Differential activation of frontoparietal attention
networks by social and symbolic spatial cues
Andrew D. Engell,1,2Lauri Nummenmaa,3,4Nikolaas N. Oosterhof,1,5Richard N. Henson,3James V. Haxby,1,6
and Andrew J. Calder3
1Center for the Study of Brain Mind and Behavior, Princeton University, Princeton, NJ 08540,2Department of Psychology, Yale University,
New Haven, CT 06511, USA,3Medical Research Council, Cognition and Brain Sciences Unit, CB2 7EF Cambridge, UK,4Department of
Psychology, University of Tampere, Tampere, Finland,5School of Psychology, Bangor University, Bangor LL57 2AS, UK, and6Department
of Psychological and Brain Sciences, Dartmouth College, Hanover NH 03755, USA
Perception of both gaze-direction and symbolic directional cues (e.g. arrows) orient an observer’s attention toward the indicated
location. It is unclear, however, whether these similar behavioral effects are examples of the same attentional phenomenon and,
therefore, subserved by the same neural substrate. It has been proposed that gaze, given its evolutionary significance, constitutes
a ’special’ category of spatial cue. As such, it is predicted that the neural systems supporting spatial reorienting will be different
for gaze than for non-biological symbols. We tested this prediction using functional magnetic resonance imaging to measure the
brain’s response during target localization in which laterally presented targets were preceded by uninformative gaze or arrow
cues. Reaction times were faster during valid than invalid trials for both arrow and gaze cues. However, differential patterns of
activity were evoked in the brain. Trials including invalid rather than valid arrow cues resulted in a stronger hemodynamic
response in the ventral attention network. No such difference was seen during trials including valid and invalid gaze cues.
This differential engagement of the ventral reorienting network is consistent with the notion that the facilitation of target
detection by gaze cues and arrow cues is subserved by different neural substrates.
Keywords: arrow; eyes; fMRI; gaze
Perceiving another person’s gaze-direction rapidly orients
one’s own attention to the gazed-at location (Driver et al.,
1999; Langton and Bruce, 1999; Friesen and Kingstone,
1998). The neurophysiological mechanisms responsible for
this rapid orienting response are currently unknown.
Dual-process theories of attention (e.g. Egeth and Yantis
1997; Corbetta and Shulman 2002) distinguish between
goal-driven (voluntary or orienting) and stimulus-driven
(reflexive or reorienting) mechanisms of attention. In the
goal-driven mode, high-level cognitive processes such as
task goals determine where attention is to be allocated. In
attention-grabbing power (typically, abrupt onsets or feature
singletons) cause a reallocation or shift of attention to occur
without conscious effort (Pashler and Harris, 2001). Is it the
case that biologically salient directional cues such as averted
eyes rely primarily on the same stimulus-driven reorienting
system that is recruited by abrupt stimulus onsets?
Studies of attention orienting to gaze and arrow cues lar-
gely rely on a modified version of a paradigm developed by
Posner and colleagues (1980), in which participants are
asked to report the appearance of a target stimulus that
appears at a location lateral to central fixation. Prior to the
onset of this target, a centrally presented directional cue (e.g.
an arrow) appears onscreen. In the valid condition this cue
will accurately indicate the subsequent target location,
whereas in the invalid condition the cue will indicate the
‘wrong’ location. A speeded response to a validly cued
target is thought to indicate an allocation of attention (i.e.
orienting) to the target’s location prior to the target’s onset.
During invalid trails, the target’s onset at the un-cued loca-
tion results in a re-orienting of attention to the target.
Earlier studies suggested that eye gaze acts as a special
attention cue that reflexively orients attention. Support for
this notion came from studies showing that gaze-cued atten-
tion shared characteristics with reflexive attention. As with
stimulus-driven reorienting, gaze cues trigger attentional
shifts even when the time interval between the presentation
of the cue and the target is short (around 100ms; Langton
and Bruce 1999; Ristic et al., 2002; Friesen and Kingstone
2003). Unlike voluntary attention orienting (Mu ¨ller and
Rabbitt 1989), orienting to gaze cues is not susceptible to
top down control (Driver et al., 1999; Downing et al., 2004;
Friesen et al., 2004; Ristic et al., 2007). In addition to the
behavioral evidence, the evolutionary and social significance
Received 7 May 2009; Accepted 23 December 2009
Advance Access publication 19 March 2010
This research was supported by the Medical Research Council (MRC) (project codes WBSE
U.1055.02.001.00001.01 to A.J.C., WBSE U.1055.05.012.00001.01 to R.N.H.); and by a National Science
Foundation Graduate Fellowship to A.D.E.
Correspondence should be addressed to Andrew D. Engell, Department of Psychology, Yale University, New
Haven, CT 06511, USA. E-mail: email@example.com.
doi:10.1093/scan/nsq008 SCAN (2010)5,432^440
? The Author(s)2010.PublishedbyOxfordUniversityPress.
of eye gaze (cf. Emery et al., 2000) and the existence of
ostensibly specialized temporoparietal brain networks for
encoding gaze direction (Hoffman and Haxby 2000;
Hooker et al., 2003; Calder et al., 2007) have also been
taken to support reflexive orienting to gaze direction.
Contrary to the notion that gaze is a special attentional
cue, many studies have since demonstrated that arrow cues
evoke similar shifts of attention (e.g. Tipples, 2002; Ristic
et al., 2002). Arrow cues even demonstrate some of the resis-
tance to top-down control that was initially attributed only
to gaze cues (Tipples, 2008; Kuhn and Kingstone, 2009). This
has led some to conclude that both cue types effectively and
reflexively re-orient spatial attention, but that response to
gaze cues is ‘more reflexive’ (Ristic et al., 2007).
Imaging studies have shown that goal-driven orienting
and stimulus-driven orienting are associated with partially
separable networks in the brain. Goal-driven control of
attention is associated with activation in the dorsal parietal
and superior frontal cortices, whereas stimulus-driven con-
trol of attention is associated with activation in the tempor-
oparietal and inferior frontal cortices (Corbetta et al., 2000;
Yantis et al., 2002; Mayer et al., 2004; Thiel et al., 2004).
Specifically, the dorsal attention system comprises the intra-
parietal sulcus (IPS) and frontal eye fields (FEF) bilaterally
and is engaged during goal-driven shifts of attention. The
ventral attention system comprises the right temporoparietal
junction (TPJ) and right ventral frontal cortex (including the
inferior frontal gyrus, middle frontal gyrus and anterior
insula) and is engaged, along with the dorsal system,
(cf. Corbetta et al., 2008). Attempts using functional mag-
netic resonance imaging (fMRI) to investigate the engage-
ment of these attention systems in the brain during gaze and
arrow-triggered orienting have yielded mixed results. Two
studies reported activation of separate attention systems by
gaze and symbolic directional cues (Kingstone et al., 2004;
Hietanen et al., 2006) while two others have reported a
common neural substrate (Tipper et al., 2008; Sato et al.,
Kingstone and colleagues (2004) used a perceptually
ambiguous cue that could either be perceived as a pair of
eyes under a top hat or as a car. Reliable behavioral cuing
effects were observed for both the eyes and car conditions.
Activation in the right superior temporal sulcus (STS) was
greater in the eyes than in the car condition, but this differ-
ence was not observed in the attention systems. However,
STS activation is known to increase when participants attend
to the eyes (gaze) vs identity of a face (Hoffman and Haxby,
2000). Therefore, the observed STS response could result
from the changed percept (eyes vs car) of the stimulus and
not necessarily from attention orienting.
Tipper and colleagues (2008) used a similar design using
fixed-effects fMRI data analysis and came to a different con-
clusion. Triangular cues that could be perceived as either a
profile view of an eye or an arrow evoked a significant
response within occipital and frontal attention controlling
areas when contrasted with a fixation baseline. These areas
comprise components of both the dorsal (e.g. right IPL) and
ventral (e.g. right TPJ) attention systems. When contrasted
directly, cues perceived as gaze evoked a stronger than cues
perceived as arrows response in a small subset of these
regions, including ventral frontal areas that are part of the
ventral attention network. The authors thus concluded that
attentional orienting by gaze and arrows share a common
neural substrate that is more effectively engaged by gaze. An
alternative interpretation is that highly schematized eyes,
such as those used in the study, do not effectively engage
the brain’s gaze perception systems. It has been suggested
that perception of gaze direction is a largely innate ability
(Hood 1998) and, as such, would rely on stereotypical phys-
ical features of the eye. Indeed, the morphology of the
human eye is distinct and, in many regards, unique among
primates (Kobayashi and Kohshima 1997). Many, if not
most, of these distinct characteristics are preserved even in
simple line drawings (e.g. Friesen and Kingstone 2003;
Hietanen et al., 2006) but are absent from the stimuli used
by Tipper and colleagues (2008). Though this might seem
inconsistent with the effective behavioral cuing achieved in
Tipper et al.’s study when the stimulus was perceived as an
eye, an alternative explanation is that participants may have
simply flipped their perception of what constituted the lead-
ing edge of the stimulus. That is, participants could have
simply perceived the ‘eye’ stimulus as an abstract symbol
pointing toward the opposite direction as the ‘arrow’ stim-
ulus, thus speeding reaction times to valid targets without
recruiting the neural system underlying gaze perception.
Hietanen and colleagues (2006) and Sato and colleagues
(2009) both investigated directional vs non-directional gaze
and arrow cues by contrasting directional cues with a direct
gaze (in the gaze condition) or a non-directional shape (in
the arrow condition). Hietanen et al. observed that orienting
by arrows rather than gaze was more contingent on the vol-
untary (particularly the FEF) attention system. Sato et al.
found directional vs nondirectional gaze cues resulted in
no significant differences, whereas directional vs nondirec-
tional arrow cues recruited the ITG/MTG and SPL. Contrary
to the results of Kingstone et al. (2004) and Tipper et al.
(2008), these studies suggest that arrow rather than gaze cues
are more effective in recruiting the brain’s attention systems.
However, the validity of the aforementioned contrasts is
predicated on the assumption that a direct gaze and a
non-directional shape are equivalent baselines. This may
not be the case, as direct gaze is known to capture or reorient
attention (von Gru ¨nau and Anston 1995; Senju and
Hasegawa 2005; Doi and Ueda 2007).
In sum, there is no conclusive neuroimaging evidence that
the dorsal and ventral attention systems are differentially
engaged by gaze and arrow cues. In the current study, we
attempt to clarify whether gaze and arrow cues evoke similar
or different activation patterns in these systems. Our study
Neuralresponse during spatialorientingSCAN (2010)433
involved three methodological advances over previous ones.
First, to compare our findings directly with those obtained in
previous neuroimaging studies on the goal-driven and
stimulus-driven attention networks (Corbetta et al., 2000;
Yantis et al., 2002; Mayer et al., 2004; Thiel et al., 2004),
we assessed the reorienting effects by comparing invalidly
cued trials with validly cued trials. We focused on the
non-predictive arrow cues result in orienting toward the
cued location (e.g. Ristic et al., 2002; Tipples, 2002).
Therefore, contrasting invalid and valid trials will reveal
any differences in reorienting attention to the onset of the
invalidly cued target. Moreover, this approach avoids the
potential confounds of using a direct gaze cue as the baseline
in our critical contrast and more directly assesses the reor-
ienting response than has been done previously (cf. Hietanen
et al., 2006).Second, we
computer-generated face images that are more ecologically
valid than schematic gaze cues. Third, we used an
event-related fMRI design and modeled the subjects as
random effects, enabling us to draw inferences at the popu-
lation level. As in most cueing studies, the participants were
engaged in a detection task in which the target appeared to
the left or right of fixation and was preceded by a
non-predictive gaze or arrow cue.
As a secondary aim, we investigated whether direct gaze
was an appropriate baseline for this type of experiment.
Direct gaze can capture attention (von Gru ¨nau and
Anston, 1995), whereas ‘direct’ arrows are non-directional
and would therefore not be expected to engage attention
control systems. Downward cues were included to address
this potential baseline confound as they are equally informa-
tive directional cues for both gaze and arrows. We were then
able to compare contrasts that used direct cue baselines with
those using downward cue baselines.
MATERIALS AND METHODS
Sixteen participants (12 women, mean age¼25.8 years,
Participants were right-handed with normal or corrected
vision. All participants gave informed consent prior to the
experiment and were fully debriefed at its completion. A
local ethics committee approved the study (LREC 07/
completedthe fMRIstudyfor payment.
Gaze stimuli were 20 near photo-realistic portraits of faces
created using the FaceGen software package (Singular
Inversions, Vancouver, BC). Each face was manipulated
with FaceGen to appear in five eye gaze positions (eyes
closed, direct gaze, downward gaze, left gaze and right
gaze; Figure 1). Faces subtended a horizontal visual angle
of 4.98 and were centered in the middle of the display.
Arrow stimuli were bold white lines on a black background
(Figure 1) created with Adobe PhotoShop CS (Adobe, CA).
As with the gaze cues, the arrow condition cues could be
nonexistent (cf. closed eyes), point down, to the left or right
or none of these three directions (cf. direct gaze). Arrows
subtended 0.78 and extended from the center of the display
in the relevant direction. The arrows extended from the
center of the display, rather than being centered in the dis-
play, in order to ensure that the arrowhead and the eye
closest to the target were equidistant from the target in the
valid cue condition. The target stimulus was a 0.358 high
letter ‘L’ presented 3.88 to the left or right of the center of
Throughout all trials the participants were required to fixate
a small cross presented at the center of the screen and mon-
itor for the appearance of the letter ‘L’ to the left or right of
the face or arrow display and, upon detection, report on
which side the letter appeared as quickly as possible by press-
ing a button with their left or right index finger. A target
localization task was used as opposed to simple target detec-
tion in an effort to maximize the behavioral effect
(cf. Friesen and Kingstone 2003). Importantly, participants
were explicitly informed that the centrally presented cues
had no predictive value regarding the subsequent target
A single gaze cuing trial would begin with a 1000ms pre-
sentation of a face with gaze averted downward. The image
was presented on the screen for 1000ms in order to mini-
mize any directional cueing effect of the image. This display
was followed immediately with a 125ms presentation of the
same face with closed eyes. Following this ‘blink’, the cue
display (face with eyes averted downward, direct, left or
right) appeared. After 300ms, the target appeared, thus
Fig. 1 (A) Example of gaze (top row) and arrow (bottom row) stimuli. From left to
right: left, direct, blink, down, right. (B) Sample trial sequence with a gaze cue.
434 SCAN (2010)A.D.Engelletal.
resulting in a 300ms stimulus-onset asynchrony (SOA).
A 300ms SOA has previously been used to successfully
elicit the cuing effect for both gaze and arrows (e.g.
Friesen and Kingstone 2003; Driver et al., 1999; Friesen
et al., 2004; Langdon and Smith, 2005; Sato et al., 2009).
Participants were instructed to report as quickly and accu-
rately as possible on which side the ‘L’ appeared via a button
press. After 300ms both the face and target-letter would be
replaced by a fixation cross that would remain on the screen
for the 1275ms remainder of the trial. The arrow trials were
similar to the gaze trials with the exception that respective
arrow cues were displayed, and the initial 1000ms presenta-
tion of a downward arrow was followed by a 125ms ‘blink’
period during which the screen was black. On ‘valid’ trials
the cue pointed toward the upcoming target location while
on ‘invalid’ trials the cue pointed opposite to the target loca-
tion. On direct trials the gaze cue pointed directly toward the
observer (i.e. direct gaze) and the arrow cue was a simple
cross, while on ‘down’ trials the cue remained averted down-
ward after the ‘blink’. During null trials a fixation cross was
displayed at the center of the screen for 3000ms.
In total, there were 160 ‘gaze’ trials, 160 ‘arrow’ trials and
160 ‘null’ trials divided across six time-series. There were an
equal number (40) of valid, invalid, direct and down trials
for both the gaze and arrow conditions. In each time-series,
28 (24 in time-series 5 and 6) null events were randomly
interspersed with 26 arrow trials and 26 gaze trials (28 of
each in time-series 5 and 6). In order to keep any effect of
switching between arrow and gaze stimuli to a minimum,
the trials were presented in a pseudo-randomized fashion
such that arrow and gaze trials were presented in interleaved
‘mini-blocks’ that comprised 10 trials each (including null
events). Subjects practiced the task outside of the scanner
prior to the scanning session.
Reaction time data preprocessing and analysis
Prior to analyses, incorrect responses (an average of 3.5%)
and reaction times 2.5 s.d.’s above or below the participants’
mean (an average of 3%) were removed. The reaction time
(RT) data were analyzed using a 2 (cue type: arrow, gaze)?4
(cue direction: valid, direct, down, invalid) within-subjects
ANOVA. Planned simple effects contrasts included indepen-
dent comparisons of cue direction for arrow and gaze trials.
The blood oxygenation level-dependent (BOLD) signal was
used as a measure of neural activation (Ogawa et al., 1990;
Kwong et al., 1992). Echo planar images (EPI) were acquired
with a Siemens Tim Trio scanner (Siemens, Erlangen,
Germany) with a standard ‘bird-cage’ coil (TR 2424ms, TE
30ms, flip angle 788, FOV 192mm, matrix size 64?64).
Nearly whole brain coverage was achieved with 40 inter-
leaved 3mm axial slices and a 1mm slice gap. In addition
to the EPI series, a high-resolution anatomical image
(T1-MPRAGE, TR 2500ms, TE 4.3ms, flip angle 88,
matrix size 256?256) was acquired for use in registering
activity to each subject’s anatomy and for spatially normal-
izing data across subjects.
NeuroImages (AFNI: Cox 1996) using standard preproces-
sing procedures. This involved six parameter 3D motion
correction, slice-scan time correction, spatial smoothing
withan 8-mm full-width-at-half-minimum
kernel and signal normalization to a percent signal change
from the mean. Spatial normalization was accomplished
using a non-linear transformation to MNI space with
analyzedwith Analysisof Functional
Each of the six time-series was convolved with a hemody-
namic response function to create a regressor for each of the
eight cuing conditions. Regressors of no interest were
included in the multiple regression model to factor out vari-
ance associated with mean, linear, quadratic and cubic
trends in each run as well as subject head motion.
Additional regressors of no interest were included to account
for variance associated with any incorrect manual responses
or RT outlier trials that were removed from the behavioral
analysis. The regression model yielded coefficients that rep-
resented the signal change from the mean for each condition
within each voxel.
For all the data analysis, experimental conditions were
used as fixed factors and subjects as random factors.
Events were time-locked to the cue presentation (and
included the cue and subsequent target), as the timing of
the paradigm precludes separating target-related activity
from cue-related activity. Data were analyzed with two dif-
ferent approaches. In our primary analysis, a 2 (cue type:
gaze, arrow)?2 (cue validity: valid, invalid) mixed-effects
repeated-measures ANOVA was used to specifically test
whether gaze and arrow cues engaged the reorienting net-
work in a similar fashion (cf. Thiel et al., 2004). We used the
AlphaSim program included in AFNI to correct for multiple
comparisons. A minimum cluster size of 175 27-mm3voxels
was used to achieve a corrected significance of P<0.05 as
determined by a Monte Carlo simulation with a voxelwise
threshold of P<0.01. Further analyses were performed in an
effort to test whether direct gaze cues are an appropriate
neutral baseline for this type of experiment. First, we did a
simple contrast of direct and downward gaze cues. Second,
for both gaze and arrow cues, we contrasted lateral cue trials
(valid and invalid) with direct cue trials (note that this con-
trast corresponds to the ‘cued–uncued’ contrast in Hietanen
et al., 2006). Lateral and down cue trials were also con-
trasted. Following the method used by Hietanen and col-
leagues (2006), we created maps displaying the intersection
of voxels from these contrasts as well as voxels unique to one
or the other contrast.
Neuralresponse during spatialorientingSCAN (2010)435
Region of interest (ROI) analysis
In order to better understand the nature of the interaction
effects revealed by the 2?2 ANOVA (see above), we tested
simple effects on the extracted mean BOLD response for
each condition averaged across voxels within given ROIs.
The regions chosen represented the intersection of areas pre-
viously implicated in shifts of spatial attention (see the
review by Corbetta and Schulman, 2002) that also showed
a significant interaction effect of cue type ? cue validity in
the current study. These were V5/MT, right posterior supe-
rior temporal sulcus/temporoparietal junction (pSTS/TPJ),
right intraparietal sulcus (IPS), right somatosensory cortex
and right inferior frontal gyrus (IFG).
The pSTS/TPJ was defined by creating two spheres
(radius¼6mm) centered on the two peak t-values given
by the interaction in the right posterior lateral temporal
cortex. All other regions were defined using the maximum
probability maps (MPM) distributed in the SPM anatomy
toolbox (Eickhoff et al., 2005). These included V5/MT
(Wilms 2005; Malikovic et al., 2007), IFG (Amunts et al.,
1999), IPS (Choi et al., 2006) and somatosensory cortex
(Geyer et al., 1999, 2000).
The reaction time data are summarized in Figure 2. Analysis
of the reaction times revealed a main effect of cue type
(arrow vs gaze), F(1, 15)¼18.99, P¼0.001 with arrow
cuing trials being significantly faster than gaze cuing trials
(Marrow¼381ms, Mgaze¼402ms). There was also a signifi-
cant main effect of cue direction (valid vs direct vs down vs
invalid), F(3, 45) ¼12.92, P<0.0001. The interaction was
not significant, P>0.05. For each cue type, we performed
multiple comparisons between all possible pairs of cue con-
ditions. These tests revealed that valid cues were significantly
faster than direct, downward and invalid cues for both gaze
and arrow cue types (P<0.05). Direct gaze cues were
significantly faster than downward gaze cues (P<0.05).
None of the remaining pairwise comparisons revealed sig-
nificant differences (P-values>0.05).
In most cases the statistically significant clusters comprised
many functional areas of the brain. In order to facilitate
identification of functional regions, we identified significant
voxels that fell within the maximum probability map of the
SPM Anatomy Toolbox (Eickhoff et al., 2005). For signifi-
cant voxels outside of these regions we identified local
t-value maxima (minimum distance between peaks¼6
Main effects of cue type and cue validity
Gaze trials evoked significantly greater activity than arrow
trials in three large clusters of voxels. Two of these clusters
represented near identical patterns in each hemisphere.
These clusters extended from extra-striate visual areas into
bilateral occipital-temporal and ventral temporal cortices.
These same clusters also ran along a path that extended
anteriorly through medial temporal regions, including the
hippocampus and amygdala, and into the temporal poles.
The third cluster was located primarily on the right inferior
frontal gyrus and partially extended onto the right middle
frontal gyrus (Supplementary Table S1). Greater activity for
arrow than for gaze trials was seen in the left parietal lobe,
including the IPS and postcentral gyrus. This cluster
extended anteriorly just past the central sulcus into the pre-
central gyrus (Supplementary Table S2).
Invalid gaze and arrow cues evoked a significantly larger
response than valid gaze and arrow cues in the right lateral
temporal cortex (including the pSTS/TPJ) and the right infe-
rior parietal lobe. However, this effect was primarily driven
by the arrow cues (see the description of the interaction
below). A second large cluster was found in medial subcor-
tical regions that included the putamen, pulvinar and cere-
bellum (Supplementary Table S3). There were no significant
clusters revealed by contrasting valid vs invalid.
Interaction of cue type and cue validity: differential
activation of the reorienting network
A 2 (cue type: gaze, arrow) ? 2 (cue validity: valid, invalid)
ANOVA revealed a significant interaction in five clusters
including the right IFG, bilateral IPS and bilateral occipi-
tal/occipitotemporal cortices (Supplementary Table S4).
The results of the interaction were expanded on by an
ROI-based analysis. Figure 3A shows the location and pat-
tern of response of all ROIs where the signal change was
extracted. These were located in the right IFG, right IPS,
right TPJ, right primary somatosensory cortex and right
V5/MT. Simple effects tests (see Table 1) revealed that the
interaction was driven by invalid arrows evoking a
Fig. 2 Means and standard errors (in ms) of manual reaction times for reporting the
appearance of the peripheral target as a function of cue type (arrow, gaze) and cue
direction (valid, direct, down, invalid).
significantly greater response than valid arrows in all ROIs
(P-values?0.05), whereas there was no significant difference
between invalid gaze and valid gaze (P-values>0.05). This
pattern was largely preserved outside of the ROIs as well.
In other words, there were no significant clusters in the
brain for valid gaze vs invalid gaze, whereas valid arrows vs
invalid arrows resulted in multiple activated regions.
However, small clusters of activity for valid gaze > invalid
gaze in early visual and ventral temporal cortices were
revealed when using a more liberal statistical threshold
(P<0.01, uncorrected, see Figure 3B).
Lateral cues vs direct or down baseline
Lateral arrow cues (i.e. valid and invalid) contrasted with
non-directional direct cues showed significantly greater
activity in bilateral occipital, occipitoparietal (including the
superior parietal lobe and intraparietal sulcus) and right lat-
eral temporal cortices, whereas contrasting lateral arrow cues
to downward arrow cues revealed no significant differences.
Conversely, lateral gaze cues contrasted with direct gaze
revealed no significant differences, whereas contrasting lat-
eral gaze cues to downward gaze cues showed significantly
greater activity in bilateral occipital and ventral temporal
cortices as well as the right precuneus, superior parietal
lobe and intraparietal sulcus. Contrasting direct and down-
ward gaze cues did not reveal any significant BOLD response
differences (Supplementary Figure S1).
The main finding of the current study is that the brain’s
response when spatially orienting to non-predictive spatial
cues is different for social eye gaze cues than symbolic arrow
cues. Reaction time data showed that both gaze and arrow
cues were effective in triggering seemingly reflexive shifts of
spatial attention. However, trials with invalid arrow cues
resulted in a stronger hemodynamic response in the ventral
‘reorienting’ attention network than those including valid
arrow cues. No such difference was seen during trials includ-
ing valid and invalid gaze cues (Figure 3B). Broadly, these
data show that arrow cues engage the ventral reorienting
network differently than gaze cues. Further, we found that
contrasting lateral cues with direct cues evoked a different
activation pattern than did contrasting lateral cues with
downward cues; this is an important methodological distinc-
tion for future investigations. In what follows, we will discuss
these findings in detail and relate them to previous neuroi-
maging and cognitive studies on voluntary and reflexive and
gaze-cued orienting of visual attention.
The engagement of the ventral attention network by
gaze and arrow cues
Previous studies on the ventral attention network have quan-
tified it by assessing the effects of cues and targets separately
(e.g. Corbetta et al., 2000), or by contrasting invalidly cued
trials with validly cued trials (Thiel et al., 2004). We followed
the latter approach with the exception that cue type (gaze vs
arrow) was used as an additional factor in the ANOVA. The
main effect of validity (Supplementary Table S1) shared
multiple regions with those reported by Thiel and colleagues
Fig. 3 (A) Voxels showing a significant interaction effect of cue type (gaze, arrow)
and cue validity (valid, invalid). Regions include those previously implicated during
reorienting to unattended targets: IPS, intraparietal sulcus; IFG, inferior frontal gyrus;
TPJ temporo-parietal junction and those implicated during preparatory shifts of
attention: SI, primary somatosensory cortex; V5/MT. Bar graphs display the sig-
nal change in each of the four conditions at the peak voxel within each region.
(B) Overlay showing both of valid > invalid (yellow) and invalid > valid (red)
contrasts for gaze (left) and arrow (right) cues. Only those voxels significant at
P<0.01, uncorrected are displayed. Left: Overlay showing the two contrasts for
gaze cues. Right: Overlay showing the two contrasts for arrow cues.
Table 1 ROI results
Regions defined by locating local maxima
Arrow invalid>ValidGaze invalid>Valid
Lateral temporal cortex
Results of simple effects tests on the extracted mean BOLD response for each
condition averaged across voxels within ROIs. ROIs represent areas previously impli-
cated in shifts of spatial attention that also showed a significant interaction between
cue type and cue validity in a 2?2 ANOVA (P<0.05, corrected, N¼16)
(see Materials and methods section).
Neuralresponse during spatialorientingSCAN (2010)437
(2004), including the right pSTS/TPJ (cf. right middle tem-
poral gyrus and right supramarginal gyrus) and the right
inferior parietal lobe (cf. right parietal operculum). An
interaction analysis yielded a network of clusters including
the right pSTS/TPJ and the right IFG, the core components
of the ventral attention network, as well as the right IPS, an
area in the dorsal attention system that is often engaged by
both reflexive and voluntary orienting of attention (Corbetta
and Shulman, 2002). However, a surprising trend was
observed when the interaction was broken down by ROI
analyses. The visual and attention networks in the brain
responded differentially to invalid vs valid arrow cues but
not invalid vs valid gaze cues. Specifically, the results for the
arrow cuing show that invalid vs valid arrows increased
activity in the ventral attention network whereas no such
difference was noted for invalid vs valid gaze cues
(Figure 3B). Indeed, the average response within regions of
this network evidenced a trend (although nonsignificant)
toward a larger response for valid than invalid gaze cues.
This pattern of results implies that gaze and arrow cues,
despite similar behavioral effects, engage the attentional
A recent study by Hietanen and colleagues (2008) suggests
that gaze and arrow cues differentially recruit the dorsal
(i.e. voluntary) and ventral (i.e. reflexive) frontoparietal
attention networks. They measured event-related potentials
(ERPs) to centrally presented gaze and arrow cues and found
that arrow but not gaze cues resulted in the early directing
attention negativity (EDAN) component associated with vol-
untary orienting of attention (Harter et al., 1989; Yamaguchi
et al., 1994). Importantly, both cue types were equally effec-
tive in shifting visual attention, as indexed by both manual
RTs and target-triggered N1 and P1 components. Hietanen
and colleagues proposed that the source of the EDAN might
be the dorsal frontoparietal attention network thus demon-
strating a strong engagement of this system by arrows cues
whereas the null effect for gaze cues could represent a
disproportionate reliance on the reflexive frontoparietal
The current data, in light of the previous findings of
Hietanen and colleagues, can be interpreted as suggesting
that the dorsal and ventral attention networks were similarly
recruited by valid and invalid gaze cues. As illustrated in
Figure 3B, no differences in the activity of the attention sys-
tems were observed when the valid and invalid gaze cues
were contrasted with each other, although behavioral data
(Figure 2) confirms that gaze cues were indeed successful in
inducing attention shifts. Together these data thus suggest
that the onset of a gaze cue engages the ventral attention
network, whose activity would remain high regardless of
the subsequent target location (valid vs invalid). Indeed, an
examination of the response to gaze cues reveals little
difference between them in both the dorsal and ventral
attention systems (Supplementary Figure S2). In contrast,
the onset of an arrow cue would initially engage the dorsal
attention system. The ventral system would only be recruited
if the subsequent target appeared at the ‘invalid’ location,
Consistent with this, an examination of the response to
arrow cues reveals a more pronounced difference between
valid and invalid cues in the ventral than in the dorsal atten-
tion system (Supplementary Figure S2). However, this dif-
ference offers only indirect evidence to support our
interpretation. To directly test this hypothesis additional
studies will be necessary. Specifically, for both cue types, it
will be necessary to be able to independently estimate the
hemodynamic response to the cue and to the target. For the
gaze condition, it will be crucial to use a common baseline
that is not likely to induce its own attentional response (see
Alternatively, the lack of gaze cue modulation of the
BOLD response in the attention network could be due to
excitatory and inhibitory neural responses cancelling each
other out. Shepherd and colleagues (2009) have shown
that some neurons in macaque lateral intraparietal area
(LIP) increase their firing rate, while others reduce their
firing rate, when the monkey views an image of a conspecific
gazing toward the cell’s response field. If cells with similar
response properties exist in intermixed clusters in humans,
contrasting BOLD responses to valid and invalid gaze cues
could potentially result in a net null effect as observed in the
present study. However, high temporal coherence was noted
only in those neurons that increased rather than decreased
their firing rate. The low temporal coherence of the
suppressive effect would exert less influence on downstream
local field potentials and, therefore, less influence on
the evoked hemodynamic response (Logothetis, 2002),
making it less likely that these responses could cancel each
The current findings support the ‘specialness’ of gaze cues
by demonstrating that they achieve reflexive orienting of
attention primarily via different cortical systems than
arrow cues, and seemingly with less processing demand
(as indexed by BOLD response) than arrows. The human
eye is unique among primates in that it has a large and
bright sclera, which increases the visibility of the pupil and
iris. Kobayashi and Kohshima (1997) have suggested that the
human eye has evolved these features to signal gaze direction
(i.e. locus of attention) to others. This notion is bolstered by
evidence that infants as young as 2–5 days old discriminate
gaze contact and gaze aversion (as indexed by ERPs; Farroni
et al., 2002) and will automatically orient to gaze cues as
early as 10 weeks old (Hood, 1998). The brain, then, might
be sensitized to gaze in the same way it is to strong low-level
sensory stimuli such as a loud sound or a bright light. This
sensitization could operate in parallel with learned contin-
gencies. Arrow cuing, on the other hand, relies solely on
learned contingencies. This could account for more ‘reflex-
ive’ brain effects to gaze direction despite near indistinguish-
able behavioral effects to gaze and arrow cues. Despite the
reorientingto the target.
438 SCAN (2010) A.D.Engelletal.
overt similarities, gaze and arrow cues are thus processed
differently in the brain.
Attentional effects of ’direct’ cues
The contrast of lateral arrow vs direct arrow cues yielded
significant differences in expansive regions of occipitotem-
poral and occipitoparietal cortices. A similar effect was not
observed for the gaze stimuli. This result largely replicates
the findings of Hietanen et al. (2006). Importantly though,
replacing the direct cues with downward cues in the contrast
resulted in a striking reversal of the pattern of results.
That is, occipitotemporal and occipitoparietal regions were
again shown to be active (though with a right hemisphere
bias); however, the differences were now significant for the
gaze, but not arrow, trials. Why would two seemingly
irrelevant cues (i.e. neither ever cued the location of the
subsequent target) evoke such disparate results? It is possible
that the effects of direct gaze cues are driven by their ability
to capture attention (von Gru ¨nau and Anston 1995) as well
as their visual salience and social relevance (Batki 2000;
Farroni et al., 2002; Mason et al., 2004; Farroni et al.,
2006). However, these differences were not large enough to
be seen when directly contrasting direct and downward cues.
Alternatively, it seems that direct gaze is perceived as a
directional, rather than a nondirectional, cue and would
therefore engage the same orienting response as lateral cues.
Unlike direct gaze cues, direct (or neutral) arrow cues are
an appropriate baseline for arrow cuing given that they
convey no spatial information and, therefore, do not affect
target detection RTs or BOLD responses within the brain’s
attention network. It is this differential effect of gaze and
arrow ‘neutral’ cues on the BOLD response that is
particularly problematic with regard to prior fMRI studies
of social vs symbolic cuing. We suggest that future studies
that include a ‘neutral’ baseline rather than contrasting valid
trials to invalid trials use either truly non-directional cues
(e.g. closed eyes and non-directional arrows) or matched
directional cues that are never predictive of stimulus location
(e.g. downward gaze and arrow cues).
These findings support the notion that the facilitation of
target detection by gaze cues and arrow cues is subserved
by different neural responses in the attention systems. We
have shown that arrow cue validity modulates the activity in
the ventral frontoparietal attention network (specifically, the
right TPJ and IFG) and the IPS. Importantly, we found no
such modulation as a function of validity for centrally pre-
sented gaze cues, suggesting that orienting of spatial atten-
tion is supported by different neural systems for social gaze
and symbolic arrow cues. Further, we have shown that the
baselines often used in arrow and gaze cuing paradigms (i.e.
a line segment and a direct-gaze face) are not equivalent and
should be interpreted accordingly. Although a line segment
faithfully represents the absence of an attentional cue, the
same cannot be said of a direct-gaze, perhaps due to its
strong social relevance.
Supplementary Data are available at SCAN Online.
Amunts, K., Schleicher, A., Burgel, U., Mohlberg, H., Uylings, H.B.,
Zilles, K. (1999). Broca’s region revisited: cytoarchitecture and intersub-
ject variability. The Journal of Comparative Neurology, 412(2), 319–341.
Batki, A. (2000). Is there an innate gaze module? Evidence from human
neonates. Infant Behavior Development, 23(2), 223.
Calder, A.J., Beaver, J.D., Winston, J.S., et al. (2007). Separate coding of
different gaze directions in the superior temporal sulcus and inferior
parietal lobule. Current Biology, 17(1), 20–25.
Choi, H.J., Zilles, K., Mohlberg, H., et al. (2006). Cytoarchitectonic identi-
fication and probabilistic mapping of two distinct areas within the ante-
rior ventral bank of the human intraparietal sulcus. The Journal of
Comparative Neurology, 495, 53–69.
Corbetta, M., Kincade, J.M., Ollinger, J.M., McAvoy, M.P., Shulman, G.L.
(2000). Voluntary orienting is dissociated from target detection in human
posterior parietal cortex. Nature Neuroscience, 3(3), 292–297.
Corbetta, M., Kincade, J.M., Shulman, G.L. (2002). Neural systems for
visual orienting and their relationships to spatial working memory.
Journal of Cognitive Neuroscience, 14(3), 508–523.
Corbetta, M., Patel, G., Shulman, G.L. (2008). The reorienting system of the
human brain: From environment to theory of mind. Neuron, 58(3),
Corbetta, M., Shulman, G.L. (2002). Control of goal-directed and stimulus-
driven attention in the brain. Nature Reviews Neuroscience, 3(3), 201–215.
Cox, R.W. (1996). AFNI: Software for analysis and visualization of func-
tional magnetic resonance neuroimages. Computers and Biomedical
Research: An International Journal, 29(3), 162–173.
Doi, H., Ueda, K. (2007). Searching for a perceived stare in the crowd.
Perception, 36(5), 773–780.
Downing, P.E., Dodds, C.M., Bray, D. (2004). Why does the gaze of others
direct visual attention? Visual Cognition, 11(1), 71–79.
Driver, J., Davis, G., Ricciardelli, P., Kidd, P., Maxwell, E., Baron-Cohen, S.
(1999). Gaze perception triggers reflexive visuospatial orienting. Visual
Cognition, 6(5), 509–540.
Egeth, H.E., Yantis, S. (1997). Visual attention: Control, representation, and
time course. Annual Review of Psychology, 48, 269–297.
Eickhoff, S.B., Stephan, K.E., Mohlberg, H., et al. (2005). A new SPM tool-
box for combining probabilistic cytoarchitectonic maps and functional
imaging data. NeuroImage, 25(4), 1325–1335.
Emery, N.J. (2000). The eyes have it: neuroethology, function and evolution
of social gaze. Neuroscience and Behavioral Reviews, 24, 581–604.
Farroni, T., Csibra, G., Simion, F., Johnson, M.H. (2002). Eye contact
detection in humans from birth. Proceedings of the National Academy of
Sciences of the United States of America, 99(14), 9602–9605.
Farroni, T., Menon, E., Johnson, M.H. (2006). Factors influencing new-
borns’ preference for faces with eye contact. Journal of Experimental
Child Psychology, 95(4), 298–308.
Friesen, C.K., Kingstone, A. (1998). The eyes have it! Reflexive orienting is
triggered by nonpredictive gaze. Psychnonomic Bulletin and Review, 5,
Friesen, C.K., Kingstone, A. (2003). Abrupt onsets and gaze direction cues
trigger independent reflexive attentional effects. Cognition, 87(1), B1–10.
Friesen, C.K., Ristic, J., Kingstone, A. (2004). Attentional effects of counter-
predictive gaze and arrow cues. Journal of Experimental Psychology:
Human Perception and Performance, 30(2), 319–329.
Geyer, S., Schleicher, A., Zilles, K. (1999). Areas 3a, 3b, and 1 of human
primary somatosensory cortex. NeuroImage, 10(1), 63–83.
Neuralresponse during spatialorientingSCAN (2010)439
Geyer, S., Schormann, T., Mohlberg, H., Zilles, K. (2000). Areas 3a, 3b, and Download full-text
1 of human primary somatosensory cortex: part 2. Spatial normalization
to standard anatomical space. NeuroImage, 11(6 Pt 1), 684–696.
Harter, M.R., Miller, S.L., Price, N.J., LaLonde, M.E., Keyes, A.L. (1989).
Neural processes involved in directing attention. Journal of Cognitive
Neuroscience, 1, 223–237.
Hietanen, J.K., Leppanen, J.M., Nummenmaa, L., Astikainen, P. (2008).
Visuospatial attention shifts by gaze and arrow cues: an ERP study.
Brain Research, 1215, 123–136.
Hietanen, J.K., Nummenmaa, L.,
Hamalainen, H. (2006). Automatic attention orienting by social and
symbolic cues activates different neural networks: an fMRI study.
NeuroImage, 33(1), 406–413.
Hoffman, E.A., Haxby, J.V. (2000). Distinct representations of eye gaze and
identity in the distributed human neural system for face perception.
Nature Neuroscience, 3(1), 80–84.
Hood, M.B., Willen, J.D., Driver, J. (1998). Adult’s eyes trigger shifts of
visual attention in human infants. Psychological Science, 9(2), 131–134.
Hooker, C.I., Paller, K.A., Gitelman, D.R., Parrish, T.B., Mesulam, M.M.,
Reber, P.J. (2003). Brain networks for analyzing eye gaze. Cognitive Brain
Research, 17, 406–418.
Kingstone, A., Tipper, C., Ristic, J., Ngan, E. (2004). The eyes have it!: an
fMRI investigation. Brain and Cognition, 55(2), 269–271.
Kobayashi, H., Kohshima, S. (1997). Unique morphology of the human eye.
Nature, 387(6635), 767–768.
Kuhn, G., Kingstone, A. (2009). Look away! Eyes and arrows engage ocu-
lomotor responses automatically. Attention, Perception and Psychophysics,
Kwong, K.K., Belliveau, J.W., Chesler, D.A., et al. (1992). Dynamic magnetic
resonance imaging of human brain activity during primary sensory sti-
mulation. Proceedings of the National Academy of Sciences of the United
States of America, 89(12), 5675–5679.
Langdon, R., Smith, P. (2005). Spatial cueing by social versus nonsocial
directional signals. Visual Cognition, 12(8), 1497–1527.
Langton, S.R.H., Bruce, V. (1999). Reflexive visual orienting in response to
the social attention of others. Visual Cognition, 6(5), 541–567.
Logothetis, N.K. (2002). The neural basis of the blood-oxygen-level-depen-
dent functional magnetic resonance imaging signal. Philosophical
Transactions of the Royal Society of London: Series B. Biological Sciences,
Malikovic, A., Amunts, K., Schleicher, A., et al. (2007). Cytoarchitectonic
analysis of the human extrastriate cortex in the region of V5/MTþ: a
probabilistic, stereotaxic map of area hOc5. Cerebral Cortex, 17(3),
Mason, M.F., Hood, B.M., Macrae, C.N. (2004). Look into my eyes: gaze
direction and person memory. Memory, 12(5), 637–643.
Mayer, A.R., Dorflinger, J.M., Rao, S.M., Seidenberg, M. (2004). Neural
networks underlying endogenous and exogenous visual-spatial orienting.
NeuroImage, 23(2), 534–541.
Mu ¨ller, H.J., Rabbitt, P.M. (1989). Reflexive and voluntary orienting of
visual attention: time course of activation and resistance to interruption.
Journal of Experimental Psychology. Human Perception and Performance,
Ogawa, S., Lee, T.M., Kay, A.R., Tank, D.W. (1990). Brain magnetic reso-
nance imaging with contrast dependent on blood oxygenation.
Proceedings of the National Academy of Sciences of the United States of
America, 87(24), 9868–9872.
Pashler, H., Harris, C.R. (2001). Spontaneous allocation of visual attention:
Dominant role of uniqueness. Psychonomic Bulletin and Review, 8(4),
Posner, M.I. (1980). Orienting of attention. The Quarterly Journal of
Experimental Psychology, 32(1), 3–25.
Ristic, J., Friesen, C.K., Kingstone, A. (2002). Are eyes special? It depends on
how you look at it. Psychonomic Bulletin and Review, 9(3), 507–513.
Ristic, J., Wright, A., Kingstone, A. (2007). Attentional control and reflexive
orienting to gaze and arrow cues. Psychonomic Bulletin and Review, 14(5),
Sato, W., Kochiyama, T., Uono, S., Yoshikawa, S. (2009). Commonalities in
the neural mechanisms underlying automatic attentional shifts by gaze,
gestures, and symbols. NeuroImage, 45(3), 984–992.
Senju, A., Hasegawa, T. (2005). Direct gaze captures visuospatial attention.
Visual Cognition, 12, 127–144.
Shepherd, S.V., Klein, J.T., Deaner, R.O., Platt, M.L. (2009). Mirroring of
attention by neurons in macaque parietal cortex. Proceedings of the
National Academy of Sciences of the United States of America, 106(23),
Singular Inversions. FaceGen 3.1. Vancouver, BC, Canada.
Thiel, C.M., Zilles, K., Fink, G.R. (2004). Cerebral correlates of alerting,
orienting and reorienting of visuospatial attention: an event-related
fMRI study. NeuroImage, 21(1), 318–328.
Tipper, C.M., Handy, T.C., Giesbrecht, B., Kingstone, A. (2008). Brain
responses to biological relevance. Journal of Cognitive Neuroscience,
Tipples, J. (2002). Eye gaze is not unique: automatic orienting in response to
uninformative arrows. Psychonomic Bulletin and Review, 9(2), 314–318.
Tipples, J. (2008). Orienting to counterproductive gaze and arrow cues.
Perception & Psychophysics, 70(1), 77–87.
von Gru ¨nau, M., Anston, C. (1995). The detection of gaze direction: a stare-
in-the-crowd effect. Perception, 24(11), 1297–1313.
Wilms, M. (2005). Human V5/MT: comparison of functional and cytoarch-
itectonic data. Anatomy and Embryology, 210(5–6), 485.
Yamaguchi, S., Tsuchiya, H., Kobayashi, S. (1994). Electroencephalographic
activity associated with shifts of visuospatial attention. Brain: A Journal of
Neurology, 117(Pt 3), 553–562.
Yantis, S., Schwarzbach, J., Serences, J.T., et al. (2002). Transient neural
activity in human parietal cortex during spatial attention shifts. Nature
Neuroscience, 5(10), 995–1002.