = 0.01 sec; novices:
M = 0.59 sec, SE = 0.02 sec; t(23) = 2.81, p = .009) and
especially when they had to indicate if the check relations
was present or not (check, experts: M = 0.58 sec, SE =
0.02 sec; novices: M = 0.71 sec, SE = 0.02 sec; t(23) =
4.15, p < .001). The speed advantage of experts was con-
fined to the chess-related tasks, as there were no signifi-
cant differences when chess experts and novices had to
individualize geometrical shapes (control, experts: M =
0.68 sec, SE = 0.03 sec; novices: M = 0.66 sec, SE =
0.03 sec; t(23) = 0.47, p = .64).
1352
Journal of Cognitive Neuroscience
Volume 28, Number 9
fMRI Univariate Analysis
Although experts were faster in chess-related tasks, their
FFA was not more activated than the FFA of novices in
any of the chess tasks (identity, experts: M = 0.23,
SE = 0.07; novices: M = 0.21, SE = 0.10; t(23) = 0.19,
p = .85; check, experts: M = 0.25, SE = 0.06; novices:
M = 0.23, SE = 0.08; t(23) = 0.22, p = .83). There were
also no differences in the activation of the FFA between
experts (M = 0.18, SE = 0.08) and novices (M = 0.20,
SE = 0.06) in the control task (t(23) = 0.22, p = .83).
It is important to note that there were no differences in
the FFA activation between the three tasks (F(2, 48) =
1.16, p = .32), despite their apparent difference in diffi-
culty as indicated by the RT. Therefore, the FFA also did
not respond differently to the individuation of a single
isolated chess object or an isolated relation between
two chess objects.
The other face area, the pSTS, was not differently acti-
vated by any of the tasks (F(2, 48) = 0.34, p = .71), nor
was it modulated by expertise in any of the three tasks
(identity, experts: M = 0.10, SE = 0.15; novices: M =
0.28, SE = 0.18; t(23) = 0.76, p = .58; check, experts:
M = 0.08, SE = 0.16; novices: M = 0.22, SE = 0.20; t(23) =
.56, p = .58; control, experts: M = 0.13, SE = 0.15; nov-
ices: M = 0.07, SE = 0.14; t(23) = 0.31, p = .76).
fMRI MVPA
As seen in Figure 4A, the lack of reliable response to
chess objects was confirmed with the more sensitive
MVPA. Experts’ FFA could not distinguish above chance
between individuating chess objects and neutral objects
(identity vs. control: t(11) = 1.47, p = .085), between re-
lations among chess objects and individuation of neutral
objects (check vs. control: t(11) = 0.78, p = .28), and be-
tween chess individuation and chess relations (check vs.
identity: t(11) = 1.27, p = .11). The FFA in novices was
not more successful as none of the comparisons reached
the necessary statistical threshold to become significant
(identity vs. control: t(12) = 0.85, p = .21; check vs. con-
trol: t(12) = 1.95, p = .037; check vs. identity: t(12) =
1.67, p = .061). As with the univariate fMRI analysis, there
Figure 4. MVPA results in
Experiment 2. (A) The
success rate for the FFA in
differentiating between check,
identity, and control tasks in
experts and novices. (B) The
success rate for the pSTS in
differentiating between check,
identity, and control tasks in
experts and novices. The dotted
line represents 50% success
rate—chance level. Error bars
indicate SEM.
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Bilalić
1353
were no significant differences between the FFA in experts
and novices (identity vs. control: t(23) = 0.94, p = .35;
check vs. control: t(23) = 0.59, p = .56; check vs. identity:
t(23) = 0.27, p = .79).
Figure 4B shows that the pSTS was not much better at
differentiating chess from control stimuli. The pSTS of ex-
perts could not reliably distinguish between individuation
of chess and neutral objects (identity vs. control: t(11) =
1.90, p = .042), between the identification of relations
among chess objects and the individuation of neutral ob-
jects (check vs. control: t(11) = 1.03, p = .17), or between
chess individuation and chess relations (check vs. identity:
t(11) = 1.32, p = .12). The pSTS in novices was also not
successful in differentiating the stimuli above chance
(identity vs. control: t(12) = 0.25, p = .82; check vs. con-
trol: t(12) = 1.11, p = .28; check vs. identity: t(12) = 2.24,
p = .022), and there were no significant differences in the
pSTS success rate of differentiation between experts and
novices (identity vs. control: t(23) = 1.29, p = .21; check
vs. control: t(23) = 0.08, p = .94; check vs. identity: t(23) =
0.49, p = .63).
Needless to say, the cross-categorization procedure
was also unsuccessful, which is not surprising given the
lack of the reliable differentiation between the stimuli
with the MVPA.
DISCUSSION
Most of the previous studies on the function of the FFA
have used stimuli visually similar to faces. Here, I em-
ployed the chess stimuli to circumvent the similarity
problem, but the novelty of the work lies in the use of
different chess stimuli as well as the employment of more
sensitive techniques of analysis (MVPA). In two experi-
ments, I demonstrated that the FFA is indeed sensitive
to expertise but in a more subtle way than previously
thought. In the first experiment, chess experts’ FFA could
reliably differentiate between chess and other neutral
stimuli, such as rooms and tools. In novices, not even a
sensitive technique such as MVPA could demonstrate the
sensibility in the FFA between chess and other stimulus
categories. Different types of chess stimuli, however, elic-
ited a different pattern in experts’ FFA. Chess positions,
complex stimuli made out of several individual objects
(see Figure 1A), were easily differentiated in experts’
FFA, whereas single isolated chess objects were not that
well indexed in experts’ FFA. The MVPA demonstrated
that even experts’ FFA may be sensitive above chance
to isolated chess objects, but the general success rate
was not reliably different from that of novices, which
was generally unsuccessful in differentiating between
chess objects and neutral objects (see Figure 2B).
The isolated chess objects might be familiar to chess
players, but they are rarely encountered in isolation.
The second experiment further examined the FFA re-
sponse to isolated chess objects by asking the chess
players to actively individualize, that is, name, the object
in question and connect it to another object on a minia-
ture chessboard. Even this explicit individuation instruc-
tion in combination with the sensitive MVPA did not
result in reliable FFA responses to isolated chess objects
in either experts or novices (see Figure 4A).
One appealing feature of chess stimuli for the investi-
gation of the FFA function is their lack of similarity with
faces. However, when one goes beyond visual similarity
and considers underlying processes in face and chess
perception, the two categories suddenly share many
common features (Tarr & Cheng, 2003). The chessboard
defines the space of chess positions, and they consist of
multiple objects, which form spatial relations. Faces also
have clearly defined spaces as well as individual features
whose spatial relations are essential to face recognition.
Chess experts grasp the essence of chess positions by
perceiving the chess objects and the relations between
them as groups (Reingold, Charness, Schultetus, &
Stampe, 2001) and not as individual objects like novices
(Gobet & Simon, 1996). People with intact face percep-
tion also grasp faces as a whole and not as a sum of indi-
vidual features. Prosopagnosic people struggle with face
recognition precisely because they perceive the individual
features separately (Van Belle et al., 2011), not unlike the
way that chess beginners perceive chess objects in chess
positions (Reingold et al., 2001; Saariluoma, 1995).
Another similarity between the processes behind
skilled face and chess perception is their high efficiency
and automaticity. A recent study by Boggan, Bartlett, and
Krawczyk (2012) tapped into the shared underlying pro-
cesses by showing that chess experts experienced the
same composite effect with chess positions as people
do with faces. Even more intriguing is the finding of neg-
ative correlation between the starting age of chess play-
ing and the face composite effect (Figure 4 in Boggan
et al., 2012). In other words, starting early with chess re-
sults in less holistic face perception. There are many pos-
sible reasons for this negative relation, but one of them is
that both face and chess perception share common
mechanisms. Once these mechanisms have been cap-
tured early by chess expertise, less is left for the develop-
ment of face perception in a holistic manner (but see,
McGugin, Van Gulick, & Gauthier, 2016; Wang, Gauthier,
& Cottrell, 2016).
The holistic processing in expertise may be a matter of
degree rather than an all-or-nothing phenomenon. The
FFA in novices was not sensitive to chess stimuli in al-
most all comparisons except the one between chess po-
sitions and rooms. It is possible that this result is a
consequence of the alpha error despite the correction
for multiple comparisons. On the other hand, it may in-
dicate that the holistic process develops with exposure
rather than being an all-or-nothing phenomenon. The
design of this study employed the expertise approach
(Bilalić et al., 2010, 2012), which enables the uncovering
of even small effects because of huge differences be-
tween experts and novices (Campitelli & Speelman,
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Journal of Cognitive Neuroscience
Volume 28, Number 9
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2013). A more subtle approach with participants at several
developmental stages (e.g., Boggan et al., 2012; Bilalić
et al., 2009; Bilalić, McLeod, & Gobet, 2008) may be used
to reveal whether holistic processing does indeed increase
gradually with expertise.
Considering the similarities between the chess and
face perception, it may not be that surprising that the
FFA has been sensitive to chess expertise. The FFA is
thought to be responsible for holistic parsing of individ-
ual parts of faces (Arcurio, Gold, & James, 2012), and the
previous results indicate that similar holistic processing
underlies the perception of chess positions (Boggan
et al., 2012). The results of the study therefore confirm
the expertise hypothesis of the FFA function and are in
accordance with the other recent study involving radio-
logical images (Bilalić et al., 2016). At first sight, they also
seem to rule out the possibility that the FFA is not re-
sponsible for holistic processing but rather responds to
curved shapes (Ohayon, Freiwald, & Tsao, 2012; Tsao,
Freiwald, Tootell, & Livingstone, 2006; Wilkinson et al.,
2000; Kosslyn, Hamilton, & Bernstein, 1995). Unlike
some radiological images, chess positions are hardly oval
in shape. The problem with this conclusion is that the
cross-categorization, a more stringent test of shared pro-
cessing between faces and chess stimuli, was not quite
successful (see Figure 3). Even experts’ FFA was unable
to reliably differentiate between chess and neutral stimuli
when the learning was initially done on the faces and the
same neutral stimuli. The same procedure, however, was
successful with the FFA of radiologists (Bilalić et al., 2016;
see Figure 2). There are many differences between chess
and radiological expertise, but one of them, as men-
tioned above, is that radiological stimuli are oval in shape
unlike chess stimuli. It is difficult to draw firm conclu-
sions about the role of the oval shape in the FFA from
the two separate studies, but it is certainly an intriguing
question for future studies.
There may be different reasons why some studies
failed to identify the expertise effect in the FFA (de
Beeck, Baker, DiCarlo, & Kanwisher, 2006; Moore,
Cohen, & Ranganath, 2006; Yue, Tjan, & Biederman,
2006; Grill-Spector et al., 2004; Rhodes et al., 2004), such
as the fact that they used test stimuli that were similar but
outside experts’ specialization (e.g., antique cars with
modern car experts). Another problem, suggested by
our results, may be the use of isolated, context-deprived
stimuli (see also Bar, 2004). Namely, the FFA was not sen-
sitive to individual chess objects (Experiment 1) even
when they are explicitly individuated, and their function
retrieved and put into relations with other objects (Ex-
periment 2). This is surprising as those individual objects
and their relations are the main building blocks of chess
positions, the very same stimuli that consistently elicit
expertise effects in the FFA. Similarly, the expertise hy-
pothesis postulates that the FFA is important for individ-
uation of objects. Yet, the explicit individuation task in
Experiment 2 (identity task) has not produced the
expected expertise modulation of the FFA. One possibil-
ity is that the performed experiment lacked the necessary
power to discover the expertise effects in the FFA with
isolated chess objects. After all, the studies featured a
dozen participants at most in each group, and the nonsig-
nificant results should not be confused with a complete
absence of effects. It is impossible to exclude this possi-
bility, but one should keep in mind that two experiments
were performed with a relatively large number of partic-
ipants for expertise studies. If anything, the expertise ef-
fect in FFA seems to be considerably smaller with isolated
chess objects than with chess positions.
The other possibility is that the identity task in Exper-
iment 2 may not be sensitive enough to the individuation
processes. The differentiation between two different
objects (knight and rook) may be too crude to elicit
the necessary individuation mechanisms for eliciting
the FFA activation. Differentiating between two visually
different versions of the same object (rook presented
in two distinct designs) may be closer to the individua-
tion process one finds in faces and other categories. This
intriguing prospect remains to be examined in future
studies, but the lack of the expertise effects in FFA with
isolated chess objects may also be taken as further evi-
dence of the FFA’s involvement in holistic processing.
Chess objects may not lack distinctive features by which
they are recognized, but they certainly seem to have
fewer features of this kind than chess positions, whose
complexity is often compared with the number of atoms
in the universe (Shannon, 1950).
There has been much talk about the role of the FFA in
visual expertise, and this study is obviously no exception,
as it confirmed its role in chess expertise. The other face
area, the pSTS, was not significantly involved in chess ex-
pertise even when the MVPA was employed. One should
not forget, however, that the cross-categorization proce-
dure, where the activation patterns of faces were used to
differentiate chess positions from other stimuli, was not
quite successful (see Figure 3B). The perception of faces
and chess positions may therefore not share the same
underlying processes but rather only some of them.
The same could hold for faces and any other visual cate-
gory ( Wang et al., 2016). The perception of any stimuli,
even faces, goes beyond a single brain region, even if it is
the FFA (Duchaine & Yovel, 2015). The previous studies
identified that the FFA is expertise modulated even by
nonchess activity, such as counting the objects in a chess
position (Bilalić, Langner, et al., 2011). However, the
chess-specific task demands were indexed in other infero-
temporal and medial parietal areas, such as collateral
sulcus and retrosplenial cortex (Bilalić et al., 2010,
2012). This is in accordance with other expertise do-
mains, where a number of areas form a neural network
necessary for many processes that visual expertise re-
quires (Harel, 2015; Harel, Kravitz, & Baker, 2013). The
FFA may indeed be an important area for chess expertise,
but its role in chess perception, and in visual expertise in
Bilalić
1355
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general, remains to be put into context with other relevant
brain areas.
Here, the game of chess was used to disentangle the
current controversy about the FFA function. The two ex-
periments confirmed the expertise hypothesis of the FFA
function but also extended it in an important way. The
FFA is not a face-specific brain module but rather a more
general piece of brain machinery, honed through experi-
ence with particular stimuli, which parses individual parts
of the stimulus into a whole. The FFA is not only
responsible for individuation, but at its heart are the pro-
cesses that enable fast and efficient perception of complex
stimuli. The more complex the stimuli, the more likely it is
that the brain will require the help of the FFA in grasping
its essence. Finally, another conclusion to take away from
the two experiments presented here is the suitability of
chess and the expertise approach of comparing experts
with novices in general (Bilalić, Kiesel, et al., 2011; Boggan
& Huang, 2011), as an exploration vehicle in cognitive
neuroscience.
Acknowledgments
I thank Michael Erb for his support and advice. The help and
cooperation from chess players are greatly appreciated. This
work was supported by the DFG Project BI 1450/1-2.
Reprint requests should be sent to Merim Bilalić, Department
of Cognitive Psychology, Klagenfurt University, Universitätstr.
65-67, 9020 Klagenfurt, Austria, or via e-mail: merim.bilalic@
aau.at, or, Department of Neuroradiology, University Hospital,
Tübingen University, Hoppe-Seyler, Str. 2, 72076 Tübingen.
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