Journal of Vision (2010) 10(3):16, 1–10

http://journalofvision.org/10/3/16/

1

Looking away from faces: Inﬂuence of high-level visual
processes on saccade programming
Stéphanie M. MorandCentre for Cognitive Neuroimaging,
Department of Psychology, University of Glasgow,
Glasgow, UK

Marie-Hélène Grosbras

Centre for Cognitive Neuroimaging,
Department of Psychology, University of Glasgow,
Glasgow, UK

Roberto Caldara

Centre for Cognitive Neuroimaging,
Department of Psychology, University of Glasgow,
Glasgow, UK

Monika Harvey

Centre for Cognitive Neuroimaging,
Department of Psychology, University of Glasgow,
Glasgow, UK

Human faces capture attention more than other visual stimuli. Here we investigated whether such face-speciﬁc biases rely
on automatic (involuntary) or voluntary orienting responses. To this end, we used an anti-saccade paradigm, which requires
the ability to inhibit a reﬂexive automatic response and to generate a voluntary saccade in the opposite direction of thestimulus. To control for potential low-level confounds in the eye-movement data, we manipulated the high-level visualproperties of the stimuli while normalizing their global low-level visual properties. Eye movements were recorded in
21 participants who performed either pro- or anti-saccades to a face, car, or noise pattern, randomly presented to the left or
right of a ﬁxation point. For each trial, a symbolic cue instructed the observer to generate either a pro-saccade or an antisaccade. We report a signiﬁcant increase in anti-saccade error rates for faces compared to cars and noise patterns, as well
as faster pro-saccades to faces and cars in comparison to noise patterns. These results indicate that human faces induce
stronger involuntary orienting responses than other visual objects, i.e., responses that are beyond the control of theobserver. Importantly, this involuntary processing cannot be accounted for by global low-level visual factors.
Keywords: eye movements, anti-saccade task, face perception, top-down control, automatic processing
Citation: Morand, S. M., Grosbras, M.<H., Caldara, R., & Harvey, M. (2010). Looking away from faces: Inﬂuence of high-levelvisual processes on saccade programming. Journal of Vision, 10(3):16, 1–10, http://journalofvision.org/10/3/16/,
doi:10.1167/10.3.16.

Introduction
A face is a salient stimulus conveying crucial information for social interactions and several lines of evidence
have suggested that human faces capture attention much
more than other stimulus categories (see Palermo &Rhodes, 2007 for review): developmental studies haveshown that newborns and infants track faces preferentiallyover scrambled faces and inverted faces (Johnson,
Dziurawiec, Ellis, & Morton, 1991; Morton & Johnson,
1991; but see Simion, Leo, Turati, Valenza, & Dalla
Barba, 2007). Moreover, patients with hemispatial visualneglect are more sensitive to faces in their neglected
hemifield than to other stimuli (Vuilleumier, 2000).
Numerous behavioral studies have further implied a
special capacity of faces to recruit attention. Changes todoi: 1 0. 11 67 / 1 0 . 3 . 1 6

faces are detected both more rapidly and more accurately
than changes to objects when competing for attentional
resources (Palermo & Rhodes, 2003; Ro, Russell, &Lavie, 2001). It has also been reported that faces “hold”
attention more than other objects in spatial cueing tasks,
i.e., it is harder to move attention away from faces(Bindemann, Burton, & Jenkins, 2005), although thiseffect can be modulated by manipulating stimulus configuration, observer expectation, and cue predictiveness(Bindemann, Burton, Langton, Schweinberger, & Doherty,2007).
In recent visual search studies, it has further been shownthat faces can be searched for efficiently (i.e., pop out)
when presented among different types of non-face objects(Hershler & Hochstein, 2005, 2006; VanRullen, 2006),
contradicting previous research that failed to show pop
out, albeit under different conditions (Brown, Huey, &Received September 11, 2009; published March 30, 2010

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ISSN 1534-7362 * ARVO

Journal of Vision (2010) 10(3):16, 1–10

Morand, Grosbras, Caldara, & Harvey

Findlay, 1997; Kuehn & Jolicoeur, 1994; Nothdurft, 1993;
Purcell, Stewart, & Skov, 1996). Using realistic humanfaces and a variety of photographs of objects, Hershler
and Hochstein (2005) found a visual search asymmetryadvantage of faces over cars and houses (although notanimal faces), suggesting that this pop-out effect might beface specific. When they replaced the original photographs
by scrambled images in which the whole facial configuration was disrupted, the pop-out effect disappeared,
leading the authors to conclude that the pop-out effectreflects high-level, holistic parallel processing of faces.
Yet, while replicating the original finding by Hershler andHochstein, VanRullen (2006) reported that impairing theholistic processing by inverting faces had only a minor
effect on search performance; he argued that the face popout effect may be explained by low-level differences
between faces and other categories, such as the Fourier
amplitude spectrum (see also Honey, Kirchner, & VanRullen, 2008). Thus there is still a controversy as towhether the face pop-out effect is driven by low-levelvisual factors or depends on high-level mechanisms.
Here, we used the anti-saccade paradigm (Hallett, 1978)
to investigate whether biases toward faces rely on
automatic (involuntary), stimulus driven or voluntary(task driven) responses. In this task, participants are
required to saccade away from a visual object, that is, togenerate an eye movement in the opposite direction of thestimulus, to its mirror position. To correctly perform ananti-saccade, participants have to inhibit the stimulusdriven response to the target and instead generate avoluntary orienting response in the opposite direction(Connolly, Goodale, Desouza, Menon, & Villis, 2000;
Hallett, 1978). Thus, the anti-saccade task is a classic
paradigm to assess voluntary control over stimulus input(Everling & Fisher, 1998; Munoz & Everling, 2004).
Anti-saccade error rates, i.e., the number of involuntarysaccades toward the stimulus, reflect the orienting
response that is beyond the control of the participant.
Pro-saccade and anti-saccade trials can be randomlyinterleaved in the same block of trials and the instruction
to which type of eye movement to generate can beconveyed by a symbolic cue at the fixation point.
Although this paradigm is widely used in studies of visual
attention and clinical neuroscience, it has (as far as we areaware) only once been applied to investigate faceprocessing, despite being a most sensitive means of
dissociating voluntary from automatic face responses.
Gilchrist and Proske (2006) investigated how the visualproperties of a face stimulus influence saccadic programming. In a paradigm similar to the one described above,
participants were instructed to perform saccades awayfrom upright and inverted face stimuli. The authors foundan increase in the anti-saccade error rate for uprightcompared to inverted faces. As the overall low-levelvisual properties of the upright and inverted face stimuli
were identical and only the high-level processing between

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these stimuli changed, the authors concluded that the
involuntary saccadic orienting response was influenced by
the high-level visual properties of the stimulus. However,their choice of using face stimuli only prevented a definite
conclusion in terms of an advantage of involuntaryorienting for faces compared to other (non-face) highlevel visual objects.
In the present study, we elaborated on the findings by
Gilchrist and Proske (2006) by assessing whether theincreased error rates are indeed specific to faces orwhether they apply to other high-level objects. To thisend, we compared face images with front-view car images
to maintain visual homogeneity between high-level categories. Importantly, to exclude the possibility of low-level
confounds driving our results, we used well-controlled
stimuli (faces, cars, and noise patterns) of identicalamplitude spectra and contrast yet with different phase
content (e.g., Rousselet, Husk, Bennett, & Sekuler, 2008;
Vizioli, Foreman, Rousselet, & Caldara, 2010). As form
information is carried mainly by phase rather thanamplitude (Oppenheim & Lim, 1981; Sekuler & Bennett,
1996), stimuli remained discriminable and semantically
relevant even after scrambling (Figure 1). This method
ensured that any differences in saccadic performancecannot rely on differences in luminance, contrast, orientation, or spatial frequency components across stimuli.
We therefore asked subjects to generate either pro- or
anti-saccades to complex high-level stimuli while keeping
their global low-level visual properties constant. We
hypothesized that if face processing is beyond the controlof the observer, faces should elicit higher anti-saccade
error rates than other stimuli. Indeed, we report here asignificant increase in anti-saccade error rates for faces
compared to cars and noise patterns, faster pro-saccades tofaces as well as shorter fixation durations for pro-saccades
to faces. These results indicate that human faces generatestronger involuntary responses than other visual objects.
Importantly, this involuntary processing cannot be
accounted for by global low-level visual factors.

Methods
Subjects
Twenty-one naive subjects (11 females and 10 males)
participated in this experiment. Twenty were righthanded according to the Oldfield–Edinburgh questionnaire(Oldfield, 1971). All participants were between 20 and
36 years old (mean age = 28, SD = 4.8) and had normal orcorrected-to-normal visual acuity. Subjects were tested on
two different days, for a total of 12 blocks. They wererecruited from the University of Glasgow, gave theirwritten informed consent, and were paid for participation.
The project was approved by the local Ethics Committee.

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Journal of Vision (2010) 10(3):16, 1–10

Morand, Grosbras, Caldara, & Harvey

Stimuli
We used a set of 12 neutral Western Caucasian faces (asused in Michel, Rossion, Han, Chung, & Caldara, 2006)
and 12 cars (database from Schweinberger, Kaufmann,Moratti, Keil, & Burton, 2007) and 12 noise phasescrambled patterns as control stimuli. Faces consisted of
6 male and 6 female faces and were cropped within acommon oval frame. Faces and cars (image size: 128 
128 pixels, 8 bits/pixel) were front-view grayscaled
photographs pasted onto a uniform gray background.
Sample displays are shown in Figure 1A. The photographs
subtended a visual angle of approximately 4.20-  3.8and appeared in each trial at one of two possible
peripheral locations on the horizontal meridian, at 10either to the left or right of the center of the screen. A
central black cross with a size of 0.8- on a whitebackground served as a fixation point.
To control for low-level visual properties between
stimuli, faces and car images were equated for spatial
frequency, luminance, and contrast. To this end, theaverage amplitude spectrum of all images in the data set
was calculated first and the phase of each image was then
combined to this average amplitude spectrum. As a result,3

face and car stimuli were normalized for their amplitude
spectra. Noise patterns were generated by randomizing thephase of the normalized face and car images. Finally, theRMS contrast was also normalized for all the images (seeFigure 1A), resulting in stimuli normalized for theirglobal low-level visual properties.

Apparatus
Displays were presented on a gamma-corrected 21W
SONY GDMF520 CRT monitor with 1024  768 pixel
resolution and 85-Hz refresh rate using E-prime 1.1. The
monitor was located at 68 cm from the chinrest. A second
PC was used to record eye position data online. Eye
movements were monitored with a video-infrared eye
tracker (EyeLink 2K SR Research, Mississauga, Canada,
spatial resolution of 0.01-). The system uses the center ofthe pupil and corneal reflection technique to define pupil
position. Eye movements were recorded at 1000 Hz. At
the beginning of each trial, the experimenter monitored
the subject’s eye movement and initiated the stimulus
presentation as soon as the eyes were stabilized on the
fixation point. Trials with an initial fixation larger thanFigure 1. Schematic representation of the anti-saccade task. (A) Examples of the images used. See Methods section for image
processing details. (B) Within a block, subjects were asked to generate either a pro-saccade (PS) or an anti-saccade (AS) depending on
the color of the cue: a green dot indicated that the participant had to perform a saccade toward the stimulus (PS) while a red dot instructed
the participant to generate a saccade in the opposite direction (AS). Stimuli were faces, cars, and noise phase scrambled patterns asshown in (A) and were presented in random order.

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Journal of Vision (2010) 10(3):16, 1–10

Morand, Grosbras, Caldara, & Harvey

0.5- away from the fixation cross were excluded offline
from the analysis.

4on the following dependent variables: error rates, saccade
latency, fixation duration after the first saccade, and finallysaccade amplitude.

Experimental paradigm
Subjects had to generate a saccade either in the
direction of the stimulus appearing on the screen (prosaccade) or in the opposite direction away from thestimulus (anti-saccade). A schematic representation of theanti-saccade paradigm is given in Figure 1B. Each trial
was initiated by the presentation of a fixation cross. Then,a cue (0.8- in size) whose color instructed the participant
to generate either a pro-saccade (green dot) or an antisaccade (red dot) was presented for 200 ms in the centerof the screen followed by a gap interval of 100 ms (blankscreen). This was done as the removal of the fixation point
before target onset reduces reaction times for both proand anti-saccades and further increases the difficulty ofanti-saccades (Munoz & Everling, 2004). The stimulus
then appeared for 1000 ms. Subjects were asked toperform the correct eye movement as quickly as possible.
Participants completed 12 blocks of 60 trials (total of
720 trials). Within a block, each face, car, and noise
scrambled pattern was presented 5 times with pro- andanti-saccade instructions, in a randomized order to the left
and to the right of the fixation cross located in the centerof the screen.
Each of the 12 blocks started with a nine-point gridcalibration and validation procedure to ensure accurate
eye tracking. Participants were asked to saccade to a gray,circular disk that appeared sequentially (but unpredictably) in a 3  3 grid. After a satisfactory validation had
been obtained, a block of trials was run. Prior to the first
block, participants were shown a 20-trial demonstration of
the task.

Data analysis
Data were analyzed offline with the Data Viewer
Software (SR Research, Mississauga, ON, Canada).
Saccades were detected using velocity and acceleration
criteria of 30-/s and 8000-/s2. Only the first saccade after
stimulus display onset was analyzed. Trials were discarded if (1) the saccade latency was shorter than 80 ms or(2) the amplitude of the first saccade was less than 2-. On
average, 5.14% of the total number of trials presented afirst saccade with a latency shorter than 80 ms and 4.18%
with an amplitude lower than 2-. These criteria led to an
exclusion of an average of 6.9% of trials per subject.
Discarded trials were equally distributed across conditions
and stimuli.
A 2  2  3 repeated measures ANOVA with task (pro-,anti-saccade instruction), side (left, right) and stimulus
type (faces, cars, noise patterns) as factors was carried out

Results
Error rates (incorrect pro-saccades)
Initially we assessed the error rates for both pro- andanti-saccades for the three stimulus types and the twosides. A 3-way analysis of variance (ANOVA) revealed
significant main effects of task type (F(1,20) = 60.7, p G
0.0001) showing greater errors for anti-saccades andstimulus type (F(2,40) = 5.20, p G 0.01) showing greater
errors for faces compared to the other stimuli, but noeffect of side. As no side effects were found on any of the
independent variables, left and right stimuli of the same
type and task conditions were merged in all the following
results.
The error rate results for both pro- and anti-saccades arepresented in Figures 2A and 2B. As expected, the twoway ANOVA revealed a significant main effect of task
(F(1,20) = 60.04, p G 0.001) in that participants showed
greater error rates for anti-saccades (24.07%) than for prosaccades (3.08%). Importantly, there was an interaction
between stimulus type and task (F(2,40) = 4.08, p = 0.02).
Pairwise comparisons showed that in the anti-saccadetask, participants performed worse for faces compared tocars or scrambled patterns (Figure 2B): participants madesignificantly more errors to faces (26%) compared to cars
(23%, F(1,20) = 18.6, p G 0.001) and scrambled patterns(23%, F(1,20) = 7.4, p = 0.01). There was no difference
between the error rates of pro-saccades in relation to thethree stimulus types.
We also performed an item analysis on the anti-saccade
error rate and carried out two ANOVAs with items andparticipants as random factors, assessing if specific stimuli
or subjects might be driving this significant face-specificeffect.
The two-way ANOVA with items as the random factor
revealed a main effect of stimulus type (F(2,40) = 6.4, p =0.004) showing greater errors for faces compared to cars
and noise, but importantly no main effect of items(F(11,220) = 1.7, p = 0.07) nor a significant interaction
between stimulus type and items (F(22,440) = 1.1, p =0.28, ns). From this analysis, it is now clear that the antisaccade error rate increase found for faces in comparison
to cars and noise stimuli, even if small (G5%), was not
driven by one or several particular items in the stimuli set.
Nor can it be attributed to the image manipulation we used
to control for the low-level visual properties.
The two-way ANOVA with participants as the random
factor revealed a significant main effect of stimulus type

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Journal of Vision (2010) 10(3):16, 1–10

Morand, Grosbras, Caldara, & Harvey

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Figure 2. Results of the error rates, saccadic reaction time, ﬁxation duration, and saccadic amplitude obtained for (left) pro-saccades and(right) anti-saccades as a function of stimulus type (faces, cars, and noise scrambled patterns). Bars indicate within-participants SEM
error bars. Asterisks represent the least signiﬁcant p-values of the tests performed (***p G 0.001).

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Morand, Grosbras, Caldara, & Harvey

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and correct pro-saccades (24 ms). More interestingly, weagain found an interaction between stimulus type and task
(F(4,80) = 3.89, p = 0.006) Pairwise comparisons showedthat correct pro-saccades to faces (p G 0.001) and to cars
(p G 0.001) were significantly faster than to noise
scrambled patterns (189 ms), yet no differences were
found between faces and cars (Figure 2C). These twohigh-level property stimuli elicited similar pro-saccadic
reaction times (both 183 ms). No effects were found forthe anti-saccades and incorrect pro-saccades.

Fixation duration after the ﬁrst saccade

Figure 3. Error rate bias across subjects: a face-speciﬁc measurecalculated as the error rate for faces minus the average of theerror rate for cars and noise (non-face objects) is represented
individually for the 21 subjects. Seventeen subjects showed aface-speciﬁc effect (positive values), while only 4 subjects showed
the reverse effect (negative values).

(F(2,22) = 4.5, p = 0.02) showing greater errors for faces
compared to cars and noise, as well as a significant main
effect of subjects (F(20,220) = 27.1, p G 0.001). No
significant interaction between stimulus type and subjects
was found. The significant main effect of subjects was
unsurprising as the anti-saccade error rate was highlyvariable over the 21 subjects. To further clarify whether
the error rate differed consistently across the visual
categories, we carried out separate two-tailed paired t-tests
on the three stimulus types across subjects. Subjects
showed significantly larger errors for faces compared tocars (p = 0.002) and faces compared to noise patterns (p =0.004), but they did not show any significant difference
between cars and noise patterns (p = 0.69, ns). Given theseresults, car and noise pattern data were collapsed to
calculate a measure of “face effect” across subjects,
defined as the error rate for faces minus the average ofthe error rate for cars and noise. A great majority ofsubjects (17/21) presented the face-specific effect, that is,a higher error rate for faces compared to noise patterns
and cars (positive values) while only 4 subjects presented
the reverse effect (negative values; see Figure 3).

Saccadic reaction times
We analyzed the saccadic reaction times for the correctpro- and anti-saccades (Figures 2C and 2D) but also for
the incorrect pro-saccades in a 3  3 repeated measures
ANOVA. There was a significant main task effect of task
(F(2,40) = 63.04, p G 0.001): as expected, anti-saccades
were on average 41 ms slower than pro-saccades. Incorrect
pro-saccades were faster than both anti-saccades (65 ms)

Analysis of the fixation duration after the first saccade,that is the time spent by the participant on the image,before going back to fixation or performing other saccades
in exploring the image, revealed an interesting effect on
pro-saccades only (F(2,40) = 36.4, p G 0.001) as
illustrated in Figure 2E. The fixation duration was much
shorter for faces (334 ms) compared to cars (486 ms, p G
0.001) and scrambled patterns (480 ms, p G 0.001) yet
there was no difference between cars and scrambled
patterns. A two-way ANOVA with items as the random
factor revealed a main effect of stimulus type (F(2,40) =35.51, p G 0.001), but no main effect of items (F(11,220) =0.7, p = 0.7) nor a significant interaction between stimulus
type and items (F(22,440) = 1.3, p = 0.7, ns). Therefore,
the shorter fixation duration found for pro-saccades tofaces in comparison to cars and noise stimuli was not
driven by one or several particular items in the stimuli set.
Nor can it be attributed to the image manipulation we used
to control for the low-level visual properties.
A two-way ANOVA with participants as the random
factor revealed a significant interaction between stimulus
type and subjects (F(40,440) = 8.5, p G 0.001), withsignificant main effects of stimulus type (F(2,22) = 223.1.5,
p G 0.001) and of subjects (F(20,220) = 60.4, p G 0.001).
Again, the significant main effect of subjects was
unsurprising as the fixation duration was highly variable
over the 21 subjects. We used a similar approach as forthe anti-saccade error rates described above (refer to“error rates” in the Results section) to clarify whether the
fixation duration differed consistently across the visual
categories. Two separate two-tailed paired t-tests werecarried out on the three stimulus types across subjects.
Subjects showed significantly shorter fixation duration for
pro-saccades to faces compared to cars (p G 0.001) andfaces compared to noise patterns (p G 0.001), but they did
not show any significant difference between cars and noise
patterns (p = 0.51, ns). Car and noise pattern data werethen collapsed to calculate a measure of “face effect”
across subjects, defined as the fixation duration for faces
minus the average of the fixation duration for cars andnoise. All subjects except one presented the face-specificeffect, that is shorter fixation duration for faces compared
to noise patterns and cars (see Figure 4).

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Morand, Grosbras, Caldara, & Harvey

Figure 4. Fixation duration bias across subjects: a face-speciﬁc
measure calculated as the ﬁxation duration for faces minus theaverage of the ﬁxation duration for cars and noise (non-face
objects) is represented individually for the 21 subjects. Twenty
subjects showed a face-speciﬁc effect (negative values), while
only 1 subject showed a marginal reverse effect (positive values).

To investigate whether this face-specific effect might berelated to any learning, repetition, or familiarity effect of
the faces, we analyzed the fixation duration for the prosaccades in the first and last runs. The results confirmedthat there were no differences between the runs. Thus,
familiarity and learning cannot account for the fact thatfaces were processed much faster compared to cars andscrambled patterns. Note that no such fixation duration
effects were found after the anti-saccades (Figure 2F), nor
after the incorrect pro-saccades.

Saccade amplitudes
The two-way ANOVA performed on the saccade
amplitudes of both pro- (Figure 2G) and anti-saccades(Figure 2H) for the three stimulus types revealed a main
effect of stimulus (F(2,40) = 5.58, p = 0.007) as well as
task (F(1,20) = 6.12, p = 0.022). Amplitudes of the antisaccades were higher than those of the pro-saccades.
Faces and cars led to higher amplitudes in comparison to
the noise scrambled patterns. There was, however, nosignificant interaction between stimulus type and task.

Discussion
Our aim was to investigate whether preferential processing of faces relies more on an automatic (involuntary)or a voluntary response, excluding the possibility that lowlevel visual properties might be driving these effects. We

7addressed this question by using an anti-saccade paradigm
in which the high-level visual properties of the stimuli(i.e., phase) were manipulated while keeping their global
low-level visual features constant (i.e., luminance, amplitude spectra, and contrast).
The significant increase in anti-saccade error rates
found for faces but not for cars and noise patterns
indicates that human faces elicit stronger involuntary,
stimulus-driven orienting responses than other visualobjects. Moreover, this automatic processing cannot be
attributed to difference in the low-level visual properties
across visual categories, as all stimuli (faces, cars, andnoise patterns) were normalized and visually homogenous.
We thus claim that the high-level visual features defining
face shapes trigger a significantly greater proportion of
saccades beyond the control of the observer than otherhigh-level stimuli.
To the best of our knowledge, there has been just one
previous study assessing how the visual properties of astimulus affect the error rates in an anti-saccade task.
Gilchrist and Proske (2006) reported higher error rates for
upright compared to inverted faces, suggesting that faces
and inverted faces are processed differently, in line withprevious studies (Haxby et al., 1999; Perrett et al., 1988;
Valentine, 1988 but see also Butler & Harvey, 2005).
Since faces and inverted faces share the same low-levelvisual properties and differ only by their high-levelproperties, Gilchrist and Proske (2006) argued that thisdifference impacts on the saccadic system. Importantly,
the higher anti-saccade error rates for faces over cars andnoise patterns reported here demonstrate that not all highlevel stimuli influence saccade programming in the same
way as faces do. The results further illustrate that eye
movement paradigms are a powerful tool to investigate
face processing (see also work by Thorpe et al. cited
below).
We also found that saccades toward faces were fasterthan those toward noise patterns, yet did not differ in theirlatencies compared to cars. Subjects were therefore more
rapid to saccade to high-level, familiar stimuli than tounrecognizable stimuli, whatever the object category. This
result is somewhat puzzling given the specificity of thefaces to capture attention as described above. Moreover, afast bias toward faces has been reported recently (Crouzet,
Thorpe, & Kirchner, 2007; Honey et al., 2008). In asaccadic choice paradigm, Crouzet et al. (2007) showedthat early selective saccades could be directed toward
faces but not toward other categories (animals and meansof transport). Yet, in the present study, faces and cars did
not differ in saccadic latencies. Our divergent result might
be explained by the use of different contexts (complex
visual scenes in their study) and by a floor effect driven by
our particular paradigm (mixing pro- and anti-saccades ina block and having a fixation offset). Maybe pro-saccades
could simply not be generated any faster, thus unlike theerror rate, failing to reveal a differential effect. Indeed
even the incorrect pro-saccades were not much faster

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Journal of Vision (2010) 10(3):16, 1–10

Morand, Grosbras, Caldara, & Harvey(24 ms on average). The duration of the fixation after the
first saccade was also significantly shorter for faces
compared to both cars and noise patterns. This observation
further suggests a faster, possibly more efficient, processing for faces (although it has to be granted that, since there
was no specific task for the subjects to perform on theseimages, this observation is open to other interpretation).
What is the visual information content in the face thatmakes it so favorable to the human visual system? Recent
neuroimaging findings have shown that brain regions
dedicated to face processing are tuned to process intrinsic
visual regularities present in human faces (i.e., a topheavy vertical bias constituted by the eyes, eyebrows,
hairs toward the mouth). Neurons in the right middle
fusiform gyrus respond to non-face curvilinear shapes
containing more high-contrast elements in the uppercompared to the lower part (Caldara & Seghier, 2009;
Caldara et al., 2006), indicating that such low-level global
properties might be used by those neurons to automatically categorize visual shapes as human faces.
At the behavioral level, in a recent study by Honey et al.
(2008), subjects were asked to saccade to the image
containing the greater contrast in image pairs. The authors
further manipulated the low-level visual content by
scrambling either the local orientation or the position(i.e., the phase) of the spatial frequency components of theimages. These manipulations had different impacts on theface bias: while disrupting the orientation content of ascene abolished the bias toward faces, the disruption of
the phase of Fourier components (but not their orientation)
did not. This suggests that the fast face bias depends inparts on local low-level information, in particular the 2-D
amplitude spectrum across orientations. In the current
study, we addressed the question differently. We kept the
global low-level features between object categories constant to see whether high-level properties of the stimuli
could account for the face bias. We used the same phasescrambling procedure as described in Honey et al. (2008)
but matched each image to the mean amplitude spectrumof all images before scrambling and keeping the RMS
contrast constant. Thus, faces, cars, and noise patterns
differed only in phase content at the global level. Our
results clearly show that the automatic attentional capture
for faces is driven by high-level visual properties (see also
Hershler & Hochstein, 2005, 2006). Future studies arenecessary to clarify the extent to which each of the local
low-level information intrinsic to faces (e.g., the highcontrast present in the eye region) contributes to thenature of the effect we report.
What neural circuits could control the involuntaryorienting bias toward faces? The generation and/or
suppression of saccadic eye movements involves several
frontal areas such as the frontal eye fields (FEF), thesupplementary eye field (SEF) and the dorsolateralprefrontal cortex (DLPFC), the lateral intraparietal area
(LIP) in the posterior parietal cortex as well as the

8superior colliculus (SC), a subcortical structure. The
DLPFC plays a crucial role in suppressing the automatic,reflexive responses and the FEF in executing voluntarysaccades: patients with focal lesion in the DLPFC, but not
those with lesions in the FEF, show specific increase inerror rates in the anti-saccade paradigms, whereas lesions
in the FEF are associated with increased latency of correct
anti-saccades (Pierrot-Deseilligny et al., 2003; PierrotDeseilligny, Rivaud, Gaymard, & Agid, 1991). It is also
well known that complex high-level visual processing,including the processing of faces, involves the inferior
temporal lobe of the ventral visual pathway (e.g., GrillSpector, 2003; Haxby et al., 1999; Ishai, Ungerleider,
Martin, Schouten, & Haxby, 1999). Current results
suggest an interaction between these two systems, which
could be through the FEF and LIP as both these structures
receive substantial projections from different areas fromthe ventral visual stream (Schall, Morel, King, & Bullier,
1995, summarized in Kirchner & Thorpe, 2006). The fastsaccadic latencies found for faces (and cars) in comparison to noise stimuli indicate that an interaction between
the high-level complex visual processing in the temporal
lobe and the saccade programming of the eye movements
occurs rapidly and most probably at an early stage ofprocessing.

Conclusions
The significant increase in anti-saccade error rates
found for faces but not for other visual categories
indicates that human faces generate stronger involuntary,
stimulus-driven orienting responses, in line with anautomatic capture of attention by faces. Moreover, thisautomatic processing cannot be attributed to global lowlevel visual properties as all stimuli (faces, cars, and noise
patterns) were normalized for this factor.

Acknowledgments
This work has been supported by a grant from the
Economic and Social Research Council and the Medical
Research Council (RES-060-25-0010) awarded to all
authors.
Commercial relationships: none.
Corresponding author: Stephanie Morand.
Email: s.morand@psy.gla.ac.uk.
Address: Centre for Cognitive Neuroimaging (CCNi),
Department of Psychology, University of Glasgow, 58,
Hillhead Street, G128QB, Glasgow, UK.

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Journal of Vision (2010) 10(3):16, 1–10

Morand, Grosbras, Caldara, & Harvey

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