Cerebral Cortex January 2011;21:35--47
Advance Access publication April 7, 2010
Lower-Level Stimulus Features Strongly Influence Responses in the Fusiform Face Area
Xiaomin Yue, Brittany S. Cassidy, Kathryn J. Devaney, Daphne J. Holt and Roger B. H. Tootell
Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School,
Charlestown, MA 02129, USA
Address correspondence to Dr Xiaomin Yue, Martinos Center for Biomedical Imaging, Massachusetts General Hospital, 149 13th street, Charlestown,
MA 02129, USA. Email: email@example.com.
An intriguing region of human visual cortex (the fusiform face area;
category. However, the potential role of lower-order stimulus
properties in FFA remains incompletely understood. To clarify those
lower-level influences, we measured FFA responses to independent
variation in 4 lower-level stimulus dimensions using standardized
face stimuli and functional Magnetic Resonance Imaging (fMRI).
These dimensions were size, position, contrast, and rotation in depth
(viewpoint). We found that FFA responses were strongly influenced
by variations in each of these image dimensions; that is, FFA
responses were not ‘‘invariant’’ to any of them. Moreover, all FFA
response functions were highly correlated with V1 responses (r 5
0.95--0.99). As in V1, FFA responses could be accurately modeled as
a combination of responses to 1) local contrast plus 2) the cortical
magnification factor. In some measurements (e.g., face size or
a combinations of multiple cues), the lower-level variations
dominated the range of FFA responses. Manipulation of lower-level
stimulus parameters could even change the category preference of
FFA from ‘‘face selective’’ to ‘‘object selective.’’ Altogether, these
results emphasize that a significant portion of the FFA response
reflects lower-level visual responses.
Keywords: CMF, contrast gain, face recognition, FFA, V1
Previous fMRI (Puce et al. 1995; Kanwisher et al. 1997; Grill-
Spectoretal.1999; Halgrenetal.1999; Haxbyetal.1999; Hasson
et al. 2001) and neuropsychology studies (McCarthy et al. 1997;
Marotta et al. 2001) have identified an area in human inferior
temporal cortex that responds selectively to images of faces,
relative to a wide range of control stimuli. This has often been
called the ‘‘fusiform face area’’ (FFA). Additional functional
specializations have also been described in this cortical region
(Haxby et al. 1999; Gauthier et al. 2000a; Kriegeskorte et al.
2008), and additional face-selective areas have also been
reported (Gauthier et al. 2000b; Hooker et al. 2003; Moeller
et al. 2008).
Empirically, this face selectivity is well established in FFA,
but the neural mechanisms producing this remarkable speci-
ficity are not fully known. Computationally, face processing is
quite challenging (e.g., Sinha 2002). How would such compu-
tations be accomplished in FFA within a few synaptic steps
from those in lower visual cortical areas—which respond best
to simple local edges in the visual field?
A fundamental challenge to face computation is posed by the
endlessvariation inreal-life lower-level features. For instance, an
ideal detector must correctly signal ‘‘face versus nonface’’ (face
detection) or ‘‘face A versus face B’’ (face recognition) in
response to either a small face viewed in profile or a large face
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facing forward, in isolation or embedded among many other
objects, in twilight or in direct sunlight, either young or old, and
so forth. For this reason, strictly invariant responses to lower-
models (Hummel and Biederman 1992; Wallis and Rolls 1997;
Riesenhuber and Poggio 1999). Analogously, some fMRI (Grill-
Spector et al. 1999; Andrews and Ewbank 2004; Sawamura et al.
2005; Murray and He 2006; Kovacs et al. 2008) and single-unit
(Rolls and Baylis 1986; Ito et al. 1995) studies have emphasized
stimulus invariance in inferotemporal cortex and in FFA in
specific. To what extent does FFA respond invariantly to faces,
despite the wide variation in lower-level features?
Other studies have instead observed variation in FFA activity
when lower-level cues are varied (Avidan et al. 2002; Yue et al.
2006; Schwarzlose et al. 2008; Andresen et al. 2009; Carlson
et al. 2009; Xu et al. 2009). To clarify the situation, here we
systematically tested the effects of 4 basic cues that are known
to challenge visual object processing, including face computa-
tion: size, position, contrast level, and viewpoint (rotation in
depth). Do these stimulus variables influence activity in FFA, as
they do in lower-level visual areas, such as V1?
Despite the higher-order face selectivity that defines FFA, we
found that FFA also responded strongly to all 4 lower-level
image variations, and the FFA response variations correlated
highly with those in V1. Accordingly, FFA responses could be
accurately modeled by a combination of stimulus contrast and
the cortical magnification factor (CMF). Each of these lower-
level influences was quite strong, accounting for up to 55% of
the response to optimized faces. Combinations of these
stimulus dimensions were even more influential. Manipulation
of lower-level cues could even reverse the defining category
selectivity in FFA, such that FFA responded better to nonface
objects compared with faces.
Materials and Methods
The face/head 3D meshes were generated by FaceGen (Singular
Inversions) and then imported into Matlab (The MathWorks) by
customized Matlab programs. All stimulus images were rendered in
Stimuli were presented in the scanner via LCD projector (Sharp XG-P25,
1024 3 768 pixels) using PsychToolbox (Brainard 1997; Pelli 1997).
In different experiments, faces were varied in either size, position,
viewpoint (rotation in depth), contrast level, or a combination thereof
(contrast 3 size). All stimulus sets included a common subset of
reference faces (frontal view and gaze, centered in the visual field, 8
identities, 4 males/females, neutral expression).
Stimulus conditions were organized into a block design. Blocks (16-s
1 face/s), along with control blocks of uniform gray, of equal mean
A fixation dot was present on the center of all face stimuli and the
baseline (uniform gray) images. Subjects performed a dot detection task
during central fixation throughout the functional scanning using
a button box in the scanner. The probe dot appeared at unpredictable
times (100 ms random shift from each stimulus onset), distributed
randomly across the display with equal spatial probability. The
detectability of the probe dot was manipulated by varying its cyan/
white ratio (decreased saturation = decreased detection). Threshold
was modulated by the staircase method, converging on 75% correct. To
reduce response variability, the dot size varied with eccentricity.
All 17 subjects gave written consent. The experimental protocol was
approved by the Institutional Review Board of Massachusetts General
Hospital. All subjects had normal or corrected-to-normal vision.
All scanning were done in a 3T Siemens Trio. Functional scans used
a gradient echo EPI sequence (repetition time = 2s, voxel size 3 mm
An additional 1840 functional volumes were excluded from analysis due
to excessive head motion. For each subject, high-resolution anatomical
scans were also collected for cortical surface reconstruction.
Each individual brain was inflated using FreeSurfer (http://surfer.
nmr.mgh.harvard.edu). Statistical analysis of the functional data was
performed with FS-FAST. All functional images were motion corrected,
spatially smoothed with a 5-mm Gaussian kernel, and normalized across
sessions individually. Average signal intensity maps were calculated for
each condition for each subject. Voxel-by-voxel statistical tests were
conducted by computing contrasts based on a General Linear Model
(GLM). For averaging across subjects, each subject’s functional and
anatomical data were spatially normalized by using the spherical
transformation (Fischl et al. 1999). Data from both hemispheres were
averaged together unless otherwise noted.
In each subject, areas FFA and parahippocampal place area (PPA)
were localized using an independent set of stimuli: groups of faces and
indoor scenes, respectively (Supplementary Fig. 1). Subsequent region-
of-interest (ROI) analyses were based on these individually localized
areas. ROIs for V1 were based on the Montreal Neurological Institute
standard inflated brain at group level. Activations for all conditions (all
vs. fixation) in each experiment were compared using random effects
analyses with a threshold of P < 0.01. Then, the group-averaged data
were extracted from that ROI for each condition.
All statistics were performed in SPSS. All error bars are standard error
of the mean.
The Lower-Order Model
We modeled the lower-order (e.g., V1) visual responses as follows. Each
image was filtered with a Gabor function composed of one spatial
frequency at 8 equally spaced orientations (i.e., 45 ? difference in angle).
The Gabor filter was modified from the model in Lades et al. (1993):
f ð~ xÞ=~k
imates the response of complex cells in V1. Then, the filtered response
was weighted with a Gaussian: gð~ xÞ=
model is the sum of all responses across all 8 orientations.
, where k is orientation. The filter approx-
2d2. The final output of the
In response to variations in each parameter, several possible
outcomes were possible in FFA.
processing in FFA.’’ For instance, variations in a parameter that is
known to affect face perception could produce a corresponding
effect in FFA but not in lower-level cortical areas, such as V1.
Second, a given parameter might be ‘‘irrelevant for FFA.’’ In
such cases, FFA would respond equivalently, no matter how
this parameter was varied. We defined this type of fMRI
response as feature invariance (see also DiCarlo and Maunsell
Third, a given parameter could affect FFA activity in a way
‘‘unrelated to face processing.’’ For instance, variation of a given
stimulus parameter might produce response covariations in
FFA—as well as in V1 and other visual cortical areas. Such
a correlated response would suggest a passive transmission of
visual activity through V1 into FFA.
In a larger parametric study, we found examples of each of
these 3 response types in FFA. Here, we describe the effect of
the 4 parameters that fell under outcome 3.
Typically, subject performance on the dot detection task
converged quickly to threshold levels (see Supplementary Fig.
2). Thus, the attentional load was relatively stabilized during
the functional scans.
Experiment 1: Size
Computationally, it is useful if a face detector responds in a way
that does not vary with face size (Hummel and Biederman
1992; Wallis and Rolls 1997; Riesenhuber and Poggio 1999).
Accordingly, size invariance has been reported in fMRI studies
of FFA and the Lateral Occipital complex (LO) (Grill-Spector
et al. 1999; Andrews and Ewbank 2004; Sawamura et al. 2005).
Figure 1a shows 2 predictions of FFA activity in response to
variations in face size. The hypothesis of size invariance is
shown as a solid line. The alternative prediction (dashed line) is
based on the response of lower-level visual areas in which
population activity increases with stimulus size. That second
function is described by the well-known CMF. Typically, the
CMF has been used to map variations in receptive field center
location. Here, we instead used the CMF to predict variations in
population activity (measured as fMRI amplitude over the
entire ROI-defined area) in response to a given face presented
at different sizes. The CMF prediction (Fig. 1c) was based on
measurements from V1 (Sereno et al. 1995; Engel et al. 1997).
Stimuli and Data Set
All faces were presented upright in frontal view. Except for the
variable of interest (size), all faces were identical to the
reference face in other experiments. Test sizes (average of
vertical and horizontal diameters) varied from 0.64 to 16.80 ? in
11 steps. All stimuli were clearly recognized as faces based on
subject report. Faces can be accurately recognized even at very
small sizes (Yip and Sinha 2002; Lindsay et al. 2008) comparable
with the smallest size tested here. The data set included 18 720
functional volumes from 15 subjects.
Figure 1b shows the resultant face size function in FFA.
Responses were not invariant over any portion of the curve
(F10,140= 5.8, P < 0.001). Instead, we found a linear increase in
the FFA responses with logarithmic increases in face size
throughout the tested range. The function correlated very
Parameter Dependence in FFA
Yue et al.
highly (r = 0.986, P < 0.001) with the prediction derived from
the CMF (Fig. 1c). Thus, the face size function in FFA also
correlated well with that measured empirically in V1 (r = 0.956,
P < 0.001) (Fig. 1d). Although our analysis focused on V1 and
FFA, the activity maps showed a clear increase in activity with
increasing size throughout many areas of visual cortex
(Supplementary Fig. 3).
If cortical area FFA has an explicit retinotopic map, this
influence of the CMF would be unsurprising—as in V1. How-
ever, previous studies have reported that FFA does not have an
explicit retinotopic map (Haxby et al. 2001). Are retinotopic
variations from lower areas instead distributed nonretinotopi-
cally in FFA?
We tested this by performing a voxel-wise correlation. For
each subject, BOLD signal changes were estimated with a GLM
model for every single voxel for each face size. Then, the BOLD
signal change of each voxel within each ROI was extracted,
which yielded a single vector for each face size. The correlation
between conditions was calculated, which generated an 11 3
11 matrix. From these data, the final correction matrix was
averaged across all subjects. This procedure produced a corre-
lation matrix for both V1 and FFA.
In V1, we found a strong positive correlation between the
activity produced by similar face sizes, especially for small face
sizes (Fig. 1e). Faces of dissimilar size were uncorrelated or
negatively correlated. All these results are consistent with a
well-ordered retinotopic map based on small receptive
fields—the hallmark of V1 organization.
However, in FFA, all correlations were positive (Fig. 1f). This
suggests that bottom-up retinotopic information is largely
Figure 1. Responses to variations in face size. (a) The hypothesis of size invariance is shown as a solid line. In the alternative hypothesis (dashed line), a population response
based on the CMF produces increasing responses as face size is increased as a linear function on a logarithmic scale. (b) Results in FFA. The FFA response is consistent with the
CMF hypothesis but not with the size invariance hypothesis. (c) The FFA response correlated highly with the CMF. The CMF function is (log(x)/0.063 þ 11.36), where x is size in
averaged diameter. (d) The FFA and V1 responses correlated highly. Two y-axes are displayed showing the absolute percent changes in both FFA (left y-axis) and V1 (right y-axis)
and their amplitude-normalized correlation. (e) Correlation in individual voxels in V1. Voxels in V1 showed clear retinotopic correlations. (f) Correlation in individual voxels in FFA.
Voxels were largely devoid of this retinotopic effect. For voxel-wise correlation analysis, each voxel’s activity within the ROI was extracted based on GLM for each condition and
converted to a percent signal change. The correlation was done for each individual subject and each hemisphere.
Cerebral Cortex January 2011, V 21 N 1 37
intermixed within FFA at least at this voxel size. Correlations
were slightly higher for large faces compared with small faces
in FFA; this pattern was the reverse of the V1 pattern. This
evidence is consistent with the presence of larger receptive
fields in FFA.
The PPA is a discrete cortical area located immediately
adjacent to FFA, which does not respond well to faces (Epstein
and Kanwisher 1998). In response to these nonoptimal stimuli,
would PPA nevertheless show a size dependence in its fMRI
responses? Here, we found that the smaller faces (less than 10?)
produced little or no response. However, in response to
progressively larger faces, PPA activity increased systematically
and significantly (F10,140= 5.8, P < 0.001) (Supplementary Fig.
4). This suggests that an activity threshold (‘‘floor’’) exists for
smaller faces in PPA, yielding response amplitudes near zero.
Above 10?, the PPA size gain function increased even more
steeply than that in FFA (interaction between FFA_PPA vs. size:
F2,28= 6.621, P < 0.01). The steeper slope in PPA indicates that
the PPA response is not simply ‘‘spillover’’ from FFA. The
steeper slope is also consistent with reports of a peripheral bias
in PPA responses (Levy et al. 2001) (see Discussion).
Experiment 2: Visual Field Position
Next, we measured the effect of variations in visual field
position. Many computational models (Hummel and Biederman
1992; Wallis and Rolls 1997; Riesenhuber and Poggio 1999)
assume invariance for face/object position in the visual field
(‘‘position invariance’’) for reasons similar to those for size
invariance. Size and position specificity are linked by common
receptive field mechanisms, at least at lower levels of the visual
system (Hubel and Wiesel 1974).
The invariance hypothesis for position (Fig. 2a) predicts that
FFA responses to a given face will be equivalent to each other,
irrespective of where that face is positioned in the visual field.
However, the hypothesis of position invariance is biologically
problematic because neural mechanisms vary profoundly with
visual field eccentricity (see Discussion). Accordingly, visual
perception also varies a great deal across the visual field.
Previous fMRI tests of position invariance have yielded mixed
results in FFA (Grill-Spector et al. 1999; Hemond et al. 2007;
Kovacs et al. 2008; Schwarzlose et al. 2008).
Given this evidence and our size results (Fig. 1b), a specific
alternative tothehypothesis ofposition invariance was based on
the CMF. This hypothesis (Fig. 2a) predicts that FFA responses
will decrease as a given face is viewed at progressively greater
distance from the center of gaze in accordance with the CMF.
Stimuli and Data Set
Faces (frontal view, 4.6? diameter) were presented in 5 3 7
equally spaced positions (vertical 3 horizontal, respectively, in
2.9? steps) on the display screen using one position per block
(Fig. 2b). The data set included 8320 functional volumes from 8
We did not find invariance for position in FFA (F3,21= 7.3, P <
0.01); instead our data again matched the CMF prediction
(Fig. 2a). In fact, the face position function in FFA matched that
in V1 near perfectly (r = 0.999, P < 0.001; Fig. 2d).
In V1, the known retinotopic organization predicts a specific
response variation to the 4 axes tested (Fig. 2e). As predicted,
we found that the largest fMRI differences occurred along the
horizontal meridian: Contralateral face positions produced
the highest activity, whereas ipsilateral face positions produced
the least. This was expected because essentially all V1 activity
is driven by contralateral stimuli (Tootell et al. 1988). In con-
trast, test positions along the vertical meridian straddled each
hemifield equally. Correspondingly, V1 activity along the vertical
meridian was similar along the dorsal and ventral axes, and inter-
mediate to activity produced along the ipsilateral/contralateral
extremes (i.e., along the horizontal meridian).
A weaker version of the same variation was found in FFA (Fig.
2f). The weaker difference is consistent with the decreased
retinotopy and larger receptive fields in FFA compared with V1.
Overall, the face position results were fully consistent with
a CMF influence in FFA.
Experiment 3: Contrast Level
Next, we tested the effects of varying contrast level. Figure 3a
illustrates the hypotheses tested for contrast level in FFA. The
contrast invariance hypothesis (solid line in Fig. 3a) predicts an
equivalent response to each contrast level across the visible
range (e.g., Rolls and Baylis 1986). In an alternative hypothesis,
FFA responses increase with increasing contrast. Such
contrast gain functions are the rule in lower-tier visual areas
in response to simple geometrical stimuli, such as gratings
and checkerboards (Kaplan et al. 1987; Tootell et al. 1988,
1995; Sclar et al. 1990; Boynton et al. 1996). For example, the
dashed line in Figure 3a shows the contrast gain function
produced by grating stimuli in V1, based on fMRI (Tootell
et al. 1995).
Stimuli and Data Set
Figure 3b shows examples of the stimuli. The face size was
12.7?. The mean luminance of the face was identical to the
mean luminance of the uniform gray background. Contrast
levels were defined as the root-mean-square (RMS) contrast
across the entire face in equal logarithmic steps ranging from
2% to 100%. Contrast detection is improved when based on
RMS compared with other measures (Bex and Makous 2002).
Corresponding Michelson contrast value varied between 2.44%
and 94.09%. Psychophysically, visual objects are reliably
detected at Michelson contrasts more than 1.5% (Murray and
The data set for this experiment included 9360 functional
volumes from 8 subjects.
Figure 3c shows the results. Averaged FFA responses increased
monotonically with increasing contrast level (F4,28= 36.91, P <
0.001); thus, the hypothesis of contrast invariance was ruled
out. Instead, the FFA data closely matched the contrast gain
curve from V1 in response to grating stimuli (see Fig. 3a). In
V1, the contrast gain for these face stimuli (Fig. 3d) was also
similar to that in earlier V1 measurements using gratings. Based
on face stimuli, the contrast gain in V1 also correlated highly
with that in FFA (r = 0.957, P < 0.05 [P = 0.011]) (Fig. 3d).
Parameter Dependence in FFA
Yue et al.
Experiment 4: Viewpoint (Rotation in Depth)
The image of the face/head changes as it rotates in 3
dimensions—often fundamentally. This image change occurs
during all axes of rotation, except in the single case of rotation
exactly in plane. To measure the effect of these ubiquitous
changes in viewpoint, we measured fMRI responses in response
to face/head views following systematic rotations in depth.
Figure 4a,b shows the predictions and stimuli. During
rotation in depth, the face is occluded by the remainder of
the head over an appreciable portion of the rotation range. In
our stimuli (Fig. 4b), the eyes and nose cannot be seen beyond
112.5?. Thus, the generic invariance hypothesis (i.e., that
responses to any face will be equivalent) is here restricted to
the rotation range within which the face is visible.
Instead,theresults inexperiments 1--3predict thataface that
is barely visible (e.g., 112.5? in Fig. 4b) will produce much less
activity, compared with the frontal (0?) view of that same face.
Multiple factors contribute to this prediction. First, as the
viewpoint changes from frontal through the profile view, and
beyond (to 112.5?), the visible portion of the face becomes
smaller, and it is positioned more peripherally in the visual field.
Thus, the CMF influence (experiments 1 and 2) predicts
Figure 2. Responses to variations in visual field position of face stimuli. (a) The hypothesis of position invariance (solid line) predicts an equivalent response in FFA irrespective of
visual field position. Alternatively, the CMF hypothesis (dashed line) predicts a response that decreases according to the CMF variation (e.g., Figure 1). (b) Stimulus configuration.
Within a given block, faces were presented in one of the visual field locations outlined by dashed black lines. (c) Results from FFA combined across all 4 axes. FFA activity
decreases systematically with increasing stimulus eccentricity consistent with the CMF hypothesis. (d) The FFA response curve (solid line) was near-identical to that in V1
(dashed line). (e) FMRI variations in V1 due to variations in position along 4 major visual field axes. As expected, the steepest decrease resulted from moving the face into the
ipsilateral visual field by the most direct path along the horizontal meridian (green). Movement in the opposite direction (contralateral visual field, red) produced the highest overall
activity. The slope of these 2 curves was a nonmonotonic function reflecting 2 competing factors: 1) activity decreased with increasing face eccentricity, as in the other
measurements, but: 2) activity increased/decreased more at the first offset from zero as the face shifted from half-viewed in a given hemisphere (central position) to whole
viewed (all other positions). As expected, activity along the vertical meridian (yellow and cyan) decreased with a slope intermediate to those along the horizontal meridian. (f)
Variations in FFA activity as a function of position, otherwise as in panel (e). The changes in activity are qualitatively similar to those in V1 (see e) except for a single point.
However, the range of response difference is compressed relative to that in V1, as one would expect from larger receptive fields in FFA.
Cerebral Cortex January 2011, V 21 N 1 39
progressively lower responses to facial viewpoints further from
Variations in contrast also influence this prediction. As it
rotates, the average RMS contrast level of our face/head
decreases from frontal view through the back-of-the-head view
(180?). Since FFA responses decrease with decreasing contrast
(experiment 3), this factor thus predicts smaller responses to
faces at viewpoints rotated further from frontal.
We formalized this prediction using a ‘‘lower-order’’ model
(see Materials and Methods) based on the well-known response
properties of V1. This model’s prediction for the rotation
experiments is illustrated in Figure 4a.
Stimuli and Data Set
We tested the full range of rotations in 22.5? increments in
semi-randomized order. To save space, only half of this rotation
range is shown in Figure 4b. All the heads were 12.7? in
diameter in frontal view. A constant virtual head ‘‘distance’’
from the viewer was maintained across rotation angles.
This data set included 11 200 functional volumes from 7
The FFA response function (Fig. 4c) did not match the
restricted invariance hypothesis. Instead, it closely matched
the prediction of the lower-order model (r = 0.961, P < 0.001)
(Fig. 4d). Thus, the FFA response decreased progressively at
rotation angles further from the frontal view to a minimum at
the back of the head.
Our model predicted that V1 would show a similar response
function. Figure 4e confirms that the V1 function did match the
lower-order model prediction quite closely. Hence, the FFA and
V1 functions were highly correlated with each other (r = 0.957,
P <0.001) (Fig. 4f). Again, theFFAresponses were indistinguish-
able from a passive reflection of lower-level activity in V1.
FFA responded fairly well (roughly half-maximum) to the
‘‘back’’ of the head, although no part of the face was visible from
that viewpoint. Did that result represent a response to an
inferred face based on prior knowledge of heads and faces
(Cox et al. 2004; Wild and Busey 2004)—or simply a visually
nonselective component in the aggregate FFA response?
To address this, we conducted an additional control experi-
ment measuring FFA responses to a sphere compared with the
back (180?) view of these bald heads. FFA responded nearly
(76%) as well to the sphere compared with the 180? head view
than the back of the head, there may be no higher-order
advantage to the head compared with the sphere. In fact, the
lower-order modelpredictsthe sphereresult within theerrorof
measurement (model prediction = 63% decrease to the sphere,
relative to the back-of-the-head view). Overall, this supports
previous evidence that a generalized (non-face-selective)
component contributes to the FFA responses (Grill-Spector
2006; Caldara et al. 2006; Yue et al. 2006; Tootell et al. 2008)—in
addition to any components that are face selective.
Above, we combined data from both hemispheres. However,
differences between the hemispheres were also expected (and
Figure 3. Responses to variations in facial contrast. (a) Hypotheses for changes in FFA activity due to variations in contrast level of face stimuli. The ‘‘contrast invariance’’
hypothesis is shown as a solid line. Alternatively, FFA responses might increase with increasing contrast, as in the V1 prediction (dashed line), based on grating stimuli (from
Tootell et al. 1995). (b) Stimulus examples. (c) FFA activity to faces increases at progressively higher contrast (relative to the uniform gray baseline stimuli), similar to the V1
prediction based on gratings (dashed line in panel a). (d) Based on the face stimuli, the contrast gain response in FFA (solid line) was similar to that in V1 (dashed line). The
contrast levels shown here are not exact due to lack of control over publication displays.
Parameter Dependence in FFA
Yue et al.
confirmed) from these stimuli in V1. Responses were
relatively higher when the face was rotated further into the
contralateral hemispheres, and lower when the face was
rotated into the ipsilateral hemisphere (F1,13= 12.2, P <0.01).
This hemispheric bias was expected because V1 responds to
local contrast in the contralateral hemifield, and in our stimuli,
the face had more local contrast compared with the back of
This contralateral bias was also observed in FFA (F1,13= 13.6,
P < 0.01). This is consistent with the idea that FFA responds in
part to local contrast, as in V1. Additionally, this result confirms
that contralateral dominance is retained through cortical levels
at least as high as FFA (Hemond et al. 2007)
Experiment 5: Interactions
Above, we varied only one stimulus dimension at a time. From
those data, can we predict the response to changes in multiple
dimensions? Do the response functions to each dimension
combine linearly? That would be computationally convenient,
and it would ease the translation of our present results from the
laboratory to real-world faces.
This assumption of linear summation predicts excep-
tionally low responses to face stimuli in FFA when multiple
low-level stimulus dimensions are nonoptimal. For instance,
in the single-dimension experiments in FFA (Fig. 1b),
Figure 4. Response to variations in face viewpoint (rotation in depth). (a) Two hypotheses for FFA activity. The solid line shows the rotation-limited invariance hypothesis. Since
the face is occluded beyond 0 ± 135?, a baseline response is predicted to those stimuli. The second hypothesis (dashed line) is the prediction from the lower-order model
incorporating the contrast and CMF influence in FFA. (b) Stimulus examples. Half of the tested rotations are illustrated; the remaining stimuli are mirror symmetrical. We also
tested the responses to a sphere similar in size to the back of the head (bottom right). (c) fMRI responses in FFA. Results from left and right rotation angles have been combined.
Normalized responses to the sphere are shown in dashed line. (d) The variation in FFA response (solid line) correlated well with the variation produced by the model (dashed line).
(e) An analogous result was found in V1. (f) Responses from FFA (solid line) and V1 (dashed line) correlated highly.
Cerebral Cortex January 2011, V 21 N 1 41
activity, compared with that produced by the larger (opti-
mized) faces. Similarly, lower contrast faces produced much
lower activity than faces at higher contrast. When these 2
dimensions are combined (a small low-contrast face), would
those decreases also combine? Alternatively, does the re-
sponse to a given face have a lower limit, as long as the visual
stimulus is a face? The latter is an extension of the category
Depending on the stimuli used, the FFA responses predicted
for such nonoptimal faces might be even lower than the
responses to specific nonface objects.
Stimuli and Data set
These predictions were tested by presenting 1) relatively
‘‘optimized’’ faces (12.7? diameter, 100% RMS contrast, frontal
viewpoint) versus 2) ‘‘nonoptimized’’ faces (1.02? diameter,
14.14% RMS contrast). Assuming a linear combination of our
single-dimension results, these nonoptimized faces should
produce 42.8% of the activity (i.e., 55% for size, x 77.8% for
contrast) compared with the optimized faces.
Several considerations ensured that both sets of stimuli were
readily perceived as faces. First, we made only 2 features non-
optimal: size and contrast level. The 2 remaining features
(position and rotation) remained optimized. Second, we chose
‘‘nonoptimal’’ values that were well above perceptual threshold.
As expected, subjects confirmed that all experimental faces
were readily visible and perceived as faces.
To test whether nonface objects could produce more activity
than nonoptimized faces, we also tested a third condition:
computer-generated nonface objects (‘‘blobs’’; Fig. 5a). These
blobs were otherwise similar to the optimized faces in terms
of the lower-level properties size, contrast, and position
(rotation values are moot in these shapes). The blob texture
was generated from images of faces using an algorithm that
markedly altered the higher-order structures while preserving
image statistics (Portilla and Simoncelli 2000). The shape of
the blobs approximated facial concavities and convexities (e.g.,
nose, cheekbone) (Yue et al. 2006; Nederhouser et al. 2007).
This data set included 4400 functional volumes from
The results (Fig. 5b) confirmed that the ‘‘preferred’’ stimulus
category in FFA could be fully reversed from faces to objects by
using 2 nonoptimal parameters for the face stimuli, and
presumably optimized parameters for unfamiliar blob-shaped
objects. The response to these objects was more than double
(increase = 113%) that of the nonoptimized faces. This
difference was significant (t4= 9.3, P < 0.001).
Post hoc calculations revealed that this stimulus category
reversal could have been achieved by changing only a single
parameter. Based on our data (Figs 1 and 3), FFA activity in
response to the blob stimuli should be equivalent to that
produced by a face of maximum (100%) contrast and a larger
(2.14?) diameter. Thus, any face that is smaller, and/or lower in
contrast, would instead produce less activity compared with
these particular blob stimuli.
Within our measurement limits, the fMRI effects of multiple
facial parameters combined near-linearly in FFA. The non-
optimized faces produced 30.0% as much activity compared
with the optimized faces. This result was statistically in-
distinguishable (t4= 1.8, P = 0.14) from the linear prediction
for this combination (42.8%).
Parahippocampal Place Area
Despite the expected small response amplitudes to face stimuli,
we found that PPA response functions were generally
consistent with those in FFA and V1 (e.g., Fig. 3c,d)—except
for the small peripheral bias described previously in PPA
(Supplementary Fig. 4a,b). Again, this emphasized the wide-
spread influence of these lower-level influences (e.g., Supple-
mentary Fig. 3).
FFA Activity Is Correlated with V1 Activity
(Puce et al. 1995; Kanwisher et al. 1997; Grill-Spector et al. 1999;
Halgren et al. 1999; Haxby et al. 1999; Hasson et al. 2001, but see
Gautier et al. 2000; Haxby et al. 2001). Computationally, face
selectivity is much easier to achieve if it is not confounded by
this expectation, invariant responses to several lower-level
properties have been reported previously from FFA and nearby
regions of visual cortex (Rolls and Baylis 1986; Ito et al. 1995;
Grill-Spector et al. 1999; Andrews and Ewbank 2004; Sawamura
et al. 2005; Murray and He 2006; Kovacs et al. 2008).
Instead, here, we found that at least 4 lower-order image
dimensions produced systematic variation in FFA responses.
Moreover, this response variation was highly correlated with
that in V1. Those V1--FFA correlations were as follows: r = 0.956
for size, 0.999 for position, 0.957 for contrast gain, and 0.957 for
Figure 5. Sensitivity to stimulus category and parameters. (a) Examples of the
stimuli from left to right: 1) optimized faces (12.7? diameter, 100% contrast); 2)
computer-generated objects (‘‘blobs’’), with lower-level features matched to the
optimized faces; 3) nonoptimized faces (1.02? diameter; 14.14% contrast). The white
scale bar represents 1? for the nonoptimized face and 6.3? for the blob and optimized
face. (b) Average FFA response to each stimulus type.
Parameter Dependence in FFA
Yue et al.
rotation in depth. Accordingly, all these results in FFA were
closely fit by a model based on V1 function. The model assumed
only a response to local contrast corrected by the CMF; it did
not include any additional face-selective component.
In the hierarchy of primate visual cortical areas, V1 is the
information bottleneck (Van Essen et al. 1992; Distler et al.
1993); most or all the input to higher-tier cortical areas
(presumably including FFA) passes through V1. For simplicity,
we measured lower-level visual cortical activity in V1. Although
we did not systematically analyze data from other visual areas
(e.g., V2, etc.), our activity maps (e.g., Supplementary Fig. 3)
revealed that many additional lower-level visual cortical areas
responded similarly to V1 and FFA. This eased the burden of
proof for our lower-level influences in FFA, and it eased certain
technical constraints. For instance, if our ‘‘V1’’ ROI encroached
slightly into neighboring V2, this presumably had little effect on
our data; both V1 and V2 are lower-level visual cortical areas,
functionally similar at the level of fMRI.
Variations in face size (e.g., in degrees of visual angle) strongly
affected responses in FFA. In fact, size was the strongest lower-
level influence that we found in FFA, given the experimental
ranges tested here. This size-driven response variation was
relatively surprising because several previous fMRI (Grill-
Spector et al. 1999; Andrews and Ewbank 2004; Sawamura
et al. 2005) and single-unit (Rolls and Baylis 1986; Ito et al.
1995) studies have reported size invariance in inferior temporal
cortex. Instead, our results are more consistent with psycho-
physical studies (Kolers et al. 1985; Fiser et al. 2001) showing
significantly longer reaction time when sequentially matching
faces of dissimilar size compared with faces of same size. A
recent fMRI study using fast event-related adaptation also
showed a significant size effect in right FFA (Xu et al. 2009).
What accounts for this apparent discrepancy between re-
sults in the present data, relative to some previous reports? One
factor may arise from differences in the stimulus ranges tested.
Specifically, some previous studies tested relatively limited
ranges of stimulus size (e.g., 2- or 4-fold in diameter). By com-
parison, here, we tested a 26-fold range of face sizes. These
differences in sampling range could account for the different
conclusions in those studies: Analogous 2- or 4-fold size vari-
ation in our own data also produced statistically insignificant
differences (i.e., size invariance). Here, the wider range of
stimulus sizes revealed the size tuning function in FFA; this
would not have been uncovered by tests using more limited
Foveal Bias in FFA
responds more to foveal stimuli, relative to adjacent area PPA,
which responds relatively more to peripheral stimuli. However,
that information alone is ambiguous. Relative to classically
retinotopic visual cortical areas such as V1, does this FFA/PPA
The present data suggest that FFA is not biased for foveal
stimuli relative to V1 because FFA and V1 share a common CMF.
However, FFA could be biased for foveal stimuli relative to PPA if
we assume that PPA has a peripheral bias. Evidence for such
a peripheral bias can be seen in Supplementary Figure 4a: The
size gain function shows a steeper slope in PPA compared with
that in FFA and V1, when all responses exceeded baseline. Thus,
(Levy et al. 2001; Hasson et al. 2003; Schwarzlose et al. 2008).
Retinal mechanisms vary profoundly with respect to eccen-
tricity. Such neural mechanisms include the rod/cone ratio
(Andrade da Costa and Hokoc 2000), photoreceptor packing
(Roorda et al. 2001), cone photoreceptor type (Knau et al.
2001), morphology and arborization (Callaway and Wiser
1996), preretinal absorbance (Yolton et al. 1974), and so forth.
Variation in some of these retinal mechanisms contributes to
the CMF (Perry and Cowey 1985).
Accordingly, visual perception also varies enormously with
(Waugh and Levi 1993), color vision (Martin 1998), spatial
frequency sensitivity (Rovamo et al. 1992), and crowding (Pelli
et al. 2007). Even face perception itself varies with eccentricity
(Melmoth et al. 2000; Afraz and Cavanagh 2008).
Given these visual field variations, and the CMF influence in
FFA (e.g., Fig. 1c), it would be surprising if otherwise-equal faces
field eccentricities. Although the converse conclusion (position
invariance) has also been reported in posterior Fusiform gyrus
that study varied over a narrower visual field range (5.6?)
a smaller range of stimulus variation may explain a conclusion of
stimulus invariance. In fact, the CMF influence predicts that one
could deliberately create an even more dramatic ‘‘position
invariance’’ by comparing only the responses with faces that
are positioned far apart in the visual field, equidistant from the
center of gaze, along the vertical meridian (e.g., Fig. 2e,f).
Schwarzlose et al. (2008) reported that foveal stimuli
produced slightly larger responses compared with peripheral
stimuli in FFA even when stimulus size was corrected by a CMF.
If the CMF completely explained the variations in FFA response,
then the CMF-corrected stimuli should have produced equal
responses in FFA. However, the CMF in Schwarzlose et al.
(2008) was extrapolated from values in the literature; con-
current measurements were not analyzed from V1 to confirm
the CMF values in that specific subject pool. If there is a dis-
crepancy between our data and those of Schwarzlose et al.
(2008), this raises an interesting question: Is size versus posi-
tion information encoded along different (vs. equal) ‘‘CMF’’
functions? From single-unit recordings in macaque V1, either
conclusion is possible (e.g., Van Essen et al. 1984, but see Hubel
and Wiesel 1974).
Facial Contrast Level (Gain)
Responses to a range of contrast levels (i.e., contrast gain
functions) have been extensively studied using gratings based
on psychophysics (Legge 1981), event-related potentials
(DiRusso et al. 2001), single units (Kaplan et al. 1987; Sclar
et al. 1990), and fMRI from the retina through mid-level visual
cortex (Tootell et al. 1995; Boynton et al. 1999; Olman et al.
2004; Gardner et al. 2005). Here, we extended such measure-
ments to face stimuli in FFA based on RMS contrast.
The contrast gain function imposes significant constraints for
the computation of face/object processing. Most importantly,
Cerebral Cortex January 2011, V 21 N 1 43
does contrast invariance exist in FFA? One fMRI study (Avidan
et al. 2002) concluded that pFs (FFA) showed an ‘‘increasing
tendency toward contrast invariance’’ in LO (and pFs, the FFA
equivalent) relative to V1. However, when those earlier data are
replotted on a conventional logarithmic scale, it also showed
anear-linear increaseinpFs(FFA)similar tothatpresented here.
Thesimilarity betweencontrast gainfunctions inthese 2studies
is notable, considering the many technical differences between
them. For instance, the earlier fMRI study was based on-
responses to luminance variations of line drawings on a constant
luminance background, rather than the equal-luminance con-
trast variations in the gray-level faces tested here. Another fMRI
study also reported contrast-varying responses in nearby region
‘‘LO’’ (Murray and He 2006). Overall, these results suggest that
contrast invariance cannot be assumed in FFA nor likely in other
ventral stream areas.
Facial Viewpoint (Rotation in Depth)
Several changes occur as the head rotates in depth (e.g., Fig. 4).
The size and averaged eccentricity of the face increases and
decreases during the head rotation, like the waxing and waning
eccentricity variations because local contrast was concentrated
on the face. Thus, all 3 stimulus variables (size, eccentricity, and
contrast) predicted higher responses to frontal views of a face
and minimum responses to the back of the head in both FFA and
V1. This prediction was formalized in our lower-order model.
Our results closely matched the model predictions in both
FFA and V1 (Fig. 5b,c). The model even accounted for the
slightly decreased FFA response to a sphere compared with the
back of the head. Again, a face-selective component was not
required to account for the FFA activity variation.
Several fMRI studies (Grill-Spector et al. 1999; Fang et al.
2007; Xu et al. 2009) have tested the effects of rotation in
depth in FFA, using sparser sampling compared with the
present study. Xu et al. (2009) showed that FFA was sensitive to
rotation angle as small as 20?. One study (Tong et al. 2000) also
tested responses to the back of the head including hair. In
general, those studies showed FFA results similar to ours:
Overall, activity decreased as viewpoint diverged progressively
from frontal views. However, previous studies did not measure
the rotation in as much detail in FFA nor V1 responses in
comparison with those in FFA. The V1 measurement especially
shaped our ultimate conclusion.
Conceivably, head stimuli with hair might yield a different
result because hair adds a fine-grained contrast. However,
hairless faces (as used here) are commonly used in studies of
face perception because such stimuli avoid confounding cues
due to hair (e.g., gender, race, culture, and age).
The Overall Role of Lower-Level Influences in FFA
Why would FFA show such a strong lower-level influence in
these experiments? First (and simplest), many previous studies
someclose V1--FFAcorrelations may haveremainedundetected.
Second, our experimental task was deliberately designed to
minimize attention to higher-order facial characteristics (e.g.,
identity, gender, etc.) by requiring subjects to attend to
a competing lower-level feature (dot detection). Thus, lower-
level influences may have been relatively uncovered in FFA
compared with other possible tasks that elicit higher-order
influences (e.g., Grill-Spector et al. 2004). In any event, FFA has
been historically defined and localized based on its sensory
(face) selectivity (Puce et al. 1995; Kanwisher et al. 1997;
Halgren et al. 1999)—not on its higher-order properties (but
see Gautier et al. 2000a).
Third, our measurements spanned a greater stimulus range
compared with previous studies, for all 4 stimulus dimensions
tested. Such extended test ranges increased our statistical
power to uncover variations in responses function, which
could have gone undetected with a more restricted test range.
A fourth possible explanation arises from the position of FFA
in the cortical visual hierarchy. Based on monkey data (Van
Essen et al. 1992; Distler et al. 1993; Nakamura et al. 1993;
Rajimehr et al. 2009), information could presumably get from
human V1 to FFA via as few as 1 or 2 intervening areas (e.g., V1
>V4 >FFA, or V1 >V4 >TEO [temporo-occipital] >FFA). This
suggests that FFA occupies a middle (not an upper) tier in the
visual cortical hierarchy (e.g., higher than V1 but lower than
anterior TE). Thus, FFA should show some residual generalized
influence from lower-tier areas.
This perspective implies that face selectivity is not yet
complete at the level of FFA. This idea is supported by the
moderate FFA responses to stimuli that are only loosely similar
to natural faces—or not face-like at all (Grill-Spector et al. 1999;
Gauthier et al. 2000; Tong et al. 2000; Tsao et al. 2003, 2006;
Caldara et al. 2006; Yue et al. 2006; Tootell et al. 2008).
Lack of Stimulus Invariance
A common view is that FFA responds selectively to faces as
a distinct category (reviewed in Kanwisher and Yovel 2006).
However, faces vary infinitely in detail: Does FFA respond
invariantly to all faces, despite this variation in individual face
images? Here, we documented that FFA activity varies a great
deal in response to 4 important face parameters: size, position,
contrast, and viewpoint.
The relative strength of each lower-level parameter cannot
be easily reduced to a single number because the strength of
that variation depends on the range of variation tested. For
instance, in our measurements, variations of face size amounted
to more than half of the FFA response to the most effective
face; in that case, the lower-level influence dominated the
response of the test faces relative to uniform gray baseline. By
comparison, the influence of face position was weaker in our
position data. However, if we had been technically able to test
faces at a wider range of visual field positions, presumably this
would have produced a correspondingly larger influence of
position in FFA in accord with a CMF-like function.
In a further experiment (e.g., Fig. 5), a combination of 2
parameters was influential enough to wholly reverse the
category selectivity of FFA from faces to objects. At least in that
case, the lower-order influences were even stronger when
combined. Our data (Figs 1 and 3) also suggest that a similar
(though smaller) ‘‘preference’’ for the ‘‘blob’’ (nonface) stimuli
could have been achieved by manipulating only one parameter
This emphasizes that face selectivity in FFA is parameter
dependent, not absolute. This has implications at the level of
a population code: Cortical neurons at higher levels cannot
simply respond according to a face/nonface threshold because
the response range to faces overlaps the response range to
Parameter Dependence in FFA
Yue et al.
nonface objects. At most activity levels, some object-driven
activity will be above a given threshold and some face-driven
activity will be below it.
Implications for Neural Models of Face Processing
Studies of FFA have influenced neural models of high-level face
processing (Sinha et al. 2006). Our data, and previous observa-
tions (Avidan et al. 2002; Murray and He 2006; Yue et al. 2006;
Carlson et al. 2009; Xu et al. 2009), emphasize that biologically
plausible models cannot assume response invariance for size,
position, and contrast nor viewpoint at a level corresponding to
FFA. Our data are more consistent with the idea that FFA
corresponds to the structural encoding stage in the proposal by
while also preserving low-level information (Biederman and
Kalocsai 1997; Yue et al. 2006; Yue 2007).
To minimize parameter explosion across our many stimulus
conditions, we tested only a single dependent measure of the
BOLD response. Given that constraint, we focused on the
nonlinear classifier, or other measurement. This allowed more
direct comparisons between our results relative to the on-
responses in the single-unit literature and in the original fMRI
Using different approaches, other studies may come to
different conclusions. For instance, single-unit techniques may
reveal functional distinctions that cannot be distinguished
using fMRI. Evolutionary differences between humans and
macaques may also temper the current conclusions. Variations
in the nature of the attention task might change the shape of
the response curves (e.g., Murray and He 2006; Li et al. 2008;
Castelo-Branco et al. 2009). Further fMRI analyses (e.g., based
on adaptation, multivoxel pattern analysis, and/or event-related
approaches) may yield additional insights compared with the
direct on-responses measured here.
As described above, the relative strength of each stimulus
dimension also depends crucially on the range of stimulus
Though V1 and FFA activity correlated highly in all the
amplitude-normalized comparisons (e.g., Figs 1d, 2d, 3d, and
4f), the slope of the gain functions was sometimes higher in V1
compared with FFA, when based on raw fMRI signal levels (e.g.,
Figs 1d, 2d, and 3d). Multiple unknown factors could underlie
this difference in slope, including 1) larger receptive fields in
FFA, 2) the significant residual response to nonface stimuli in
FFA (e.g., Fig. 4b; Grill-Spector et al. 1999; Gauthier et al. 2000;
Tong et al. 2000; Tsao et al. 2003, 2006; Caldara et al. 2006; Yue
et al. 2006; Tootell et al. 2008), and 3) known differences in the
physiology and anatomy of V1 relative to extrastriate cortex
(e.g., a higher cell packing density and denser vasculature
[Perry and Cowey 1985], a specialized laminar structure, and
high spontaneous and driven single-unit activity). It is possible
that a lower slope in FFA could indicate ‘‘an increasing
tendency toward invariance,’’ in LO or FFA, relative to V1, as
described by Avidan et al. 2002. However, only a strict
invariance (not an ‘‘increasing tendency toward’’ invariance)
for a given variable will aid the computation of faces/objects by
allowing the computation to ignore that variable; whether the
slope is higher or lower is relatively moot for the computation.
The fact that we were able to reverse FFA selectivity for faces
versus objects by manipulating only lower-level properties
(Fig. 5) strongly suggests that any ‘‘tendency toward invariance’’
remains incomplete at the level of FFA.
These results emphasize that FFA activity can be strongly
affected by lower-level stimulus parameters. Receptive field
properties similar to those known in V1 can account for
essentially all the response variation we found in FFA. Given
the strength of these effects, it is possible that previous
controversies about function in FFA were inadvertently compli-
cated by lower-level stimulus differences. At least, our results
reemphasize the importance of specifying and standardizing
stimulus parameters, and of acquiring control measurements in
V1, in studies of higher-order function.
These results do not rule out the presence of an apparently
face-selective component in the FFA response. Although we
were able to systematically reverse the normal category selec-
tivity in FFA by manipulating lower-level parameters, relatively
atypical parameters were used for those nonoptimal faces.
Moreover,the face-driven activityinFFAwas alwaysmuchhigher
than that in adjacent area PPA, as expected. Thus, overall, our
to the sensitivity to lower-level features emphasized here.
National Institutes of Health (EY017081 to R.B.H.T., MH076054
to D.J.H.); the National Alliance for Research on Schizophrenia
and Depression (NARSAD) (R.B.H.T., D.J.H.); the Sidney J. Bear
Trust (D.J.H.); Martinos Center for Biomedical Imaging,
National Center for Research Resources (NCRR) (P41RR14075).
materialcanbe foundat: http://www.cercor
We thank Jeremy Young, Samantha Huang, and Spencer Lynn for
helping with pilot experiments on face size. Natalia Bilenko helped
with the scanning on this study. Conflict of Interest: None declared.
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