Gender in facial representations: a contrast-based study of adaptation within and between the sexes.
ABSTRACT Face aftereffects are proving to be an effective means of examining the properties of face-specific processes in the human visual system. We examined the role of gender in the neural representation of faces using a contrast-based adaptation method. If faces of different genders share the same representational face space, then adaptation to a face of one gender should affect both same- and different-gender faces. Further, if these aftereffects differ in magnitude, this may indicate distinct gender-related factors in the organization of this face space. To control for a potential confound between physical similarity and gender, we used a Bayesian ideal observer and human discrimination data to construct a stimulus set in which pairs of different-gender faces were equally dissimilar as same-gender pairs. We found that the recognition of both same-gender and different-gender faces was suppressed following a brief exposure of 100 ms. Moreover, recognition was more suppressed for test faces of a different-gender than those of the same-gender as the adaptor, despite the equivalence in physical and psychophysical similarity. Our results suggest that male and female faces likely occupy the same face space, allowing transfer of aftereffects between the genders, but that there are special properties that emerge along gender-defining dimensions of this space.
- SourceAvailable from: Ipek Oruç[Show abstract] [Hide abstract]
ABSTRACT: Faces have both shape and skin texture, but the relative importance of the two in face representations is unclear. Our goals were first, to determine the contribution of shape versus texture to aftereffects for facial age and identity and second, to assess whether adaptation transferred between shape and texture, suggesting integration in a single representation. In our first experiment we examined age aftereffects. We obtained young and old images of two celebrities and created hybrid images, one combining the structure of the old face with the skin texture of the young face, the other combining the young structure with the old skin texture. This allowed us to create adaptation contrasts where the two adapting faces had the same facial structure but different skin texture, and vice versa. In the second experiment, we performed a similar study but this time examining identity aftereffects between two people of a similar age. We found that both skin texture and facial shape generated significant age aftereffects, but the contribution was greater from texture than from shape. Both texture and shape also generated significant identity aftereffects, but the contribution was greater from shape than from texture. In the last experiment, we used the normal and hybrid images to determine if adaptation to one property (i.e., texture) could create aftereffects in the perception of age in the other property (i.e., shape). While there was significant within-component adaptation for texture and shape, there was no evidence of cross-component adaptation. We conclude that shape and texture contribute differently to different face representations, with texture dominating for age. The lack of cross-component adaptation transfer suggests independent encoding of shape and texture, at least for age representations.Cortex 10/2011; · 6.16 Impact Factor
Gender in Facial Representations: A Contrast-Based
Study of Adaptation within and between the Sexes
Ipek Oruc ¸1,2*, Xiaoyue M. Guo1,2,4, Jason J. S. Barton1,2,3
1Department of Ophthalmology and Visual Sciences, University of British Columbia, Vancouver, Canada, 2Department of Medicine (Neurology), University of British
Columbia, Vancouver, Canada, 3Department of Psychology, University of British Columbia, Vancouver, Canada, 4Neuroscience Department, Wellesley College, Wellesley,
Massachusetts, United States of America
Face aftereffects are proving to be an effective means of examining the properties of face-specific processes in the human
visual system. We examined the role of gender in the neural representation of faces using a contrast-based adaptation
method. If faces of different genders share the same representational face space, then adaptation to a face of one gender
should affect both same- and different-gender faces. Further, if these aftereffects differ in magnitude, this may indicate
distinct gender-related factors in the organization of this face space. To control for a potential confound between physical
similarity and gender, we used a Bayesian ideal observer and human discrimination data to construct a stimulus set in which
pairs of different-gender faces were equally dissimilar as same-gender pairs. We found that the recognition of both same-
gender and different-gender faces was suppressed following a brief exposure of 100ms. Moreover, recognition was more
suppressed for test faces of a different-gender than those of the same-gender as the adaptor, despite the equivalence in
physical and psychophysical similarity. Our results suggest that male and female faces likely occupy the same face space,
allowing transfer of aftereffects between the genders, but that there are special properties that emerge along gender-
defining dimensions of this space.
Citation: Oruc ¸ I, Guo XM, Barton JJS (2011) Gender in Facial Representations: A Contrast-Based Study of Adaptation within and between the Sexes. PLoS ONE 6(1):
Editor: Susanne Hempel, Rand, United States of America
Received July 30, 2010; Accepted December 18, 2010; Published January 18, 2011
Copyright: ? 2011 Oruc et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This work was supported by Discovery Grant RGPIN 355879-08 from the Natural Sciences and Engineering Research Council. MG was supported by a
Canadian Institutes of Health Research Summer Studentship from the University of British Columbia graduate program in neuroscience. JB was also supported by
a Canada Research Chair and Senior Scholar Award from the Michael Smith Foundation for Health Research. The funders had no role in study design, data
collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: email@example.com
Adaptation aftereffects are changes in the perception of a
stimulus following exposure to another. Aftereffects are ubiquitous
in the visual system, commonly found for many visual dimensions,
such as luminance, contrast, spatial frequency, orientation and
motion [1,2,3,4,5,6,7] among others. One key aspect of adaptation
aftereffects is their selectivity, in that the size of the aftereffect is
modulated by the similarity between adapting stimuli and test
stimuli. This selectivity is thought to reflect the tuning of the
mechanisms that encode these stimuli . According to this view,
perception is based on a population of units, each tuned to a
limited range of values for a stimulus property (e.g., orientation,
direction of motion, spatial frequency). Adapting to a specific value
of a stimulus property (e.g., high spatial frequency), affects only
those units that respond to that value, and leaves others
unaffected. Thus, adaptation has been successfully used as a
psychophysical tool to infer the tuning properties of various
mechanisms underlying the perception of basic visual properties
such as orientation, spatial frequency and direction of motion
Aftereffects also occur for more complex stimuli, such as faces.
For example, adapting to a male face causes a gender-neutral face
to appear more female . Similar aftereffects have been shown
for many different facial attributes, such as identity, ethnicity,
expression, and viewpoint [12,13,14,15]. Faces are higher-level
stimuli and less is known about their neural encoding compared to
our understanding of lower-level ‘‘channels’’ and their tuning
properties. The systematic examination of how face aftereffects are
modulated by various facial attributes offers a means to explore
how faces are represented in the human visual system.
A commonly used metaphor for the organization of face
representations is the concept of a ‘face space’ [16,17], where faces
are encoded along multiple physical dimensions. Precisely what
physical dimensions are represented in human face space is not yet
clear; nevertheless, it is these attributes that we use to discriminate
one face from another. The concept of face space has been used
successfully to explain various effects of ethnicity in face
processing, as due to denser representation for faces of an ethnic
group not frequently encountered by the observer and therefore,
greater distance between faces of one’s own race than between
faces of the other race [18,19].
How gender is reflected in the organization of face represen-
tations has also been a subject of interest and studied using
adaptation techniques. Studies of gender-contingent aftereffects
and cross-gender transfer of adaptation have yielded a variety of
opinions. Some have suggested that male and female faces
constitute distinct populations that are functionally independent
and show little interaction , which others have interpreted as
possibly suggesting separate face spaces for males and females
PLoS ONE | www.plosone.org1 January 2011 | Volume 6 | Issue 1 | e16251
. Such a strong version of gender-selectivity would have some
difficulty accounting for simple gender aftereffects, in which
adapting to e.g. a male face causes a gender-neutral face to appear
female , and would predict both lack of cross-gender transfer
of adaptation, as well as minimal if any influence of the properties
of different-gender faces in gender-contingent aftereffects. Others
have suggested that male and female faces likely occupy the same
face space, largely on the basis that cross-gender transfer of
adaptation does occur, but with both gender-selective dimensions
and dimensions common to both genders .
If male and female faces are represented in the same face space,
which the best evidence tends to support [14,21,22], it is also
possible to ask a slightly different question: do the dimensions that
define gender differ in some way from other dimensions in face
space? (Note that the concept of ‘gender-defining dimensions’
differs from that of the ‘gender-selective dimensions’ proposed
elsewhere . A gender-selective dimension is a dimension along
which the faces of one gender vary but those of the other gender
do not. A gender-defining dimension is one along which the faces
of both genders vary, but in such a way that those of one gender
tend to have different values than those of the other, permitting
this dimension to contribute to the discrimination of female from
Evidence that gender dimensions have a special status could
come from adaptation studies showing that aftereffects differ
according to whether the adaptor and test faces share the same
gender or not. However, if differences are found, it is possible that
these could simply reflect the fact that, for a given stimulus set, the
stimulus faces of a different gender may be more dissimilar to the
test faces than the stimulus faces of the same gender and therefore
are located further away in face space from the test faces. However,
as others have remarked , male and female faces have a high
an experimental set in such a way that the physical similarity
between different-gender faces is equivalent to that between same-
gender faces. If gender influences are found in adaptation with a
stimulus set that controls for similarity, this would be more definitive
evidence of a gender-specific effect in face-space.
Most previous studies of gender aftereffects have examined
perceptual bias aftereffects, in that they report on relative shifts of
perception along a continuum between two values of a facial
property, to one of which the subject is adapted. Ideally, to
quantify effects in face space it would be helpful to have a reported
metric that is orthogonal and therefore not relative to the facial
dimension being adapted. In this study we use a novel face
adaptation paradigm first introduced in Oruc & Barton  [also
see, 24,25] that measures changes in recognition contrast
thresholds as a result of face adaptation. In more traditional
examples of visual adaptation (e.g. orientation) the impact of
adaptation on the underlying neural mechanisms has commonly
been observed in two main phenomena: selective changes in the
sensitivity to the adapting stimulus, and perceptual bias aftereffects
[e.g., 9]. Thus in orientation adaptation, prolonged exposure to
one specific orientation causes both a selective loss of sensitivity at
this orientation, and also a bias towards perceiving a nearby
orientation as tilted in the opposite direction, e.g., after adapting to
a counterclockwise tilt of 10u, vertically oriented stimuli appear to
have a clockwise tilt [4,11,26,27]. These two phenomena are
thought to be mediated by a common process in which the
response of the adapted unit is selectively suppressed, resulting in
the sensitivity loss that in turn brings about an imbalance in the
population response to a similar stimulus and thus a shifted or
biased percept [3,28]. While many examples of perceptual bias
aftereffects of face adaptation have been reported in the last
decade [12,13,14,15,29,30], studies on how face adaptation affects
sensitivity to faces have generally been lacking.
In Oruc & Barton  we measured changes in contrast
sensitivity for recognizing a face for a wide range of adapting
durations (10ms–6400ms). These results showed that face adapta-
tioninvolves multiple complex effects. First, as expected on the basis
of hypothesized suppression of adapted representations, adaptation
decreased sensitivity for recognizing the same face: however, this
was found only for adapting durations longer than 500ms. At
shorter durations sensitivity actually improved, indicating facilita-
tion and thus imparting a complex non-monotonic pattern to the
temporal dynamic of adaptation. Second, adaptation increased
recognition thresholds for ‘‘different’’ faces, a result not predicted by
simple models based on suppression of adapted representations
alone, but which is suggested by more complex views that
incorporate a degree of lateral inhibition . In Oruc & Barton
 we presented modeling results and discussed various
implications of this pattern of aftereffects on our understanding of
face recognition. In the present study, we chose an adapting
duration to test foreffects ofgender, withthe goal of maximizingthe
ability to detect differences between same- and difference-face
aftereffects, preferably by showing facilitation for the former but
suppression for the latter.
We used recognition contrast thresholds to determine first, if
aftereffects show cross-gender transfer, and second, if these
aftereffects differ when the test stimulus has a different gender
than the adapting stimulus. Our stimulus set was composed of two
male and two female faces, all to be used both as adapting and test
faces, so that in any given trial the test face could be the same as
the adaptor, a different face of the same gender, or a different face
of the other gender (see Figure 1). We chose the particular faces
with a selection process that controlled for similarity as measured
physically by an ideal observer technique and verified psycho-
physically with discrimination experiments in human observers
(Figure 2). If gender effects in adaptation merely reflect the fact
that, on average, faces of a different gender are located further
away in face-space than faces of the same gender, then aftereffects
should be similar in same-gender and different-gender conditions
once we have controlled for similarity. On the other hand, if there
is something distinct about the dimensions that determine gender,
then differences in adaptation effects should still be evident despite
the fact that same-gender and different-gender face pairs are
matched for degree of similarity.
Materials and Methods
The protocol was approved by the review boards of the
University of British Columbia and Vancouver Hospital, and
written informed consent was obtained in accordance with the
principles in the Declaration of Helsinki.
There were nine subjects (five females, ages 18–35) with normal
or corrected vision. All but two of the subjects were naı ¨ve with
respect to the purposes of the experiment.
Subjects were seated 99 cm from the display screen in an
otherwise dark room. The stimuli were presented on a SONY
Trinitron 17-in GDM-G500 monitor at 10246768 resolution and
100Hz refresh rate. A Cambridge Research Systems (CRS) VSG
2/3 video card was used to display stimuli via the CRS VSG
toolbox for Matlab and the Psychophysics Toolbox [32,33]. The
Gender in Neural Representation for Faces
PLoS ONE | www.plosone.org2 January 2011 | Volume 6 | Issue 1 | e16251
display was gamma-corrected using an OptiCAL photometer
(Model OP200-E) and software by CRS. Mean luminance was
Two female and two male faces with neutral expression from
the Karolinska Database of Emotional Faces  were used as
stimuli (see Figure 1). Selection of these stimuli was based on two
criteria: gender ratings and discriminability.
28 male and 33 female faces in the database were first converted
to grayscale using Adobe Photoshop (www.adobe.com). An oval
mask that was 2836400 pixels in size was superimposed onto the
face images. 15 volunteers were asked to rate the faces in terms of
their femaleness or maleness on an 11 point scale, 0 and 10
representing the highest maleness and femaleness scores, respec-
tively. Raters viewed and scored faces in a random order. The
average rating for the female faces was 7.161.5 (mean 6 sd), and
the average rating for the male faces was 2.261.3. Six female faces
with the top femaleness scores (ranging between 8.2–8.7), and 6
male faces with the top maleness scores (ranging between 1.1–1.4)
were selected as candidate stimuli. The 12 candidate faces were
then aligned horizontally and vertically with respect to the oval
mask such that the tip of the nose and pupils were level across all
faces. The luminance values inside the oval mask were normalized
for each face such that mean luminance was equal to half
maximum luminance (i.e., 35 cd/m2), and the root-mean-squared
(rms) contrast (defined as the standard deviation of luminance
divided by mean luminance) was set to 1. Luminance outside the
oval mask was set to half the maximum luminance. At this point,
the faces that contained trivially distinguishing marks (such as hair
visible inside the oval mask, moles, etc.) were eliminated which
resulted in the exclusion of 3 of the candidate male faces. The
remaining set of 9 faces (6 female, 3 male) were submitted to a
discriminability analysis using a Bayesian ideal observer simulation
To avoid confounding gender differences with physical
similarity, we aimed to create a stimulus set (comprising 2 female
and 2 male faces) where pair-wise similarities between all pairs of
faces were equal (see Figure 2A for an illustration). This is
equivalent to picking four pair-wise equidistant points in face
Figure 1. Illustration of a typical trial. Each trial starts with a 100-ms adapting period during which either one of four faces (two females and two
males) or a blank stimulus is shown. Following this is a 50-ms noise mask, a 150-ms fixation cross, and a 150-ms blank. After this a test face is shown
for 150 ms. The test face is randomly chosen from the same set of four faces that were used as adapting stimuli. The task of the subject is to indicate
which one of the four test faces they saw by choosing it from a choice display that remained until the subject entered their response. Contrast
thresholds were measured for recognizing each face in a 4-alternative forced-choice (4-AFC) paradigm. A psychophysical staircase controls the
contrast of the test face at each trial to estimate thresholds for 82% accuracy. The adapting faces are shown at a fixed rms contrast of 60%. The 20
adapting/test stimulus pairs (five adapt6four test) were further classified into three main conditions: (1) the same-face condition, where the test and
adapting faces were the same, (2) the same-gender condition, where they were different faces of the same gender, (3) the different-gender condition,
where they were different faces of different genders, in addition to the baseline condition, where the adapting stimulus was a blank.
Gender in Neural Representation for Faces
PLoS ONE | www.plosone.org3January 2011 | Volume 6 | Issue 1 | e16251
space. How do we accomplish this? If we knew what the
dimensions and the distance metric of face space were then it
would simply be a matter of computing pair-wise distances across
many pairs of faces and finding a set of four (two males and two
females) where all pair-wise distances (a total of six distances given
by four choose two) are equal. Lacking this information we used an
indirect way to measure distances in face space. While the
dimensions of face space are unknown, it is commonly agreed that
they are those attributes that enable discrimination of faces. In this
respect, discriminability of faces provides a distance measure. We
measured discrimination contrast thresholds for a Bayesian ideal
observer and two human observers, and used these results to guide
the selection of our stimuli (Figure 2).
A total of 45 distinct sets of four faces with two faces from each
gender can be constructed using the 6 female and 3 male
candidate faces obtained. To ascertain the suitable stimulus sets
Figure 2. Selection of face stimuli. (A) Our stimulus set contains four faces, two male and two female. In general, the physical dissimilarity
between different-gender face pairs would on average be larger than that between same-gender pairs, because a male and a female face differ in
those attributes that determine gender in addition to all the other ways in which faces can vary. Therefore, in a randomly selected stimulus set,
gender and physical similarity are potentially confounded. To prevent this confound we set out to assemble a set of four faces in which physical
dissimilarity between all pairs are equivalent. If the dimensions of the representational space of faces were known, this would simply be a matter of
selecting four pair-wise equidistant points in this space. An illustration of such an arrangement is shown. (B) Although dimensions of face space are
unknown it is generally accepted that they are those qualities that enable discrimination. In other words, the more similar faces are to each other, the
closer they are in face space and the harder it is to discriminate between them. We used a Bayesian ideal observer in a two-alternative forced-choice
(2-AFC) task to measure discrimination thresholds between all pairs of faces for a large number of candidate stimulus sets. The ideal observer
discrimination contrast thresholds are shown on the left for the best choice that provided the closest approximation to equal discrimination
thresholds across all six pairings (a set of two males and two females yield two same-gender and four different-gender pairs). Importantly, for this set
of stimuli, discriminating the same-gender face pairs (blue) was as easy as discriminating the different-gender face pairs (red). This was verified by
similar results obtained from two human observers (shown on the right).
Gender in Neural Representation for Faces
PLoS ONE | www.plosone.org4 January 2011 | Volume 6 | Issue 1 | e16251
among these that meet the condition of equal discriminability
between all pairs, i.e., pairs of same-gender faces equally
distinguishable as pairs of different-gender faces, we submitted
all 45 potential stimulus sets to an ideal observer analysis .
Each potential stimulus set contained 2 female and 2 male faces.
This gave 6 pair-wise comparisons within a given stimulus set, two
of which were same-gender, and the remaining four were
different-gender pairs. A separate discrimination contrast thresh-
old was measured for each pair of faces in a two-alternative forced-
choice (2-AFC) paradigm. At each trial one of the two faces, Fi,
i=1,2, was chosen at random as the test face and shown at
variable contrast with added white noise of fixed variance. The test
face contrast (controlled by a staircase procedure), the variance of
the Gaussian noise (fixed throughout), and the prior probability of
either face being presented (equal), were known to the ideal
observer. The Bayesian ideal observer based its response on
maximum posterior probability, which was equivalent to a
minimum distance rule given equal prior probability for the two
test faces and our use of Gaussian white noise  given by
template at contrast c. Further details of this model can be found in
Fox, Oruc, & Barton . The contrast of the test face was
controlled by a 40-trial staircase that estimated threshold at 82%
accuracy. The ideal observer’s contrast threshold for each pair of
faces was measured independently 200 times, and the average is
This procedure was repeated for each of the 45 potential
stimulus sets to determine the most suitable candidate set of four
faces. Note that the specific values of the ideal observer’s
thresholds were arbitrary as they depend on the noise variance.
Rather, what we are looking for is that the 6 thresholds measured
for all face pairs in a candidate stimulus set be approximately
equal. Upon visual inspection of the ideal observer results for all 45
potential stimulus sets, we selected the set shown in Figure 1. In
Figure 2B, left panel, discrimination thresholds of the ideal
observer for the six pairs of faces are plotted, in blue for same-
gender pairs and in red for different gender pairs. The data plot is
quite flat, indicating that all face pairs in this stimulus set were
approximately equally discriminable. Most importantly, the same-
gender thresholds (blue) were not higher than different-gender
thresholds (red) indicating that the similarity among same-gender
faces was equivalent to the similarity among different-gender faces.
After using these ideal observer data to select the candidate
stimulus set, we then tested two human observers on this stimulus
set, to confirm that human perceptual discriminability follows that
of physical discriminability with the ideal observer. There was
substantial agreement between the human and ideal observer
results (Figure 2B, right panel), indicating that both physical and
psychophysical similarity between different-gender face pairs was
not significantly different than that of the same-gender face pairs,
in the stimulus set shown in Figure 1.
Finally, to make sure that our face images, which were equated
for pair-wise discriminability, do not retain other distinctions that
confer differences in the appearance of female and male faces, we also
collected similarity ratings. We asked 15 naı ¨ve volunteers to rate the
similarity of all six face pairs (two same-gender, four different-
gender) on a 11-point scale, 0 and 10 representing the lowest and
highest similarity, respectively. A Tukey-Kramer multiple compar-
ison test showed no significant differences in the ratings obtained for
the six pairs. In addition, a linear contrast (26same – different)
showed no significant difference (p.0.5) between same-gender
versus different-gender ratings (3.73 versus 3.54). Thus, the
similarity ratings are in line with our discriminability results,
eliminating perceived appearance as a potential confound.
ðÞ2, where S is the noisy stimulus, and Fi,cis the ithface
We measured recognition contrast thresholds in a four-
alternative forced-choice (4-AFC) paradigm and examined how
thresholds were impacted as a result of adapting to a face. Each
trial started with a 100-ms adaptation to one of four faces (two
males and two females), or a blank (see Figure 1). Previously Oruc
& Barton  have shown that a 100-ms exposure to the adapting
face lowers thresholds in the same-face condition below that of
baseline (threshold change ratio=0.67), but elevates thresholds in
the different-face condition above baseline (threshold change
ratio=1.21). We chose this adapting duration to take advantage of
the cost of a nearly two-fold increase in recognition thresholds
associated with adapting to a different face compared to adapting
to the same face. The adapting stimulus was followed by a 50-ms
white noise mask, a 150-ms fixation period, and a 150-ms blank.
Then the test stimulus, one of four faces chosen at random, was
shown at low contrast for 150 ms. After a 150-ms blank, an answer
display showing all four faces was presented until the subject
indicated which one of the four faces they saw by a key-press.
Auditory feedback was provided: a single click for a correct
response and a double-click for an incorrect response. The
contrast threshold for each of 20 adapt-test pairs (five adaptors6
four test faces) were measured separately via 20 randomly
interleaved staircases each of which ran for a fixed length of 40
To minimize the contribution of aftereffects from lower-level
image properties in our face adaptation paradigm, we incorpo-
rated a size and location mismatch between the adapting and test
stimuli. The adapting face was presented centrally at fixation with
a fixed rms contrast of 0.6. The test faces were 10% smaller in size
and were presented 1u left or right of fixation determined
randomly at each trial. Since the subjects did not know whether
the test face would be displayed on the left or right at any given
trial, they were instructed to fixate in the center. The duration of
the test face was too short (150 ms) for the subjects to make a
foveating saccade and therefore the test faces were viewed slightly
peripherally relative to the adapting stimulus. The contrast of the
test faces were determined at each trial by a psychophysical
staircase implemented in the Psychophysics Toolbox [32,33] based
on the Quest procedure .
To familiarize the subjects to the 4-AFC task and the four faces
used in the experiment, a short training session was provided prior
to the adaptation experiment. During the training, subjects
performed the 4-AFC recognition task (without any adaptation
period) for four blocks of 40-trials each, or until their performance
stabilized determined based on visual inspection of their thresholds
in each block.
There were three main conditions: (1) same-face, where the
adapting and test faces were the same, (2) same-gender, where the
test face was a different face of the same gender, and (3) different-
gender, where the test face was a different face of the different
gender, in addition to a baseline condition, where the adapting
stimulus was a blank. A threshold change ratio was computed by
dividing the contrast threshold measured for a given adapting
condition by that of the corresponding baseline condition (i.e. for
that particular test face). If adaptation does not affect performance,
then the threshold change ratio would be 1. Values above 1
represent impairment of performance (i.e., threshold elevation),
and values below 1 represent facilitation (i.e., threshold reduction).
For each subject threshold change ratios for the three main
conditions were given by geometric averages of the corresponding
Gender in Neural Representation for Faces
PLoS ONE | www.plosone.org5 January 2011 | Volume 6 | Issue 1 | e16251
test-adapt face pairs. Group data was in turn obtained by taking
geometric averages across all subjects.
Threshold change ratio data were then submitted to a Kruskal-
Wallis one-way ANOVA to test for a main effect of condition
(same-face, same-gender, and different-gender). This was followed
by pair-wise comparisons between the three main conditions, as
well as between each condition and the baseline using Wilcoxon
signed-rank tests. Error bars for the group data were obtained by a
non-parametric bootstrap simulation , in which the individual
data ware re-sampled with replacement a large number of times
and analyzed the same way as the experimental data. 68%
confidence intervals were then obtained by sorting the resulting
data set and selecting the upper and lower 16thpercentile values
separately for each condition.
Contrast thresholds for recognizing faces in a four-alternative
forced-choice (4-AFC) task were measured following exposure to
the same face (the same-face condition), a different face of the same
gender (the same-gender condition), and a different face of the other
gender (the different-gender condition). Threshold change ratios were
computed by dividing the contrast thresholds in each condition by
the baseline threshold determined by using a blank adaptor.
Figure 3 shows threshold change ratios plotted for the three main
conditions. There was a significant main effect of condition
(p,0.01). Adapting to the same face showed a trend to decreased
thresholds below baseline (p=0.07), while adapting to a different
face of either gender significantly increased thresholds above
baseline (both p’s,0.02). All pair-wise comparisons between the
three main conditions were significant, with the same-face condition
significantly lower than both the same-gender and the different-gender
conditions (both p’s,0.02), and, most importantly, the same-gender
condition significantly lower than the different-gender condition
We showed previously that at brief adapting durations an
adapting face lowers contrast recognition thresholds for that face
but elevates thresholds for other faces, compared to baseline
performance [23,24,25]. The results of the current experiment
replicate this pattern. In addition, our results showed two
important findings regarding adaptation effects in the different-
gender condition. The first is that there was an aftereffect
resembling that in the same-gender condition, consistent with
cross-gender transfer of adaptation. The second is that the
congruency of gender did influence adaptation, with greater
elevation of thresholds occurring in the different-gender than the
same-gender condition, even though we had controlled for
The finding of cross-gender transfer of adaptation is consistent
with a number of previous reports that showed either complete
cross-gender transfer for expression  and shape aftereffects
, or partial cross-gender transfer of ethnicity aftereffects .
These contrast with another report that variations in the degree of
‘average-ness’ or sexual dimorphism did not generate cross-gender
aftereffects in ratings of attractiveness . This discrepancy is
likely due to the differences in the properties being adapted in each
study. While the structural properties underlying expression, shape
and ethnicity are likely similar for both female and male faces,
those determining attractiveness differ at least partly between male
and female faces . For example, the role of sexual dimorphism
may be not only opposite but also asymmetric, with highly
feminized faces being most attractive in female faces, but average
or only moderately masculinized faces being preferred for male
faces. The complex differences in what makes a male versus a
female face attractive complicate the interpretation of the results of
Little et al. . If the dimensions in face space that generate
attractiveness for male faces differ from those for female faces, this
alone can account for minimal cross-gender transfer of adaptation
for attractiveness ratings. This would imply that such data cannot
be taken as evidence of separate neural populations for male and
Another class of finding used to support the possibility of
gender-selective mechanisms or distinct populations is that of
gender-contingent aftereffects. This has been shown for inter-
ocular distance , facial contraction/expansion , ethnicity
[22,30] and perceived normality of facial structure . For
example, viewing both males with close-set eyes and females with
wide-set eyes in the same adapting period leads to a perception
that male test images with more wide-set eyes and female test
images with more close-set eyes are more normal in appearance
. As has been pointed out by Ng et al. , completely
separate representations for each gender would predict gender-
contingent aftereffects as large as same-gender aftereffects when
viewing a single face or only faces of one gender, because the
presence or absence of the face of the other gender would be
irrelevant. However, in those experiments that measured both
gender-contingent and same-gender aftereffects, the gender-
contingent aftereffects have been weaker than same-gender
The fact that both cross-gender adaptation transfer occurs and
that gender-contingent aftereffects exist has led to proposals that
multiple mechanisms are involved – ‘‘common and sex-selective
mechanisms’’  or ‘‘singly and jointly tuned mechanisms’’ .
Figure 3. Experimental results. Threshold change ratios for the
three main conditions are computed by dividing the contrast threshold
in each case with the corresponding baseline threshold, such that a
value of one indicates no effect of adaptation. Values below one
represent lowered thresholds, i.e., a facilitation effect, and values above
one represent elevated thresholds, i.e., a suppression effect. Geometric
averages across nine subjects are shown. Adapting to the same face as
the test face lowered thresholds below baseline performance, and
adapting to a different face elevated thresholds. Most importantly,
adapting to a different face elevated thresholds even more if it also
differed in gender from the test face. Stars represent significant pair-
wise differences. Errorbars are 68% bootstrap confidence intervals.
Gender in Neural Representation for Faces
PLoS ONE | www.plosone.org6 January 2011 | Volume 6 | Issue 1 | e16251
However, it is also possible, and perhaps more parsimonious, to
explain these effects as naturally arising from partially overlapping
distributions in a single face space of the representations of male
and female faces, if aftereffects have some selectivity for the region
of face space in which they are evoked (Figure 4). The fact that
male and female faces are located in the same face space accounts
for cross-gender transfer of adaptation; the fact that they occupy
slightly different regions of face space can account for smaller
aftereffects when gender is incongruent between test and adaptor
. Larger aftereffects in same-gender than in cross-gender
conditions will generate gender-contingent aftereffects, though
these will be weaker than same-gender aftereffects, since gender-
contingent effects emerge from a balance between the same-
gender and cross-gender effects in these paradigms.
So far then, the results of previous studies can be explained by
spatially separated but not independent distributions of female and
male face representations in a single face space. The spatial
separation is not controversial, as our ability to distinguish male
from female faces indicates that they must differ along some
dimensions in face space. However, if faces of different genders
occupy differing portions of a single face space, then it becomes
important to determine whether gender-related effects in specific
experiments can be accounted for simply by greater separation in
face space between stimuli differing in gender than between stimuli
of the same gender. If so, this would imply that there is no special
status derived from the category of gender, vis a ` vis other sources
of structural variation in faces. Few studies to date have attempted
to control for face similarity. One study of gender-contingent
aftereffects used a prototype-based transformation method to
create pairs of female/male faces and pairs of female/hyper-
female faces, in which the physical image differences within the
female/male pair were linearly similar to those within the female/
hyper-female pair . This study reported contingent aftereffects
only when male and female faces were paired, not when female
and hyper-female faces were paired. This suggests a categorical
effect of gender beyond the effects of physical similarity. However,
it is not know whether the metrics of face space follow the linear
distance metrics used to make physically equivalent stimuli in this
experiment, and the authors acknowledged that controlling for
physical difference in a two-dimensional image does not guarantee
equivalent perceptual differences between faces.
Nevertheless, our second finding, that same-gender aftereffects
differ from different-gender aftereffects, also supports a conclusion
that the effects of gender are not reducible to the psychophysical
separation of male and female faces in this face space. We used a
different approach to control similarity in same-gender and
different-gender pairs than that used by , with measures that
equated both physical and perceptual differences in the stimuli. In
the absence of knowledge of the dimensions that define face space,
we, like others in the past [18,42,43], used measures of
distinctiveness or similarity to index the distance between
representations in face space. Our ideal observer and human
observer data show that, in both physical and psychophysical
terms, the faces in our stimulus set were as similar to the faces of
the opposite gender in the set as much as they were to faces of the
same gender. Despite this, adaptation increased thresholds more
for different-gender faces than for same-gender faces. This suggests
that there is something anomalous occurring along gender-
relatedz dimensions in face space. The nature of this anomaly is
not yet clear, but there are several potential, inter-related
Figure 4. Hypothetical model predictions for magnitude of figural aftereffects in same-gender, cross-gender and gender-
contingent conditions based on proximity in face space. (A) Effects of adaptation are dependent on the similarity between the adapting and
test stimuli. Perceptual aftereffects peak at a neighboring location, then gradually fall off as the test stimuli become more dissimilar. For example, the
effect of adapting to a contracted female face will have greater impact on a female test face (red curve, ‘a’) than a male test face (red curve, ‘d’) simply
due to greater similarity between faces of the same gender compared to faces of different genders. The result is cross-gender transfer of aftereffects
(‘d’) that is less than the aftereffect for the same-face (‘a’). The same logic applies to the effects of adapting to an expanded male face (blue curve). (B)
Gender-contingent aftereffects are obtained by simultaneously adapting to a male and a female face with opposing figural distortions. The fact that
contingent aftereffects are usually found to be smaller in magnitude than same-gender aftereffects are predicted by an additive effect of the
simultaneous adaptation: Adapting to a contracted female face generates a large perceptual bias on a female test face (red curve, ‘a’), which is offset
by a smaller but opposite bias caused by adapting to an expanded male face (blue curve, ‘b’). Thus the contingent aftereffect magnitude ‘c’ will be
equivalent to the same-gender aftereffect ‘a’ reduced by the cross-gender aftereffect ‘b’.
Gender in Neural Representation for Faces
PLoS ONE | www.plosone.org7 January 2011 | Volume 6 | Issue 1 | e16251
explanations upon which one could speculate. One may be that
distances in face-space do not reflect merely similarity, but include
factors that distort or increase perceptual distances along gender-
defining dimensions. How this would occur is not clear. However,
one possible physiological basis for such an effect might be that
there are differences in lateral connectivity between representa-
tions along gender-defining dimensions compared to other
dimensions, so that the spread of adaptation-related activity differs
when gender changes. This would be in line with the suggestion
that adaptation suppresses the representation of other faces
through lateral inhibition [23,31], causing the elevated contrast
recognition thresholds we observe in the different-face conditions
in this and other similar experiments [23,24,25]. Last, from a
cognitive point of view, it may be that this anomaly is introduced
by the crossing of a categorical boundary, which during adaptation
confers an added degree of suppression upon representations on
the other side of the boundary. A categorical effect would also be
supported by the finding of  that, with hyper-female, female
and male faces all modified along the same physical dimensions,
contingent aftereffects arise only when the paired faces differ in
gender. It is also consistent with previous work using classic
paradigms that show better discrimination across categorical
boundaries then within categories, which conclude that such
effects can be found for face gender [44,45]. just as they can be
found for face identity  and face expression [47,48].
Of course, these different levels of explanation are not mutually
exclusive. The physiological foundations of the categorical effect in
high-level vision remain elusive: others have pointed out that the
‘‘mechanisms by which faces are perceived categorically have yet
to be adequately accounted for by any theoretical approach’’ .
Some suggest that part of the categorical effect reflects a ‘between-
categorical separation effect’, in which acquired distinctiveness
reflects ‘‘an increase in perceptual sensitivity to differences that are
relevant for a categorization’’ . As is evident, this corresponds
closely to the first proposal of distortion or increase in perceptual
distances along gender dimensions and it may be that increased
lateral inhibition along gender dimensions could be one means of
achieving this in physiological terms.
In conclusion, while our results and those of others best support
overlapping or adjacent distributions of male and female face
representations within the same face space, our findings go beyond
those of prior studies, to provide evidence of additional effects
introduced by the category of gender, such that adaptation is
associated with even greater suppression of different-gender face
representations. Thus, while it is unlikely that male and female
faces occupy separate and independent face spaces, gender may
nevertheless confer some special status to certain dimensions
within face space.
Results were presented at the annual meeting of the Vision Sciences
Society, Naples 2009.
Conceived and designed the experiments: IO MG JS. Performed the
experiments: IO MG. Analyzed the data: IO. Wrote the paper: IO MG JS.
1. Blakemore C, Campbell FW (1969) On the existence of neurones in the human
visual system selectively sensitive to the orientation and size of retinal images.
J Physiol 203: 237–260.
2. Blakemore C, Sutton P (1969) Size adaptation: a new aftereffect. Science 166:
3. Clifford CW (2002) Perceptual adaptation: motion parallels orientation. Trends
Cogn Sci 6: 136–143.
4. Gibson JJ, Radner M (1937) Adaptation, after-effect, and contrast in the
perception of tilted lines. I. Quantitative studies. Journal of Experimental
Psychology 20: 453–467.
5. Shapley R, Enroth-Cugell C (1984) Visual Adaptation and Retinal Gain
Controls. In: Osborne N, Chader G, eds. Progress in Retinal Research. London:
Pergamon. pp 263–346.
6. Anstis S, Verstraten AJ, Mather G (1998) The motion aftereffect. Trends in
Cognitive Sciences 2: 111–117.
7. Levinson E, Sekuler R (1976) Adaptation alters perceived direction of motion.
Vision Res 16: 779–781.
8. Graham N (1989) Visual pattern analyzers. New York: Oxford University Press.
9. Blakemore C, Nachmias J (1971) The orientation specificity of two visual after-
effects. J Physiol 213: 157–174.
10. Sekuler RW, Rubin EL, Cushman WH (1968) Selectivities of human visual
mechanisms for direction of movement and contour orientation. J Opt Soc Am
11. Regan D, Beverley KI (1985) Postadaptation orientation discrimination. J Opt
Soc Am A 2: 147–155.
12. Webster MA, Kaping D, Mizokami Y, Duhamel P (2004) Adaptation to natural
facial categories. Nature 428: 557–561.
13. Leopold DA, O’Toole AJ, Vetter T, Blanz V (2001) Prototype-referenced shape
encoding revealed by high-level aftereffects.[see comment]. Nature Neurosci-
ence 4: 89–94.
14. Fox CJ, Barton JJ (2007) What is adapted in face adaptation? The neural
representations of expression in the human visual system. Brain Res 1127:
15. Fang F, He S (2005) Viewer-centered object representation in the human visual
system revealed by viewpoint aftereffects. Neuron 45: 793–800.
16. Valentine T (1991) A unified account of the effects of distinctiveness, inversion,
and race in face recognition. Q J Exp Psychol A 43: 161–204.
17. Valentine T (2001) Face-space models of face recognition. In: Wenger MJ,
Townsend JT, eds. Computational geometric and process perspectives on facial
cognition: contexts and challenges. MahwahN.J.: Lawrence Erlbaum Associates,
Inc. pp 83–113.
18. Valentine T, Endo M (1992) Towards an exemplar model of face processing: the
effects of race and distinctiveness. Q J Exp Psychol A 44: 671–703.
19. Valentine T, Chiroro P, Dixon R, eds. (1995) An account of the own-race bias
and the contact hypothesis based on a ‘face space’ model of face recognition.
New York: Routledge.
20. Little AC, DeBruine LM, Jones BC (2005) Sex-contingent face after-effects
suggest distinct neural populations code male and female faces. Proc Biol Sci
21. Jaquet E, Rhodes G (2008) Face aftereffects indicate dissociable, but not distinct,
coding of male and female faces. J Exp Psychol Hum Percept Perform 34:
22. Ng M, Boynton GM, Fine I (2008) Face adaptation does not improve
performance on search or discrimination tasks. Journal of Vision 8: 1–20.
23. Oruc I, Barton JJ (2010) A novel face aftereffect based on recognition contrast
thresholds. Vision Res 50: 1845–1854.
24. Guo XM, Oruc I, Barton JJ (2009) Cross-orientation transfer of adaptation for
facial identity is asymmetric: a study using contrast-based recognition thresholds.
Vision Res 49: 2254–2260.
25. Rostamirad S, Barton JJ, Oruc I (2009) Center-surround organization of face-
space: evidence from contrast-based face-priming. Neuroreport 20: 1177–1182.
26. He S, MacLeod DI (2001) Orientation-selective adaptation and tilt after-effect
from invisible patterns. Nature 411: 473–476.
27. Magnussen S, Johnsen T (1986) Temporal aspects of spatial adaptation. A study
of the tilt aftereffect. Vision Res 26: 661–672.
28. Coltheart M (1971) Visual feature-analyzers and after-effects of tilt and
curvature. Psychol Rev 78: 114–121.
29. Webster MA, MacLin OH (1999) Figural aftereffects in the perception of faces.
Psychon Bull Rev 6: 647–653.
30. Ng M, Ciaramitaro VM, Anstis S, Boynton GM, Fine I (2006) Selectivity for the
configural cues that identify the gender, ethnicity, and identity of faces in human
cortex. Proc Natl Acad Sci U S A 103: 19552–19557.
31. Huber DE, O’Reilly RC (2003) Persistence and accommodation in short-term
priming and other perceptual paradigms: temporal segregation through synaptic
depression. Cognitive Science 27: 403–430.
32. Brainard DH (1997) The Psychophysics Toolbox. Spatial Vision 10: 433–436.
33. Pelli DG (1997) The VideoToolbox software for visual psychophysics:
transforming numbers into movies. Spatial Vision 10: 437–442.
34. Lundqvist D, Litton JE (1998) The Averaged Karolinksa Directed Emotional
Faces - AKDEF. CD ROM from Clinical Neuroscience, psychology section,
35. Green DM, Swets JA (1988) Signal detection theory and psychophysics. Los
Altos, CA: Peninsula Publishing.
Gender in Neural Representation for Faces
PLoS ONE | www.plosone.org8 January 2011 | Volume 6 | Issue 1 | e16251
36. Tjan BS, Braje WL, Legge GE, Kersten D (1995) Human efficiency for
recognizing 3-D objects in luminance noise. Vision Res 35: 3053–3069.
37. Fox CJ, Oruc I, Barton JJ (2008) It doesn’t matter how you feel. The facial
identity aftereffect is invariant to changes in facial expression. J Vis 8: 11 11–13.
38. Watson AB, Pelli DG (1983) QUEST: a Bayesian adaptive psychometric
method. Perception & Psychophysics 33: 113–120.
39. Efron B, Tibshirani RJ (1993) An introduction to the bootstrap. New York:
40. Rhodes G (2006) The evolutionary psychology of facial beauty. Annu Rev
Psychol 57: 199–226.
41. Bestelmeyer PE, Jones BC, Debruine LM, Little AC, Perrett DI, et al. (2008)
Sex-contingent face aftereffects depend on perceptual category rather than
structural encoding. Cognition 107: 353–365.
42. Sergent J (1984) An Investigation into Component and Configural Processes
Underlying Face Perception. British Journal of Psychology 75: 221–242.
43. Johnston RA, Milne AB, Williams C, Hosie J (1997) Do distinctive faces come
from outer space? An investigation of the status of a multidimensional face-
space. Visual Cognition 4: 59–67.
44. Campanella S, Chrysochoos A, Bruyer R (2001) Categorical perception of facial
gender information: behavioural evidence and the face-space metaphor. Visual
Cognition 8: 237–262.
45. Bu ¨lthoff I, Newell FN (2004) Categorical perception of sex occurs in familiar but
not unfamiliar faces. Visual Cognition 11: 823–855.
46. Beale JM, Keil FC (1995) Categorical effects in the perception of faces.
Cognition 57: 217–239.
47. Etcoff NL, Magee JJ (1992) Categorical perception of facial expressions.
Cognition 44: 227–240.
48. Young AW, Rowland D, Calder AJ, Etcoff NL, Seth A, et al. (1997) Facial
expression megamix: tests of dimensional and category accounts of emotion
recognition. Cognition 63: 271–313.
Gender in Neural Representation for Faces
PLoS ONE | www.plosone.org9January 2011 | Volume 6 | Issue 1 | e16251