Spatial attention modulates initial afferent activity in human primary visual cortex.
ABSTRACT It is well established that spatially directed attention enhances visual perceptual processing. However, the earliest level at which processing can be affected remains unknown. To date, there has been no report of modulation of the earliest visual event-related potential component "C1" in humans, which indexes initial afference in primary visual cortex (V1). Thus it has been suggested that initial V1 activity is impenetrable, and that the earliest modulations occur in extrastriate cortex. However, the C1 is highly variable across individuals, to the extent that uniform measurement across a group may poorly reflect the dynamics of V1 activity. In the present study we employed an individualized mapping procedure to control for such variability. Parameters for optimal C1 measurement were determined in an independent, preliminary "probe" session and later applied in a follow-up session involving a spatial cueing task. In the spatial task, subjects were cued on each trial to direct attention toward 1 of 2 locations in anticipation of an imperative Gabor stimulus and were required to detect a region of lower luminance appearing within the Gabor pattern 30% of the time at the cued location only. Our data show robust spatial attentional enhancement of the C1, beginning as early as its point of onset (57 ms). Source analysis of the attentional modulations points to generation in striate cortex. This finding demonstrates that at the very moment that visual information first arrives in cortex, it is already being shaped by the brain's attentional biases.
[show abstract] [hide abstract]
ABSTRACT: The response properties of cells in the primary visual cortex (V1) were measured while the animals directed their attention either to the position of the neuron's receptive field (RF), to a position away from the RF (focal attention), or to four locations in the visual field (distributed attention). Over the population, varying attentional state had no significant effect on the response to an isolated stimulus within the RF but had a large influence on the facilitatory effects of contextual lines. We propose that the attentional modulation of contextual effects represents a gating of long range horizontal connections within area V1 by feedback connections to V1 and that this gating provides a mechanism for shaping responses under attention to stimulus configuration.Neuron 04/1999; 22(3):593-604. · 14.74 Impact Factor
Article: Functional MRI reveals spatially specific attentional modulation in human primary visual cortex.[show abstract] [hide abstract]
ABSTRACT: Selective visual attention can strongly influence perceptual processing, even for apparently low-level visual stimuli. Although it is largely accepted that attention modulates neural activity in extrastriate visual cortex, the extent to which attention operates in the first cortical stage, striate visual cortex (area V1), remains controversial. Here, functional MRI was used at high field strength (3 T) to study humans during attentionally demanding visual discriminations. Similar, robust attentional modulations were observed in both striate and extrastriate cortical areas. Functional mapping of cortical retinotopy demonstrates that attentional modulations were spatially specific, enhancing responses to attended stimuli and suppressing responses when attention was directed elsewhere. The spatial pattern of modulation reveals a complex attentional window that is consistent with object-based attention but is inconsistent with a simple attentional spotlight. These data suggest that neural processing in V1 is not governed simply by sensory stimulation, but, like extrastriate regions, V1 can be strongly and specifically influenced by attention.Proceedings of the National Academy of Sciences 03/1999; 96(4):1663-8. · 9.68 Impact Factor
[show abstract] [hide abstract]
ABSTRACT: Recordings of event-related potentials (ERPs) were combined with structural and functional magnetic resonance imaging (fMRI) to study the spatio-temporal patterns of cortical activity that underlie visual-spatial attention. Small checkerboard stimuli were flashed in random order to the four quadrants of the visual field at a rapid rate while subjects attended to stimuli in one quadrant at a time. Attended stimuli elicited enhanced ERP components in the latency range 80-200 ms that were co-localized with fMRI activations in multiple extrastriate cortical regions. The earliest ERP component (C1 at 50-90 ms) was unaffected by attention and was localized by dipole modeling to calcarine cortex. A longer latency deflection in the 150-225 ms range that was accounted for by this same calcarine source, however, did show consistent modulation with attention. This late attention effect, like the C1, inverted in polarity for upper versus lower field stimuli, consistent with a neural generator in primary visual cortex (area V1). These results provide support to current hypotheses that spatial attention in humans is associated with delayed feedback to area V1 from higher extrastriate areas that may have the function of improving the salience of stimuli at attended locations.Cerebral Cortex 06/2003; 13(5):486-99. · 6.54 Impact Factor
Spatial Attention Modulates Initial
Afferent Activity in Human Primary Visual
Simon P. Kelly1,2, Manuel Gomez-Ramirez1,2and John J. Foxe1,2
1The Cognitive Neurophysiology Laboratory, Nathan S. Kline
Institute for Psychiatric Research, Program in Cognitive
Neuroscience and Schizophrenia, 140 Old Orangeburg Road,
Orangeburg, NY 10962, USA and2Program in Cognitive
Neuroscience, Department of Psychology, City College of the
City University of New York, 138th Street & Convent Avenue,
New York, NY 10031, USA
It is well established that spatially directed attention enhances
visual perceptual processing. However, the earliest level at which
processing can be affected remains unknown. To date, there has
been no report of modulation of the earliest visual event-related
potential component ‘‘C1’’ in humans, which indexes initial
afference in primary visual cortex (V1). Thus it has been suggested
that initial V1 activity is impenetrable, and that the earliest
modulations occur in extrastriate cortex. However, the C1 is highly
variable across individuals, to the extent that uniform measurement
across a group may poorly reflect the dynamics of V1 activity. In the
present study we employed an individualized mapping procedure to
control for such variability. Parameters for optimal C1 measurement
were determined in an independent, preliminary ‘‘probe’’ session
and later applied in a follow-up session involving a spatial cueing
task. In the spatial task, subjects were cued on each trial to direct
attention toward 1 of 2 locations in anticipation of an imperative
Gabor stimulus and were required to detect a region of lower
luminance appearing within the Gabor pattern 30% of the time at
the cued location only. Our data show robust spatial attentional
enhancement of the C1, beginning as early as its point of onset
(57 ms). Source analysis of the attentional modulations points to
generation in striate cortex. This finding demonstrates that at the
very moment that visual information first arrives in cortex, it is
already being shaped by the brain’s attentional biases.
Keywords: C1, ERP, spatial attention, V1, visual
Voluntarily directing one’s attention to a specific location in
visual space results in improved detection and discrimination of
stimuli appearing at that location (Posner 1980; Hillyard et al.
1998). Functional magnetic resonance imaging (fMRI) studies
have demonstrated that modulations of cortical processing
accompanying this improvement can extend to the lowest
hierarchical level, primary visual cortex (V1; e.g., Gandhi et al.
1999; Kastner et al. 1999). However, whether V1 modulation
occurs during initial sensory afference cannot be determined
using fMRI due to inadequate temporal resolution, and so
V1 response modulations in nonhuman primates (Motter 1993;
McAdams and Reid2005), and of modulated anticipatory activity
in V1 (Kastner et al. 1999; Silver et al. 2007), there has been no
report of spatial attentional modulation of the ‘‘C1’’ component
of the human event-related potential (ERP) (see Martinez et al.
1999). This has led to the prevailing view that attention only
influences V1 activity during delayed re-entrant feedback
(Noesselt et al. 2002).
That the C1 component (peaking between 65--90 ms)
reflects mainly activity of V1 has been shown by ERP studies
using topographic and source localization techniques (Gomez
Gonzalez et al. 1994; Clark et al. 1995; Di Russo et al. 2002).
This was already a widely held tenet, based on the observation
that the scalp distribution of the C1 is highly dependent on
retinal location, in a way that is consistent with retinal
representation within V1 (Jeffreys and Axford 1972; Butler
et al. 1987). Lying along the banks and within the depths of the
calcarine fissure, which itself takes a convoluted path along the
medial occipital cortical surface, V1 has been said to show
‘‘almost an infinity of individual variation’’ (Polyak 1957). It has
been found to vary widely in shape, size, and areal extent
relative to anatomical landmarks in histological studies
(Rademacher et al. 1993). Although major consistent features
enable characterization of a ‘‘typical’’ C1 topography (e.g.,
upper-field projects to lower calcarine banks, leading to
negative scalp potential), subject-by-subject analysis of the C1
strongly reflects such anatomical variability (Jeffreys and
Axford 1972; Clark et al. 1995; Foxe and Simpson 2002;
Proverbio et al. 2007). This motivates the question whether
measures of initial afferent V1 activity in earlier ERP studies
have been sufficiently reliable to make the claim that initial V1
activity cannot be influenced by attention (see Mangun et al.
1993; Gomez Gonzalez et al. 1994; Clark and Hillyard 1996).
much fewer individuals are likely to exhibit a robust C1 for
a single selected location than would be the case for later, larger
components generated on the lateral cortical surface such as the
P1 or N1. Hence, uniform measurement of the C1 across the
sample may not offer sufficient power for detecting what may
be subtle modulations thereof. To control for intersubject var-
iability in the present study, we employed a simple individual-
ized mapping procedure, whereby both the optimal spatial
locations for stimulation and the optimal electrode locations for
derivation were determined in an independent preliminary
‘‘probe’’ session, and were applied subsequently in a follow-up
session involving a spatial attention task.
Though ERP studies have provided the ultimate support for
early, perceptual-stage attentional selection as opposed to
postperceptual selection (Hillyard et al. 1998), theoretical
arguments for early selection have often been made solely on
the basis of behavioral findings. In particular, that attention can
influence the detection of simple luminance increments (e.g.,
Luck et al. 1994) and increase the contrast sensitivity of stimuli,
thus altering appearance (Carrasco et al. 2004), strongly points
to selection in early processing stages (Vogel et al. 2005).
However, tasks placing demands on such elementary, low-level
information processing have not been employed in ERP studies
addressing the modulation of the earliest components. It has
been shown that attention can operate flexibly so that the
? The Author 2008. Published by Oxford University Press. All rights reserved.
For permissions, please e-mail: email@example.com
Cerebral Cortex Advance Access published March 4, 2008
locus of selection varies according to the processing stages
most overloaded by a particular task (Lavie 1995; Vogel et al.
2005). Along these lines, we reasoned that selection at the
lowest level may be contingent on the task heavily relying on
low-level information. Accordingly, we employ a novel task in
the present study that involves detection of low-contrast
luminance decrements within high-contrast pattern stimuli.
Materials and Methods
Sixteen healthy paid volunteers (4 females), aged 20--34 years partici-
pated in this study, carried out in accordance with the principles laid
down in the Declaration of Helsinki and approved by the Institutional
informed consent, and reported normal or corrected-to-normal vision.
Each subject underwent 2 recording sessions, the first to ‘‘probe’’ 8
spatial locations and characterize the C1 response independent of
spatially directed attention (Fig. 1a), and the second to apply a priori
chosen optimal stimulus locations in a spatial attention task (Fig. 2).
Stimuli and Tasks
Standard stimuli in both tasks consisted of a Gabor patch with a spatial
frequency of 6 cycles/degree, a diameter of 1? at half-contrast, and
duration of 100 ms. The patch could be oriented at 45? or 135? with
equal probability so that subjects had no prior knowledge of
orientation. Data were collapsed across orientation for all analyses.
Subjects fixated on a white central cross on a gray background for the
duration of both tasks.
In the probe task, Gabor stimuli were presented in random sequence
at 8 locations in an annulus of 4? eccentricity, with 1 location lying in
each visual octant. The locations were numbered as on a clock-face
such that the (x, y) coordinates of locations 1 and 2 in degrees of visual
angle were, respectively (2.33, 3.1) and (3.55, 1.7), location 3 was at
(3.55, -1.7), and so on (at polar angles of 25.6? or 53.1? from the
horizontal meridian; see Fig. 1a). Subjects responded with a left mouse
button press to targets, consisting of the standard Gabor patch with
a superimposed black ring of diameter 1.7? and thickness 0.07?,
appearing at any location 11% of the time. This task, which was
performed at >99% accuracy for all subjects, ensured that subjects
maintained fixation and spread attention evenly among the 8 locations
at all times. The stimulus onset asynchrony (SOA) was fixed at 833 ms.
At least 18 blocks (mean 22) of 180 stimuli were run per subject (20
standards at each location plus 20 targets).
In the visuospatial attention task of the second session 2 diagonally
opposite optimal locations determined in the preliminary session for
the subject (see below) were each marked by 4 white dots outlining
a 2.75? 3 2.75? square centered on the location. A central cue
instructed the subject on each trial to covertly attend to 1 of the 2
marked locations, in anticipation of an imperative stimulus (‘‘S2’’)
appearing 733 ms later (Fig. 2b). Cue stimuli (duration 100 ms)
consisted of a small rotated L-shape whose corner pointed in the
direction attention was to be deployed, and appeared at a distance of
0.4? from the center of the fixation cross. Cue direction was
randomized, with equal probability. Standard stimuli were identical to
those in the probe task. Target stimuli, which appeared randomly on
30% of trials, consisted of the standard Gabor pattern with a ring of
reduced luminance of diameter 0.8? and thickness 0.11? superimposed
(see Fig. 2a), also lasting 100 ms. Subjects were instructed to respond
to targets presented at the cued location but to ignore stimuli
presented at the uncued location. The cue-S2 SOA was fixed at
833 ms. The intertrial interval was fixed at 1533 ms. Each subject
underwent at least 20 blocks (mean 24), each composed of 100 trials
The difficulty of target detection, defined by the drop in luminance of
the ring region in targets (Fig. 2a), was varied adaptively across 11
levels based on online performance. The targets at each level were
created simply by multiplying grayscale brightness values within the
ring region by a factor of 0.4--0.9, increasing in steps of 0.05. Each block
began at level 7. Thereafter difficulty dropped a level in the event of
either a single miss or 2 false alarms in a row and increased a level in
the event of 2 hits in a row. As a result, all subjects achieved an average
hit rate of ~80%. Feedback on the average and maximum level reached
was givenat the endof each block.Subjectswereencouragedto achieve
and maintain performance at as high a difficulty level as possible.
Continuous electroencephalographic (EEG) data, digitized at 512 Hz,
were acquired from 164 scalp electrodes and 4 electrooculographic
(EOG) electrodes with a pass-band of 0.05--100 Hz and low-pass filtered
up to 45 Hz offline. Noisy channels, identified by taking the standard
deviation over the block and checking whether it is more than 50%
greater than that of at least 2 of the 4 closest surrounding channels,
were interpolated. During the attention task, eye movement was
recorded using an ISCAN infrared eye-tracker (120 Hz sample rate;
0.03? resolution), the output of which was both monitored online to
ensure fixation and also analyzed offline Preliminary calibration runs
were carried out to ensure precise mapping of eye-position data to
visual angle, wherein subjects performed 10 brief, randomly cued eye
movements to each of 16 locations corresponding to the 8 probe
Figure 1. Probe task and procedure carried out in the preliminary session, independent of spatially directed attention. Data from a single subject (S#6) are shown. (a) Gabor
stimuli were presented to 8 locations in a randomized sequence. Based on the resulting waveforms, we identified the pair of diagonally opposite locations from which the highest
amplitude response within the C1 interval (50--80 ms) was elicited. (b) For these optimal locations, the negative and positive foci were identified in the scalp topography in the
same C1 time frame for upper- and lower-field locations, respectively. (c) Average-reference waveforms were extracted from electrodes lying at the center of these foci.
Page 2 of 8
Spatial Attention and V1 Afference
Kelly et al.
locations and the half-way point of each relative to the fixation cross.
Attention task trials were rejected offline if eye gaze deviated by more
than 0.5?. Horizontal EOG data recorded from the outer canthi were
also calibrated in this run, and the same rejection criterion was applied
to EOG data of 3 subjects for whom eye-tracker data were not available.
Forboth tasks, average-referencedatawere epoched from –80ms before
to 200 ms after stimulus onset, and baseline-corrected relative to the
interval –80 to 0 ms, with an artifact rejection threshold of 60 lV applied.
The purpose of the probe task was to provide an unbiased estimate of
C1 amplitude for each stimulus location, when attention is not directed
to any one point in space, but is presumably spread equally among all
locations. For each individual, ERP waveforms were derived for each of
the 8 locations and examined both in terms of morphology and the
Timing was emphasized as the principal criterion for C1 identification,
such that only initial components with an early onset of 50--60 ms, and
whose amplitude rose to a level well above baseline fluctuations by
80 ms, were considered. Topographical characteristics established in
previous studies additionally guided the identification, referring in
particular to studies that sampled from many stimulus locations (e.g.,
Jeffreys and Axford 1972; Clark et al. 1995). (Because the majority of
ERP studies of spatial attention have used a small number of locations
lying close to or on the horizontal meridian, the C1 is often regarded as
having a strictly dorsal-midline distribution. However, studies such as
these that more fully covered the visual field show that C1 topography is
much more sensitive to stimulus location, having distinctly lateralized
distributions for stimuli located closer to the vertical meridian.) A pair of
diagonally opposite locations (e.g., upper-left location 7 and lower-right
location 3) was then selected on the basis that each elicited a robust
C1. Pairing diagonally opposite locations ensured that the distance
between attended and unattended locations in the attention task was
constant across subjects, and the fixation point lay always on a line
joining the locations. In 2 cases, a reliable C1 could not be measured for
any probe location; therefore these subjects were excluded from
Having selected stimulus locations, electrode sites of maximal C1
amplitude were then identified so that a single trace could be derived
for each location for a given subject. In order to collapse data across
subjects, it was necessary to group responses of like polarity. To this
end, we identified the earliest onsetting negative focus in the scalp
topographies for upper-field stimuli and the positive focus for lower-
field stimuli (Fig. 1b,c). Although this is in line with the property of
polarity inversion for upper- versus lower-field stimuli for locations
close to the horizontal meridian (Di Russo et al. 2002), we applied this
constraint here merely as a convention to group responses, providing
reliability through having 2 separate observations of modulation in the
attention task data.
Attention Task Data
For the attention task data, ERPs to the upper- and lower-field stimuli
were derived for the conditions of attention toward and away from
each location. Trials containing deviations of gaze from central fixation
of greater than 0.5? during the cue-S2 interval were rejected. Three
subjects who made such deviations on more than 30% of trials were
excluded from further analysis. Only nontarget stimuli were analyzed in
both sessions, excluding false alarm trials, resulting in an average sweep
count of 350 per condition for the probe data and 330 per condition for
the attention task data.
To test for attentional modulation of the C1, we first took as the
dependent variable the average amplitude over the interval 50--80 ms,
measured from waveforms at the optimal electrodes determined
independently in the first session. An analysis of variance (ANOVA)
with the factors of attention (toward vs. away) and field of S2 (upper vs.
lower) was then carried out. It was necessary to invert the upper-field
values for this test so that polarity was all positive, enabling comparison
of the strength of effects across fields.
To follow up early attention effects found in the initial ANOVA,
a second analysis was conducted to estimate both the onset of the
unbiased ‘‘probe’’ C1 and the onset of attentional modulation in the
cueing task for comparison. For both the probe and attention task data,
upper- and lower-field waveforms were combined by subtraction,
giving a single waveform for each of the probe, attended, and
unattended conditions. To estimate the onset of cortical activity in
the absence of biased attention, we computed running t-tests com-
paring probe waveform amplitude at each sample point to zero. The
onset was defined as the point at which the difference reached sig-
nificance at the 0.05 level for 10 or more consecutive points (>20 ms)
beginning at that point (see Foxe and Simpson 2002; Molholm et al.
2002). To determine modulation onset in the attention data, we
computed running t-tests comparing the attended to the unattended
waveform at each point, with the same constraints applied.
Effects of attention on the later P1 component were also investigated
for the purposes of comparison with previous studies. Though more
consistently observed across subjects, the P1 has been found to vary
also as a function of stimulus location, albeit to a lesser degree than the
C1 (e.g., Clark et al. 1995). Thus, electrodes for P1 amplitude
measurement were determined on the basis of grand average probe
data for each of the 8 locations, and applied in the attention task data to
test for modulation effects. In line with many studies distinguishing an
earlier contralateral P1 phase from a later ipsilateral phase (e.g., Di
Russo et al. 2002), we measured and tested early (90--110 ms;
contralateral electrodes for all locations) and late (110--140 ms;
ipsilateral electrodes for all locations but 1 and 8) phases of the P1
separately. As for the C1, an ANOVA with the factors attention and field
was carried out for each P1 phase in the attention task data.
We estimated intracranial sources of attentional modulation using
a distributed linear inverse solution based on a Local Auto-Regressive
Figure 2. Spatial cueing task of the second session, incorporating the optimal pair of
locations determined in session 1. (a) standard Gabor stimulus and target stimulus at
difficulty level 7. (b) Task structure. In this example an invalid (uncued) target is
presented, which is to be ignored.
Cerebral Cortex Page 3 of 8
Average (LAURA) model of the unknown current density in the brain
(Grave de Peralta et al. 2001), implemented in the Cartool analysis
package. LAURA uses a realistic head model with a solution space of
4024 nodes, where voxels are restricted to the gray matter of the
Montreal Neurological Institute’s (MNI’s) average brain divided into
a regular grid with 6-mm spacing. For each subject the inverse solution
was estimated for the difference waveforms (attended minus un-
attended) in the attention task data. We then found the maximally
activated node within the set of all nodes lying within Brodmann areas
17 (57 nodes across hemispheres), 18 (259) or 19 (290) over the
interval 50--70 ms, that is, just shy of the typical onset of the earliest P1
(Martinez et al. 1999; Di Russo et al. 2002).
Probe Task and Mapping Procedure
Attesting the utility of the mapping procedure, optimal
locations selected on the basis of the probe data varied
considerably across the 11 included subjects (see Fig. 4). In the
majority of cases a reliable C1 was observed for less than half of
the probed locations, such that the selection of location pairs
was guided most often by the presence or absence of the C1,
rather than a comparison of relative amplitudes. For 9 of the 11
subjects, the timing and topography of the C1 for selected
locations closely matched those demonstrated in previous
studies (e.g., Clark et al. 1995; Di Russo et al. 2002). Consistent
with the cruciform model of V1 (Jeffreys and Axford 1972;
Butler et al. 1987), subjects with optimal locations lying close
to the vertical meridian (subjects 1, 2, 7, 9, 11) exhibited
bipolar C1 distributions reflecting the projection of these
locations onto parts of V1 lying furthest outside the calcarine
sulcus. Of the 6 subjects whose optimal locations lay close to
the horizontal meridian, 4 (subjects 3, 6, 8, 10) exhibited
a distinct midline dorsal distribution for upper-field stimuli,
matching the ‘‘classic’’ C1 topography observed in many studies
(e.g., Martinez et al. 1999; Di Russo et al. 2003). The more
lateral negative foci observed for the remaining 2 subjects
(4, 5) were strong exceptions to the classic pattern, highlight-
ing the extent of variability accounted for by the mapping
Behavioral Results of Spatial Cueing Task
As stimuli appearing at the uncued location were to be ignored,
we cannot derive a behavioral measure of attentional modula-
tion as is often done for traditional Posner tasks involving
probabilistic cues. However, the effectiveness of the adaptive
difficulty manipulation in maintaining task difficulty at a high
level, and thus keeping subjects highly engaged, is demon-
strated in a mean ± SD hit rate of 80.7 ± 3.3% and d# of 2.36 ±
Electrophysiological Results of Spatial Cueing Task
Figure 3a shows the ERP responses averaged over all 11
subjects, contrasting the conditions of attention toward and
away from each location, with waveforms to upper- and lower-
field stimuli superimposed. The ANOVA testing the C1
component revealed a significant main effect of attention
(F1,10 = 20.25, P < 0.001). There was no effect of field or
interaction between factors. Follow-up t-tests in each field
revealed a significant attention effect for both the upper-field
stimuli (t(10) = 4.10, P <0.002) and lower-field stimuli (t(10) =
4.28, P < 0.002). The ANOVA testing P1 amplitude revealed
a significant effect of attention for both the early (F1,10= 23.02,
P < 0.001) and the late phase (F1,10= 15.58, P < 0.005).
The timing of attentional modulation with reference to the
onset of the unbiased probe C1 onset represents a crucial
indicator of striate cortex generation. Figure 3b plots the series
of P-values resulting from point-wise t-tests in the time frame of
C1 onset for the deviation of probe amplitude from baseline,
and for the difference in amplitude between the attended and
unattended conditions. As the figure indicates, the point at
which significance is reached for the probe C1 coincides
precisely with that of the attention effect, at 57 ms.
Figure 4 displays the data of each individual subject,
illustrating the selected optimal measurement points on the
probe topographies at 80 ms and the waveforms derived at
these electrodes in the attention task data. Also shown are
the probe ERP topographies at 100 ms, the peak latency of
the contralateral P1, for the lower-field locations. As both the
lower-field C1 and early phase P1 manifest as contralateral
positivities, their topographies tend to overlap. This overlap has
not yet been quantified systematically, possibly due to in-
adequate electrode density in earlier studies (Jeffreys and
Axford 1972; Clark et al. 1995). In addition, differences in exact
stimulus locations across studies make direct comparison
difficult. Nevertheless, it can be seen from Figure 4 that the
majority of subjects show a marked shift in the positive
contralateral focus from 80 to 100 ms, indicating that the 2
components are well dissociated. It is worth noting, for
example, that all 3 subjects having location 5 as their lower-
field location exhibit a lateral shift in topography from the C1
to the P1, which is highly similar to that seen in 2 recent
studies wherein stimulus locations were ~0.5? from this
location (Di Russo et al. 2002, 2003). The average absolute
shift in the focus of positive potential across subjects was
measured as 2.8 ± 1.8 cm—almost twice the average in-
terelectrode distance on the 160-channel electrode cap used.
Figure 3. (a) Grand average waveforms for attention toward and away from upper-
and lower-field stimuli. (b) P-values derived from running t-tests to determine the
onset of the probe C1 from session 1 and the onset of the attention effect in session 2.
Page 4 of 8
Spatial Attention and V1 Afference
Kelly et al.
We estimated the intracranial sources of attentional modu-
lation of the C1 using a distributed inverse solution (LAURA).
Specifically, the site of maximum modulation in visual cortex in
the time range of C1 onset (50--70 ms), was determined for
each subject. Following the procedure of Martinez et al. (1999)
we averaged Talairach coordinates of the source sites across
subjects, which revealed coordinates of (x = 24, y = –80, z = 3)
and (x = –23, y = –78, z = 10) for left and right hemifield stimuli
respectively, consistent with striate cortex generators (note
again that only 9% of included nodes in MNI space were from
area 17). Offline analysis of eye-tracking data for the accepted
trials in the attention task revealed an average absolute gaze
deviation in any direction across subjects of 0.09? ± 0.05?
(mean ± SD), illustrated in Figure 5?.
Figure 4. Individual subject scalp topographies at the 80-ms time point for upper-field locations and at 80 and 100 ms for lower-field locations from the probe data, and attended
and unattended waveforms (average reference) from the attention task data for the pair of diagonally opposite locations selected for each individual. Scalp electrodes selected to
measure individual C1s on the basis of probe topographies are shown as green circles. C1 (80 ms) and P1 (100 ms) topographies for lower-field locations are shown on the same
scale for each subject to highlight changes in amplitude as well as topographical focus.
Cerebral Cortex Page 5 of 8
In the present study intersubject variability of the C1 com-
ponent of the human ERP was controlled for in a simple
individualized mapping procedure, resulting in robust mea-
surement of initial V1 activity. We applied this procedure
to data recorded during a spatial attention task involving
elementary luminance decrement detection, and observed
significant modulation of the C1. Further, a fine-grained timing
analysis showed that the onset of attentional modulation
precisely coincided with the onset of the ‘‘probe’’ C1 measured
without spatially focused attention. Source localization results
provide further support for a striate source of the modulation.
These findings count against the theory that V1 activity is
impenetrable during initial afference and may only modulate
during delayed re-entrant feedback, which has emerged on the
basis of combined ERP and fMRI studies showing V1
modulations in fMRI data but not modulation of the C1
(Martinez et al. 1999; Noesselt et al. 2002; Di Russo et al. 2003).
Conversely, our results are consistent with the interpretations
of previous fMRI studies finding attentional modulation in V1
(Gandhi et al. 1999; Somers et al. 1999) and also with single-
unit studies in nonhuman primates (Motter 1993; Ito and
Gilbert 1999; McAdams and Reid 2005).
Although the C1, measured in the same latency interval (e.g.,
Clark et al. 1995; Martinez et al. 1999) or even later (e.g., Di
Russo et al. 2002, 2003), has consistently been shown to
originate in striate cortex, and is here observed to modulate
with attention, we must still rule out the possibility that an
overlapping P1 modulation, which has been seen to onset as
early as 70 ms (Martinez et al. 1999), contributed to the effect.
First of all, we found equally strong modulations for negative
upper-field C1s as positive lower-field C1s. As in every other
study on the subject, the P1 modulation found here was
a relative enhancement with attention, resulting in a positive
shift. If there were contributions from an overlapping P1 effect,
we would have found greater modulations for positive than
negative C1s, or might not have observed modulation of the
negative C1 at all. Secondly, the point of onset of the attention
effect, calculated as 57 ms, is a good deal earlier than the
earliest observed modulations of the P1, and not only coincides
with the unbiased probe C1 onset calculated here, but co-
incides with or even precedes C1 onset latencies expressed in
the vast majority of previous studies relating to the issue (e.g.,
Gomez Gonzalez et al. 1994; Clark et al. 1995; Clark and
Hillyard 1996; Martinez et al. 1999; Di Russo et al. 2002, 2003;
Pourtois et al. 2004; Stolarova et al. 2006; Proverbio et al. 2007).
Finally, the average distance on the scalp by which the
contralateral positive focus shifted between 80 and 100 ms
for lower-field stimuli is large enough to render a common
generator for the C1 and P1 extremely unlikely. Moreover,
where valid comparison is possible, the temporal and spatial
characteristics of the C1 and P1 measured here match those in
previous studies where separate generators in striate and
asserted (e.g., Di Russo et al. 2002, 2003).
It is worth pointing out again that it was by convention that
we selected electrodes lying within the negative topographical
focus for measurement of upper-field C1s and within the
positive focus for lower-field C1s. Polarity inversion of the C1 at
midline sites has become a routine indicator of a striate cortical
source. This is because the majority of studies have used
stimulus locations near or on the horizontal meridian, which
project to parts of V1 lying well inside the calcarine fissure
(e.g., Martinez et al. 1999; Di Russo et al. 2002, 2003; Noesselt
et al. 2002). However, it is well known that locations close to
the vertical meridian project to the part of V1 lying on the
outer banks of the calcarine cortex (see Clark et al. 1995).
Approximate dipolar sources for upper- and lower-field
locations near the vertical meridian would thus have roughly
the same orientation. This anatomical feature, along with its
assured variability across individuals (see Stensaas et al. 1974),
casts doubt on whether polarity inversion can be used as a valid
diagnostic of V1 generation that can be generalized to all spatial
locations. We would thus emphasize here that it is the timing of
attentional modulation with respect to the probe activity onset
that we have considered the crucial indicator of the earliest
striate source activity.
Many recent studies have focused on demonstrating the
flexibility of selective attention and its expression in visual
cortex. For instance, the locus of attentional selection has been
shown to vary among hierarchical levels of processing accord-
ing to perceptual load (Lavie 1995), the spatial scale of attended
items (Hopf et al. 2006), and the involvement of perceptual
versus memory systems (Vogel et al. 2005). Given these
dramatic manifestations of flexibility, it seems somewhat
arbitrary that gating could occur early in the visual system,
but never reach down to the very first stage. It is certainly not
the case that the pattern of feedback inputs to V1 from higher
regions is any sparser than in later regions of extrastriate cortex
(see Sincich and Horton 2005). The C1 component is not
invulnerable to contextual influences, such as the motivational
relevance of aversive stimuli (Pourtois et al. 2004; Stolarova
et al. 2006), or indeed to concurrent auditory input (Molholm
et al. 2002). Moreover, spatially specific increases in V1 baseline
activity with attention in the absence of stimulation have been
found in human fMRI studies (Kastner et al. 1999; Silver et al.
2007), suggestive of anticipatory priming of V1 neurons. In
human EEG studies, anticipatory changes in alpha-band
oscillatory power have been found to be retinotopically specific
(Worden et al. 2000; Kelly et al. 2006), consistent with priming
of the very earliest cortical stages. It seems paradoxical then
that there has been no report of modulation of the C1—why
would anticipatory priming of V1 neurons affect processing not
in the first volley but only during later rounds of feedback?
Though it is clear that increased sensitivity has been afforded
by the mapping procedure, it is unlikely that this factor alone
Figure 5. Average gaze deviations for trials accepted to the ERP analysis,
superimposed on the stimulus display. Both upper and lower locations are shown for
each subject in different colors. The stimulus is a level 7 target.
Page 6 of 8
Spatial Attention and V1 Afference
Kelly et al.
fully accounts for our detecting a C1 modulation, and why
many other studies have not. Indeed, without controlling for
variability as we have done here, several studies have measured
relatively high-amplitude C1s that were not observed to
modulate (e.g., Martinez et al. 1999; Di Russo et al. 2003).
Recently, Proverbio et al. (2007) also found large individual
variability in the C1, with only half of subjects showing
a negative C1, which would be expected for stimuli centered
on the horizontal meridian (see Clark et al. 1995; Martinez et al.
1999). Even for this subgroup of subjects, there was no effect of
spatial attention on the C1. It is therefore of interest to
consider differing experimental parameters, which, individually
or together, may have further contributed to the outcome.
Of potential relevance is that trial-by-trial cueing was
employed in the present study, whereas more continuous,
rapid stimulation (1--5 stimuli per s) has been used in previous
studies, with attention alternated between 2 locations every
20 s or so (e.g., Martinez et al. 1999) or directed to 1 location
for an entire run of 1 minute or longer (e.g., Mangun et al.
1993). Theoretical arguments on this issue do not clearly
favor either task type as being more likely to induce early
modulations—the potential roles of refractory effects (such as
inhibition of return) at play in rapid stimulation paradigms, or
on the other hand, negative priming effects associated with
trial-by-trial shifting of attention in cueing paradigms, are
unknown. Such phenomena appear not to compromise the
modulation of later components such as P1 and N1, as these are
almost invariably observed to modulate. In previous studies
looking at ERP attentional modulations for trial-by-trial cueing
paradigms with instructional (not probabilistic) cues, the C1
component has not been directly tested (Eimer 1994; Hopf and
Mangun 2000). Though not the most compelling explanation
for the current results, a systematic investigation of C1 mod-
ulation during sustained versus trial-by-trial attention deploy-
ments may be warranted.
Another factor that distinguishes the present paradigm from
most previous studies observing no C1 modulation is the spatial
configuration of the attended/unattended locations, which
were diagonally opposite here, rather than symmetrical about
the vertical meridian. One study, however, did use a display
with both types of unattended location, where subjects
attended to 1 of 4 stimulus streams, 1 in each quadrant, and
no effects of attention were found on the C1 (Mangun et al.
Stimulus differences are likely important, particularly in the
comparison of our results with those of Martinez et al. (1999)
and Noesselt et al. (2002). In the latter studies, the task
involved discrimination of a symbol in the center of the
stimulus among surrounding distracters, all of which were
superimposed on a background checkerboard pattern. Thus,
the part of the stimulus primarily responsible for evoking
a strong scalp-measured C1, that is, the background, is not the
part that is relevant to task performance. An important factor
may be that the majority of V1 neurons whose receptive fields
lie within the stimulus space actually receive input from
distracter symbols—though enhancement of the entire stimu-
lus may occur at extrastriate levels where receptive field sizes
are large, enhancement of the entire stimulus at the level of V1
would be disadvantageous for task performance and therefore
might not occur. In contrast, attentional enhancement of the
entire stimulus pattern is required in our task and this may be
a necessary condition for observing early modulation. That it is
not a sufficient condition clearly follows from the many studies
showing that C1 does not modulate during spatially cued size
discrimination tasks (Mangun et al. 1993; Clark and Hillyard
1996; Di Russo et al. 2003).
An alternative explanation may lie in the further consider-
ation of task demands. A unique feature of the present task
among other ERP studies is that simple pattern detection was
required, as opposed to more complex discrimination. Though
strong attention effects on near-threshold detection abound in
behavioral studies, the examination of ERP correlates thereof
has been largely precluded by the inability to measure reliable
visual ERPs to low-contrast stimuli (Luck et al. 1994). We have
effectively surmounted this problem by infusing a low-contrast
target pattern within a high-contrast, uniform pattern stimulus.
Task performance then boils down to a simple presence--
absence judgment, which, computationally, would involve
a relatively direct translation from low-level features analysis
to final decision.
Depending on the stimulus aspects that distinguish a target
from nontarget in a given visual task, the fidelity of information
in certain processing stages will be more critical to successful
performance than that in other stages. In our task, crucial
evidence of the low-contrast ‘‘break in context’’ within the
otherwise uniform pattern that defines a target may be
contained in feed-forward activity through V1. Indeed, it has
been shown that successful figure-ground processing of this
kind is strongly dependent on V1 activity (Supe ` r et al. 2003).
Conversely, the output of low-level analyzers in V1 may be the
point at which insufficient signal-to-noise most often gives rise
to an erroneous response. Higher-order attentional control
processes may then work to adapt the structure of cue-
contingent anticipatory attentional sets such that a boost in
‘‘gain’’ is instantiated at this crucial stage. This targeted
enhancement may be equivalent to a sharpening of contrast
sensitivity similar to that shown behaviorally with transient
attention (Carrasco et al. 2004). It is interesting in this context
to note that a task involving near-threshold pattern detection of
the kind under discussion here was used in an fMRI study
showing preparatory modulations in V1 to be strongly pre-
dictive of behavioral performance (Ress et al. 2000).
The implication of this adaptive gain account is that when
complex computations are crucial in the performance of a task,
correspondingly complex processing stages will be favored for
attentional enhancement. In other words, the level of
attentional selection may follow the level of complexity of
discrimination. In Proverbio et al. (2007), subjects were
required to fully identify and compare animals and objects
within the attended stimulus. The studies of Clark and Hillyard
(1996) and Di Russo et al. (2003) employed size discrimination
tasks; whereas this appears to be a simple process, size
estimation may involve the interaction of levels with larger
receptive fields, and the task certainly involves interaction with
working memory in the comparison with a ‘‘standard’’ size
template. On the other hand, one could reason retrospectively
that the lowest levels may be targeted for enhancement in
some tasks used in previous primate studies, such as the
detection of a red/green color pixel within grayscale noise
(McAdams and Reid 2005), and the discrimination of the
orientation of small bars (fitting inside a V1 receptive field) in
the presence of competing distracters (Motter 1993). Clearly,
more systematic, direct investigation will be necessary to
substantiate these ideas.
Cerebral Cortex Page 7 of 8
U.S. National Science Foundation (BCS-0642584) and National
Institute of Mental Health (MH65350) to J.J.F.
We thank S. A. Hillyard, S. Molholm, A. Martinez, and E. C. Lalor for
valuable comments, and J. Montesi for technical assistance. The Cartool
software was programmed by D. Brunet, and supported by the Center
for Biomedical Imaging of Geneva and Lausanne. Conflict of Interest:
Address correspondence to John J. Foxe, PhD, The Cognitive
Neurophysiology Laboratory, Nathan S. Kline Institute for Psychiatric
Research, Program in Cognitive Neuroscience and Schizophrenia, 140
Old Orangeburg Road, Orangeburg, NY 10962, USA. Email foxe@nki.
Butler SR, Georgiou GA, Glass A, Hancox RJ, Hopper JM, Smith KR.
1987. Cortical generators of the CI component of the pattern-onset
visual evoked potential. Electroencephalogr Clin Neurophysiol.
Carrasco M, Ling S, Read S. 2004. Attention alters appearance. Nat
Clark VP, Fan S, Hillyard SA. 1995. Identification of early visual evoked
potential generators by retinotopic and topographic analyses. Hum
Brain Mapp. 2:170--187.
Clark VP, Hillyard SA. 1996. Spatial selective attention affects early
extrastriate but not striate components of the visual evoked
potential. J Cogn Neurosci. 8:387--402.
Di Russo F, Martinez A, Hillyard SA. 2003. Source analysis of event-
related cortical activity during visuo-spatial attention. Cereb Cortex.
Di Russo F, Martinez A, Sereno MI, Pitzalis S, Hillyard SA. 2002. Cortical
sources of the early components of the visual evoked potential.
Hum Brain Mapp. 15:95--111.
Eimer M. 1994. ‘‘Sensory gating’’ as a mechanism for visuospatial
orienting: electrophysiological evidence from trial-by-trial cueng
experiments. Percept Psychophys. 55:667--675.
Foxe JJ, Simpson GV. 2002. Flow of activation from V1 to frontal cortex
in humans. A framework for defining ‘‘early’’ visual processing. Exp
Brain Res. 142:139--150.
Gandhi SP, Heeger DJ, Boynton GM. 1999. Spatial attention affects brain
activity in human primary visual cortex. Proc Natl Acad Sci USA.
Gomez Gonzalez CM, Clark VP, Fan S, Luck SJ, Hillyard SA. 1994.
Sources of attention-sensitive visual event-related potentials. Brain
Grave de Peralta MR, Gonzalez AS, Lantz G, Michel CM, Landis T. 2001.
Noninvasive localization of electromagnetic epileptic activity I
Method descriptions and simulations. Brain Topogr. 14:131--137.
Hillyard SA, Vogel EK, Luck SJ. 1998. Sensory gain control (amplifica-
tion) as a mechanism of selective attention: electrophysiological and
neuroimaging evidence. Philos Trans R Soc Lond B Biol Sci.
Hopf JM, Luck SJ, Boelmans K, Schoenfeld MA, Boehler CN, Rieger J,
Heinze HJ. 2006. The neural site of attention matches the spatial
scale of perception. J Neurosci. 26:3532--3540.
Hopf JM, Mangun GR. 2000. Shifting visual attention in space: an
electrophysiological analysis using high spatial resolution mapping.
Clin Neurophysiol. 111:1241--1257.
Ito M, Gilbert CD. 1999. Attention modulates contextual influences in
the primary visual cortex of alert monkeys. Neuron. 22:593--604.
Jeffreys DA, Axford JG. 1972. Source locations of pattern-specific
components of human visual evoked potentials: I. Component of
striate cortical origin. Exp Brain Res. 16:1--21.
Kastner S, Pinsk MA, De Weerd P, Desimone R, Ungerleider LG. 1999.
Increased activity in human visual cortex during directed attention
in the absence of visual stimulation. Neuron. 22:751--761.
Kelly SP, Lalor EC, Reilly RB, Foxe JJ. 2006. Increases in alpha oscillatory
power reflect an active retinotopic mechanism for distracter
suppression during sustained visuospatial attention. J Neurophysiol.
Lavie N. 1995. Perceptual load as a necessary condition for selective
attention. J Exp Psychol Hum Percept Perform. 21:451--468.
Luck SJ, Hillyard SA, Mouloua M, Woldorff MG, Clark VP, Hawkins HL.
1994. Effects of spatial cuing on luminance detectability: psycho-
physical and electrophysiological evidence for early selection. J Exp
Psychol Hum Percept Perform. 20:887--904.
Mangun GR, Hillyard SA, Luck SJ. 1993. Electrocortical substrates of
visual selective attention. In: Meyer D, Kornblum S, editors.
Attention and performance XIV. Cambridge (MA): MIT Press.
Martinez A, Anllo-Vento L, Sereno MI, Frank LR, Buxton RB,
Dubowitz DJ, Wong EC, Hinrichs H, Heinze HJ, Hillyard SA. 1999.
Involvement of striate and extrastriate visual cortical areas in spatial
attention. Nat Neurosci. 2:364--369.
McAdams CJ, Reid RC. 2005. Attention modulates the responses of
simple cells in monkey primary visual cortex. J Neurosci.
Molholm S, Ritter W, Murray MM, Javitt DC, Schroeder CE, Foxe JJ.
2002. Multisensory auditory-visual interactions during early sensory
processing in humans: a high-density electrical mapping study.
Cogn Brain Res. 14:115--128.
Motter BC. 1993. Focal attention produces spatially selective process-
ing in visual cortical areas V1, V2, and V4 in the presence of
competing stimuli. J Neurophysiol. 70:909--919.
Noesselt T, Hillyard SA, Woldorff MG, Schoenfeld A, Hagner T, Jancke L,
Tempelmann C, Hinrichs H, Heinze HJ. 2002. Delayed striate
cortical activation during spatial attention. Neuron. 35:575--587.
Polyak S. 1957. The vertebrate visual system. Chicago (IL): University of
Posner MI. 1980. Orienting of attention. Q J Exp Psychol. 32:3--25.
Pourtois G, Grandjean D, Sander D, Vuilleumier P. 2004. Electrophys-
iological correlates of rapid spatial orienting towards fearful faces.
Cereb Cortex. 14:619--633.
Proverbio AM, Del Zotto M, Zani A. 2007. Inter-individual differences in
the polarity of early visual responses and attention effects. Neurosci
Rademacher J, Caviness VS, Jr, Steinmetz H, Galaburda AM. 1993.
Topographical variation of the human primary cortices: implications
for neuroimaging brain mapping and neurobiology. Cereb Cortex.
Ress D, Backus BT, Heeger DJ. 2000. Activity in primary visual cortex
predicts performance in a visual detection task. Nat Neurosci.
Silver MA, Ress D, Heeger DJ. 2007. Neural correlates of sustained
spatial attention in human early visual cortex. J Neurophys.
Sincich LC, Horton JC. 2005. The circuitry of V1 and V2: integration of
color form and motion. Annu Rev Neurosci. 28:303--326.
Somers DC, Dale AM, Seiffert AE, Tootell RB. 1999. Functional MRI
reveals spatially specific attentional modulation in human primary
visual cortex. Proc Natl Acad Sci USA. 96:1663--1668.
Stensaas SS, Eddington DK, Dobelle WH. 1974. The topography and
variability of the primary visual cortex in man. J Neurosurg.
Stolarova M, Keil A, Moratti S. 2006. Modulation of the C1 visual event-
related component by conditioned stimuli: evidence for sensory
plasticity in early affective perception. Cereb Cortex. 16:876--887.
Supe ` r H, van der Togt C, Spekreijse H, Lamme VA. 2003. Internal state
of monkey primary visual cortex (V1) predicts figure-ground
perception. J Neurosci. 23(8):3407--3414.
Vogel EK, Woodman GF, Luck SJ. 2005. Pushing around the locus of
selection: evidence for the flexible-selection hypothesis. J Cogn
Worden MS, Foxe JJ, Wang N, Simpson GV. 2000. Anticipatory biasing of
visuospatial attention indexed by retinotopically specific alpha-band
electroencephalography increases over occipital cortex. J Neurosci.
Page 8 of 8
Spatial Attention and V1 Afference
Kelly et al.