The role of stimulus salience and attentional capture across the neural hierarchy in a stop-signal task.
ABSTRACT Inhibitory motor control is a core function of cognitive control. Evidence from diverse experimental approaches has linked this function to a mostly right-lateralized network of cortical and subcortical areas, wherein a signal from the frontal cortex to the basal ganglia is believed to trigger motor-response cancellation. Recently, however, it has been recognized that in the context of typical motor-control paradigms those processes related to actual response inhibition and those related to the attentional processing of the relevant stimuli are highly interrelated and thus difficult to distinguish. Here, we used fMRI and a modified Stop-signal task to specifically examine the role of perceptual and attentional processes triggered by the different stimuli in such tasks, thus seeking to further distinguish other cognitive processes that may precede or otherwise accompany the implementation of response inhibition. In order to establish which brain areas respond to sensory stimulation differences by rare Stop-stimuli, as well as to the associated attentional capture that these may trigger irrespective of their task-relevance, we compared brain activity evoked by Stop-trials to that evoked by Go-trials in task blocks where Stop-stimuli were to be ignored. In addition, region-of-interest analyses comparing the responses to these task-irrelevant Stop-trials, with those to typical relevant Stop-trials, identified separable activity profiles as a function of the task-relevance of the Stop-signal. While occipital areas were mostly blind to the task-relevance of Stop-stimuli, activity in temporo-parietal areas dissociated between task-irrelevant and task-relevant ones. Activity profiles in frontal areas, in turn, were activated mainly by task-relevant Stop-trials, presumably reflecting a combination of triggered top-down attentional influences and inhibitory motor-control processes.
- SourceAvailable from: Robert Hester[Show abstract] [Hide abstract]
ABSTRACT: In everyday life, people use self-control to withhold actions. This ability is particularly important when the consequences of action withholding have an impact on the individual's well-being. Despite its importance, it is unclear as to how the neural nodes implicated in action withholding contribute to this real-world type of self-control. By modifying an action withholding paradigm, the go/no-go task, we examined how the brain exerts self-control during a scenario in which the implications of withholding an action are meaningful and motivationally significant. A successfully withheld response contributed to long-term monetary rewards, whereas failure to withhold a response incurred an immediate monetary punishment. Compared with neutral action withholding, participants significantly improved their performance when these contingencies were applied. Crucially, although the right IFG and pre-SMA were found to promote overall action withholding, the enhancement in behavioral performance relative to a neutral condition was only reflected by a physiological change in a region encompassing the right inferior frontal junction and precentral gyrus. We speculate that the ability to flexibly modulate attention to goal-relevant stimuli is crucial to enhanced, motivationally driven action withholding and that this ability is subserved by the right inferior frontal junction. These findings suggest that control-modulating factors, rather than action withholding processes per se, can be critical to improving motivationally significant action withholding outcomes.Journal of Cognitive Neuroscience 08/2014; · 4.69 Impact Factor
- [Show abstract] [Hide abstract]
ABSTRACT: Impulsivity has been linked to three main factors: performing without direct involvement of the frontal lobe functions, an increase in the speed of response, and the acquisition of immediate gratification. This behavioral inhibition deficit involves a variety of behaviors including aspects of hyperexcitability, behavioral disinhibition and higher order decision making. Although by tradition, the definition of this executive function has been conceptualized from a psychopathological view, currently, the wide variety of neuropsychological, developmental and animal models assessment techniques encourage us to establish dialogues that integrate the knowledge of these theoretical perspectives for the interpretation and understanding of impulsivity.Avances en Psicologia Latinoamericana 04/2013; 31(1):241-251.
- [Show abstract] [Hide abstract]
ABSTRACT: Disorders such as borderline personality disorder (BPD) or attention-deficit/hyperactivity disorder (ADHD) are characterized by impulsive behaviors. Impulsivity as used in clinical terms is very broadly defined and entails different categories including personality traits as well as different cognitive functions such as emotion regulation or interference reso-lution and impulse control. Impulse control as an executive function, however, is neither cognitively nor neurobehaviorally a unitary function. Recent findings from behavioral and cognitive neuroscience studies suggest related but dissociable components of impulse control along functional domains like selective attention, response selection, motivational control, and behavioral inhibition. In addition, behavioral and neural dissociations are seen for proactive vs. reactive inhibitory motor control.The prefrontal cortex with its sub-regions is the central structure in executing these impulse control functions. Based on these con-cepts of impulse control, neurobehavioral findings of studies in BPD and ADHD were reviewed and systematically compared. Overall, patients with BPD exhibited prefrontal dysfunctions across impulse control components rather in orbitofrontal, dorsomedial, and dorsolateral prefrontal regions, whereas patients with ADHD displayed disturbed activity mainly in ventrolateral and medial prefrontal regions. Prefrontal dysfunctions, however, varied depending on the impulse control component and from disorder to disorder. This suggests a dissociation of impulse control related frontal dysfunctions in BPD and ADHD, although only few studies are hitherto available to assess frontal dysfunctions along differ-ent impulse control components in direct comparison of these disorders.Yet, these findings might serve as a hypothesis for the future systematic assessment of impulse control com-ponents to understand differences and commonalities of prefrontal cortex dysfunction in impulsive disorders.Frontiers in Human Neuroscience 09/2014; 8:698. · 2.90 Impact Factor
The Role of Stimulus Salience and Attentional Capture
Across the Neural Hierarchy in a Stop-Signal Task
Carsten N. Boehler1,2*, Lawrence G. Appelbaum1, Ruth M. Krebs1,2, Ling-Chia Chen1, Marty G.
1Center for Cognitive Neuroscience, Duke University, Durham, North Carolina, United States of America, 2Department of Experimental Psychology, Ghent University,
Ghent, Belgium, 3Department of Psychiatry, Duke University, Durham, North Carolina, United States of America
Inhibitory motor control is a core function of cognitive control. Evidence from diverse experimental approaches has linked
this function to a mostly right-lateralized network of cortical and subcortical areas, wherein a signal from the frontal cortex
to the basal ganglia is believed to trigger motor-response cancellation. Recently, however, it has been recognized that in
the context of typical motor-control paradigms those processes related to actual response inhibition and those related to
the attentional processing of the relevant stimuli are highly interrelated and thus difficult to distinguish. Here, we used fMRI
and a modified Stop-signal task to specifically examine the role of perceptual and attentional processes triggered by the
different stimuli in such tasks, thus seeking to further distinguish other cognitive processes that may precede or otherwise
accompany the implementation of response inhibition. In order to establish which brain areas respond to sensory
stimulation differences by rare Stop-stimuli, as well as to the associated attentional capture that these may trigger
irrespective of their task-relevance, we compared brain activity evoked by Stop-trials to that evoked by Go-trials in task
blocks where Stop-stimuli were to be ignored. In addition, region-of-interest analyses comparing the responses to these
task-irrelevant Stop-trials, with those to typical relevant Stop-trials, identified separable activity profiles as a function of the
task-relevance of the Stop-signal. While occipital areas were mostly blind to the task-relevance of Stop-stimuli, activity in
temporo-parietal areas dissociated between task-irrelevant and task-relevant ones. Activity profiles in frontal areas, in turn,
were activated mainly by task-relevant Stop-trials, presumably reflecting a combination of triggered top-down attentional
influences and inhibitory motor-control processes.
Citation: Boehler CN, Appelbaum LG, Krebs RM, Chen L-C, Woldorff MG (2011) The Role of Stimulus Salience and Attentional Capture Across the Neural Hierarchy
in a Stop-Signal Task. PLoS ONE 6(10): e26386. doi:10.1371/journal.pone.0026386
Editor: Nicole Wenderoth, Katholieke Universiteit Leuven, Belgium
Received May 26, 2011; Accepted September 26, 2011; Published October 17, 2011
Copyright: ? 2011 Boehler et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This research was supported by NIH grants R01-MH060415 and R01-NS051048 to M.G.W. and funds from the Deutsche Forschungsgemeinschaft (BO
3345/1-1) to C.N.B. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: firstname.lastname@example.org
Inhibitory motor control — i.e. the ability to suppress unwanted
behavioral responses — provides crucial flexibility in goal-directed
behavior, allowing individuals to quickly adjust to a changing
environment and to overcome pre-potent responses when they are
inadequate or inappropriate (see  for a review). Interest in this
topic has dramatically increased over the past several years in
accord with the central role of this function in normal human
behavior and development, as well as in a range of neurological
and psychiatric conditions, such as attention-deficit hyperactivity
disorder and substance abuse [2–6].
One of the most prominent experimental paradigms designed to
investigate response-inhibition capabilities is the Stop-signal task
[7,8]. In this task, a choice-reaction Go-stimulus is rapidly
followed, on a minority of trials, by a Stop-stimulus requiring
participants to withhold the response to the Go-stimulus. Variants
of this and related tasks have been used extensively with a variety
of methodological approaches to investigate brain processes
underlying response inhibition. Converging evidence from these
studies has led to the view that a mostly right-hemisphere network
of brain areas plays a critical role in response inhibition (but see
). This network includes the inferior frontal gyrus (IFG;
especially the frontal operculum extending into the insula) and
the pre-supplementary motor area (pre-SMA), which in turn
interacts with the basal ganglia and the thalamus (for reviews see
Although it is very likely that the loop between the frontal cortex
and the basal ganglia/thalamus described above is a core structure
subserving response inhibition, it is increasingly recognized that
other mechanisms play an important role leading up to response
inhibition and in determining whether it will be successful or not.
Specifically, it has been reported that selective attention to the
task-relevant stimuli can play an important role in determining
trial outcome in the Stop-signal task. Numerous studies have
reported transient modulations of sensory processing of the
relevant stimuli in the time-range of the sensory evoked N1 ERP
component, which precedes the implementation of response
inhibition, and that these modulations are predictive of the
outcome of the process [11–15]. Due to the timing and the
posterior topography, such effects can be compellingly attributed
to differences in sensory processing, including due to attentional
modulations of that processing.
Unfortunately, such conclusions are much more difficult to
draw for activity at later time-ranges and in other brain areas, so
that a separation of perceptual/attentional processes from those
PLoS ONE | www.plosone.org1October 2011 | Volume 6 | Issue 10 | e26386
that are directly related to response inhibition has proven difficult.
A case in point relates to the right IFG, which has received a lot of
experimental support as a key structure in response inhibition.
This area is reliably activated in human fMRI studies investigating
response inhibition (for a recent comprehensive review, see ),
while lesion and electrophysiological studies in humans have
provided corroborating evidence [16–20]. Recent studies, howev-
er, have challenged the view that the right IFG is directly involved
in response inhibition (but see ). For example, strong right IFG
activations have also been reported in response to other rare
stimuli besides task-relevant Stop-stimuli ([22–24], see also [25–
29]), consistent with its reported participation in the ventral
attention system that has been implicated in bottom-up attentional
processes triggered relatively automatically by salient environ-
mental events [30,31].
An important distinction in this context that has not yet been
established (neither for the right IFG nor for other involved brain
areas) is the degree of automaticity with which Stop-trial
stimulation elicits neural activity. Specifically, in the existing
attention literature it is appreciated that the presentation of rare,
and/or physically salient, stimuli (note that Stop-trials meet both
criteria) tend to automatically capture attention and activate at
least parts of the ventral attention system [30,32]. Such processes
can even occur if these stimuli are entirely task-irrelevant (for a
recent discussion, see ). In the context of the Stop-signal task,
however, the degree to which neural activity is related to such
salience-triggered processes is not clear, versus how much such
activation may depend on the general task context, in which the
behavioral relevance of stimuli other than Go-stimuli needs to be
determined. Establishing such a distinction is important in order to
gain insights into which areas and processes are under active top-
down control during a Stop-signal task, even if their function is
related to control processes that are not directly related to response
inhibition. Moreover, because these other functions could also be
derailed in psychopathology, thereby potentially mimicking
deficits directly in motor control (e.g., ), disentangling and
understanding these processes better is an important goal.
In the present report, we have carried out additional sets of
analyses of the data from a recent study  that included, as a
control condition, task-irrelevant Stop-trials from separate task
blocks (see Fig. 1). In our previous report, this control condition
was used specifically to subtract out activity related to the sensory
processing of Stop-stimuli. Here, we expanded our analyses of
these data in order to gauge, on a brain-wide level, the degree to
which activity in different brain areas is related to the sensory and
attention-attracting features of Stop-stimuli. Additionally, we
performed an ROI analyses to investigate the relative degree of
activation by Go-trials, task-irrelevant Stop-trials, and task-
relevant Stop-trials in key brain areas. These analyses provide
activity profiles indicative of separable neural operations that have
important implications towards a better understanding of the
specific systems-level neural circuits that lead to and implement
Participants and Ethics Statement
Eighteen participants took part in this study, two of which had
to be excluded due to technical problems, and another one due to
particularly poor behavioral performance. The 15 remaining
participants (nine female) had a mean age of 22.9 years, all with
correct or corrected-to-normal visual acuity, and none reporting a
history of psychiatric or neurological disorders. All participants
gave written informed consent and the study was approved by the
Figure1.Paradigm andbehavioral data. (A)InStop-relevantblocks,
a choice-reaction stimulus (a green German traffic-light symbol oriented
to the left or right) was either presented for the entire stimulus duration
of 800 ms (Go-trial) or replaced by a red Stop-stimulus (Stop-trial) after a
variable SOA set trial-to-trial by a tracking algorithm. The Stop-stimulus
indicated that the response to the Go-stimulus was to be cancelled,
yielding successful (SSTs) and unsuccessful Stop-trials (USTs). (B) In Stop-
irrelevant blocks the visual stimulation was identical, but the Stop-stimuli
were all irrelevant, i.e. responses were required for all the Go-trials
regardless of whether they were followed by a Stop-stimulus. (C)
Response times were slowest for Stop-relevant (SR) Go-trials but similar
for unsuccessful Stop-trial, Stop-irrelevant (SI) Stop-trials, and Stop-
irrelevant Go-trials. TheStop-signalreactiontime(SSRT) was calculatedto
be 230 ms (grand-average data + standard error of the mean (SEM)).
Attentional Processes in the Stop-Signal Task
PLoS ONE | www.plosone.org2 October 2011 | Volume 6 | Issue 10 | e26386
Duke University Health System Institutional Review Board.
Participants were compensated $20 per hour.
The present experiment entailed two variants of the typical
Stop-signal task  that differed only in the instructions given to
the participants. During Stop-relevant blocks, participants were
instructed to try to withhold their response when a Stop-stimulus
followed a Go-stimulus, whereas in Stop-irrelevant blocks the
visual stimulation was identical, but participants were instructed to
ignore the Stop-stimuli and thus to respond to all Go-stimuli
irrespective of whether they were followed by a Stop-stimulus .
Each task was performed once per experimental run (approxi-
mately 2.5 minutes each), separated by a 16-sec break (i.e., task
break, with continuing MR data acquisition). Odd runs began
with the Stop-relevant task, followed by the Stop-irrelevant task,
with even runs in the opposite order. Ten runs were collected for
each participant, yielding a total of 943 trials across all conditions
per participant. The time between trial onsets was varied pseudo-
randomly between 2 and 8 seconds (gamma distribution; average
3.2 sec) to allow for the separation of different conditions in an
event-related fMRI analysis .
Stop-relevant blocks used a standard Stop-signal task (using
German traffic-light signs, see Fig. 1A), entailing a random
sequence of frequent Go-trials and less-frequent Stop-trials. On
Go-trials (80% of all trials), only a Go-stimulus was presented,
requiring a rapid choice response. On Stop-trials (20% of trials),
the Go-stimulus was followed shortly after by the presentation of a
Stop-stimulus, indicating that the response to the Go-stimulus was
to be canceled. On Go-trials, a green symbol was presented for
800 ms, and participants had to decide whether it was oriented to
the left or right (mapped to the right index and middle finger).
Stop-trials started identically, but after a variable stimulus onset
asynchrony (SOA) the Go-stimulus was replaced by a red Stop-
stimulus until the end of the total stimulus duration of 800 ms. The
SOA between the Go- and the Stop-stimulus is an important
determinant for whether participants are able to withhold the
response to the Go-stimulus (successful Stop-trials, SST) or not
(unsuccessful Stop-trials, UST; see ). Note that for discussion of
the SST and UST trials below, the respective block is usually not
specified because these conditions are exclusive to the Stop-
A common approach for controlling performance is to titrate
the Go-Stop SOA using an adaptive staircase procedure to yield
approximately equivalent numbers of SST and UST for each
participant. We implemented such a procedure here, increasing
the SOA by 17 ms (one refresh screen) after SSTs and decreasing
it by the same amount after USTs (starting SOA: 200 ms). This
procedure allowed us to calculate the Stop-signal response time
(SSRT), which is viewed as reflecting the mean amount of time
that is required to implement the inhibition of a motor response
and is derived by subtracting the mean Go-Stop SOA from the
average Go-trial response time .
During Stop-irrelevant blocks, visual stimulation was identical
to the Stop-relevant ones (Fig. 1B), but participants were instructed
to respond to all Go-stimuli irrespective of the occurrence of Stop-
stimuli. To equate the sensory stimulation as much as possible
between the two block types, we also varied the Go-Stop SOA
during Stop-irrelevant blocks. Specifically, the SOA value
resulting from the staircase procedure of the preceding Stop-
relevant block was used as the initial value, which was then varied
in a random one-up/one-down fashion after each Stop-trial,
staying within +/- three 17-ms steps of the initial value. Stop-
relevant blocks used the end value of the preceding Stop-relevant-
block staircase as their starting value.
Data acquisition and basic analysis
MR data was acquired on a 3-Tesla GE Signa MRI system.
Functional images were acquired with a reverse spiral imaging
sequence (TR=2000 ms, TE=25 ms; flip angle =75u; 32 slices
with36363 mm resolution;
coverage approximately from the top of the brain down to the
pons). The first five functional images were excluded from the
analysis, to allow the scanner to reach steady-state magnetization.
For anatomical reference, a high-resolution structural T1 (3D Fast
Spoiled Gradient Recalled (FSPGR); 16161 mm resolution) was
obtained. The fMRI data were analyzed using SPM5 (http://
www.fil.ion.ucl.ac.uk/spm/). All functional images were corrected
for acquisition time delay, spatially realigned, and normalized by
applying the normalization parameters used to warp the high-
resolution T1 image to the SPM template. Images were resliced to
a voxel size of 26262 mm and smoothed with an isotropic 8-mm
full-width half-maximum Gaussian kernel. For each participant, a
statistical model was computed by applying a canonical hemody-
namic response function (HRF) combined with time and
dispersion derivatives for each of the conditions, including a
128-sec high-pass filter . All conditions were modeled
separately, restricting the analyses on the trials with correct
responses (or with a successfully withheld response in the case of
successful Stop-trials). Additional regressors were included to
model trials with incorrect responses, misses, and break onsets, as
well as for modeling the six realignment parameters measuring the
participants’ movements during the experiment. For visualization
purposes, activation maps were rendered on the SPM single-
The parameter estimates resulting from each condition/contrast
and participant (first-level analysis) were entered into a second-
level, random-effects group analysis using one-sample t-tests. In
order to test for areas that were more active for Stop-irrelevant
Stop-trials than Stop-irrelevant Go-trials on a brain-wide level, a
voxel-wise analysis was performed. The respective group-level
results were thresholded at T.3 (uncorrected) and a minimum
cluster size of k=10 contiguous voxels. Additionally, cluster-level
correction for multiple comparisons was performed. Clusters
surviving this correction (p,0.05) are highlighted in the Results
tables, and strong inferences are limited to these areas. Despite the
danger of false positives, we also report those activations that did
not survive this correction. Such two-stage procedure was
employed to meet our inferential goals to simultaneously not
underestimate activity differences in areas that are typically
associated with the Stop-signal task (i.e., to not make strong
claims about the absence of activity differences based on a highly
conservative threshold), while also highlighting which activations
are quite certainly not false positives.
Additionally, a region of interest (ROI) analysis was performed
to compare activity elicited by the different conditions in the key
regions involved in this task. In order to define ROIs that would
allow for a comparison between Stop-trials from the Stop-relevant
and the Stop-irrelevant task blocks, a t-contrast was employed that
tested the average of these Stop-trial responses across the blocks
against the average of all Go-trial responses across the blocks. Due
to the very robust and widespread activations identified by this
Attentional Processes in the Stop-Signal Task
PLoS ONE | www.plosone.org3 October 2011 | Volume 6 | Issue 10 | e26386
contrast, the group-level effects were thresholded comparatively
conservatively (p,0.01; FDR-corrected corrected on the voxel
level with an extent threshold k=50 contiguous voxels; note that
the resulting clusters also survived cluster-level multiple-compar-
ison correction). Defining ROIs on the basis of this contrast
enabled quantitative comparisons between the Stop-relevant and
Stop-irrelevant Stop-trials in these regions, because the ROI
selection was not biased in favor of either of those conditions .
Ten 4-mm-radius spherical ROIs were selected (see Results).
Marsbar (http://marsbar.sourceforge.net/) was used to extract
percent-signal-change values from these ROIs. Statistical assess-
ment of the ROI data and the behavioral data was accomplished
using repeated-measures analyses of variance (rANOVAs), with
non-sphericity correction of the degrees of freedom (Greenhouse-
Geisser algorithm) where necessary, or using paired t-tests
reporting two-tailed p-values if not indicated otherwise. For the
ROI analysis, our inferential goals also had an influence on the
choice of statistical significance criterion. Specifically, p-values are
reported without correction for multiple comparisons. Although
this risks some false-positive results, applying strict correction
would have artificially biased our results towards the conclusion
that there are no differences between Stop-trials from the two task-
blocks (as well as between SST and UST). Nevertheless, we note
that some differences could represent false positives and need to be
interpreted with a degree of caution.
Participants performed very accurately during both the Stop-
relevant and Stop-irrelevant task blocks. No significant differences
in accuracy were observed for the three trial types that always
required a response (i.e., Stop-relevant Go-trials [97.6%], Stop-
irrelevant Go-trials [96.6%], and Stop-irrelevant Stop-trials
[97.1%]; F(1.5,21.4)=2.2, p=0.15). Response times were slower
on Stop-relevant Go-trials (520 ms) relative to unsuccessful Stop-
trials (446 ms) and relative to Stop-irrelevant Go- and Stop-trials
(436 and 439 ms; overall F-Test: F(1.8,25.3)=32.3, p,0.001), but
were similar between the latter three conditions (F(1.3,17.61)=0.9,
p=0.37; see Fig. 1C). During Stop-relevant Stop-trials, partici-
pants managed to withhold their behavioral response on
approximately half of the trials (52.7%), indicating the success of
our staircase SOA-adjustment procedure. The average SSRT
across subjects was 230 ms.
Activity related to Stop-irrelevant Stop-trials.
identify brain areas that respond differentially to Stop-stimuli, as
compared to Go-stimuli, even if those stimuli are entirely task-
irrelevant, we performed a voxel-wise comparison between Stop-
trials and the Go-trials in the Stop-irrelevant blocks (T.3; k=10;
additionally, cluster-level correction for multiple comparison was
employed, and clusters surviving this procedure are highlighted
below and in table 1). Differences were not only found in lateral
occipital areas, as can be expected based on the differences in
sensory stimulation between these trial types, but also bilaterally in
widespread clusters in the inferior parietal lobules (IPL; Fig. 2 and
Table 1). Importantly, these main posterior clusters all survived
multiple-comparison correction on the cluster level. Turning to the
frontal cortex, two clusters were identified, one in the right IFJ,
and another one in the right pre-SMA. Importantly, these clusters
were too weak/smallto survive
correction employed. Although these could reflect false-positive
results, the locations of these clusters are well in line with typical
activations in the Stop-signal task in these areas (see also ROI
analysis below), thus giving some more credibility to both effects.
Due to the failure to reach cluster-level-corrected significance,
however, these activations need to be interpreted cautiously.
Taken together, activity that is purely triggered by the perceptual
and attention-attracting aspects of Stop-trials are not limited to
ventral sensory areas, but are also present in inferior parietal areas,
with possible contributions from the right IFJ and pre-SMA.
ROI selection and predicted activity profiles.
above analyses provide a formal brain-wide test for which areas
are activated during Stop-trials (as compared to Go-trials) even
when these stimuli are task-irrelevant, the relationship to activity
triggered by task-relevant Stop-trials is hard to evaluate without
direct reference to these other trial types. Importantly, activity in
some areas might not be triggered in an all-or-none fashion by
Stop-stimuli. Rather it is possible that some areas may be activated
In order to
Table 1. fMRI activations for the contrast ‘‘Stop-irrelevant Stop-trials vs. Stop-irrelevant Go-trials’’.
Anatomical structure Hemi-sphereCluster size [voxel] T-Value
Peak coordinates MNI (mm)
x y z
Inferior frontal junction (IFJ)R 1244.73 42 8 36
Pre-SMAR 834.22 4 20 48
Inferior parietal lobule (IPL)*R16516.3730 -56 50
Inferior parietal lobule (IPL)*L 647 5.06-54 -34 36
Supramarginal gyrusL524.18-62 -18 26
Middle occipital gyrus (MOG)*L 5768.51-50 -76 0
Middle occipital gyrus (MOG)*R 7507.1546 -72 0
Main local maxima. Data are thresholded at T.3 (uncorrected), with a cluster-level of k=10.
(*) denotes clusters that are significant after correction for multiple comparisons on the cluster level.
Attentional Processes in the Stop-Signal Task
PLoS ONE | www.plosone.org4 October 2011 | Volume 6 | Issue 10 | e26386
in a graded fashion, wherein a certain amount of activity is
triggered even by task-irrelevant Stop-stimuli, which gets more
pronounced if those stimuli are in fact task-relevant. A good
example process for which such a pattern might be present is
attentional capture, although other processes might also be
engaged in a graded fashion. Specifically, attentional capture has
an automatic component that does not depend on task-relevance.
However, attentional capture effects can get enhanced if the
capturing stimulus is furthermore relevant to the task. Accordingly,
in order to provide a more detailed analysis of the contributions of
different key areas to the processing of task-relevant and task-
irrelevant Stop-trials, we performed additional analyses within
ROIs that were delineated based on both kinds of Stop-trials.
Another advantage of such an ROI analysis is that voxel-wise
comparisons are necessarily quite conservative, whereas ROI-
analyses can focus on the most relevant areas derived from
orthogonal contrasts, thus ameliorating the multiple-testing
problem. In order to allow for an unbiased comparison between
the Stop-trials from the different task blocks, ROIs were selected
on the basis of a contrast comparing all Stop-trials from the two
task blocks (i.e., Stop-relevant and Stop-irrelevant) against all Go-
trials from those tasks. The present ROI analysis is related to an
ROI analysis of some of these data applied in our earlier paper
. As compared to this earlier report, however, we used the
Stop-irrelevant Stop-trials here as an active part of the ROI-
defining contrast instead of using it as a baseline condition.
Moreover, our earlier report used a conjunction of SST and UST
for the ROI definition (rather than their average), which could
have introduced some bias towards finding similar activity
estimates for the two conditions in the subsequent analysis. Such
bias would be avoided with the present analysis.
Due to the very robust and widespread activations that were
identified by this contrast, we opted for a comparatively
k=50). This contrast yielded eight activation clusters (note that all
clusters furthermore survived cluster-level correction for multiple
comparisons; note also that the present set of areas is very similar to
other studies that have compared Stop-trials with Go-trials, which
presumably indicates that activity levels in Stop-relevant Stop-trials
were sufficient to identify typical stopping-related areas even when
averaged with Stop-irrelevant Stop-trials that may have failed to
elicit substantial activity in some of these areas.). The respective
maxima of seven of these were highly distinctive and were thus
directly used for further analysis (see Figs. 3, 4 and Table 2). Five of
these were found in frontal cortex, including right-hemispheric
lateral frontal areas IFG (protruding into theanteriorinsula) andthe
inferior frontal junction (IFJ). Additionally, this contrast revealed
activity in a middle frontal gyrus (MFG) area, along with right pre-
SMA and the left anterior insula. In the left hemisphere two
additional substantial clusters were found, namely in the lateral
middle occipital gyrus (MOG) and in the inferior parietal lobule
(IPL). A final eighth cluster was identified in the posterior part of the
right hemisphere, but seemed to be a grouping of three subclusters
(see Figs. 3, 4). Accordingly, the three main local maxima in this
cluster were each analyzed separately (right MOG, right IPL, and a
cluster in the superior temporal gyrus close to the right temporo-
parietal junction (TPJ/STG), yielding 10 locations total. (Note that
we will use the combined abbreviation TPJ/STG here because the
present local maximum is a bit ventral to the typical TPJ location.
However, a slightly more dorsal local maximum displayed a very
similar activity pattern. Moreover, there is some heterogeneity
between studies reporting activity in TPJ (see e.g., ), so that the
present activation seems quite likely to relate to the functions that
tend to be ascribed to the right TPJ.) Percent signal change values
were determined for spherical ROIs around these ten maxima (see
Methods; also see , for other functional contrasts of data from
Among these ROIs, we predicted finding three distinctive activity
profiles for the different conditions: (1) Sensory-driven activity that
would be present for Go-trials and further enhanced for Stop-trials
(due to the extra sensory stimulation), but not differing significantly
between Stop-relevant and Stop-irrelevant Stop-trials (dark blue
bars in Fig. 3). (Note that the ROI selection favored Stop-trials, as it
is based on a direct comparison of Stop-trials with Go-trials.
Therefore, statistical tests between Stop- and Go-trials within the
ROIs were avoided in our analyses here. Similarly, tests comparing
activity estimates for Go-trials against zero were also not performed,
and the respective results are only displayed to serve as an
approximate reference and for qualitative comparisons between the
activity profiles in different areas. To highlight this fact and to
further set them apart from Stop-trials, bargraphs referring to Go-
trials are represented without a fill color in Fig. 3 and 4.) (2) Activity
associable with automatic attentional capture by the rare Stop-
on Stop-trials irrespective of their task relevance (i.e., not differing
significantly for stop-irrelevant versus stop-relevant Stop-trials; light
blue bars in Fig. 3, 4). (3) Activity profiles dominated by responses to
the Stop-relevant Stop-stimuli, with significantly stronger responses
to task-relevant than task-irrelevant Stop-trials and little or no
response to Go-trials (red bars in Fig. 3, 4). This activity pattern
would be indicative of top-down control processes in response to the
Stop-stimuli, including both directed attention toward them due to
their relevance and response inhibition following their detection.
Posterior brain regions.
Analyses of the five posterior ROIs
revealed three different activity profiles that were largely
symmetrical for the bilaterally activated areas (Fig. 3). Occipital
ROIs (left and right MOG) revealed a pattern in line with simple
visual processing, in that activity estimates were similarly
prominent for both types of Go-trials (i.e., in both the Stop-
relevant and Stop-irrelevant task blocks), but were substantially
Figure 2. Grand-average comparison of Stop-trials versus Go-
trials from the Stop-irrelevant task blocks (activation maps
thresholded at T. .3 (uncorrected) and cluster size k. .10).
Activity differences were most prominent in occipito-temporal and
parietal areas but were also present in the right IFJ and pre-SMA (note
that only the large parietal and occipital clusters survived strict cluster-
level correction for multiple comparisons).
Attentional Processes in the Stop-Signal Task
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larger for Stop-trials of either task block, reflecting the presentation
of an additional salient stimulus on all Stop-trials. In contrast, the
bilateral IPL regions produced very little activity for either kind of
Go-trial, but showed strong responses for all Stop-trials. These IPL
activations, however, did not differ significantly between the Stop-
hemispheres). Finally, the TPJ/STG ROI also displayed little
activity related to Go-trials from either trial block type. More
importantly, however, this area yielded a statistically significant
difference between the average of Stop-relevant Stop-trials vs.
Stop-irrelevant Stop-trials (t(14)=2.2; p=0.04), distinguishing this
area from all other posterior regions.
Frontal brain regions.
Of the five frontal clusters identified,
all areas except the right IFJ displayed qualitatively the same
pattern of activity. More specifically, these areas did not respond
strongly to Go-trials in either task block, nor to the Stop-trials from
the Stop-irrelevant blocks, but responded strongly to Stop-relevant
Stop-trials. In all these areas the Stop-relevant Stop-trials yielded
significantly stronger activations than the Stop-irrelevant Stop-
trials (all p,0.05; see Fig. 4 for further significant differences).
Comparisons among only the Stop-relevant Stop-trials indicate
that the right IFG displayed a trend for stronger activity for
successful than for unsuccessful Stop-trials (t(14)=2; p=0.07).
Interestingly, the pre-SMA showed a significant effect in the
opposite direction (t(14)=2.4; p=0.03). The right IFJ displayed a
different general activity profile, with stronger activity for
successful Stop-trials than for unsuccessful Stop-trials (t(14)=2.9;
p=0.01) or for Stop-irrelevant Stop-trials (t(14)=2.4; p=0.03),
but the average of all Stop-relevant Stop-trials did not differ
significantly from Stop-irrelevant Stop-trials in this area (p.0.3).
Figure 3. Grand-average activity estimates in posterior brain areas for the comparison of all Stop-trials in the two tasks (average of
Stop-relevant (SR) and Stop-irrelevant (SI)) versus the average of all the Go-trials in the two tasks (MNI coordinates; activation
maps thresholded at p, ,0.01 (FDR-corrected) and cluster size k. .50). Areas in the lateral occipital cortex displayed a pattern of activity that
mostly reflected sensory stimulation (i.e., no significant difference between task-relevant [average of SST and UST] and task-irrelevant Stop-trials,
along with substantial response to Go-trials; dark blue bars). Bilateral responses in the inferior parietal lobule appeared to mainly reflect attentional
capture by the infrequent Stop-stimulus, irrespective of its task relevance (i.e., no significant difference between task-relevant [average of SST and
UST] and task-irrelevant Stop-trials, accompanied by a weak response to Go-trials; light blue bars). The only area in the posterior part of the brain that
reflected the task-relevance of Stop-stimuli was in the superior temporal gyrus (STG) close to the TPJ (red bars; significantly larger response to Stop-
relevant Stop-trials [average of SST and UST] than Stop-irrelevant Stop-trials, along with a weak response to Go-trials). Error bars depict the SEM;
activity estimates for Go-trials are represented without a fill color to set them apart from Stop-trials and to indicate that the ROI definition favored
Stop-trials so that statistical comparisons including Go-trials were avoided.
Attentional Processes in the Stop-Signal Task
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The present fMRI study aimed at delineating the neural
processes that are involved in the context of response inhibition
during the Stop-signal task and to distinguish different neural
underpinnings of the various cognitive processes engaged during
such tasks. In an attempt to identify areas that respond to the rare
and salient sensory stimulation of Stop-trials in an automatic
fashion, we found that occipital and inferior parietal areas respond
more strongly to Stop-trials than to Go-trials, even if the Stop-
stimuli are completely task-irrelevant. Interestingly, this analysis
also identified clusters in the right IFJ and the right pre-SMA that
responded in a similar fashion, albeit only on a comparatively
lenient uncorrected significance level. An additional ROI analysis
that focused on the comparison of neural responses to Stop-trials
from task blocks in which the Stop-stimuli were versus were not
task-relevant identified three major activity profiles for different
cortical areas. These profiles indicate a hierarchy in which pure
sensory processing is mostly restricted to occipital areas, whereas
some degree of automatic attentional capture by rare Stop-stimuli
regardless of their task-relevance occurs in the inferior parietal
lobules. In contrast, in the third profile, activity in a wide range of
frontal areas and the right TPJ/STG was prominent only for Stop-
trials that were task-relevant. These findings provide an important
step towards establishing a framework in which sensory processes,
bottom-up attentional processes, and top-down control functions
can be attributed to specific portions of the wider cortical network
that is typically associated with response inhibition during the
Visual stimulation, attention, and response inhibition
At least three recent publications have highlighted the difficulty
of distinguishing processes directly involved in response inhibition
from those related to the attentive processing of the relevant
stimuli, focusing on the role of right IFG ([22–24], see also [25–
29]). The majority of these studies concluded that the right IFG,
which has typically been considered a crucial node in response
inhibition, may only be indirectly related to this function, and that
this area may actually be more generally involved in the attentive
processing of the Stop-stimuli. Using functional connectivity
patterns derived from Granger causality analyses of fMRI data,
Figure 4. Grand-average activity estimates in frontal brain areas for the comparison of all Stop-trials (average of Stop-relevant (SR)
and Stop-irrelevant (SI)) versus the respective Go-trials (MNI coordinates; activation maps thresholded at p, ,0.01 (FDR-corrected)
and cluster size k. .50). None of the frontal areas displayed strong activity estimates for Go-trials. All frontal areas except for IFJ displayed a clear
difference between the average response to the task-relevant Stop-trials and the response to the task-irrelevant ones (red bars). Additional significant
differences between the individual Stop-trial types are indicated in the bar plots (*,0.05; **,0.01; ***,0.001; two-tailed; error bars depict the SEM.).
Right IFJ displayed a somewhat different pattern, in that SST responses were larger than both the UST and Stop-irrelevant Stop-trial responses, but
that the average response to Stop-relevant Stop-trials (i.e., averaged across SSTs and USTs) was not larger than that to Stop-irrelevant ones. Error bars
depict the SEM; activity estimates for Go-trials are represented without a fill color to set them apart from Stop-trials and to indicate that the ROI
definition favored Stop-trials so that statistical comparisons including Go-trials were avoided.
Attentional Processes in the Stop-Signal Task
PLoS ONE | www.plosone.org7 October 2011 | Volume 6 | Issue 10 | e26386
Duann and colleagues observed that the right IFG influenced
activity in the motor system only indirectly via the pre-SMA (,
see also ). They concluded that the connectivity pattern of the
right IFG suggested an attentional role based on its close
functional relationship with temporal and parietal brain structures
(see also ). Two subsequent studies have made similar
arguments by investigating modified Stop-signal tasks that used
high-level control stimuli that did not require response inhibition
In addition to an attentional account of the function of the IFG,
the ubiquity of its activation across different tasks might also relate
to recent accounts that assign more global control functions to this
area (for recent reviews, see [41,42]). Moreover, it has recently
been suggested in the context of a modified Go-NoGo task that the
requirements for response control irrespective of response
inhibition can also strongly activate the right IFG .
Specifically, these authors report strong activations of the right
IFG (exceeding the activation level of NoGo trials) for a control
condition where subjects had to press an additional button in
response to a third class of stimuli that were equally infrequent as
NoGo trials. Note, however, that there is still controversy about
whether the right IFG is really not directly related to response
inhibition . Either way, the example of the right IFG thus
highlights the importance of further distinguishing related
functions on a brain-wide level, both for understanding the basic
underlying cognitive functions and because psychopathological
derailment of other functions could potentially mimic deficits in
motor control (e.g., ). For example, a number of studies have
reported that fluctuations in attentional engagement can strongly
influence the outcome of Stop-trials in this task ([11–15]; see also
), thereby mimicking fluctuations in response-inhibition
Importantly, none of the studies mentioned above included a
condition in which Stop-stimuli were entirely task-irrelevant, thus
leaving open the question whether the right IFG and other areas
would respond to Stop-stimuli even when they are entirely task-
irrelevant. Such activation could arise by means of bottom-up
attentional capture by the Stop-stimuli simply due to their rarity
and physical salience. The present study identified activation by
task-irrelevant Stop-stimuli bilaterally in occipital areas and in
IPL, as well as in the right IFJ and pre-SMA (albeit on a more
lenient, uncorrected significance threshold). Other frontal areas
such as the IFG, however, did not respond in this condition, thus
indicating that their task involvement depends on some level of
task-relevance of the Stop-stimuli. This relevance, in turn, does not
have to be based on the necessity to withhold a motor response,
but at a minimum on the requirement that such a stimulus needs
to be discriminated from the other stimuli in the sequence to
determine its task-relevance.
Thus, the area that appeared to be most consistently activated
by the mere rarity and salience of Stop-stimuli, irrespective of their
task relevance, was the bilateral IPL, thus arguing against notions
that have ascribed a direct involvement of this area in response
inhibition (e.g., ). Activity in the IPL was highly similar in
response to task-relevant and task-irrelevant Stop-stimuli, even
when directly comparing activity estimates in the ROI analysis
(i.e., avoiding conservative voxel-wise tests), thus also arguing
against a graded involvement in the sense of a weak involvement
in task-irrelevant Stop-trials that is enhanced for task-relevant
ones. Based on this activity pattern, we would suggest that the
IPL’s role would be described in terms of automatic attentional
capture by rare salient events. Given this, it is slightly surprising
that there is in fact no behavioral effect of attentional capture for
task-irrelevant Stop-trials (i.e., no RT decrement as compared to
the corresponding Go-trials). Nevertheless, the IPL appears to be
mostly responsive to Stop-trials, thus arguing against a simple
sensory role. However, it is not entirely atypical to find neural
indications of attentional capture in the absence of a significant
behavioral effect (e.g., ). Moreover, independent of its precise
function, it appears that IPL is fulfilling a role during the Stop-
signal task that is quite exclusive to Stop-trials, yet not directly
related to response inhibition.
For areas that respond to Stop-trials only when they are task-
relevant, however, response inhibition processes cannot be easily
distinguished from those related to enhanced attentive processing
of the Stop-stimuli, or those related to increased response control
Table 2. fMRI activations for the contrast ‘‘all Stop-trials vs. all Go-trials’’ (average Stop-relevant and Stop-irrelevant blocks).
Anatomical structure Hemi-sphereCluster size [voxel] T-Value
Peak coordinates MNI (mm)
x y z
Inferior frontal gyrus (IFG)/anterior insulaR 3938.1644 18 -2
Inferior frontal junction (IFJ)R 2597.3150 14 34
Middle frontal gyrus (MFG)R 957.2440 42 28
Pre-SMAR 3006.46 4 20 48
Anterior insulaL 2746.15-36 20 -2
Inferior parietal lobule (IPL)R3613#
8.6236 -44 44
Inferior parietal lobule (IPL)L14717.06 -34 -48 42
Temporo-parietal junction/ superior temporal gyrus (TPJ/STG)R3613#
8.2150 -54 8
Middle occipital gyrus (MOG)L 75410.01-50 -76 0
Middle occipital gyrus (MOG)R3613#
9.0646 -72 0
Main local maxima. Data are thresholded at p,0.01 (FDR-corrected), with a cluster-level of k=50.
(#) the three main local maxima were taken from this larger cluster subtending the right occipito-temporal and parietal cortex.
Attentional Processes in the Stop-Signal Task
PLoS ONE | www.plosone.org8 October 2011 | Volume 6 | Issue 10 | e26386
demands that are not inhibitory (e.g., ). This paradigmatical
problem is difficult to overcome if it is the case that task-relevant
Stop-stimuli require more attention or other top-down control
mechanisms than control stimuli that require a different response.
Consequently, areas that are more active during task-relevant
Stop-trials could generally subserve several different functions.
The present data suggest that for most of the frontal activations, as
well as for those in the TPJ/STG, Stop-stimuli do not elicit a
robust neural response if they are completely task-irrelevant.
However, while this defines a ‘‘lower limit’’ of basic processes that
do not elicit activity in typical response-inhibition areas, the
present data cannot further distinguish between different high-
level operations such as top-down attentional engagement,
response demand complexity, and response inhibition.
The role of right IFJ and MFG
While the right IFG and pre-SMA have commonly been
discussed in the context of response inhibition, other frontal areas
such as the right IFJ, MFG, and left anterior insula have also been
frequently implicated in such tasks. Considerably less is known
about the functional role of these other areas, however. For
example, the right IFJ has been argued to play a role in detecting
infrequent NoGo-trials in a Go-NoGo task, including because it
has been shown to also respond to an additional type of Go-trials
during a Go-NoGo task when they are presented as infrequently as
the NoGo-trials . This finding dovetails with our observation
that the right IFJ also responds to Stop-trials that are entirely task-
irrelevant (see also ). The present data furthermore revealed
that the right IFJ was more active during successful than during
unsuccessful Stop-trials (with the latter triggering similar levels of
activity as the task-irrelevant Stop-trials), echoing the results from
another recent report . Interestingly, it has also been reported
that the right IFJ is more active for Go-trials that might turn into a
Stop-trial than for Go-trials that could not do so (‘‘conditional’’
Stop-signal task, see ), suggesting that such activations may
represent a process related to the preparation to inhibit a response
, rather than being part of the inhibition-generating processes
itself (see also ). More generally, the right IFJ has been
implicated in maintaining, updating, and/or activating task sets
[51,52]. Our finding that the right IFJ activity is enhanced during
successful versus unsuccessful Stop-trials suggests that it may play a
role either specifically in preparing response inhibition, or in
representing and enforcing the task rules more generally to
influence the outcome of Stop-trials.
The current results also revealed a large cluster of activity in the
right MFG during task-relevant Stop-trials, in line with results
from earlier response-inhibition paradigms [9,25,53–55]. In
general, the role of this area during response inhibition has not
been well characterized, but it has been suggested to be involved in
task-related, top-down control processes , potentially related to
working memory demands . Given that the present MFG
activation did not differentiate between successful and unsuccessful
Stop-trials, we cannot attribute a more specific role to it, beyond
the fact that it only responds to Stop-trials when they are task-
The cortical control of response inhibition
Given recent reports of right IFG activation by control stimuli
that do not require response inhibition (e.g., [22,23]), it is possible
that the role of this brain area in response inhibition may be
relatively indirect, which would be counter to most earlier notions
that identified it as being critical for this function. Accordingly, it
would not be clear which cortical area, if not the right IFG,
actually initiates the cancellation of a motor response. Although
one possible candidate is the right pre-SMA (e.g., [23,24]), it is
noteworthy that in our study, as well as in another recent report
, the pre-SMA was more active during unsuccessful than during
successful Stop-trials. This raises the question whether inhibition-
related activity is really stronger in successful than in unsuccessful
Stop-trials , as is usually assumed. Alternatively, it is
conceivable that other control-modulating factors, such as
fluctuations in the perceptual/attentional processing of the task-
relevant stimuli, are critical in determining the outcome of the
process, while activity fluctuations related to actual response
inhibition may not be the critical determining factor as to whether
a Stop-trial is responded to successfully or not. Another possibility
would be that the fMRI signal, due to its low temporal resolution,
additionally includes processes that occur in the pre-SMA before
or after the response has been (or has not been) generated, which
could also differ between the successful and unsuccessful Stop-
trials without reflecting response inhibition processes. The fact that
the pre-SMA might perform additional operations during this task
that are not related to response inhibition is further supported by
our finding that there appears to be a weak pre-SMA response
even for task-irrelevant Stop-trials. Although it is important to note
that the respective cluster was only weakly activated and did not
survive multiple-comparison correction, the fact that the peak
coordinate was identical to the one that resulted from the analysis
that also included task-relevant Stop-trials would appear to give
some more credibility to this activation. Based on the observation
that the pre-SMA generally tracks response speed in this context,
even for Go-trials (e.g., ), it is possible that this activity is
related to the small amount of slowing that occurs during task-
irrelevant Stop-trials (as compared to the respective Go-trials).
Nonetheless, other functions are conceivable, including for
example a role in attentional shifting . Regardless, the current
findings underscore that further research is needed to more clearly
disentangle the functional components of processes engaged
during the inhibition of a motor output in response to the
detection of a unique stimulus type in a stream of other stimuli.
Conceived and designed the experiments: CNB RMK MGW. Performed
the experiments: CNB LCC. Analyzed the data: CNB LGA RMK LCC.
Wrote the paper: CNB LGA RMK LCC MGW.
1. Aron AR (2007) The neural basis of inhibition in cognitive control.
Neuroscientist 13: 214–228.
2. Williams BR, Ponesse JS, Schachar RJ, Logan GD, Tannock R (1999)
Development of inhibitory control across the life span. Dev Psychol 35: 205–213.
3. Aron AR (2009) Introducing a special issue on stopping action and cognition.
Neurosci Biobehav Rev 33: 611–612.
4. Aron AR, Durston S, Eagle DM, Logan GD, Stinear CM, et al. (2007)
Converging evidence for a fronto-basal-ganglia network for inhibitory control of
action and cognition. J Neurosci 27: 11860–11864.
5. Verbruggen F, Logan GD (2008) Response inhibition in the stop-signal
paradigm. Trends Cogn Sci 12: 418–424.
6. Groman SM, James AS, Jentsch JD (2009) Poor response inhibition: at the nexus
between substance abuse and attention deficit/hyperactivity disorder. Neurosci
Biobehav Rev 33: 690–698.
7. Logan GD, Cowan WB, Davis KA (1984) On the ability to inhibit simple and
choice reaction time responses: a model and a method. J Exp Psychol Hum
Percept Perform 10: 276–291.
8. Logan GD (1994) On the ability to inhibit thought and action: a user’s guide to
the stop signal paradigm. In: Dagenbach D, Carr TH, eds. Inhibitory processes
in attention, memory, and language. San Diego: Academic Press. pp 189–239.
9. Swick D, Ashley V, Turken AU (2008) Left inferior frontal gyrus is critical for
response inhibition. BMC Neurosci 9: 102.
Attentional Processes in the Stop-Signal Task
PLoS ONE | www.plosone.org9 October 2011 | Volume 6 | Issue 10 | e26386
10. Chambers CD, Garavan H, Bellgrove MA (2009) Insights into the neural basis
of response inhibition from cognitive and clinical neuroscience. Neurosci
Biobehav Rev 33: 631–646.
11. Bekker EM, Kenemans JL, Hoeksma MR, Talsma D, Verbaten MN (2005) The
pure electrophysiology of stopping. International Journal of Psychophysiology
12. Boehler CN, Munte TF, Krebs RM, Heinze HJ, Schoenfeld MA, et al. (2009)
Sensory MEG responses predict successful and failed inhibition in a stop-signal
task. Cereb Cortex 19: 134–145.
13. Bekker EM, Overtoom CC, Kooij JJ, Buitelaar JK, Verbaten MN, et al. (2005)
Disentangling deficits in adults with attention-deficit/hyperactivity disorder.
Arch Gen Psychiatry 62: 1129–1136.
14. Overtoom CC, Bekker EM, van der Molen MW, Verbaten MN, Kooij JJ, et al.
(2009) Methylphenidate restores link between stop-signal sensory impact and
successful stopping in adults with attention-deficit/hyperactivity disorder. Biol
Psychiatry 65: 614–619.
15. Knyazev GG, Levin EA, Savostyanov AN (2008) A failure to stop and attention
fluctuations: An evoked oscillations study of the stop-signal paradigm. Clin
Neurophysiol 119: 556–567.
16. Aron AR, Fletcher PC, Bullmore ET, Sahakian BJ, Robbins TW (2003) Stop-
signal inhibition disrupted by damage to right inferior frontal gyrus in humans.
Nat Neurosci 6: 115–116.
17. Chambers CD, Bellgrove MA, Stokes MG, Henderson TR, Garavan H, et al.
(2006) Executive "Brake Failure" following Deactivation of Human Frontal
Lobe. J Cogn Neurosci 18: 444–455.
18. Schmajuk M, Liotti M, Busse L, Woldorff MG (2006) Electrophysiological
activity underlying inhibitory control processes in normal adults. Neuropsycho-
logia 44: 384–395.
19. Swann N, Tandon N, Canolty R, Ellmore TM, McEvoy LK, et al. (2009)
Intracranial EEG reveals a time- and frequency-specific role for the right inferior
frontal gyrus and primary motor cortex in stopping initiated responses.
J Neurosci 29: 12675–12685.
20. Pliszka SR, Liotti M, Woldorff MG (2000) Inhibitory control in children with
attention-deficit/hyperactivity disorder: event-related potentials identify the
processing component and timing of an impaired right-frontal response-
inhibition mechanism. Biological Psychiatry 48: 238–246.
21. Aron AR (2011) From Reactive to Proactive and Selective Control: Developing
a Richer Model for Stopping Inappropriate Responses. Biological Psychiatry In
Press, Corrected Proof.
22. Hampshire A, Chamberlain SR, Monti MM, Duncan J, Owen AM (2010) The
role of the right inferior frontal gyrus: inhibition and attentional control.
Neuroimage 50: 1313–1319.
23. Sharp DJ, Bonnelle V, De Boissezon X, Beckmann CF, James SG, et al. (2010)
Distinct frontal systems for response inhibition, attentional capture, and error
processing. Proc Natl Acad Sci U S A 107: 6106–6111.
24. Duann JR, Ide JS, Luo X, Li CS (2009) Functional connectivity delineates
distinct roles of the inferior frontal cortex and presupplementary motor area in
stop signal inhibition. J Neurosci 29: 10171–10179.
25. Chikazoe J, Jimura K, Asari T, Yamashita K, Morimoto H, et al. (2009)
Functional dissociation in right inferior frontal cortex during performance of go/
no-go task. Cereb Cortex 19: 146–152.
26. Fassbender C, Simoes-Franklin C, Murphy K, Hester R, Meaney J, et al. (2006)
The role of a right fronto-parietal network in cognitive control. Journal of
Psychophysiology 20: 286–296.
27. Mostofsky SH, Schafer JGB, Abrams MT, Goldberg MC, Flower AA, et al.
(2003) fMRI evidence that the neural basis of response inhibition is task-
dependent. Cognitive Brain Research 17: 419–430.
28. Neubert FX, Mars RB, Buch ER, Olivier E, Rushworth MF (2010) Cortical and
subcortical interactions during action reprogramming and their related white
matter pathways. Proc Natl Acad Sci U S A 107: 13240–13245.
29. Dodds CM, Morein-Zamir S, Robbins TW (2011) Dissociating Inhibition,
Attention, and Response Control in the Frontoparietal Network Using
Functional Magnetic Resonance Imaging. Cereb Cortex 21: 1155–1165.
30. Corbetta M, Shulman GL (2002) Control of goal-directed and stimulus-driven
attention in the brain. Nat Rev Neurosci 3: 201–215.
31. Eckert MA, Menon V, Walczak A, Ahlstrom J, Denslow S, et al. (2009) At the
heart of the ventral attention system: the right anterior insula. Hum Brain Mapp
32. de Fockert J, Rees G, Frith C, Lavie N (2004) Neural correlates of attentional
capture in visual search. J Cogn Neurosci 16: 751–759.
33. Burnham BR, Neely JH, Naginsky Y, Thomas M (2010) Stimulus-driven
attentional capture by a static discontinuity between perceptual groups. J Exp
Psychol Hum Percept Perform 36: 317–329.
34. Lijffijt M, Kenemans JL, Verbaten MN, van Engeland H (2005) A meta-analytic
review of stopping performance in attention-deficit/hyperactivity disorder:
deficient inhibitory motor control? J Abnorm Psychol 114: 216–222.
35. Boehler CN, Appelbaum LG, Krebs RM, Hopf JM, Woldorff MG (2010)
Pinning down response inhibition in the brain — Conjunction analyses of the
Stop-signal task. Neuroimage 52: 1621–1632.
36. Logan GD, Cowan WB (1984) On the ability to inhibit thought and action: A
theory of an act of control. Psychol Rev 91: 295–327.
37. Hinrichs H, Scholz M, Tempelmann C, Woldorff MG, Dale AM, et al. (2000)
Deconvolution of event-related fMRI responses in fast-rate experimental
designs: tracking amplitude variations. J Cogn Neurosci 12(Suppl 2): 76–89.
38. Friston KJ, Fletcher P, Josephs O, Holmes A, Rugg MD, et al. (1998) Event-
related fMRI: characterizing differential responses. Neuroimage 7: 30–40.
39. Kriegeskorte N, Simmons WK, Bellgowan PS, Baker CI (2009) Circular analysis
in systems neuroscience: the dangers of double dipping. Nat Neurosci 12:
40. Padmala S, Pessoa L (2010) Moment-to-moment fluctuations in fMRI amplitude
and interregion coupling are predictive of inhibitory performance. Cogn Affect
Behav Neurosci 10: 279–297.
41. Nelson SM, Dosenbach NU, Cohen AL, Wheeler ME, Schlaggar BL, et al.
(2010) Role of the anterior insula in task-level control and focal attention. Brain
Struct Funct 214: 669–680.
42. Menon V, Uddin LQ (2010) Saliency, switching, attention and control: a
network model of insula function. Brain Struct Funct 214: 655–667.
43. Kramer UM, Knight RT, Munte TF (2011) Electrophysiological evidence for
different inhibitory mechanisms when stopping or changing a planned response.
J Cogn Neurosci 23: 2481–2493.
44. Wager TD, Sylvester CY, Lacey SC, Nee DE, Franklin M, et al. (2005)
Common and unique components of response inhibition revealed by fMRI.
Neuroimage 27: 323–340.
45. Fenker DB, Heipertz D, Boehler CN, Schoenfeld MA, Noesselt T, et al. (2010)
Mandatory processing of irrelevant fearful face features in visual search. J Cogn
Neurosci 22: 2926–2938.
46. Cai W, Leung HC (2011) Rule-guided executive control of response inhibition:
functional topography of the inferior frontal cortex. PLoS One 6: e20840.
47. Cai W, Leung HC (2009) Cortical activity during manual response inhibition
guided by color and orientation cues. Brain Res 1261: 20–28.
48. Aron AR, Behrens TE, Smith S, Frank MJ, Poldrack RA (2007) Triangulating a
cognitive control network using diffusion-weighted magnetic resonance imaging
(MRI) and functional MRI. J Neurosci 27: 3743–3752.
49. Chikazoe J, Jimura K, Hirose S, Yamashita K, Miyashita Y, et al. (2009)
Preparation to inhibit a response complements response inhibition during
performance of a stop-signal task. J Neurosci 29: 15870–15877.
50. Verbruggen F, Aron AR, Stevens MA, Chambers CD (2010) Theta burst
stimulation dissociates attention and action updating in human inferior frontal
cortex. Proc Natl Acad Sci U S A 107: 13966–13971.
51. Brass M, von Cramon DY (2002) The role of the frontal cortex in task
preparation. Cereb Cortex 12: 908–914.
52. Derrfuss J, Brass M, Neumann J, Cramon DYv (2005) Involvement of the
inferior frontal junction in cognitive control: Meta-analyses of switching and
Stroop studies. Human Brain Mapping 25: 22–34.
53. Nakata H, Sakamoto K, Ferretti A, Gianni Perrucci M, Del Gratta C, et al.
(2008) Somato-motor inhibitory processing in humans: an event-related
functional MRI study. Neuroimage 39: 1858–1866.
54. Xue G, Aron AR, Poldrack RA (2008) Common Neural Substrates for
Inhibition of Spoken and Manual Responses. Cereb Cortex 18: 1923–1932.
55. Leung H-C, Cai W (2007) Common and Differential Ventrolateral Prefrontal
Activity during Inhibition of Hand and Eye Movements. J Neurosci 27:
56. Chikazoe J (2010) Localizing performance of go/no-go tasks to prefrontal
cortical subregions. Curr Opin Psychiatry 23: 267–272.
57. Boehler CN, Bunzeck N, Krebs RM, Noesselt T, Schoenfeld MA, et al. (2011)
Substantia nigra activity level predicts trial-to-trial adjustments in cognitive
control. J Cogn Neurosci 23: 362–373.
Attentional Processes in the Stop-Signal Task
PLoS ONE | www.plosone.org10 October 2011 | Volume 6 | Issue 10 | e26386