Cognitive Brain Research 15 (2002) 17–29
www.bres-interactive.com
Interactive report
How does attention attenuate target–distractor interference in vision?
Evidence from magnetoencephalographic recordings
J.-M. Hopf a , *, K. Boelmans a , A.M. Schoenfeld a , H.-J. Heinze a , S.J. Luck b
a
Department of Neurology II, Otto-von-Guericke-University, Leipziger Str. 44, D-39120 Magdeburg, Germany
b
Department of Psychology, University of Iowa, Iowa City, IA 52242 -1407, USA
8 September 2002
Abstract
This study used magnetoencephalographic and electroencephalographic recordings to investigate the neural mechanisms that underlie
the attentional resolution of ambiguous feature coding in visual search. We addressed this issue by comparing neural activity related to
target discrimination under conditions of more versus less feature overlap between the target and distractor items. The results show that
increasing feature overlap leads to a focal enhancement of neural activity in ventral occipito-temporal areas, consistent with the larger
need to attenuate distractor interference. Furthermore, the results suggest that distractor attenuation proceeds as a stepwise operation, with
different spatial locations containing interfering features being suppressed successively. These findings support theories of visual search
that emphasize location-based attentional selection as a key mechanism in resolving ambiguous feature coding in vision.
 2002 Elsevier Science B.V. All rights reserved.
Theme: Neural basis of behaviour
Topic: Neural plasticity
Keywords: Attentional resolution; Visual search; Distractor attenuation; Neural activity
1. Introduction
It is commonly assumed that searching for a target item
among distractors requires attentional focusing. According
to an influential proposal [36,37], visual processing begins
with a stage of automatic, preattentive feature registration.
This initial step produces spatially organized maps of
simple features (color, orientation, form etc.), which can be
used to detect the presence of features but is not sufficient
to detect multifeature targets. For multifeature targets,
attention is thought to mediate target identification by
binding the independent feature representations into a
coherent object representation [35].
Consistent with this proposal, there is considerable
evidence showing that the allocation of attention differs
depending on the particular distribution of features across
*Corresponding author. Tel.: 149-391-671-3430; fax: 149-391-6715032.
E-mail address: [email protected] (J.-M.
Hopf).
target and distractors [8,37,38,43]. For example, a target
item containing a unique feature may be detected easily
because the preattentive feature analysis can indicate
whether the target is present or absent without the use of
focused attention. In contrast, when the features of the
target are also present in the distractors, the preattentive
feature analysis is insufficient to determine whether the
target-defining features are present in the same object or in
different objects, and focused attention is therefore necessary to bind together the features of an object and
determine whether it is a target. Electrophysiological
studies have supported this proposal, showing that the
amount of attention allocated to a target depends on
whether feature binding is necessary [20,21]
Many theories propose that the feature-binding role of
attention is mediated, at least in part, by inhibitory
mechanisms that suppress information from the distractors
surrounding the target. Indeed, neurophysiological evidence from single-unit recordings in monkeys, event-related brain potentials (ERPs) in humans, and functional
brain imaging in humans suggests that the suppression of
0926-6410 / 02 / $ – see front matter  2002 Elsevier Science B.V. All rights reserved.
PII: S0926-6410( 02 )00213-6
18
J.-M. Hopf et al. / Cognitive Brain Research 15 (2002) 17–29
distractors is an important mechanism of attentional selection [4,5,15,19,21,24,25,32,33,41]. For example, singleunit recordings in macaque extrastriate cortex during visual
search revealed that, when two objects are within a
neuron’s receptive field, attending to one of the objects
will suppress the neuron’s responses to the other object
[3,4]. These and related studies [19,24] led to the proposal
that attention may filter out irrelevant visual input by
means of a principle called biased competition [6,7,31].
That is, multiple stimuli inside one RF give rise to
competitive interactions, and attention acts by biasing
these competitive interactions in favor of the attended
stimulus [6,7,31].
The investigation of visual search tasks with ERPs in
humans has also provided evidence that attention may act,
in part, by suppressing interference from distractor items.
A particularly revealing ERP component has been the
N2pc component (N2 posterior contralateral)—a negativegoing voltage deflection in the N2 latency range, with a
maximum over the posterior scalp contralateral to the
attended item. This component, usually elicited between
180 and 300 ms after search frame onset, has been
demonstrated to reflect the operation of focusing attention
onto potential target items [10,22,23,44]. Three important
properties of the N2pc are that: (1) it is larger for targets
surrounded by competing distractor items than for isolated
targets [23]; (2) it is larger for multifeature targets than for
single-feature pop-out targets [21]; and (3) it is generated
primarily in lateral occipito-temporal regions, with a small
contribution from posterior parietal cortex [13]. Thus, the
N2pc component is similar to single-unit attention effects
observed in macaque IT cortex [4] and area V4 [5] in terms
of both its time course and its response to experimental
manipulations. Both measures may therefore reflect the
same neural activity linked to distractor suppression within
the ventral stream (see Ref. [21] for a discussion).
The present MEG / ERP experiments further investigated
the relationship between distractor interference and attention. According to the theoretical accounts sketched above,
enhancing ambiguities of feature coding should require
more suppressive neural activity that should, in turn, be
reflected by the N2pc component and it’s magnetic counterpart, the mN2pc. The amount of ambiguous feature
coding was manipulated by changing the number of
features that overlap between target and distractors. In one
condition, target and distractors shared two feature-dimensions (color and orientation), in another condition they
shared only one feature (color). We predicted that the N2pc
and mN2pc would be larger when the target and distractors
shared two features than when they shared only one, which
would be taken as evidence that suppression of competing
features is an important role of attention within visual
cortex. Furthermore, parallels between single-unit attention
effects in macaque extrastriate cortex and the N2pc in
humans [21] suggest that source activity related to distractor interference may be found extrastriate regions of the
ventral stream. To test this prediction, we compared results
of inverse source modeling (current source localization
based on minimum norm least square estimates) of the
mN2pc under conditions of more or less interference. This
allowed to identify neural activity in cortical regions where
distractor interference impacts attentional processing. Two
experiments were conducted, one to test these predictions
and another to rule out a potential confound.
2. Methods
2.1. Subjects
Both experiments were undertaken with the understanding and written consent of the subjects. Subjects were paid
for participation. Twelve subjects (mean age 25.4 years,
nine female) participated in the first experiment; six subject
participated in the second control experiment (mean age
24.8 years, five female). All subjects were right handed,
with normal color vision and normal or corrected-tonormal visual acuity.
2.2. Stimuli and procedure
2.2.1. Experiment 1
The stimuli used in this experiment are illustrated in Fig.
1A. They were presented via microcomputer-controlled
back projection at a viewing distance of 120 cm. The
background was grey (8 cd / m 2 ), and a white fixation cross
was continuously visible in the center of the display.
Subjects were instructed to fixate this point and to minimize blinking. Fixation was monitored by an infrared
video camera with a zoom lens.
Each stimulus array contained 12 double-colored circles
presented below the fixation point, with six in the left
visual field (LVF) and six in the right visual field (RVF).
Stimuli were arranged such that a central target circle was
surrounded by five distractor circles in each visual field.
The central circle’s distance from fixation (8 of visual
angle) was 3.58 to the left or right and 3.38 towards the
lower visual field. The five distractor circles were even
spaced around the central circle, with a center-to-center
distance of 1.78 from the central circle.
Each circle was divided into two differently colored
halves, blue on one side and either red or yellow on the
other. The two halves formed either a left-right pair with a
vertical border or a top-bottom pair with a horizontal
border (see Fig. 1A). Blue-and-yellow squares were presented in one visual field and blue-and-red squares were
presented in the other; the assignment of colors to visual
field varied unpredictably across trials. The luminance of
each color was 95 cd / m 2 . On double-interference trials,
the central target and surrounding distractors were presented in the same orientation, which was intended to
produce interference along two dimensions (color and
orientation), thereby increasing target–distractor similarity
and the need for attentional suppression of the distractors.
J.-M. Hopf et al. / Cognitive Brain Research 15 (2002) 17–29
19
Fig. 1. (A) Stimulus types (double, single) and principal structure of an experimental run of experiment 1. (B) Stimulus types used for the central and
peripheral control task in experiment 2.
In single-interference trials, the target and distractors were
presented in orthogonal orientations, producing interference in only one dimension (color) and thereby decreasing
target–distractor similarity and the need for attentional
suppression. Single- and double-interference trials were
randomly intermixed within trial blocks.
For both types of trials, the task was to determine which
of the two central target items contained a particular color
and to report the relative location of this color within the
target circle. In half of the trial blocks, subjects were
instructed to find the central target circle that contained the
color yellow; they pressed one button (right index finger) if
the yellow color was on the left side or the top of the
circle, and they pressed a different button (right middle
finger) if the yellow color was on the right side or the
bottom of the circle. In the other blocks, they performed
this same task with the color red. Speed and accuracy were
stressed equally.
Stimulus arrays were presented for 700 ms, with a
variable-duration blank interval of 1500–1600 ms between
arrays. Subjects performed 10 trial blocks, five with yellow
as the target color and five with red as the target color.
20
J.-M. Hopf et al. / Cognitive Brain Research 15 (2002) 17–29
Each block consisted of 160 stimulus presentations, 80
double-interference and 80 single-interference, yielding at
total of 400 trials for every combination of target visual
field and single-interference / double-interference display
type for each subject.
In this experiment, the same arrays were used for LVF
and RVF targets, and only the instructions differed (e.g. an
array with a yellow item in the LVF and a red item in the
RVF would be a LVF target trial when yellow was the
target and a RVF target when red was the target). This was
important for controlling for any effects of stimulus color
per se. However, the single- and double-interference trials
were physically different, and any neurophysiological
differences between these trial types may have been due to
low-level physical differences (e.g. differences in orientation contrast) rather than differences in attentional allocation. Experiment 2 was designed to assess the possibility
that these low-level physical differences contributed to the
effects observed in experiment 1.
2.2.2. Experiment 2
To assess the effects of the physical differences between
the single- and double-interference displays, we presented
these two stimulus configurations while the subjects were
performing tasks in which these stimuli were irrelevant.
The timing, the sequence of stimulation, and the number of
runs and trials were identical to those in experiment 1, but
additional stimuli were added to the displays, as illustrated
in Fig. 1B.
Two different tasks were used, a central task in which
attention was focused narrowly around the fixation point
and a peripheral task in which attention was distributed
broadly. In the central task, small uppercase letters (0.33
0.38 of size) were presented 0.48 above and below fixation
(see Fig. 1B, left). These two characters were randomly
chosen from a set of six letters (A, E, F, H, L, T). The two
letters were identical on 50% of the trials and differed on
50% of trials; the subjects were instructed to press one
button when the two letters were identical (right index
finger) and a different button when they differed (right
middle finger). The peripheral task was identical, except
that the letters were 0.730.78 in size and presented 78
lateral to and 3.38 below the fixation point. Match and
non-match trials were randomly combined with doubleinterference and single-interference arrays. Subjects performed the central and peripheral tasks in separate trial
blocks; peripheral and central runs alternated across the
session.
2.3. Recording and analysis
The MEG and EEG signals were recorded simultaneously using a BTi Magnes 2500 whole-head MEG magnetometer system with 148 sensors (Biomagnetic Technologies, Inc.) for the MEG and an electrode cap in
conjunction with a 32-channel Synamps amplifier (Neuro-
Scan, Inc.) for the EEG. Electrode locations were chosen
according to standard electrode montage of the American
Electroencephalographic Society [34] (Fpz, Fz, Cz, Pz, Oz,
Iz, Fp1, Fp2, F7, F8, F3, F4, FC1, FC2, T7, T8, C3, C4,
CP1, CP2, P7, P8, P3, P4, PO3, PO4, PO7, PO8, IN3,
IN4). The EEG was recorded with reference to the right
mastoid. The MEG and EEG signals were filtered with a
bandpass of DC 50 Hz and digitized with a sampling rate
of 254 Hz. Artifact rejection was performed offline by
removing epochs with peak-to-peak amplitudes exceeding
a threshold of 3.0310 212 T for the MEG and 100 mV for
the EEG.
EEG electrode positions and the sensor frame coordinate
system were spatially co-registered by using a Polhemus
3Space Fastrak system. That is, the individual electrode
setup together with anatomical landmarks (nasion and left
and right preauricular points) were digitized. The locations
of these landmarks in relation to sensor positions were
derived on the basis of precise localization signals provided by five spatially distributed coils attached to the head
with a fixed spatial relation to the landmarks. The 32 EEG
electrode locations were also digitized to permit co-registration with the coordinate system. To compute the grand
average activity over all subjects, the coordinate system for
each subject was readjusted to the coordinate system of the
Montreal MNI brain (average of 152 T1-weighted stereotaxic volumes from the ICBM project, see
www.bic.mni.mcgill.ca / cgi / icbm]view / ) whose anatomical surface structure was segmented (see below) and used
for the grand-average source analyses.
Separate averages for both MEG and EEG were computed for targets occurring in the LVF and the RVF. The
data were collapsed over the two color combinations (redblue and yellow-blue). The N2pc was then isolated by
computing LVF-minus-RVF difference waves (i.e. difference waves constructed by subtracting the RVF-target
waveforms from the LVF-target waveforms). These difference waves eliminated activity due to purely sensory
responses, because all arrays contained red-blue items in
one visual field and yellow-blue items in the other, with
red-blue items being attended in some trial blocks and
yellow-blue items being attended in others. Any higherlevel cognitive activity that was equal for ipsilateral and
contralateral targets was also eliminated in these difference
waves, leaving only lateralized cognitive responses.
Statistical analyses of the N2pc were conducted using
within-subjects analyses of variance (ANOVAs) with the
Greenhouse–Geisser epsilon adjustment for nonsphericity.
The N2pc was quantified as the mean voltage or field
strength between 180 and 300 ms, relative to an 100-ms
prestimulus baseline.
MEG source analysis was performed using the multimodal neuroimaging software Curry 4.0 (Philips Electronics N.V.) in the following way. First, on the basis of a
high resolution MR scan, a 3-D reconstruction of the head,
cerebrospinal fluid space (CSFS), and cortical surface was
J.-M. Hopf et al. / Cognitive Brain Research 15 (2002) 17–29
created for each subject using the boundary element
method [12]. Second, this realistic volume conductor
model was used in conjunction with the observed MEG
fields to estimate a model of the distribution of currents
over the cortical surface by means of the minimum norm
least squares method [9,11]. We will refer to these distributions as source density estimates (SDEs).
Source analysis was performed in several steps. We first
computed SDEs based on the LVF-minus-RVF difference
waves to characterize the source activity underlying the
mN2pc. To analyze its general time course and distribution, the initial analysis was conducted on the data after
collapsing across single- and double-interference trials.
The effect of feature interference was analyzed in a second
step in which SDEs of the mN2pc (based on the LVFminus-RVF difference waves) were separately computed
for single- and double-interference trials and then compared (mN2pc analysis). In a third step, the single- and
double-interference trials were compared for each attended
visual field separately (within-VF analysis).
Computations were always performed for each subject
individually, and the resulting SDEs were then averaged
across subjects to maximize the signal-to-noise ratio. For
visualization, SDE distributions are shown as 2D-azimuth
projection in form of a schematically flattened cortex
surface (see inset ‘azimuth projection’ in Fig. 4A). In this
projection regions from the lateral and bottom part of the
brain (i.e. regions ventral to the axial AC–PC plane of the
Talairach reference space) become situated anteriorly
(frontal and fronto-basal regions) and laterally (lateral and
inferior-temporal regions) as indicated by areas with
corresponding color in Fig. 4A (inset). To construct these
21
2D distribution maps, average SDEs were taken from 164
even spaced adjacent regions of the 3D cortical surface and
interpolated at the 2D distribution map using the splineinterpolation method [30].
3. Results
3.1. Experiment 1
3.1.1. Behavioral data
The comparison of error rates between conditions (Fig.
2, left) revealed a slightly larger mean error rate on the
double-interference trials (7.5%) than on the single-interference trials (3.9%). A two-way repeated measures
ANOVA with factors of interference (double, single) and
visual field (LVF, RVF) revealed a significant main effect
of interference (F(1,11)55.33, MSE527.53, P,0.05). A
significant interference3visual field interaction was also
observed (F(1,11)59.09, MSE52.11, P,0.05), which was
due to larger error rate difference between the double and
single condition in the LVF. Slower mean reaction times
were observed on the double-interference trials (655 ms) in
comparison to the single-interference trials (609 ms) (Fig.
2, right), but this difference was only marginally significant (F(1,11)55.57, MSE55532.7, P50.055). A significant main effect of VF (F(1,11)540.7, MSE562.6, P,
0.05) and a significant interference3visual field interaction
(F(1,11)513.8, MSE560.2, P,0.05) were observed, indicating that responses were generally slower for RVF
targets, especially for double-interference trials.
Fig. 2. Behavioral data. Percent correct responses (left) and mean response times (correct responses, right) of the single and the double condition of
experiment 1. Separate averages are shown for LVF and RVF targets. The error bars indicate the standard error of the mean.
22
J.-M. Hopf et al. / Cognitive Brain Research 15 (2002) 17–29
3.1.2. Electrical and magnetic waveforms and
distributions
Electrical waveforms for the single- and double-interference trials are illustrated in Fig. 3A. Shown are grand
average data (n512) at two selected electrode sites (PO7 /
PO8), with waveforms for LVF targets and RVF targets
superimposed. A prominent N2pc effect can be observed
approximately 170–300 ms poststimulus (indicated by the
colored regions). Confirming previous studies, the effect
appeared as a relative negativity over the parieto-occipital
scalp contralateral to the attended target item. This effect
was similar in size for the single- and double-interference
trials.
For statistical validation, a three-way within-subjects
ANOVA was computed with factors of electrode hemisphere (PO7, PO8), target side (ipsilateral or contralateral
relative to the electrode site), and interference (double,
simple). The presence of the N2pc effect was reflected by a
significant main effect of target lateralization (F(1,11)5
8.67, P,0.05). The N2pc effect was larger in the left
hemisphere than in the right hemisphere, leading to a
significant interaction between target lateralization and
electrode hemisphere (F(1,11)528.54, P,0.0005). The
N2pc effect was slightly larger on double-interference
trials than on single-interference trials, but the corresponding interference3lateralization interaction was only
marginally significant (F(1,11)53.96, P50.072).
Fig. 3B illustrates the scalp topography of the LVFminus-RVF difference waves between 230 and 290 ms
poststimulus. Nearly identical voltage distributions were
observed for single- and double-interference trials.
Fig. 3C shows the magnetic waveforms at representative
sensor sites. Waveforms for LVF targets and RVF targets
are superimposed, with the mN2pc effect indicated by the
colored regions. As in the electrical data, the mN2pc effect
began around 170 ms and ended around 300 ms. Like the
N2pc, the mN2pc was slightly larger for double-interference trials than for single-interference trials. To validate
this statistically, a two-way ANOVA with factors of target
lateralization and interference (double, simple) was computed. To cancel out the effects of the field polarity
inversion across sensor sites, we inverted the values from
sensor sites S127 and S99 and added them to the values
from sites S61 and S60, respectively. In addition, the data
were collapsed over corresponding sensor sites in the two
hemispheres (S1271S99, S611S60). The presence of the
mN2pc effect in the magnetic data was reflected by a
significant main effect of target lateralization (F(1,11)5
9.59, P,0.05). The larger mN2pc effect for the doubleinterference trials resulted in a significant lateralization3
interference interaction (F(1,11)511.74, P,0.05).
Fig. 3D illustrates corresponding average magnetic field
distributions of the LVF target-minus-RVF target difference waves between 230 and 290 ms poststimulus. The
mN2pc is characterized by peaks of magnetic flux leaving
the head (red lines) over both the left and right occipital
regions, with a transition to magnetic flux entering the
head (blue lines) close to the left and right occipitotemporal borders. Note that this transition zone is over the
cortical region in which the origin of the generating current
sources would be expected. The polarity of the magnetic
field for the LVF-minus-RVF difference wave is the same
over the left and right hemisphere, whereas the corresponding ERP distribution shows opposite polarities (see
Fig. 3B). Note, the finding of opposite polarities in the left
and right hemispheres in the ERP distributions, but similar
polarities across hemispheres for the MEG distributions,
was expected and matches previous studies [13]. Specifically, the magnetic flux leaving left occipital lobe and
entering into the left occipito-temporal region is consistent
with a downward-pointing current dipole; the magnetic
flux leaving the right occipital lobe and entering into the
right occipito-temporal region is consistent with an upward-pointing current dipole. Therefore, the magnetic field
distributions in Fig. 3D indicate the presence of opposite
polarity current sources in the left and right hemispheres,
consistent with the ERP voltage distributions in Fig. 3B.
3.1.3. Source density estimates
To provide an initial characterization of the current
sources underlying the mN2pc for the single- and doubleinterference trials, average source density estimates (SDEs)
of the LVF-minus-RVF difference waveforms were computed from the MEG data, averaged over the single- and
double-interference conditions. SDEs were computed at
each time point (every 4 ms) over a time window that
spanned the entire N2pc component in the ERP waveforms
(150–350 ms). Fig. 4A shows the resulting SDE at the
peak of activation (¯250 ms) in two different views, a
surface representation of the reference MR scan and an
azimuth projection map (see Section 2 and Fig. 4A inset).
SDE maxima were observed in the lateral inferior
Fig. 3. (A) Grand-average ERP waveforms for the double-interference (upper row) and single-interference (lower row) trials. Waveforms for RVF targets
(solid lines) and LVF targets (broken lines) are superimposed. The colored areas indicate the time-range of the N2pc-effect. Waveforms shown on the left
side were recorded from the left occipito-parietal electrode site (PO7); waveforms shown on the right side were recorded from the right occipito-parietal
electrode (PO8). (B) Average voltage maps (230–290 ms) of the LVF-target minus RVF-target difference waves for the double-interference (top) and
single-interference (bottom) trials. These voltage distributions correspond to the potential differences indicated by the colored areas in A. (C)
Grand-average magnetic waveforms from selected sensors at opposite sites of the magnetic efflux–influx transition zone in the occipital cortex of the left
(S60, S99) and right (S61, S127) hemisphere. Waveforms for RVF targets (solid lines) and LVF targets (broken lines) are superimposed. The colored areas
indicate the time-range of the magnetic analog of the N2pc effect (mN2pc). (D) Grand-average magnetic field maps (230–290 ms) of the LVF target minus
RVF target difference waves (colored areas in C).
J.-M. Hopf et al. / Cognitive Brain Research 15 (2002) 17–29
23
24
J.-M. Hopf et al. / Cognitive Brain Research 15 (2002) 17–29
occipito-temporal cortex of both hemispheres, and the
distribution of activity remained largely constant at each
time point from 180 to 350 ms. A highly similar SDE
distribution was previously observed for the mN2pc by
Hopf et al. [13] in a time range of 220–280 ms. In this
previous study, SDE maxima were also found over the
J.-M. Hopf et al. / Cognitive Brain Research 15 (2002) 17–29
posterior parietal cortex from 180 to 220 ms, but very little
activity was observed in that region in the present experiment, suggesting that attentional modulations in these
parietal regions may be less important for the present task
(see Section 4).
The primary goal of this study was to investigate the
cortical sites in which interference modulates the operation
of attention. We therefore compared the time course and
distribution of the mN2pc source density estimates between the single- and double-interference trials. For each
observer, SDEs from the LVF-minus-RVF difference
waveforms were computed for successive time samples in
the interval from 150 to 350 ms, and the resulting maps
were then averaged across observers. Difference images
were then computed by subtracting the single-interference
maps from the double-interference maps, making it possible to visualize the regions in which mN2pc activity was
greater under conditions of greater interference. Fig. 4B
shows the distribution of this SDE difference at its
maximum (approximately 250 ms), as well the time course
of the difference at its maximal location in each hemisphere. The difference was largest in occipito-temporal
cortex, especially in the left hemisphere. No difference was
observed in occipito-parietal regions.
The N2pc analysis reported so far was based on attendLVF-minus-attend-RVF difference waveforms. One useful
consequence of computing SDEs from these difference
waveforms is that any brain activity that was insensitive to
the visual field of the target was subtracted away. A
shortcoming of this approach, however, is that it is not
possible to separately examine activity elicited by LVF and
RVF targets. We therefore conducted a within-VF analysis
that was intended to assess the effects of feature interference separately for attend-LVF and attend-RVF trials. In
this analysis, we removed brain activity unrelated to our
hypotheses by subtracting the single-interference trials
from the double-interference trials (rather than by subtracting RVF trials from LVF trials). Specifically, SDEs for the
single- and double-interference trials were computed separately for LVF and RVF targets, and the SDEs for the
single-interference trials were then subtracted from SDEs
for the double-interference trials. This analysis makes it
possible to observe brain activity that is enhanced for the
double-interference trials, whether or not this activity is
25
lateralized (and may therefore differ from the mN2pc
analyses, which focused solely on lateralized activity).
Fig. 3C shows the resulting difference SDEs at the time
points of maximum difference, along with the time course
at the location of strongest activity within each hemisphere. Red traces represent source activity from the
hemisphere contralateral to the target, and blue traces
represent activity from the ipsilateral hemisphere. Note
that positive values indicate greater source density in the
double condition.
We first consider the results for LVF targets (Fig. 4C,
left). Activity began and ended at similar times in both the
ipsilateral (left) hemisphere and the contralateral (right)
hemisphere, but the ipsilateral activity peaked earlier than
the contralateral activity (¯200 versus 300 ms). A complementary pattern was observed for RVF targets. In both
cases, the early ipsilateral and late contralateral peaks
occurred at almost identical locations, within the general
region of the mN2pc double-minus-simple difference, as
estimated from the LVF-minus-RVF analyses shown at the
right side in Fig. 4C (SDE maxima, see red and blue
squares). Thus, conditions of increased interference modulated both ipsilateral and contralateral activity, but the
modulation was initially larger in the ipsilateral hemisphere and then became larger in the contralateral hemisphere.
In addition to these occipito-temporal effects, we also
observed SDE differences in frontal cortical regions (see
Fig. 3C, frontal regions). Specifically, frontal activity was
greater for the double-interference trials than for the
single-interference trials, but this effect was not lateralized
and was primarily observed for LVF targets. We will not
consider this effect further in the present article.
3.2. Experiment 2
A possible objection to experiment 1 deserves consideration. Namely, it is possible that the observed SDE
differences reflect bottom-up sensory mechanisms like
feature contrast processing and not attentional processing
per se. Low-level sensory mechanisms may have come
into play because the distractors were closely surrounding
the target and were always uniform regarding their orientation. Therefore, in the single but not in the double-interfer-
Fig. 4. (A) Source density estimates (SDEs) from LVF-minus-RVF target MEG difference waves (mN2pc analysis). Waveforms were collapsed across the
single- and double-interference trials to reveal the principal current distribution that underlies the mN2pc effect. SDEs are shown as a surface
representation using the reference MR scan (top) and as an azimuth projection map (bottom). The inset illustrates the relative location of cortical regions in
the 2D azimuth map in relation to the 3D cortical surface map. (B) Average difference map (double minus single) of SDEs from LVF-minus-RVF target
MEG difference waves (mN2pc analysis). SDEs were first computed for each condition and observer, then averaged across observers and subtracted. The
traces at the bottom show the time course of the SDE difference between 150 and 350 ms at both occipito-temporal maxima. Note, the difference is always
positive throughout the complete time-range, indicating larger SDEs in the double condition. (C) SDEs from the double-minus-single MEG difference
waves of the within-VF analysis. The traces below show the time course of these estimates between 150 and 350 ms at cortical locations with maximum
effects in the ipsilateral (blue traces) and contralateral (red traces) hemispheres. At the right side, locations of peak estimates from the within-VF analysis
are superimposed onto the double-minus-simple SDE difference map of the mN2pc analysis shown in B. Small squares indicate peak locations for LVF
targets, and small circles show peak locations for RVF targets. Colors refer to the ipsilateral (blue) and contralateral (red) hemispheres.
26
J.-M. Hopf et al. / Cognitive Brain Research 15 (2002) 17–29
ence condition, orientation contrast between the target and
the orthogonally oriented surround may have given rise to
orientation pop-out [26]. Such feature contrast effects have
been shown to modulate cell-firing in visual cortex including V1 [16–18] even in aesthesized and paralyzed mon-
keys [27]. Hence, it is possible that the observed SDE
differences reflect such pre-attentional bottom-up processes. Experiment 2 explores this possibility.
In this experiment, the task requirements were changed
to make the target stimuli task-irrelevant. If the effects
Fig. 5. (A) Grand-average event-related potential waveforms for the double-interference (upper row) and single-interference (lower row) trials of
experiment 2. Waveforms for RVF targets (solid lines) and LVF targets (broken lines) as well as the peripheral (blue traces) and central (red traces)
conditions are superimposed. Averages for LVF and RVF targets for experiment 2 were computed by selectively averaging over stimulus frames that served
as LVF and RVF targets in experiment 1. The principal differences between the waveforms of the peripheral and the central condition arise from physical
stimulus differences and differences with respect to the size of the attentional focus. (B) Grand-average magnetic waveforms from selected sensors (S60,
S99) at opposite sites of the magnetic efflux–influx transition zone in the occipital cortex of the left hemisphere. Waveforms for RVF targets (solid lines)
and LVF targets (dashed lines) as well as the peripheral (blue traces) and central (red traces) conditions are superimposed.
J.-M. Hopf et al. / Cognitive Brain Research 15 (2002) 17–29
observed in experiment 1 were due to bottom-up effects of
the low-level sensory differences between the single- and
double-interference stimuli, then these effects should also
be present in experiment 2. If the previous effects were
attention-related, however, they should be eliminated in
experiment 2.
In experiment 2, attention was directed towards the
region of the fixation point (central task, see Fig. 1B, left)
or towards two peripheral locations that were situated
beyond the extent of the stimulus arrays (peripheral task,
see Fig. 1B, right). Fig. 5A shows the ERP waveforms
from the central task (red traces) and the peripheral task
(blue traces), separately averaged for single- and doubleinterference trials. Fig. 5B shows the analogous MEG
waveforms from two left-hemisphere sensor sites. For both
the central and peripheral tasks, neither the ERP
waveforms nor the MEG waveforms exhibited an N2pclike pattern of lateralization. In addition, there was no
discernable difference between the responses elicited by
single- versus double-interference stimuli. A two-way
ANOVA confirmed the absence of significant effects of
lateralization and interference. Taken together, the results
of the experiment 2 make it unlikely that the effects of
feature-interference observed in experiment 1 arose from
more elementary bottom-up mechanisms like orientation
contrast. In addition, the negative results of the peripheral
condition speak also against an explanation in terms of
nonspecific effects of attention that may have enhanced the
neural responses at irrelevant locations within a wide focus
of attention.
4. Discussion
4.1. Localization of mN2 pc
Source density estimates of the mN2pc for both the
single- and double-interference trials revealed neural activity in focal areas of the ventral occipito-temporal cortex
of both hemispheres. Similar source locations were obtained in a previous MEG study [13] with a different
search task and different stimuli. The stability of source
locations across experiments allows increased confidence
in these mN2pc localizations and provides additional
evidence for the importance of these ventral stream cortical
areas for attentional focusing in visual search [4,19].
In the study of Hopf et al. [13], the mN2pc was found to
consist of an early parietal source (180–200 ms) followed
by later ventral occipito-temporal sources (220–240 ms).
In the present study, however, no significant source
activity was observed in the parietal cortex. Although, this
seems unexpected at the first glance, it is not completely
unpredicted in view of the interpretation of the parietal
current source offered by Hopf et al. [13]. The parietal
source was proposed to reflect the initiation of spatial
shifts of attention to the target locations, and the need for
27
attention shifting differed substantially between experiments. Specifically, the target location was highly predictable in the present study, always appearing at one of two
stable locations, whereas the targets in the Hopf et al. [13]
study varied unpredictably in location throughout the entire
visual field. Thus, the pattern of results across experiments
is consistent with the proposal that the parietal subcomponent reflects the mechanisms that initiates shifts of attention, whereas the occipito-temporal subcomponent reflects
the mechanisms the implement the selection of relevant
information once attention has been shifted. A recent MEG
investigation of visual discriminative processing that did
not involve search supports this view [14]. In this study,
current source activity related to color discrimination was
observed in similar regions of the ventral stream, with no
parietal activity.
4.2. Suppression and interference
The primary goal of the present study was to investigate
the neural mechanisms that underlie the attentional resolution of ambiguous feature coding. We addressed this by
comparing current source activity underlying the mN2pc
under conditions of more or less feature-overlap with the
distractor items. The results clearly show that increasing
feature overlap leads to a modulation of mN2pc activity in
ventral occipito-temporal regions, which is consistent with
the enhanced need for suppressive neural activity to
attenuate the increased interference. This also provides
evidence for the proposal that the neural activity underlying the N2pc reflects suppression of interference from
distractor items, as has been suggested previously [21].
The larger mN2pc in the double-interference condition
is suggestive of an underlying suppressive mechanism, and
the within-VF comparison of the single- and double-interference trials in the mN2pc time-range provides further
support for this claim. In line with the mN2pc findings, the
within-VF comparison revealed larger source activity for
the double- than the single-interference trials in ventral
occipito-temporal regions, and the maximum differences
were localized within the general region of the mN2pc.
More importantly, the expected maximum difference in the
contralateral visual cortex is regularly preceded (90–100
ms) by a maximum difference in the ipsilateral visual
cortex. In other words, the difference between the singleand double-interference conditions was initially larger at
locations ipsilateral to the target, and then became larger at
contralateral locations. On the assumption that suppressive
activity is likely to be observed in the hemisphere contralateral to the suppressed items, this pattern suggests that the
distractors in the visual field opposite to the target were
suppressed first, leading to a large effect ipsilateral to the
target and contralateral to the opposite-field distractors.
This was then followed by activity contralateral to the
target, which presumably reflects suppression of the distractors that surrounded the target in the same visual field
28
J.-M. Hopf et al. / Cognitive Brain Research 15 (2002) 17–29
(although it may also represent enhancement of the target
itself). This ipsilateral-followed-by-contralateral pattern
would be difficult to explain by a pure enhancement
mechanism because enhanced processing of irrelevant
information in the unattended hemisphere is less plausible.
Furthermore, the presence of an ipsilateral effect suggests that although target-color would have been sufficient
to unambiguously highlight the target-region, it did not
prevent interference from the non-target VF. This may
relate to psychophysical evidence showing that attention
relies on location-based selection even when selection by
color would be more effective [1]. In general, our results
are in line with theories on visual search that assume
feature-driven spatial selection to be followed by a
location-based operation of focused attention [2,37,38,42].
Finally, the ipsilateral-followed-by-contralateral sequence of source activity indicates that the attenuation of
irrelevant input does not proceed simultaneously across all
distractor items in the search array. Rather, suppressive
operations are applied step by step, with input from the
irrelevant visual field being discarded first, and unwanted
input from within the relevant visual field being discarded
subsequently. Several explanations of this sequence are
viable. First, this sequence may reflect a selection hierarchy of distractor attenuation that moves stepwise from
course to fine. Such mechanism of attentional selection has
been proposed by several computational models
[28,39,40]. For example, the selective tuning model of
Tsotsos et al. [40] proposes a hierarchical winner-take-all
process that selects the target by recursively pruning away
input from distractor items in a coarse-to-fine manner.
Second, the ipsilateral-followed-by-contralateral sequence
may just reflect the fact that interfering items could be
spatially chunked together to allow collective suppression.
The particular stimulus configuration in this study may
have invited such strategy. Then, the observed sequence
would rather be a consequence of the distribution of
interfering features than evidence for a hierarchical courseto-fine selection process. There is some psychophysical
evidence that, when the number of items exceeds a certain
limit, attention parses items into chunks which are
searched through one at a time [29]. Since the stimulus
configuration was not changed in the present experiments,
we cannot decide among these explanations, and further
experiments are necessary to clarify this issue.
Acknowledgements
This research was made possible by grant HE1531 / 3-5
from the Deutsche Forschungsgemeinschaft and grant R01
MH63001 from the National Institutes of Health. We thank
Alan Richardson Klavehn for helpful comments on earlier
versions of the manuscript.
References
[1] K.R. Cave, H. Pashler, Visual selection mediated by location:
selecting successive visual objects, Percept. Psychophys. 57 (1995)
421–432.
[2] N.J. Cepeda, K.R. Cave, N.P. Bichot, M.-S. Kim, Spatial selection
via feature-driven inhibition of distractor locations, Percept. Psychophys. 60 (1998) 727–746.
[3] L. Chelazzi, J. Duncan, E.K. Miller, R. Desimone, Response of
neurons in inferior temporal cortex during memory-guided visual
search, J. Neurophysiol. 80 (1998) 2918–2940.
[4] L. Chelazzi, E.K. Miller, J. Duncan, R. Desimone, A neural basis for
visual search in inferior temporal cortex, Nature 363 (1993) 345.
[5] L. Chelazzi, E.K. Miller, J. Duncan, R. Desimone, Responses of
neurons in macaque area V4 during memory-guided visual search,
Cereb. Cortex 11 (2001) 761–772.
[6] R. Desimone, Visual attention mediated by biased competition in
extrastriate visual cortex, Philos. Trans. R. Soc. Lond. B 353 (1998)
1245–1255.
[7] R. Desimone, J. Duncan, Neural mechanisms of selective visual
attention, Annu. Rev. Neurosci. 18 (1995) 193–222.
[8] J. Duncan, G.W. Humphreys, Visual search and stimulus similarity,
Psychol. Rev. 96 (1989) 433–458.
[9] M. Fuchs, M. Wagner, T. Kohler, H.A. Wischmann, Linear and
nonlinear current density reconstructions, J. Clin. Neurophysiol. 16
(1999) 267–295.
[10] M. Girelli, S.J. Luck, Are the same attentional mechanisms used to
detect visual search targets defined by color, orientation, and
motion?, J. Cogn. Neurosci. 1 (1997) 1.
[11] M.S. Hamalainen, R.J. Ilmoniemi, Interpreting magnetic fields of the
brain: minimum norm estimates, Med. Biol. Eng. Comput. 32
(1994) 35–42.
¨ ¨¨
[12] M.S. Hamalainen,
J. Sarvas, Realistic conductivity geometry model
of the human head for interpretation of neuromagnetic data, IEEE
Trans. Biomed. Eng. 36 (1989) 165–171.
[13] J.-M. Hopf, S.J. Luck, M. Girelli, T. Hagner, G.R. Mangun, H.
Scheich, H.-J. Heinze, Neural sources of focused attention in visual
search, Cereb. Cortex 10 (2000) 1233–1241.
[14] J.-M. Hopf, E.K. Vogel, G. Woodman, H.J. Heinze, S.J. Luck,
Localizing visual discrimination processes in time and space, J.
Neurophysiol. (2002) in press.
[15] S. Kastner, P. De Weerd, R. Desimone, L.G. Ungerleider, Mechanisms of directed attention in the human extrastriate cortex as
revealed by functional MRI, Science 282 (1998) 108–111.
[16] S. Kastner, H.-C. Nothdurft, I.N. Pigarev, Neuronal correlates of
pop-out in cat striate cortex, Vision Res. 37 (1997) 371–376.
[17] J.J. Knierim, D.C. Van Essen, Neuronal responses to static texture
patterns in area V1 of the alert macaque monkey, J. Neurophysiol.
67 (1992) 961–980.
[18] V.A.F. Lamme, The neurophysiology of figure–ground segregation
in primary visual cortex, J. Neurosci. 15 (1995) 1605–1615.
[19] S.J. Luck, L. Chelazzi, S.A. Hillyard, R. Desimone, Mechanisms of
spatial selective attention in areas V1, V2, and V4 of macaque visual
cortex, J. Neurophysiol. 77 (1997) 24–42.
[20] S.J. Luck, M.A. Ford, On the role of selective attention in visual
perception, Proc. Natl. Acad. Sci. USA 95 (1998) 825–830.
[21] S.J. Luck, M. Girelli, M.T. McDermott, M.A. Ford, Bridging the
gap between monkey neurophysiology and human perception: an
ambiguity resolution theory of visual selective attention, Cogn.
Psychol. 33 (1997) 64–87.
[22] S.J. Luck, S.A. Hillyard, Electrophysiological correlates of feature
analysis during visual search, Psychophysiology 31 (1994) 1.
[23] S.J. Luck, S.A. Hillyard, Spatial filtering during visual search:
evidence from human electrophysiology, J. Exp. Psychol. Hum.
Percept. Perform. 20 (1994) 1000–1014.
J.-M. Hopf et al. / Cognitive Brain Research 15 (2002) 17–29
[24] J. Moran, R. Desimone, Selective attention gates visual processing
in the extrastriate cortex, Science 229 (1985) 782–784.
[25] B.C. Motter, Neural correlates of feature selective memory and
pop-out in extrastriate area V4, J. Neurosci. 14 (1994) 2190–2199.
[26] H.C. Nothdurft, Texture segmentation and pop-out from orientation
contrast, Vision Res. 31 (1991) 1073–1078.
[27] H.C. Nothdurft, J.L. Gallant, D.C. Van Essen, Response modulation
by texture surround in primate area V1: correlates of ‘popout’ under
anesthesia, Vis. Neurosci. 16 (1999) 15–34.
[28] B.A. Olshausen, C.A. Anderson, D.C. Van Essen, A neurobiological
model of visual attention and invariant pattern recognition based on
dynamic routing of information, J. Neurosci. 13 (1993) 4700–4719.
[29] H. Pashler, Detecting conjunctions of color and form: reassessing
the serial search hypothesis, Percept. Psychophys. 41 (1987) 191–
201.
[30] F. Perrin, J. Pernier, O. Bertrand, J. Echallier, Spherical splines for
scalp potential and current density mapping, Electroencephalogr.
Clin. Neurophysiol. 72 (1989) 184–187.
[31] J.H. Reynolds, L. Chelazzi, R. Desimone, Competitive mechanisms
subserve attention in macaque areas V2 and V4, J. Neurosci. 19
(1999) 1736–1753.
[32] B.J. Richmond, R.H. Wurtz, T. Sato, Visual responses of inferior
temporal neurons in awake rhesus monkey, J. Neurophysiol. 50
(1983) 1415–1432.
[33] A.T. Smith, K.D. Singh, M.W. Greenlee, Attentional suppression of
activity in the human visual cortex, NeuroReport 11 (2000) 271–
277.
[34] A.E. Society, Guideline seven: a proposal for standard montages to
[35]
[36]
[37]
[38]
[39]
[40]
[41]
[42]
[43]
[44]
29
be used in clinical EEG, Am. Electroencephalogr. Soc. J. Clin.
Neurophysiol. 11 (1994) 30–36.
A. Treisman, Solutions to the binding problem: progress through
controversy and convergence, Neuron 24 (1999) 105–110.
A. Treisman, G. Gelade, A feature-integration theory of attention,
Cogn. Psychol. 12 (1980) 97–136.
A. Treisman, S. Gormican, Feature analysis in early vision: evidence
from search asymmetries, Psychol. Rev. 95 (1988) 15–48.
A. Treisman, S. Sato, Conjunction search revisited, J. Exp. Psychol.
Hum. Percept. Perform. 16 (1990) 459–478.
J.K. Tsotsos, S.M. Culhane, F. Cutzu, From foundational principles
to a hierarchical selection circuit for attention, in: J. Braun, C. Koch,
J.L. Davis (Eds.), Visual Attention and Cortical Circuits, MIT Press,
Cambridge, MA, 2001, pp. 285–306.
J.K. Tsotsos, S.M. Culhane, W.Y.K. Wai, Y. Lai, N. Davis, F. Nuflo,
Modeling visual attention via selective tuning, Artif. Intell. 78
(1995) 507–545.
W. Vanduffel, R.B.H. Tootell, G.A. Orban, Attention-dependent
suppression of metabolic activity in early stages of the macaque
visual system, Cereb. Cortex 10 (2000) 109–126.
J. Wolfe, Guided search 2.0 A revised model of visual search,
Psychonom. Bull. Rev. 1 (1994) 202–238.
J.M. Wolfe, K.R. Cave, S.L. Franzel, Guided search: an alternative
to the feature integration model for visual search, J. Exp. Psychol.
Hum. Percept. Perform. 15 (1989) 419–433.
G.F. Woodman, S.J. Luck, Electrophysiological measurement of
rapid shifts of attention during visual search, Nature 400 (1999)
867–869.