FMRI analysis of contrast polarity processing in face-selective cortex in humans and monkeys.
ABSTRACT Recognition is strongly impaired when the normal contrast polarity of faces is reversed. For instance, otherwise-familiar faces become very difficult to recognize when viewed as photographic negatives. Here, we used fMRI to demonstrate related properties in visual cortex: 1) fMRI responses in the human Fusiform Face Area (FFA) decreased strongly (26%) to contrast-reversed faces across a wide range of contrast levels (5.3-100% RMS contrast), in all subjects tested. In a whole brain analysis, this contrast polarity bias was largely confined to the Fusiform Face Area (FFA; p < 0.0001), with possible involvement of a left occipital face-selective region. 2) It is known that reversing facial contrast affects three image properties in parallel (absorbance, shading, and specular reflection). Here, comparison of FFA responses to those in V1 suggests that the contrast polarity bias is produced in FFA only when all three component properties were reversed simultaneously, which suggests a prominent non-linearity in FFA processing. 3) Across a wide range (180(o)) of illumination source angles, 3D face shapes without texture produced response constancy in FFA, without a contrast polarity bias. 4) Consistent with psychophysics, analogous fMRI biases for normal contrast polarity were not produced by non-face objects, with image statistics similar to the face stimuli. 5) Using fMRI, we also demonstrated a contrast polarity bias in awake behaving macaque monkeys, in the cortical region considered homologous to human FFA. Thus common cortical mechanisms may underlie facial contrast processing across ~ 25 million years of primate evolution.
- SourceAvailable from: Shahin Nasr[Show abstract] [Hide abstract]
ABSTRACT: Fifteen years ago, an intriguing area was found in human visual cortex. This area (the parahippocampal place area [PPA]) was initially interpreted as responding selectively to images of places. However, subsequent studies reported that PPA also responds strongly to a much wider range of image categories, including inanimate objects, tools, spatial context, landmarks, objectively large objects, indoor scenes, and/or isolated buildings. Here, we hypothesized that PPA responds selectively to a lower-level stimulus property (rectilinear features), which are common to many of the above higher-order categories. Using a novel wavelet image filter, we first demonstrated that rectangular features are common in these diverse stimulus categories. Then we tested whether PPA is selectively activated by rectangular features in six independent fMRI experiments using progressively simplified stimuli, from complex real-world images, through 3D/2D computer-generated shapes, through simple line stimuli. We found that PPA was consistently activated by rectilinear features, compared with curved and nonrectangular features. This rectilinear preference was (1) comparable in amplitude and selectivity, relative to the preference for category (scenes vs faces), (2) independent of known biases for specific orientations and spatial frequency, and (3) not predictable from V1 activity. Two additional scene-responsive areas were sensitive to a subset of rectilinear features. Thus, rectilinear selectivity may serve as a crucial building block for category-selective responses in PPA and functionally related areas.Journal of Neuroscience 05/2014; 34(20):6721-35. · 6.75 Impact Factor
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ABSTRACT: Fear generalization is the production of fear responses to a stimulus that is similar-but not identical-to a threatening stimulus. Although prior studies have found that fear generalization magnitudes are qualitatively related to the degree of perceptual similarity to the threatening stimulus, the precise relationship between these two functions has not been measured systematically. Also, it remains unknown whether fear generalization mechanisms differ for social and non-social information. To examine these questions, we measured perceptual discrimination and fear generalization in the same subjects, using images of human faces and non-face control stimuli ("blobs") that were perceptually matched to the faces. First, each subject's ability to discriminate between pairs of faces or blobs was measured. Each subject then underwent a Pavlovian fear conditioning procedure, in which each of the paired conditioned stimuli (CS) were either followed (CS+) or not followed (CS-) by a shock. Skin conductance responses (SCRs) were also measured. Subjects were then presented with the CS+, CS- and five levels of a CS+-to-CS- morph continuum between the paired stimuli, which were identified based on individual discrimination thresholds. Finally, subjects rated the likelihood that each stimulus had been followed by a shock. Subjects showed both autonomic (SCR-based) and conscious (ratings-based) fear responses to morphs that they could not discriminate from the CS+ (generalization). For both faces and non-face objects, fear generalization was not found above discrimination thresholds. However, subjects exhibited greater fear generalization in the shock likelihood ratings compared to the SCRs, particularly for faces. These findings reveal that autonomic threat detection mechanisms in humans are highly sensitive to small perceptual differences between stimuli. Also, the conscious evaluation of threat shows broader generalization than autonomic responses, biased towards labeling a stimulus as threatening.Frontiers in Human Neuroscience 09/2014; 8:624. · 2.90 Impact Factor
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ABSTRACT: In this manuscript, using a novel wide-view visual presentation system that we developed for vision research and functional magnetic resonance imaging (fMRI), we studied contrast response functions in regions of the brain that are central and peripheral to the entire set of visual areas (V1, V2, V3, V3A, MT+), regions that have not been all investigated in previous vision research. Under the stimulus conditions which were 0-20 deg, 20-40 deg, and 40-60 deg eccentricity black-and-white checkerboard patterns, we measured the blood oxygenation level-dependent fMRI contrast response at five contrast levels (6, 12, 24, 48, and 96%) in the visual areas. On the basis of these data, the central and pericentral visual areas had low-contrast gain, whereas the peripheral visual areas had high-contrast gain. In addition, our results showed that the signals fundamentally shift during visual processing through posterior visual cortical areas (V1, V2, and V3) to superior visual cortical areas (V3A and MT+).Perception 01/2014; 43(7):677-93. · 1.11 Impact Factor
FMRI analysis of contrast polarity in face-selective cortex in humans and monkeys
Xiaomin Yue⁎,1, Shahin Nasr, Kathryn J. Devaney, Daphne J. Holt, Roger B.H. Tootell
Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA 02129, USA
a b s t r a c ta r t i c l ei n f o
Accepted 23 February 2013
Available online 19 March 2013
Recognition is strongly impaired when the normal contrast polarity of faces is reversed. For instance,
otherwise-familiar faces become very difficult to recognize when viewed as photographic negatives. Here,
we used fMRI to demonstrate related properties in visual cortex: 1) fMRI responses in the human Fusiform
Face Area (FFA) decreased strongly (26%) to contrast-reversed faces across a wide range of contrast levels
(5.3–100% RMS contrast), in all subjects tested. In a whole brain analysis, this contrast polarity bias was large-
ly confined to the Fusiform Face Area (FFA; p b 0.0001), with possible involvement of a left occipital
face-selective region. 2) It is known that reversing facial contrast affects three image properties in parallel
(absorbance, shading, and specular reflection). Here, comparison of FFA responses to those in V1 suggests
that the contrast polarity bias is produced in FFA only when all three component properties were reversed
simultaneously, which suggests a prominent non-linearity in FFA processing. 3) Across a wide range (180°)
of illumination source angles, 3D face shapes without texture produced response constancy in FFA, without
a contrast polarity bias. 4) Consistent with psychophysics, analogous fMRI biases for normal contrast polarity
were not produced by non-face objects, with image statistics similar to the face stimuli. 5) Using fMRI, we also
demonstrated a contrast polarity bias in awake behaving macaque monkeys, in the cortical region considered
homologous to human FFA. Thus common cortical mechanisms may underlie facial contrast processing across
~25 million years of primate evolution.
© 2013 Elsevier Inc. All rights reserved.
ences facial recognition. Faces of reversed (‘negative’) contrast polarity
are much less recognizable than faces of normal (‘positive’) polarity
(Bruce and Langton, 1994; Bruce and Young, 1998; Galper, 1970;
Galper and Hochberg, 1971; Kemp et al., 1996; Nederhouser et al.,
2007; Russell et al., 2006). For instance, otherwise-familiar faces are
very difficult to recognize when viewed as photographic negatives
Analogously, one previous study reported reduced fMRI activity in
the vicinity of the Fusiform Face Area (FFA, although that area was not
independently localized), in response to color reversal of red-green
faces (Georgeet al.,1999).A subsequent study(Giladet al., 2009) dem-
onstrated a reduced response in rightFFAin response tocontrast rever-
sal in achromatic faces or facial regions. Moreover, a recent study (Nasr
and Tootell, 2012) showed reduced recognition-related activity in FFA
in response to contrast-reversed faces compared to the normal faces.
Here we further explored this intriguing contrast polarity effect in
multiple experiments. In Experiment 1, we first tested for a contrast po-
larity bias using achromatic faces of quantitatively calibrated contrast
and equal mean luminance, across a wide range of contrast levels.
This quantitative information was not available from the previous liter-
ature. Experiment 1 also furnished baseline results for subsequent
of contrast polarity actually reverses three independent image proper-
ties in parallel: absorbance, illumination, and specular reflection. Al-
though psychophysical studies have debated this issue (e.g. Bruce and
periments distinguishing which of these properties produce a polarity
bias in the brain. Experiment 2 tested which of these three properties
were necessary and/or sufficient to produce a contrast polarity bias.
Previous perceptual studies (Hill and Bruce, 1996; Liu et al., 1999)
showed that the contrast polarity bias is also found in faces lacking
normal textures (i.e. in face shapes without the normal covariation
in surface absorbance). To systematically test for a cortical counter-
part of this perceptual effect, Experiment 3 measured whether the
fMRI contrast polarity bias (Experiment 1) is eliminated in response
to such ‘shape only’ face stimuli, consistent with the psychophysics.
By systematically varying the illuminant position over more than
180°, this experiment also tested for ‘illuminant direction constancy’
NeuroImage 76 (2013) 57–69
⁎ Corresponding author at: Martinos Center for Biomedical Imaging, Massachusetts
General Hospital, 149 13th street, Charlestown, MA 02129, USA. Fax: +1 617 726 7422.
E-mail address: email@example.com (X. Yue).
1Current working address: Laboratory of Brain and Cognition, NIMH/NIH 49 Con-
vent Drive Bldg 49, Room 6A68 Bethesda, MD 20892, USA.
1053-8119/$ – see front matter © 2013 Elsevier Inc. All rights reserved.
Contents lists available at SciVerse ScienceDirect
journal homepage: www.elsevier.com/locate/ynimg
in the fMRI responses — as one might predict if FFA responses corre-
late with processing of faces per se, independent of local contrast
variation. Such illuminant constancy has not been reported previously
based on fMRI, to our knowledge.
Psychophysically, contrast reversal has little effect on the recogni-
tion of non-face objects (Galper, 1970; Nederhouser et al., 2007).
Why would contrast reversal affect recognition of faces, but not ob-
jects? One idea is that faces have certain universal features based on
local contrast that facilitate recognition, whereas objects are variable
enough to defy this rule. One exception to this rule is shading: by def-
inition, shadows are always darker (not lighter) than their surround-
ings. Experiment 4 tested for a contrast polarity effect in shaded
non-face objects. Like faces, these object stimuli included a funda-
mental lighting property.
Experiment 5 tested whether the contrast polarity bias is specific to
humans, or whether it is a general feature of primate vision, including
macaque monkeys. This question is especially pressing because
human faces differ so markedly from faces of non-human primates.
Materials and methods
The face/head 3D meshes were generated using FaceGen (Singular
Inversions, Canada), imported into Matlab (The MathWorks, US) using
customized Matlab programs. The faces were rendered by projecting
the absorbance variations onto its 3D meshes, with precise control of
lighting, viewpoint, and rotation angle. All stimulus faces were 12.7°
in diameter (average of width and height), in frontal view. Stimuli
were presented in the scanner via a LCD projector (Sharp XG-P25,
1024 × 768 pixels) using PsychToolbox (Brainard, 1997; Pelli, 1997).
All experiments included a common subset of reference faces (frontal
view and gaze, centered in the visual field, using eight identities, all
with a neutral expression). Stimulus conditions were organized in a
block design. Blocks (16 s duration) were presented in semi-random
order (8 different faces/block, one face/s), along with control blocks of
uniform gray. Mean luminance was kept constant (253 cd/m2) for all
stimuli, unless specified otherwise. Luminance contrast levels were de-
fined as the root-mean-square (RMS) contrast across the entire face, in
equal logarithmic steps ranging from 5.3 to 100%. Compared to other
measures, contrast detection is improved when based on RMS (Bex
and Makous, 2002).
Visual stimuli for Experiment 2
not just one. First, surface absorbance is reversed. For instance, the
white part of the eyes (the sclera) becomes black, and the black part
(the pupil) becomes white. Secondly, the effect of illumination is re-
versed: shadows (e.g. the nostrils) become bright rather than dark,
whereas directly illuminated regions become dark instead of bright.
The third property is specular reflection, which manifests as a ‘shine’
on the facial surface. A prime example is the bright white ‘light in the
eye’ (due to the liquid film on the cornea); this bright patch becomes
black when contrast polarity is reversed. Moisture on the skin can pro-
duce similar specular reflections.
To distinguish which of these image properties contribute to the
fMRI-based polarity bias in FFA, the following stimulus conditions
were presented at both normal and reversed contrast polarity: 1) nor-
mal faces, 2) three-dimensional head/face shapes including reflection
variations, but lacking absorbance variations, and 3) two-dimensional
maps of absorbance variations, lacking reflection differences. The ratio-
nale for condition 2 is as follows. Reflection differences are intrinsically
determined by the interaction of shape and illuminant location(s), and
such differences are masked by any concurrent differences in the nor-
mal face/head. Therefore we tested reflection differences on head
shapes, which lacked all absorption variation. The rationale for condi-
tion 3 was complementary: absorption differences are independent of
(and masked by) concurrent variations in reflection. Such reflection
variations were eliminated by rendering the absorbance variations on
a 2-D sheet. In condition 2, variations in specular reflection were com-
bined with shading variations, since both properties are differences in
In condition 4, we also tested ‘chimeric’ faces, in which facial con-
trast was reversed in the eye region, but not elsewhere in the face
(eye-positive chimeras), and vice versa (eye-negative chimeras). A
prior study using eye-positive chimeras (Gilad et al., 2009) concluded
that the contrast polarity effect in FFA was strongly influenced by the
contrast of the eye regions, rather than the contrast of the whole face.
However that study did not test the effects of eye-negative chimeras.
Here we tested that hypothesis in more detail, by comparing the effect
of both polarities of chimeric face, aswell aswholefaces of both normal
and contrast-reversed polarities.Accordingtothe previousconclusions,
aneye-negative chimera should produce lower fMRIactivity, compared
to eye-positive chimeras.
ing vertices and facets, and a face texture. Those two components were
imported into Matlab using a customized Matlab code to render a full
face (condition 1 in Experiment 2). For condition 2 in Experiment 2,
an image was generated in Matlab using a 3D mesh data from FaceGen
stimuli, with a uniformed gray texture substituted for the face texture.
The images for condition 3 in Experiment 2 were obtained from the
original FaceGen face textures, after smoothing (Photoshop) to reduce
detailed shape noise. The images for condition 4 in Experiment 2 were
generated in Photoshop, as illustrated in Gilad et al. (2009).
Visual stimuli for Experiment 3
This experiment tested the effects of variation in facial illumina-
tion. We used face shapes rather than faces with texture, to better iso-
late the effects of illumination apart from texture confounds. The
location of the virtual illuminant was varied in 22.5° steps, along a
225° arc from top-lit to bottom-lit and beyond. Because changes in il-
lumination location usually change the mean luminance of the illumi-
nated object, we tested the illuminant effects in two ways, using 1)
head shapes (reflection maps) with normal, uncorrected lighting var-
iation (i.e., unequal mean luminance), and 2) head shapes in which
mean luminance was equated.
Visual stimuli for Experiment 4
Three-dimensional artificial objects (‘blobs’) were computer-
generated in two steps. First, a given orientation was added to the 2nd
and 3rd harmonics (3D shapes with either two or six equally-spaced
convex lobes, respectively), and to a sphere and the 4th harmonic of a
sphere, using Matlab. Second, by rotating the 2nd and 3rd harmonics,
a toroidal space of smooth, asymmetric 3D blobs was created (Yue et
al., 2006). Then the 3D mesh of each blob was imported from Matlab
to Blender (www.blender.org) for rendering as an image with or with-
Visual stimuli for Experiment 5
Task, human subjects
A fixation dot was present on the center of all face stimuli and the
baseline (uniform gray) images. In addition, a small probe dot was
superimposed on all stimuli (face stimuli and uniform gray), throughout
X. Yue et al. / NeuroImage 76 (2013) 57–69
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 timing of the probe dot
presentation was unrelated to the timing of the face presentation (e.g.
Yue et al., 2011). The detectability of the probe dot was manipulated
by slightly varying its low/high luminance ratio (decreased local con-
trast = decreaseddetection). Threshold was modulatedby the staircase
method, converging on 75% correct. To reduce response variability, the
dot size varied with eccentricity (Yue et al., 2011).
All subjects (n = 29) gave written consent. The number of subjects
in each experiment is given in the Results. The experimental protocol
was approved by the Institutional Review Board of Massachusetts Gen-
eral Hospital. Subjects had normal or corrected-to-normal vision, and
radiologically normal brains. Inline Supplementary Table S1 furnishes
additional details of subjects used in different experiments.
Inline Supplementary Table S1 can be found online at http://dx.
All scanningwas done in a 3 T SiemensTrio. Functional scansused a
gradient echo EPI sequence (TR = 2 s; TE = 30 ms; flip angle = 90°;
33 slices with in-plane matrix size 64 × 64. Voxel size is 3 mm
isotropic). For each subject, high-resolution anatomical scans (TR =
2530 ms; TE = 3.39 ms; flip angle = 7°; Voxel size is 1 mm isotropic)
were also collected for cortical surface reconstruction.
fMRI data analysis
Each individual brain was inflated using FreeSurfer (http://surfer.
nmr.mgh.harvard.edu). Statistical analysis of the functional data was
performed with the Free-Surfer Functional Analysis Stream (FS-FAST)
(Friston et al., 1995). All functional images were pre-processed with
motion correction, slice-timing correction, spatial smoothing (a
5 mm Gaussian kernel), and normalized across sessions individually
before submitting to General Linear Model (GLM) fitting. After those
pre-processing steps, the functional data was regressed with GLM,
where each condition was modeled as a convolution of a boxcar
with a hemodynamic response function. The 3 motion measurements
generated from the 3D motion correction were also included in the
GLM fitting in addition to the experimental condition, to reduce the
influence of head movement.
Average signal intensity maps were calculated for each condition,
for each subject. Voxel-by-voxel statistical tests were conducted by
computing contrasts based on the GLM described above. For averag-
ing across subjects, each subject's functional and anatomical data
were spatially normalized by using a spherical transformation
(Fischl et al., 1999).
In each subject, area FFA was localized using an independent set of
stimuli: group faces versus indoor scenes, of equal retinotopic extent,
as described elsewhere (e.g. Rajimehr et al., 2011; Tootell et al., 2008;
Yue et al., 2011). MR levels during baseline conditions (an otherwise
uniform gray screen including a small central fixation point, 32 s dura-
tion) were measured at the beginning and the end of each run. The in-
tervening 10 conditions (16 s/condition) were presented without
intervening baseline conditions. Therefore, each run lasted 224 s. Each
subject had 8 runs. Each image (scenes or faces) was presented for
1 s. Each condition included 8 images, repeated twice per condition.
The order of stimulus conditions (faces vs. scenes) was semi-random.
During scanning, the subjects were required to press a key with their
index figure on an fMRI-compatible button box every time the fixation
color changed from red to green, or green to red. Subjects were
instructed to keep their fixation on the fixation dot. As defined here,
FFA was bounded by non-face-selective regions dorsally, ventrally and
anteriorly, and the terminus of the fusiform gyrus posteriorly. By
those criteria, FFA was easily localized in all hemispheres tested in this
study. The group averaged map of FFA is presented in Supplementary
Evidencefor a topographically-distinct Occipital Face Area (i.e. OFA)
(Gauthier et al., 2000a,b) was less clear. Although activity biased for
faces (relative to scenes) was often found in the location reported for
OFA (averaged Talairach coordinates: (−38, −80, −11) for left OFA,
(36, −76, −9) for right OFA based on Goffaux et al., 2011; Pitcher et
al., 2007; Rossion et al., 2003; Steeves et al., 2006), such face-selective
activity was topographically somewhat scattered and variable across
subjects and across hemispheres, at least in our results. Similarly vari-
ability in OFA has been noted previously (for a review see Pitcher et
al.,2011). Giventhisvariability, ROIsforpresumptiveOFAweredefined
here as discrete patches of activity located within ~4 mm of the aver-
old of p b 10−3. The number of voxels included in the OFA ROIs ranged
from 9 to 25. These volume-based patches were then translated onto
sual areas. Based on these criteria, presumptive OFA activity was found
in 23 of the 28 hemispheres in which OFA activity was relevant and
measured (e.g. Experiment 1).
Monkey face-selective regions were defined using the same
localizer used in the human experiments. Thus this localizer was
equated for lower level cues but differed between species based on
higher-level cues. Supplementary Fig. 2 shows these face selective re-
gions from the two monkeys scanned for this study.
In Experiment 4, which probed the responses to non-face objects,
thelateraloccipitalcomplex (LOC) wasalsolocalized usinganindepen-
dent set of stimuli: gray scale objects versus a scrambled version of
these same objects (11.5° in averaged diameter) (Malach et al., 1995).
For each subject, data in this experiment was based on 8 runs with
160 s/run.Eachcondition(16 s/condition)wasrepeated4timesduring
each run. Other aspects of the experimental design were identical to
those used in the FFA localizer. Subsequent ROI analyses for LOC were
based on these individually localized regions. ROIs for V1 were based
on two criteria. The peripheral retinotopic limit (anterior-dorsal versus
posterior-ventral, along the calcarine fissure) corresponded to the pe-
ripheral representation of the face stimuli, based on the subjects' activ-
ity map of faces versus uniform gray activity. The V1–V2 border was
defined based on earlier retinotopic data (Hinds et al., 2008; Sereno
et al., 1995, Tootell et al., 1998), and the cortical anatomy (e.g. Hinds
et al., 2009). The orthogonal border in V1 (the peripheral extent of
the stimulus-driven activity) was defined based on the independent
localizers of equivalent retinotopic extent.
In all experiments, individual voxel activity for all conditions (vs.
uniform gray baseline) was calculated with a univariate General Lin-
ear Model (GLM) using the Fs-Fast. The group maps were computed
with Fs-Fast using random effects analyses with an uncorrected
threshold of p b 0.01. All ROI statistical analyses were performed in
SPSS. All error bars in the ROI analysis plot are S.E.M.
MRI in monkeys
Two male rhesus monkeys (6–8 kg) were used in the monkey fMRI
experiments. Surgical details and the training procedure are described
elsewhere (Tsao et al., 2003a,b; Vanduffel et al., 2001) and summarized
here. Each monkey was implanted with an MRI-compatible plastic
headset. All surgical procedures conformed to institutional guidelines
(Massachusetts General Hospital; National Institutes of Health). After
X. Yue et al. / NeuroImage 76 (2013) 57–69
recovery, monkeys were adapted to sit in a sphinx position inside a
plastic restraining chair. They were trained to fixate a small
(0.35° × 0.35°) central fixation target, and eye position was monitored
using an infrared pupil tracking system (ISCAN) at 120 Hz. Monkeys
were rewarded for maintainingfixationwithin a square-shaped central
fixation window (2° × 2° in size) surrounding the fixation spot. After
20–40 training sessions, when fixation performance reached asymp-
tote, we began functional scanning. Only scanning sessions with
adequately high behavioral performance (>90% central fixation
throughout the duration of each scan) were analyzed further. Before
each scanning session, an exogenous contrast agent (MION; 12 mg/kg)
was injected intravenously to enhance the contrast-to-noise ratio and
functional sensitivity (Leite et al., 2002; Vanduffel et al., 2001). For ease
of comparison, the polarity of the MION-based MR response was
The EPI scan parameters were as follows: TR = 2 s; TE = 19 ms;
flip angle = 90°; 50 slices, in-plane matrix size = 84 × 96. Voxel size
was 1.5 mm isotropic. As in the human MRI experiments, anatomical
scans were also collected from each monkey (TR = 2.5 s; TE =
4.35 ms; flip angle = 8°. 0.5 mm isotropic, MP-RAGE). These data
were reconstructed to create the cortical surfaces for each monkey
Human behavioral performance
Across all face-based stimuli, performance on the dot detection
dummy task (measured as increases versus decreases in the threshold
level) did not vary between normal versus reversed contrast polarity
(t13= −0.77, p = 0.46).
Experiment 1: Contrast level × contrast polarity
Face stimuli were presented at normal versus reversed polarity, at
four levels of RMS contrast: 5.3%, 14.1%, 37.6%, and 100% (see Fig. 1a),
spanning the range of readily visible face stimuli. Fourteen subjects
were tested in Experiment 1. For each subject, this data was based on
10 runs of 192 s/run.
Fig. 1. Stimuli and results from Experiment 1. a: Stimulus examples. Top row: faces of normal contrast. Bottom row: faces of reversed polarity. For both polarities, face contrast level
was varied in logarithmically equal steps from 5.3 to 100%, calculated as a root mean square (RMS; see Materials and methods). In the experimental display, all faces (including
those at the lowest contrast) were readily visible, for all subjects. b: Results in FFA, averaged across subjects (n = 14). At all contrast levels, the response was lower for reversed
polarity faces, compared to faces of normal polarity. The differences in MR signal were statistically equivalent, across the four levels. Thus the change due to contrast polarity was
additive rather than multiplicative (see supplementary Fig. 1). c: Results in V1. There was no significant difference between the effects of normal versus reversed polarity, at any
contrast level. The error bar indicates the standard derivation calculated from the group data. d: Inter-subject variability in FFA (n = 14). For each subject, data was collapsed across
the four contrast levels. The bias for normal contrast polarity was relatively consistent; no subject showed a converse bias for reversed polarities.
X. Yue et al. / NeuroImage 76 (2013) 57–69
Althoughtheabsolutesignalamplitudeswere larger intherightFFA
compared to the left (Supplementary Fig. 5), we did not find significant
differences in response properties or amplitude between hemispheres.
Thus, data from both hemispheres were averaged together. The faces of
normal polarity produced consistently higher activity in FFA, compared
with faces of reversed polarity (Fig. 1b). This difference was present at
allcontrastlevels(F(1,13) = 113.54,p b 0.0001).Theposthocanalysis
showed a significantly higher response to normal polarity faces com-
pared to reversed polarity faces at every contrast level (four pairs, all
p b 0.01).
Unlike the result in FFA, V1 showed no significant difference be-
tween responses to normal versus contrast-reversed faces (t3= 0.63,
p > 0.1, Fig. 1c), at any contrast level. This result is consistent with
many single unit studies showing that V1 responses are driven largely
by edges (local contrast variation), of either/both polarities.
In FFA, the size of the polarity bias remained roughly constant over
the entire range of contrasts, rather than decreasing with contrast
level. Thus there was no interaction between contrast polarity and con-
trast level (F(3,39) = 1.9, p > 0.1). The change due to contrast polarity
parison, the change due to contrast level was roughly log-linear, consis-
tent with previous results (Yue et al., 2011).
To increase statistical sensitivity, we averaged the effect of polari-
ty across all contrast levels. This analysis showed that the bias for nor-
mal face polarity was relatively consistent across subjects. In all 14
subjects tested, mean values were biased for normal contrast polarity,
to some extent (Fig. 1d).
the contrast polarity bias was stronger in FFA than anywhere else in the
visual cortex. At a typical statistical level (threshold = p b 10−3.5,
uncorrected), the preference for normal face polarity was effectively
unique to FFA (see Fig. 2 and Supplementary Fig. 4).
A ROI for right OFA could be localized in 12 out of 14 subjects. Al-
though there was a significant effect of contrast gain (F(3, 33) =
18.39, p b 0.001) in this ROI, there was no effect of contrast polarity
(F(1,11) = 6.77, p > 0.01) and no interaction between contrast gain
and contrast polarity (F(3,33) = 2.55, p > 0.01) (Supplementary
Fig. 6a). In the left hemisphere, a ROI for OFA could be localized in 11
out of 14 subjects. In this hemisphere, there was a significant (but
weaker) effect of contrast polarity (F(1, 10) = 23.19, p b 0.01) and
contrast gain (F(3,30) = 15.46, p b 0.01) (Supplementary Fig. 6b).
Experiment 2: Image components underlying the polarity bias
The RMS contrast level was 100% for all stimuli in this experiment.
Stimulus examples from the different conditions are shown in Fig. 3a.
This experiment included 9 subjects. For each subject, the data was
based on 12 runs of 192 s/run.
Responses from FFA and V1 are shown in Figs. 3c, and d, respective-
ly. To isolate the contribution of FFA (relative to V1), we defined a con-
trast polarity index as ((FFApos− FFAneg) / (V1pos− V1neg)). An index
responses in V1. This normalized polarity index (Fig. 3b) corrected for
the variation in contrast polarity effects at higher cortical levels (e.g.
FFA) which passively reflect activity differences present at lower levels
(e.g. V1) (Yue et al., 2011).
(a robust contrast polarity bias) for the normal faces (t8= 3.82,
p b 0.01). Normal faces were the only condition that included all three
lighting properties at normal polarity. In comparison, the polarity
index did not differ significantly from chance (unity) for the other
three conditions, which were based on a subset of these image proper-
ties (condition 2, t8= 0.44, p > 0.1, and condition 3, t8= −0.20,
p > 0.1), or internally inconsistent polarity (condition 4, t8= −0.41,
p > 0.1). According to this index, a polarity bias was produced only by
an internally consistent combination of all the component image prop-
erties: absorbance, shape, and specular reflection. However this ‘syner-
gistic’ conclusion is less apparent if the FFA responses are considered in
isolation, without correction for the variation in V1 responses. The OFA
ditions, in either hemisphere.
Fig. 2. Map of activity produced to faces of normal contrast polarity, compared to reversed contrast polarity, across visual cortex. Flattened views of the left and right hemispheres are
shown on the left and right side of the figure, respectively. The cortical anatomy (sulci = darker gray; gyri = lighter gray) and the activity maps were averaged across all subjects
(n = 14). FFA was localized using independent face-versus-place stimuli. The resultant ROI for FFA is shown as a dashed white line (threshold = p b 0.01, random effects). The
stimulus-activated portion of V1 is indicated as a solid white line. The activity bias produced by a normal polarity in the left/right hemispheres is coded in red/yellow (threshold =
p b 10−3.5) with the cluster threshold larger than 20 mm; no regions showed the converse bias to reversed polarity faces, at the equivalent thresholds. The contrast polarity bias was
higher in FFA, compared to other regions of visual cortex. Local orientation of the brain axes are indicated in white (D = dorsal; V = ventral; P = posterior; A = anterior).
X. Yue et al. / NeuroImage 76 (2013) 57–69
Our data was consistent with that of Gilad et al. (2009), in that FFA
responses for eye-positive chimeras were statistically indistinguishable
from responses to normal faces (t8= 3.24. p > 0.01). However we also
found that responses to eye-negative chimeras were not lower than re-
sponses to eye-positive chimeras (t8= 0.56, p > 0.01) — which indi-
cates that facial regions outside the eye region also contribute to the
contrast polarity effect. This conclusion is consistent with results from
single unit recordings in macaque (Ohayon et al., 2012).
Experiment 3: Variations in illuminant location
Examples of the test stimuli are illustrated in Fig. 4a and Supplemen-
tary Fig. 8. These stimuli were tested in a total of 10 subjects (n = 6 for
stimulusset1,andn = 4forstimulusset2).Eachsubjectparticipatedin
12 runs of 208 s/run.
Consistent with the above results at a single illuminant location
(Experiment 2, condition 2), we found no contrast polarity bias at any
illuminant location tested (for stimuli set 1: F(1,5) = 0.02, p > 0.1;
for stimuli set 2, F(1,3) = 0.03, p > 0.1). Despite the enormous varia-
tions in the local contrast and mean luminance of these stimuli (see
Fig. 4a and Supplementary Figs. 8 and 9), responses were remarkably
visual cortex. This invariance for illumination extended a full 180°, de-
creasing only when the illuminant was positioned behind the face
(e.g. the silhouette at 225°). This result was obtained both when mean
luminance was varied (F(8,40) = 1.62, p > 0.1; Supplementary Fig.
10a), and when it was not (F(8,24) = 1.16, p > 0.1; Supplementary
Fig. 10b). This remarkably robust illuminant constancy is consistent
with the general idea that FFA is involved in face processing, because
it eliminates one otherwise-complicating variable.
ROIs for OFA did not show a contrast polarity bias across illumina-
tion angles, in either hemisphere.
Experiment 4: Contrast polarity bias in non-face objects?
jects, we presented computer-generated objects at positive versus neg-
ative contrast. These objects (‘blobs’) were based on lower-order image
statistics matched to those of faces (Yue et al., 2006). To emphasize the
universal shading properties on such objects, explicit shadows were
Fig. 3. a: Stimulus examples from experiment 2. The leftmost vertical pair (‘Face’) shows faces of 100% contrast, at normal and reversed contrast polarity (top and bottom, respec-
tively). The normal polarity ‘face’ stimuli were equivalent to those in the 100% contrast condition in experiment 1. In the adjacent stimulus pair (‘Reflection’; second from the left),
variations in surface reflection (shading and specular reflection) were extracted from the normal ‘face’ stimuli, and presented on 3-D face/head shapes. Normal polarity images
(normal illumination) are illustrated in the top; images based on contrast-reversed illumination are shown below. The next pair of stimulus examples (‘Absorbance’; second
from the right) show the 2-D map of surface absorbance from the original faces, after removal of the 3-D reflection cues. Again, normal polarities are shown above, and reversed
polarities are shown below. The rightmost stimulus pair shows ‘chimera’ stimuli (e.g. Gilad et al., 2009), in which the contrast polarity of the eye region was reversed, relative to
that in remaining regions of the face. The top example has reversed contrast polarity in the eye region, and normal polarity in remaining face regions. In the bottom chimera, these
polarities were reversed. b: Polarity index for all four classes. The polarity index is high in the normal faces, consistent with the data in Figs. 1 and 2. However the index is not sig-
nificantly different from unity (chance) for any of the other three conditions. c, d: Activity in response to the four different stimulus types, relative to a uniform gray field of equal
mean luminance, in FFA and V1, respectively.
X. Yue et al. / NeuroImage 76 (2013) 57–69
added in the background in half of the stimulus conditions (see Fig. 5a).
Experimental procedures were otherwise identical to those in Experi-
ments 1–3. This experiment included 8 subjects. Each subject partici-
pated in 12 runs of 160 s/run.
Overall these stimuli did not show a bias for normal contrast po-
larity, in LOC or elsewhere in the cortex (F(1,7) = 5.69, p > 0.01)
(see Fig. 5b and Supplementary Fig. 11). These results are consistent
with behavioral studies showing that variations in contrast polarity
do not affect object recognition (Galper, 1970; Nederhouser et al.,
2007). Blobs with shadows produced a slightly larger response than
those without shadows (F(1,7) = 10.28, p = 0.015), but only in
those specific retinotopic representations predicted by the additional
retinotopic extent of the shadows per se (e.g. Supplementary Fig. 3 of
Yue et al., 2011). Thus shading variations did not affect the fMRI
responses in FFA, on either face shapes or non-face shapes (experi-
ments 3 and 4, respectively).
Experiment 5: Facial contrast polarity in the macaque
Finally, we tested whether the normal face contrast polarity bias is
mates. FMRI experiments were conducted in two trained macaque
monkeys (see Materials and methods). Multiple runs were collected
from each monkey across 2–4 weeks (40 runs for the first monkey
and 43 runs for the second monkey). Runs were discarded during the
data analysis when contaminated by poor fixation during the scans, or
napping, or large body motion-induced shim distortions. Each run
lasted 192 s. Each condition (16 s duration) included 8 monkey faces
witha neutral facial expression,and each face presented for 1 s, and re-
peated twice. 3648 volumes were used for data analysis for the first
monkey, and 3840 volumes for the second monkey. Like the human
subjects, the monkey subjects were awake, with each monkey fixating
the center of frontal views of faces throughout the functional scans.
Fig. 4. a: Examples of the stimuli in Experiment 3, set 1, at normal contrast polarity (top row) and reversed contrast polarity (bottom row). Face/head shapes were equivalent to
those in Fig. 2, in the ‘Reflection’ condition, except that here, the location of the virtual illuminant was varied. The location was varied in 22.5° steps, from directly above (0°)
through directly below (180°), and beyond (225°), along a plane oriented parallel with the line of sight (90°). For brevity, this illustration includes only angular changes of 45°.
In this stimulus set, mean luminance was not equated; thus the strongly shaded faces are darker overall. The full set of experimental stimuli (shown in Supplementary Figs. 6a,
b, c, d) included faces of normal and reversed polarity, both corrected and uncorrected for mean luminance. b: FFA responses from Experiment 3. Results from stimulus sets 1
and 2 (c.f. supplementary Figs. 4a and b) were statistically equivalent, so those two datasets are combined here. Responses were essentially equivalent across the 180° range of
frontal-through-profile illuminant locations, relative to a baseline condition (uniform gray field). At greater angles, responses decreased as the head shapes were increasingly
silhouetted (‘backlit’), in which shape features were not highlighted.
X. Yue et al. / NeuroImage 76 (2013) 57–69
In the monkey experiments, we used monkey faces as stimuli, rather
than human faces. Thus both the human and monkey subjects viewed
faces of their conspecifics, so that the face stimuli were matched evolu-
tionarily. In terms of lower level cues, the monkey faces differ signifi-
cantly from human faces (see Figs. 6 and 7a, and Discussion).
In the activity map in macaque visual cortex, we found a clear bias
for normal face contrast polarity in a discrete patch in macaque visual
cortex. Fig. 7b shows the topographical relationship between this
polarity-biased patch, relative to the face-selective patch based on
the independent face-place localizer. The face-selective patch in the
present study is the large face patch located in the posterior bank
and adjacent lip of the posterior temporal sulcus, which has been pre-
viously referred to as the ‘middle’ face patch (e.g. Tsao et al., 2006), or
the ‘posterior’ face patch (e.g. Bell et al., 2009; Pinsk et al., 2009;
Rajimehr et al., 2009), or more recently as the ‘middle lateral’ (ML)
face patch (Freiwald and Tsao, 2010). Consistent with our hypothesis,
the polarity bias was largely confined to a subdivision within that
main face-selective cortical patch, which is considered the likely ho-
mologue of human FFA (Rajimehr et al., 2009; Tsao et al., 2003a,b).
To quantify this further, ROIs for this face patch were defined in
each of the four monkey hemispheres, using an equivalent threshold
(p b 10−10), based on localization stimuli equivalent to those used in
the human subjects. Then the contrast polarity responses were aver-
aged from all voxels within those ROIs. The resultant data showed
higher activity to normal contrast polarity across all contrast levels
(see Fig. 7c and Supplementary Fig. 12). Consistent with the human
data (Fig. 1b), the differences across contrast level in monkeys
(Fig. 7c) were relatively constant. Those differences were also smaller
Fig. 5. Stimulus and results from blob experiment. a: Blob stimuli. Blobs are shown in normal contrast in the top row, and in reversed contrast in the bottom row. Blobs in the left
column are shaded but without shadows on the background. Shadows are included in the blobs in the right columns. b: The blobs did not produce a contrast polarity bias in LOC,
either with or without background shadows.
X. Yue et al. / NeuroImage 76 (2013) 57–69
in monkeys compared to those in humans. The smaller polarity bias in
monkey may reflect actual species variation, or it could simply reflect
sampling differences (2 monkey subjects versus 14 human subjects),
and/or task differences. In any event, the overall results suggest that a
bias for normal contrast polarity exists in a homologous face-selective
cortical area, in both humans and monkeys.
Contrast polarity and facial recognition
Previous studies (George et al., 1999; Gilad et al., 2009; Nasr and
Tootell, 2012) have emphasized the intriguing parallel between rec-
ognition, facial contrast polarity and fMRI activity in/near FFA: both
measures decrease when face contrast polarity is reversed. Indeed,
the fMRI results here and previously (George et al., 1999; Gilad et
al., 2009) showed a bias for normal face polarity even though subjects
were not explicitly required to recognize faces. FFA may respond bet-
ter to faces at normal contrast polarity simply because these stimuli
are more ‘face-like’, compared to faces of reversed polarity. All normal
polarity faces include consistent contrast-specific features (e.g. white
sclera, dark pupils and nostrils). Standard localization experiments
are consistent with this: even during passive viewing or attention to
non-face stimuli, FFA is activated more by normal faces, compared
to less face-like stimuli (e.g. Grill-Spector et al., 1999; Halgren et al.,
1999; Hasson et al., 2001; Haxby et al., 1999; Kanwisher et al.,
1997; Puce et al., 1995).
It could also be argued that subjects were recognizing faces covertly,
although the scanning task required no face recognition (George et al.,
1999; Nasr and Tootell, 2012), or actively competed with it (Gilad et
al., 2009, and present results). However our evidence suggests that the
contrast polarity bias is at least largely sensory-driven, and less likely
to be a covert recognition process. We found an equivalent fMRI prefer-
ence for normal contrast in the much smaller brain of monkeys, who
were concentrating intensely on a fixation task for juice reward. Our
human subjects also performed a competing (dummy) non-face atten-
tion task. However, our current data cannot completely rule out a possi-
ble influence of covert recognition during task performance (e.g. Denys
et al., 2004).
These data suggest that the facial polarity differences produce an
automatic, bottom-up bias in FFA activity, which affects facial recog-
nition at a higher stage (e.g. the anterior face area; Rajimehr et al.,
2009; Kriegeshorte et al., 2007; Nasr and Tootell, 2012). This supports
a role for FFA in face discrimination (relative to non-face objects) —
but not necessarily face recognition (relative to other faces)
(Kriegeshorte et al., 2007; Nasr and Tootell, 2012; Nestor et al.,
2011; Steeves et al., 2006; Xu et al., 2009; Yue et al., 2006).
The ROI analysis also found a weak but significant effect of con-
trast polarity in the left (but not the right) OFA. One interpretation
is that the contrast polarity effect arises (perhaps less selectively) as
early as OFA. Another possibility is that this result reflects feedback
(top down) activity from FFA back to left OFA.
Contrast gain versus contrast polarity
In FFA, activity increased monotonically with contrast level, ap-
proximating a log-linear function for both polarities (Fig. 1b). Similar
contrast gain functions are present from the retina through early vi-
sual cortex (Boynton et al., 1996; Kaplan et al., 1987; Sclar et al.,
1990; Tootell et al., 1988, 1995). Analogous contrast gain functions
have been reported in single units recorded in anterior temporal cor-
tex from the macaque monkey, in response to macaque faces (Fig. 4b;
Rolls and Baylis, 1986). Presumably, the contrast gain function in FFA
reflects the contrast gain function in lower level areas such as V1
(Boynton et al., 1996; Kaplan et al., 1987; Tootell et al., 1995).
One fMRI study (Avidan et al., 2002) concluded that pFS (~poste-
rior FFA) and LO showed an ‘increasing tendency towards contrast in-
variance’, relative to V1. However, when that earlier data is re-plotted
on a conventional logarithmic scale, it also showed a near-linear in-
crease in pFS, similar to that presented here. The similarity between
contrast gain functions in these two studies is notable, considering
the many technical differences between them. For instance, the earli-
er fMRI study was based on responses to luminance variations of line
drawings on a constant luminance background, rather than the
equal-luminance contrast 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.
By comparison, the difference in contrast polarity was essentially
constant across all contrast levels tested. That is, the polarity change
in FFA was additive rather than multiplicative. Towards threshold,
FFA responses are increasingly driven by the sign of the contrast,
and relatively less by the presence or absence of a face per se. In
Fig. 6. Species-specific differences in the eye region, in humans (top) compared to macaque monkeys (bottom). Eyebrows (the discrete band of hair on the lower edge of the brow)
are present in humans, but not in macaques. Also, during frontal gaze, the sclera (the ‘white of the eye’) is prominent in humans, but not visible (covered by the eyelids) in the
X. Yue et al. / NeuroImage 76 (2013) 57–69
previous single unit studies at lower cortical levels (e.g. Williford and
Maunsell, 2007), additive versus multiplicative response functions
have been used to distinguish between common versus distinct in-
puts, respectively. Here, the additive change is consistent with the
idea of distinct brain circuits: the contrast gain reflects input varia-
tions from lower levels, whereas facial contrast polarity may be com-
puted locally, in FFA or just prior to it.
Earlier fMRI studies described higher polarity-related activity in/
near the fusiform gyrus (George et al., 1999), or in right FFA (Gilad et
al., 2009), but did not systematically test for activity elsewhere. Our
whole brain maps showed that the contrast polarity bias was highest
in human FFA (e.g. Fig. 2 and Supplementary Fig. 2), and in its monkey
homolog (Fig. 7), compared to any other regionof the brain.At conven-
tional thresholds (e.g. 10−3for the group-averaged human map, and
10−10for individual macaque map), the contrast polarity effect was es-
sentially confined to FFA. More sensitive analyses revealed a significant
contrast polarity bias in the left OFA. However, considering the large
amount of data in the present sample (11,200 functional volumes),
and the inverse square law of signal averaging (I = 1/d2), a very large
amount of additional fMRIdata would berequired to resolve statistical-
ly robust effects in additional cortical areas. In any event, any hypothet-
Parsing the polarity bias
Psychophysical studies (Bruce and Langton, 1994; Russell et al.,
2006) suggest a prominent role for facial pigmentation (absorbance)
in the contrast polarity bias. Here, the facial polarity bias was
produced in FFA only when all component cues (absorbance, illumi-
nation and specular reflection) were combined in an internally
Fig. 7. FMRI tests of facial contrast polarity in awake fixating macaques. a: Stimulus examples for experiment 5. Analogous to the human faces in Fig. 1a, the monkey faces were
presented at four RMS contrast levels from 5.3 to 100% (from left to right), in both normal contrast polarity (top row), and in reversed polarity (bottom row). b: Map of fMRI activity
in a flattened hemisphere, comparing normal (red-yellow) versus reversed (cyan-blue) contrast polarity. Responses to four contrast levels were combined, for each polarity. The
activated region of V1 is indicated with solid white lines. Face-selective regions were localized using the same stimuli used in the human experiments. As in human FFA, the monkey
homologue of FFA (dotted white line; threshold = p b 10−10) is the prominent patch of face-selective activity anterior to retinotopic visual areas; in monkeys this region is located
in the lower bank and lip of the posterior superior temporal sulcus. The center of this main face selective patch showed a clear preference for normal face polarity (yellow-red).
c: Graph of activity in the main face selective patch, averaged across all hemispheres, at each contrast level tested. The format is similar to that in Fig. 1b.
X. Yue et al. / NeuroImage 76 (2013) 57–69
coherent manner — i.e. as in a real world face. Experiment 3 con-
firmed that the effect of shading (illumination) alone is insufficient
to produce a contrast polarity bias in visual cortex.
This synergistic effectof thethreelightingcuescanbeinterpretedin
terms of either lower or higher levels of visual processing. As described
is reminiscent of sub-threshold summation (Jancke et al., 2004), or an
all-or-none response. In higher-level terms, the contrast polarity bias
can be considered an example of ‘holistic’ face processing (Farah et al.,
1998; Tanaka and Farah, 1993), because only the most face-like stimuli
ingthesetwo levelsof explanation,non-linear(e.g. subthreshold)sum-
mation may underlie some mechanisms of holistic face processing.
Face processing in FFA
Strictly invariant responses to lower level properties are extremely
helpful in face computation (Zhao et al., 2003), because each invariant
evidence for strictly invariant responses has been scarce in previous
studies of FFA. Here, FFA responses were strictly invariant to at least
one cue, the direction of illumination (Fig. 4 and Supplementary
Based on the available evidence, different face-related image di-
mensions can affect FFA activity either: 1) in a graded, systematic
way (size, position, contrast gain and rotation in depth; Yue et al.,
2011), 2) not at all (e.g. direction of illumination; Figs. 4), or 3) in a
binary manner (contrast polarity; Figs. 1 and 2). For the former six di-
mensions, fMRI responses in FFA were qualitatively unrelated to face
perception, and/or similar to those in primary visual cortex (V1); i.e.
those cues were not ‘face-selective’. By comparison, the FFA response
to contrast polarity does appear related to the psychophysics of face
perception, at least qualitatively.
Contrast polarity bias in faces versus non-face objects
Psychophysically, the contrast polarity effect is strong for faces, but
negligible for non-face objects (Galper, 1970; Nederhouser et al.,
2007). This is consistent with the idea that the contrast polarity bias re-
flects universal features in face stimuli (white sclera, dark pupils/
nostrils) — because such universal features are more rare in non-face
objects. However even when we tested shaded objects (which, like
faces, reflect a universal lighting feature), we found no preference for
normal contrast polarity anywhere in the brain, in either the non-face
blob shapes (Fig. 6b and Supplementary Fig. 8), or in head/face shapes
in which shading is not the only lighting property that is reversed (e.g.
Our fMRI data indicated a homologous contrast polarity bias in the
corresponding face-selective region, in both macaques and humans.
The simplest interpretation is that a polarity-sensitive cortical mech-
anism evolved at least ~25 million years ago in Old World primates,
and was retained at least in macaques and humans.
However, facial features evolve over time. Consider species differ-
ences in the eye region, which is especially implicated in the contrast
polarity effect in humans (Gilad et al., 2009; Sadr et al., 2003;
Tomalski et al., 2009). Although many primate species share longer
hair along the brow ridge, hair is confined to discrete ‘eyebrows’ only
in humans(see Fig. 6). Scleral visibilityis alsospecies-specific.Inessen-
tially all non-human primate species, the white part of the human eye
(the sclera) is invisible during frontal gaze (see Fig. 6). Thus, although
the sclera and the eyebrows contribute prominently to the contrast po-
not contribute to a polarity bias, if an identical hard-wired mechanism
mediates this effect across many primate species.
Alternatively, the fMRI bias for normal face polarity could be
mediated by a more flexible mechanism that ‘learns’ consistent visual
properties, either within each individual's early lifespan, or more incre-
mentally during the evolution of each species. Such a model would not
be limited to specific facial features in a given primate species. The
within-lifespan hypothesis is consistent with the time course of face se-
expertise hypothesis in FFA (Gauthier et al., 2000a,b).
Outside of the main face patch in monkeys, contrast reversed faces
produced more activity than normal contrast faces (Fig. 7b, Supple-
mentary Fig. 12), compared to that ratio in human visual cortex
(Fig. 2, Supplementary 4). However such an apparent difference
across species could be influenced by multiple factors, including dif-
ferences in spatial attention during the task and familiarity to contrast
reversed faces. Moreover in the monkey, the contrast reversed face
images may not be recognized or interpreted as faces. Consistent
with the conclusions of Murray et al. (2002), early visual areas may
retain information that cannot be interpreted longer than those that
can be recognized at a later stage. Further experiments would be nec-
essary to clarify this issue.
Comparison of FMRI and single units
A recent single unit study (Ohayon et al., 2012) in macaque dem-
onstrated a positive contrast polarity bias in some cells in the main
face patches, which is considered a homologue of human FFA. In
some respects this data is consistent with our fMRI. For instance,
the fact that some of the face selective cells did not show the contrast
polarity bias is consistent with our fMRI results in monkeys, which
showed a positive bias restricted to a portion of the main face patch
(Fig. 7b), and a generally weaker bias in monkey compared to
humans overall (Fig. 7c). A more sensitive fMRI analysis, such as mul-
tiple voxel pattern analysis, might reveal more information that could
reconcile the fMRI with the unit recording results. For example, with a
spotlight search, it may be possible to tease apart the subregion(s) of
the main face patches that process facial information which is invari-
ant from contrast level and/or contrast polarity.
present results. For instance, Ohayon et al. (2012) reported that modu-
lating the contrast polarity of different facial regions produced a re-
sponse as strong as to whole faces in 50% of the cells. Those data imply
a contrast polarity bias in the fMRI response to chimeric faces. However
our data from humans did not confirm that expectation. Possible rea-
sons for this apparent discrepancy are manifold, including differences
in species, stimuli, and measurement (fMRI versus single units).
Manipulations of contrast polarity offer a unique window into face
processing. Contrast polarity selectively affects face perception and
FFA activity in a qualitatively similar way, in a way that several
other visual cues do not (e.g. Yue et al., 2011; current Figs. 1, 2, 4).
Moreover this contrast polarity bias is apparently conserved evolu-
tionarily, which suggests that it is fundamental for facial processing.
More practically, the presence of a contrast polarity bias in macaques
also makes it amenable to systematic experimental dissection using
classic neurobiological system tools.
This study was supported by the National Institute of Health
X. Yue et al. / NeuroImage 76 (2013) 57–69
DJH), with theSidney J. Bear Trust (DJH). Additional support came from
the Martinos Center for Biomedical Imaging, NCRR (P41RR14075).
Conflict of interest
There was no conflict of interest for this research.
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