Neural circuitry of emotional and cognitive conflict revealed through facial expressions.
ABSTRACT Neural systems underlying conflict processing have been well studied in the cognitive realm, but the extent to which these overlap with those underlying emotional conflict processing remains unclear. A novel adaptation of the AX Continuous Performance Task (AX-CPT), a stimulus-response incompatibility paradigm, was examined that permits close comparison of emotional and cognitive conflict conditions, through the use of affectively-valenced facial expressions as the response modality.
Brain activity was monitored with functional magnetic resonance imaging (fMRI) during performance of the emotional AX-CPT. Emotional conflict was manipulated on a trial-by-trial basis, by requiring contextually pre-cued facial expressions to emotional probe stimuli (IAPS images) that were either affectively compatible (low-conflict) or incompatible (high-conflict). The emotion condition was contrasted against a matched cognitive condition that was identical in all respects, except that probe stimuli were emotionally neutral. Components of the brain cognitive control network, including dorsal anterior cingulate cortex (ACC) and lateral prefrontal cortex (PFC), showed conflict-related activation increases in both conditions, but with higher activity during emotion conditions. In contrast, emotion conflict effects were not found in regions associated with affective processing, such as rostral ACC.
These activation patterns provide evidence for a domain-general neural system that is active for both emotional and cognitive conflict processing. In line with previous behavioural evidence, greatest activity in these brain regions occurred when both emotional and cognitive influences additively combined to produce increased interference.
- SourceAvailable from: John Eastwood[show abstract] [hide abstract]
ABSTRACT: Four experiments were conducted to evaluate whether focal attention can be guided by an analysis of the emotional expression in a face. Participants searched displays of 7, 11, 15, and 19 schematic faces for the location of a unique face expressing either a positive or a negative emotion located among distractor faces expressing a neutral emotion. The slopes of the search functions for locating the negative face were shallower than the slopes of the search functions for locating the positive face (Experiments 1A and 2A). When the faces were inverted to reduce holistic face perception, the slopes of the search functions for locating positive and negative faces were not different (Experiments 1B and 2B). The results suggest that the emotional expression in a face can be perceived outside the focus of attention and can guide focal attention to the location of the face.Perception & Psychophysics 09/2001; 63(6):1004-13. · 1.37 Impact Factor
- [show abstract] [hide abstract]
ABSTRACT: Participants searched for discrepant fear-relevant pictures (snakes or spiders) in grid-pattern arrays of fear-irrelevant pictures belonging to the same category (flowers or mushrooms) and vice versa. Fear-relevant pictures were found more quickly than fear-irrelevant ones. Fear-relevant, but not fear-irrelevant, search was unaffected by the location of the target in the display and by the number of distractors, which suggests parallel search for fear-relevant targets and serial search for fear-irrelevant targets. Participants specifically fearful of snakes but not spiders (or vice versa) showed facilitated search for the feared objects but did not differ from controls in search for nonfeared fear-relevant or fear-irrelevant, targets. Thus, evolutionary relevant threatening stimuli were effective in capturing attention, and this effect was further facilitated if the stimulus was emotionally provocative.Journal of Experimental Psychology General 10/2001; 130(3):466-78. · 3.99 Impact Factor
- [show abstract] [hide abstract]
ABSTRACT: A neglected question regarding cognitive control is how control processes might detect situations calling for their involvement. The authors propose here that the demand for control may be evaluated in part by monitoring for conflicts in information processing. This hypothesis is supported by data concerning the anterior cingulate cortex, a brain area involved in cognitive control, which also appears to respond to the occurrence of conflict. The present article reports two computational modeling studies, serving to articulate the conflict monitoring hypothesis and examine its implications. The first study tests the sufficiency of the hypothesis to account for brain activation data, applying a measure of conflict to existing models of tasks shown to engage the anterior cingulate. The second study implements a feedback loop connecting conflict monitoring to cognitive control, using this to simulate a number of important behavioral phenomena.Psychological Review 08/2001; 108(3):624-52. · 9.80 Impact Factor
Neural Circuitry of Emotional and Cognitive Conflict
Revealed through Facial Expressions
Kimberly S. Chiew*, Todd S. Braver
Department of Psychology, Washington University in St. Louis, St. Louis, Missouri, United States of America
Background: Neural systems underlying conflict processing have been well studied in the cognitive realm, but the extent to
which these overlap with those underlying emotional conflict processing remains unclear. A novel adaptation of the AX
Continuous Performance Task (AX-CPT), a stimulus-response incompatibility paradigm, was examined that permits close
comparison of emotional and cognitive conflict conditions, through the use of affectively-valenced facial expressions as the
Methodology/Principal Findings: Brain activity was monitored with functional magnetic resonance imaging (fMRI) during
performance of the emotional AX-CPT. Emotional conflict was manipulated on a trial-by-trial basis, by requiring contextually
pre-cued facial expressions to emotional probe stimuli (IAPS images) that were either affectively compatible (low-conflict) or
incompatible (high-conflict). The emotion condition was contrasted against a matched cognitive condition that was
identical in all respects, except that probe stimuli were emotionally neutral. Components of the brain cognitive control
network, including dorsal anterior cingulate cortex (ACC) and lateral prefrontal cortex (PFC), showed conflict-related
activation increases in both conditions, but with higher activity during emotion conditions. In contrast, emotion conflict
effects were not found in regions associated with affective processing, such as rostral ACC.
Conclusions/Significance: These activation patterns provide evidence for a domain-general neural system that is active for
both emotional and cognitive conflict processing. In line with previous behavioural evidence, greatest activity in these brain
regions occurred when both emotional and cognitive influences additively combined to produce increased interference.
Citation: Chiew KS, Braver TS (2011) Neural Circuitry of Emotional and Cognitive Conflict Revealed through Facial Expressions. PLoS ONE 6(3): e17635.
Editor: Hans Op de Beeck, University of Leuven, Belgium
Received December 5, 2010; Accepted February 4, 2011; Published March 9, 2011
Copyright: ? 2011 Chiew, Braver. 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 work was supported by grant RO1 MH66078 from the National Institutes of Health to T.S.B. and a Natural Sciences and Engineering Research
Council of Canada Postgraduate Scholarship to K.S.C. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: email@example.com
‘Cognitive control’ refers to the coordination and direction of
lower-level cognitive processes critical to complex, goal-directed
behaviour. These processes, including attentional selection,
conflict resolution, and the online maintenance of goal-relevant
information (and inhibition of goal-irrelevant information), may
underlie higher many cognitive functions, permitting the flexibility
and sophistication of human thought and behaviour across a wide
variety of task situations. While cognition has traditionally been
conceptualized as separate from affect, it has been increasingly
recognized that affective significance is a major factor in goal-
directed behaviour, both in establishing goals and in shaping how
information is processed during goal pursuit. Emotionally salient
stimuli in the environment may be prioritized for processing over
non-emotional stimuli [1,2], but it remains unclear whether
qualitatively distinct neural circuitry is engaged for the processing
of affectively-valenced stimulus dimensions. The present study
examines the neural systems engaged in the detection and
management of conflict, a canonical control function, when the
information being processed (i.e., the source of conflict) is
emotional versus non-emotional in nature.
Conflict can be defined mechanistically in terms of cross-talk
caused by the simultaneous concurrent processing of goal-relevant
and goal-irrelevant information competing for common resources
. The Stroop task  is a classic conflict task: participants must
name the colour of presented words while ignoring the word’s
meaning. In some trials, goal-irrelevant information is congruent
with goal-relevant information (e.g., the word ‘RED’ printed in
red ink); in other trials, the goal-relevant and irrelevant
information are incongruent (e.g., ‘RED’ printed in green ink),
leading to conflict. Using tasks such as the Stroop (as well as
related incompatibility paradigms such as Simon, flanker and
others, e.g., ), conflict has been extensively studied in the
cognitive realm. Functional neuroimaging methods have been
used to identify a number of frontal and parietal brain regions
canonically associated with cognitive control in this and other tasks
[6,7,8,9], with the ACC in particular being associated with conflict
processing functions [10,11,12,13].
Evidence from early neuroimaging studies examining conflict
elicited by emotional versus non-emotional distracters resulted in an
influential hypothesis postulating that emotional and cognitive
conflict detection are mediated by distinct rostral and dorsal
subdivisions of the ACC, respectively (Bush et al., 2000). However,
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subsequent investigations of emotional and cognitive conflict
processing have yielded mixed evidence regarding the domain-
specificity of their underlying neural systems. Most studies focusing
on emotion conflictusing Stroop-like variants tend to find activation
in dorsal rather than ventral ACC, as well as other areas associated
with cognitive control, such as the lateral PFC [14,15]. In a recent
study comparing activity in closely matched emotion and non-
emotional variants of a face-word Stroop paradigm, Egner and
colleagues  again reported that conflict detection was associated
with dorsal ACC in both conditions; activation was observed in the
rostral ACC (and amygdala) only during conditions examining
emotional conflict resolution (i.e., modulation based on previous
trial conflict). Ochsner and colleagues  compared an emotional
versusnon-emotional flanker task, and also found a number of areas
commonly engaged by conflict in both tasks, including the dorsal
ACC. However, consistent with the cognitive/emotion division
hypothesis, they also observed that affective conflict selectively
engaged the rostral medial PFC, with brain-behavior correlations
observed in rostral ACC. Likewise, another recent study 
reported distinct patterns of conflict-related neural activity in
conditions involving emotional stimulus-response (S-R) incompat-
ibility (emotion expression interference; elicited via making facial
expressions incongruent with those of presented faces) with
cognitive S-R incompatibility (elicited via the Simon task).
A major challenge in this research area has been to utilize
appropriate paradigms that enable valid and closely matched
comparisons of emotional and cognitive forms of conflict. The
hypothesis that emotional versus cognitive conflict may depend on
distinct subdivisions of the ACC was based on evidence from
emotional adaptations of the Stroop task, which examine
interference from emotional distracters (e.g., performance of the
colour-naming task for emotional relative to non-emotional words;
). However, it has been asserted that interference in the
colour-naming emotional Stroop task may occur because of lower-
level lexical effects  or general attention capture  rather
than the direct conflict effects present in the traditional Stroop. To
improve upon this design, face-word Stroop variants have been
utilized, in which positive and negatively valenced words (e.g.,
‘HAPPY’ or ‘FEAR’) are superimposed on compatible or
compatible facial expressions [15,16,22,23]. This design improves
on the colour-naming emotional Stroop in that the responses
require affective classification and the task-relevant and irrelevant
information are semantically related, leading to affective incom-
patibility effects more closely related to the direct conflict present
in the traditional cognitive Stroop. However, all of these tasks
involve an incompatibility between a task-relevant stimulus and a
task-irrelevant stimulus (thus, stimulus-stimulus [S-S] incompati-
bility). In contrast, studies of cognitive conflict have explored both
S-S and S-R incompatibilities . The emotion expression
interference paradigm developed by Lee and colleagues  is a
first step in exploring S-R incompatibility in the context of
emotional conflict: this paradigm examines interference when
participants make emotional facial expressions as a behavioral
response, capitalizing on their role as an index of emotional
experience and expression . However, the Lee et al paradigm
requires participants to make an expression in response to a
presented face. As such, it leaves open the possibility that
interference effects in the task may be caused by overriding
imitation tendencies instead of being due to conflicting emotional
influences, per se. In view of these considerations, our goal was to
examine emotional conflict with a paradigm that similarly
capitalized on emotional facial expressions to index stimulus-
response incompatibility, but that improved upon this paradigm
by avoiding possible imitative influences.
Accordingly, we developed a new paradigm to examine
emotional conflict via S-R incompatibility using emotional facial
expressions to emotional, but non-face stimuli . This task was
adapted from the AX Continuous Performance Task (AX-CPT), which
has been repeatedly established as a robust probe of context
processing, cognitive conflict, and cognitive control [13,26,27,28].
The emotional AX-CPT requires participants to respond to
emotionally evocative cue-probe combinations with emotionally
congruent or incongruent facial expressions. This task was
developed on the rationale that interference elicited by a mismatch
between evoked emotion and required facial response may more
closely approximate situations of emotional conflict that people
experience in ‘real-life’ (e.g., acting pleasant to a rude customer;
smiling graciously after a defeat), thus achieving a higher level of
ecological validity. In prior work using facial electromyography
(EMG) to index expression responses in this task, we demonstrated
that behavioural interference can be robustly elicited, and
furthermore, that such interference was greater when emotional
influences were present relative to when they were absent .
In the AX-CPT, conflict and cognitive control are varied on a
trial-by-trial basis through the use of contextual pre-cues. Certain
cue-probe combinations require a target response (e.g., ‘A’
followed by ‘X’), whereas all other cue-probe combinations
require a non-target response. The target (‘AX’) combination
occurs with high frequency, which leads to high levels of
interference in two low-frequency cue-probe combinations: AY
(target cue, non-target probe) and BX (non-target cue, target
probe). In AY trials, interference arises from expectancy
established by the target cue, while in BX trials interference arises
via a dominant target response bias to the probe. In both trial
combinations, target-related response biases produce stimulus-
response interference because a non-target response is required. In
the emotional AX-CPT we developed, text instructions (‘SMILE’
and ‘FROWN’) were used as cues and emotionally evocative
[IAPS]; served as probes; participants were required to smile
or frown in response. The target cue-probe-response combination
was always emotionally congruent (i.e., smiling to ‘SMILE’+plea-
sant picture, or frowning to ‘FROWN’+unpleasant picture). BX
trials (non-target cue, target probe) involved incompatibility
between the probe presented and the required facial response
(e.g., smiling to an unpleasant picture); in contrast, interference in
AY trials (target cue, non-target probe) was due to incompatibility
between the instructions of the cue and the required facial
response (e.g., frowning after ‘SMILE’ cue). When contrasting
performance in the emotion AX-CPT relative to a tightly matched
non-emotional condition (in which probes were emotionally
neutral), utilizing EMG measures to quantify the facial expression
response, we observed that interference effects were present under
both emotional and non-emotional conditions, but were strongest
in the emotional AX-CPT, when both emotional and non-
emotional sources of incompatibility were present . In this
condition, interference was due not only to standard sources of S-
R incompatibility, but also because of the automatic, but
inappropriate affective response to the target (e.g. being cued to
smile to a negative IAPS picture).
This paradigm is unique among present tasks probing emotional
conflict, in that it requires integrated processing of both cue and
probe in order to perform successfully, as opposed to requiring
inhibition of the emotional information. Additionally, a major
strength of the paradigm is the ability to create a closely matched
analog task that permits a direct comparison of emotional vs. non-
emotional conflict. Specifically, by changing probe stimuli to be
affectively neutral (i.e., arbitrary symbol categories instead of
Affective Picture System
Neural Circuitry of Emotional & Cognitive Conflict
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emotionally evocative pictures), but retaining the other aspects of
the task structure (including using facial expressions as response
modality), sources of S-R incompatibility in the affective
dimension are eliminated, while the standard non-affective S-R
association effects driving AX-CPT effects remain (i.e., probe-
driven biases and cue-driven expectancies). By comparing effects
in the two conditions, it is possible to isolate the additive conflict
effects specifically associated with S-R incompatibility in the
The present study builds on our previous behavioural work by
using event-related fMRI to examine whether brain activity
associated with processing emotional vs. non-emotional conflict
involves the same general control-related regions or qualitatively
different neural circuits. Such a comparison may help to clarify
further some of the outstanding contradictions present in previous
emotion conflict research. On the basis of previous neuroimaging
evidence, we hypothesized that both emotional and non-emotional
versions of this task would engage common control-related regions
including the dorsal ACC and lateral PFC. Further, based on our
previous behavioural evidence, we predicted that conflict-related
interference would be greater in the emotional task than in the
non-emotional task, and that this would be reflected in increased
levels of elicited activity within these control-related brain regions.
Finally, we tested whether the emotional task was associated with
the activation of potentially affectively-specialized regions, such as
the rostral ACC/ventromedial PFC and amygdala, that might be
selectively recruited to detect emotional conflict.
Ethics approval to conduct this study was granted by the
Institutional Review Board of Washington University. Each
participant provided written, informed consent prior to participa-
tion, in accordance with the human subjects guidelines established
by Washington University.
Twenty-four healthy young adults (8 males, 16 females; mean
age=25.5 years, SD=5.63) were scanned using fMRI while
participating in the task. All fMRI participants were right-handed,
native English speakers, and screened to ensure no neurological or
psychiatric disorders, psychotropic medications, or other factors
were present that contraindicated fMRI.
Participants performed an emotional (Emotion condition) and
non-emotional (Neutral condition) variant of the AX-CPT. The
AX-CPT paradigm follows a cue-probe trial structure, in which
cue stimuli set a context that is needed for appropriate response
selection to the subsequent probe. The Emotion and Neutral
conditions were identical in all respects except for the category of
stimuli used as probes. Cue stimuli in the task were the words
‘SMILE’ and ‘FROWN’. For probes, the Emotion condition used
IAPS pictures as probes and the Neutral condition used
alphanumeric symbols (i.e., letters served as target probes, and
digits served as nontarget probes). New pictures/symbols were
used as probes on each trial, except for a pre-specified neutral
picture/punctuation mark on no-go trials (described below).
Across participants the particular cue-probe combination that
comprised the ‘‘AX’’ target trial type was counter-balanced. Thus,
for approximately half of the participants (11/24) the AX target
was ‘SMILE’/positive picture (‘‘SMILE’’/letter in Neutral)
requiring a smile response (facial expression) and the other half
(‘‘FROWN’’/letter in Neutral) requiring a frown response.
However, on nontarget trials (AY, BX, BY), the opposite facial
expression was required. All other details of the task paradigms
described below were identical for the Emotion and Neutral
conditions, and for both participant groups.
Trials were presented in pseudorandom sequence, with target
(AX) trials occurring at a 7:1 frequency compared to all non-target
task trials, leading to a total of 84 AX trials, 12 AY trials (target
cue, non-target probe), 12 BX trials (non-target cue, target probe),
12 BY trials (non-target cue, non-target probe). Although the
absolute numbers of high conflict (BX and AY) trials is somewhat
low, our prior results suggest that this number was sufficient to
robustly detect significant interference effects. In addition to
primary task trials, no-go trials were also included to ensure that
participants responded on the basis of the cue-probe combination
and not solely and prematurely to the cue. No-go trials were
indicated by a pre-specified neutral picture in Emotion (punctu-
ation mark in Neutral), to which no response was to be made (24
no-go trials total; occurring both after target and non-target cues).
Participants performed four scanning runs each of the Emotion
and Neutral conditions of the AX-CPT (eight runs in total). Within
each run, task blocks (three per run; 135 seconds each) alternated
with short fixation blocks (four per run; 30 seconds each). Each
scanning run began with 10 seconds of rest (later discarded) to
allow the scanner to reach steady state; total run duration was
,9 minutes. Each of the three task blocks within a scanning run
consisted of 12 trials; thus participants performed eight runs of 36
AX-CPT trials each for 288 trials in total (144 Emotion, 144
Neutral). AX-CPT trials consisted of cue-probe pairs shown in
sequence. Trial structure (Figure 1) was as follows: cue (750 ms),
inter-stimulus-interval (ISI; 3250 ms), probe (2500 ms), and
minimum inter-trial-interval (ITI) of 1000 ms (for a minimum
total trial length of 7.5 seconds). ITIs included additional jittering
to facilitate event-related response estimation, in increments of
2500 ms (no jitter, 2500 ms, 5000 ms, or 7500 ms). 72 trials were
presented at each of the four ITI lengths.
theAX target was‘FROWN/negative picture
fMRI Data Collection
Structural and functional imaging data was collected on a 3T
Siemens TIM Trio whole-body scanner at Mallinckrodt Institute
of Radiology at Washington University School of Medicine. High-
resolution anatomical images were acquired for each participant
using a sagittal T1-weighted MP-RAGE sequence (TE=3.16 ms,
TR=2400 ms, flip angle=8u 176 slices, 16161 mm voxels).
Anatomical images were aligned with each individual’s functional
images. To facilitate registration of the T1 and functional scans, a
T2-weighted image was also acquired in the same space as the
functional scans [TE=96 ms, TR=5000 ms, 1896256 acquisi-
tion matrix, 48 slices, 1.026163 mm voxels]. The functional
images were collected in eight 210TR (,9 minutes) runs using an
asymmetric spin-echo echo-planar sequence sensitive to blood
oxygenation level-dependent (BOLD) contrast (T2*) [TE=27 ms,
TR=2500 ms, flip angle=90u, FOV=256 mm, skip=0 mm, 36
slices, 46464 mm voxels].
Stimuli were presented using E-Prime (Psychology Software
Tools, Pittsburgh, PA) on a Dell PC. As described in the Task
Procedure section, participants responded to each trial using
emotional facial expressions. A custom-built mirror apparatus
positioned over the head coil served both to reflect the projected
image of the task screen towards the participant and to reflect the
view of the participant’s face such that it could be recorded using a
videocamera positioned at the head end of the bore. Video
recording served to ensure participant compliance in the task and
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PLoS ONE | www.plosone.org3March 2011 | Volume 6 | Issue 3 | e17635
was visually inspected to verify that such compliance was
occurring. However, due to technical difficulties and poor video
quality, this video was not quantitatively evaluated for measures of
behavioural performance. A fiber-optic button box interfaced with
E-Prime facilitated communication with the participant.
fMRI Data Analysis
The fMRI data were analyzed with in-house software. Data
analysis was conducted with a general linear model (GLM),
including nuisance regressors for linear trends within runs and
baseline shifts between runs. Additionally, the GLM contained
task-related regressors for block and event-related activity. Block-
related activity related to each task condition (Emotion and
Neutral) were modeled as boxcar functions, but because
examining sustained activity did not permit the examination of
conflict effects in the data, these functions were also treated as
regressors of no interest. Our experimental design follows the
specifications of Visscher et al.  in permitting the dissociation
of block and event-related fMRI activity (using alternating blocks
of task and rest, as well as jittered trials within each task block);
using event-related regressors that are estimated (using delta or
FIR functions) rather than assumed via a model of the
hemodynamic response function. With this estimation approach,
multicollinearity between the sustained and event-related regres-
sors has been shown not to be a major concern.
The primary task-related analysis focused on event-related
activity as a function of trial type and task condition. Event-related
estimates were created for each trial type within task conditions
(AX, AY, BX, BY, no-go within Emotion and Neutral task
versions). Given the complex trial structure, event-related effects
were analyzed without reference to a fixed hemodynamic response
function, using a delta-function estimation approach. Thus, within
a 25-second response epoch following trial onset, independent
values were estimated for each of 10 timepoints (corresponding to
the 10 TR frames). The estimates from the individual subject
GLMs were analyzed using appropriately designed analyses of
variance (ANOVAs) that treated participants as a random factor.
Regions of interest identification.
related brain activity in analyses within a priori defined regions of
interest (ROIs). Analyses were conducted within two ‘networks’ of
interest (selected not on the basis of functional connectivity but as
coherent sets of regions observed in prior literature to be
We examined event-
functionally related to cognitive control and reward processing).
The first analysis examined activity within regions associated with
cognitive control and working memory (established using meta-
analyses; primarily including dorsal medial and lateral prefrontal
and parietal regions [8,9]. The ROI mask for the cognitive control
network (CCN) was created by using anatomical coordinates
identified by the aforementioned meta-analyses as seed points with
10 mm radius spheres drawn around them. The second analysis
examined activity within anatomical regions associated with
emotion and reward processing (hereafter EMO network)
including the amygdala, portions of the basal ganglia (putamen,
caudate, substantia nigra and nucleus accumbens), anterior insula,
medial orbitofrontal cortex, and ventromedial prefrontal cortex,
with regions drawn according to anatomical criteria identified
using the Talaraichatlas
[32,33,34,35,36,37,38,39,40]. A separate region of interest
included the rostral ACC, defined anatomically, which was near
to, but not overlapping the ventromedial PFC ROI . For
coordinates for ROIs in both networks, please refer to Table S1
and Table S2. The exact masks for both networks are available
from the authors by request.
Significant activity within each network mask was corrected for
multiple comparisons using a cluster size criterion based on Monte
Carlo simulations [42,43], via the AlphaSim software within AFNI
. To assure a multiple comparisons corrected p,.05 criteria,
significant regions were identified based on a per-voxel minimum
z.2.32 and minimum cluster size of 37 voxels within the CCN
mask (or 30 voxels within the EMO mask).
Within each mask, we were interested in identifying regions
demonstrating general sensitivity to conflict (e.g., across both the
emotional and non-emotional tasks) and then examining whether
brain activity within these conflict-associated regions differed as a
function of emotional task content. Thus, the first stage analysis
consisted of the following voxelwise contrast: high conflict trials (AY +
BX collapsed, averaged across timepoints 4–7) . low conflict trials
(AX + BY collapsed, averaged across timepoints 4–7). This analysis
further collapsed across the Emotion and Neutral conditions,in order
to enable unbiased identification of regions. Timepoints 4–7 were
selected to capture probe-related activity, which is necessary for the
elicitation of conflict in the AX-CPT paradigm.
In the second stage of analysis, we conducted ROI-based
ANOVAs on significant regions identified as sensitive to conflict in
, andprevious studies
Figure 1. Trial structure with timing. (A) Example of the target (AX) cue-probe-response for the smile-AX condition of the Emotion AX-CPT; (B)
Example of BX (non-target cue, target probe) and (C) AY (target cue, non-target probe) conflict trials for smile-AX condition of the task.
Neural Circuitry of Emotional & Cognitive Conflict
PLoS ONE | www.plosone.org4 March 2011 | Volume 6 | Issue 3 | e17635
the first-stage analysis. Two different kinds of region-wise analyses
were carried out. In the first ANOVA, we examined which, if any,
of these conflict-defined regions showed independent differences in
brain activity as a function of task condition, i.e., Emotion vs.
Neutral, and time (the ANOVA included all 10 timepoints). This
analysis enabled a direct test of whether conflict-related regions
showed increased responsivity under emotion conditions. In the
second ANOVA, we only included the high-conflict trials AY and
BX, to examine whether task condition effects were still exhibited
selectively during conflict. Additionally, by including trial-type as a
factor, we tested whether condition effects differed by the type of
conflict elicited (AY=cue-based; BX=probe-based; again, time-
point was also included as a factor in the ANOVA).
In addition to analyses within these networks of interest, we
conducted more focused analyses within the rostral ACC and
amygdala ROIs, as these regions have been specifically implicated
in emotional conflict processing [16,45]. The amygdala was part of
the general EMO mask, but the additional analyses focused
exclusively on amygdala and rACC regions, and as such utilized a
more liberal corrected threshold specific to the size of each ROI
(i.e., small-volume correction). Thus, for these analyses a reduced
cluster-size criterion of 12 voxels for rostral ACC and 9 voxels for
amygdala was employed (again with voxelwise minimum z.2.32).
In addition to the analyses described above, we also conducted a
focused test with the rACC and amygdala ROIs to examine
whether these regions show a selective response to conflict only
under Emotion conditions. As such, a voxelwise contrast of high
conflict (AY + BX, timepoints 4–7) . low conflict (AX + BY,
timepoints 4–7) was conducted, but only using trials from the
As described in Methods, participants performed the emotional
AX-CPT with voluntary emotional facial expressions as the
response modality. Facial expressions were monitored in the
scanner using video recording and video footage was inspected
following each participant to ensure compliance with the task, but
poor video quality and technical difficulties rendered this video
unusable for the purposes of evaluating behavioural performance.
Previously published data from our laboratory  investigating
the Emotion and Neutral versions of the AX-CPT used here found
no significant main effects of task condition on performance
(indexed by error rates and response onsets), suggesting that the
overall difficulty of emotional and non-emotional versions of the
task may be comparable. Additionally, that study indexed
performance using facial electromyography (EMG), which enables
a much more fine-grained behavioural analysis than video coding
would have permitted in the present study. We discuss issues with
the present study’s behavioural data and present behaviour from
our previous EMG study in Text S1 and Figure S1. We compared
areas defined by task conflict (high . low conflict) within the CCN
and EMO networks with and without discernable errors, and found
relatively few differences. These results are shown in Table S3.
Imaging Results: ROI Analyses
As described in the Methods section, event-related brain activity
was examined within two ‘networks’ of interest: the cognitive
control network (CCN) and the emotion/reward processing
network (EMO). We also analyzed brain activity within more
focused ROIs of the rostral ACC and bilateral amygdala.
Within each ROI, we identified regions showing
within CCN andEMO
conflict-related increases in activity through the high-conflict .
low-conflict contrast, collapsing across Emotion and Neutral
conditions to provide an unbiased test. Fourteen regions within
the CCN, as well as five regions within the EMO network, were
identified as showing conflict responses. These conflict-defined
regions are summarized in Table 1, with cortical regions shown in
Figure 2. As expected, conflict-related regions within the CCN
included the dorsal ACC and bilateral PFC, along with additional
activation in the inferior parietal lobule, precuneus, thalamus, and
cerebellum. The EMO regions showing sensitivity to conflict
included bilateral dopaminergic midbrain, bilateral anterior
insula, and left putamen. However, in this contrast, conflict-
related activation was not observed in ventromedial PFC or
Condition-related effects within the high versus low
In the next stage of analysis, each of these
conflict-defined ROIs was subjected to an ANOVA that tested for
effects of condition type, using timepoint as an additional factor to
define event-related effects (i.e., in terms of a condition 6 time
interaction). Nine ROIs showed such condition 6 time effects –
these areas are marked in a column in Table 1. The areas showing
sensitivity to both conflict and emotional task content included,
most prominently, the dorsal ACC, right dorsolateral PFC, and
bilateral posterior PFC, near the inferior frontal junction. The
examination of timecourses in these nine regions revealed that, in
all of them, the condition 6time interaction was due to Emotion
. Neutral activation, especially in the middle timepoints where
activity peaked (approximately timepoints 4–7). The timecourse of
the effect within the dorsal ACC is shown in Figure 3, as a
representative illustration of this pattern. In this and the other
regions, the effects of condition did not interact with conflict, but
instead were present as an additive increase in activation. In only
one region, the right dopaminergic (DA) midbrain, was there
evidence of a condition*conflict interaction (at trend-level,
p=.057). However, this interaction was due to increased activity
in both the high and low conflict trials of the Emotion condition
(i.e., with a reduced conflict-related increase), compared to the
Emotion effects that did not interact with conflict, we conducted
a follow-up ANOVA to address two additional questions: 1) Was
the Emotion-related increase in activation present even when only
considering high-conflict trials (i.e., AY and BX)? 2) Were there
any differential effects of Emotion related to the type of conflict
experienced, i.e., cue-based (AY trials) versus probe-based (BX
trials)? To address these questions, the second ANOVA included
only the high-conflict trial types (AY, BX) and excluded the low-
conflict trials (AX,BY), to examine potential effects of condition
(Emotion, Neutral) and high-conflict trial-type (AY,BX) as primary
factors of interest (additional factors again included timepoint, and
The primary pattern observed in the first ANOVA, a condition
6time interaction, was replicated in the second ANOVA. Eight
regions showed this effect, denoted in Table 1; again, these
included dorsal ACC, right dorsolateral PFC, and bilateral PFC
regions. Importantly, the same Emotion . Neutral pattern was
observed in these regions, confirming that high emotion-conflict
trials increased activation of the cognitive control system relative to
non-emotion conflict conditions.
A second pattern that was observed in the ANOVA was a trial-
type6time interaction, which was significant in 11 ROIs. In all of
these regions, the pattern was due to increased activation on BX
trials relative to AY, during the early part of the trial (timepoints
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2–5), but then comparable activation later in the trial (timepoints
6–10). Figure 4 demonstrates this timecourse pattern in an
example region, the right lateral PFC. Although a BX . AY
pattern is consistent with conflict being increased under probe-
based conditions, the early, rather than late timecourse of the
effect suggests that the trial-type effect might be anticipatory or
expectancy-related. Note that the expectancy for high conflict is
significantly greater following a B-cue (probability BX | B-
cue,0.4) than following an A-cue (probability AY | A-cue,0.1).
Thus, differential conflict anticipation or expectancy may account
for the trial-type effects, rather than a differential response to
experienced conflict during probe processing. Similar conflict
expectancy effects have been observed in prior studies of the AX-
CPT [46,47] and other conflict paradigms .
Although effects of condition and trial-type were present, the
two factors did not appear to interact, as no regions showed
evidence of condition6trial-type or condition6trial-type6time
interactions. Thus, the BX . AY pattern did not differ
significantly between Emotion & Neutral conditions.
Focused analysis of rostral ACC and amygdala activity:
conflict and condition effects.
CCN and EMO masks, we computed contrasts (high . low
conflict) within the rostral ACC and amygdala ROIs. However, to
test whether these regions were particularly sensitive to emotion
conflict per se, we conducted a follow-up ANOVA using a high .
low conflict contrast, but restricting to the Emotion condition only.
No voxels within the rostral ACC or amygdala survived this
contrast, contrary to evidence from previous studies suggesting
their sensitivity to emotional conflict.
As a final test to ensure that we did not produce any false
negatives, we tested the high . low conflict contrast, using all the
data (Emotion and Neutral), but with lowered statistical thresholds,
utilizing a small-volume correction for each region individually.
Even with these more liberal thresholds, no rostral ACC clusters
were observed; however, a small voxel cluster within the left
amygdala was identified (see Table 1). Within this conflict-sensitive
left amygdala region, there was a significant effect of task condition
As done previously within the
in the full ANOVA (i.e., involving conflict, condition and timepoint
asfactors;seeFigure3).Thisinteraction wasdueto a similarpattern
of activity to that observed in several other regions within the CCN
and EMO networks (i.e., Emotion . Neutral activity). Similarly, as
with these other regions, no condition*conflict interaction was
observed. Indeed, if anything, the high . low conflict effect was
weaker in the Emotion condition relative to Neutral (Figure 3),
consistent with the absence of a significant conflict effect in this
region when only the Emotion condition was examined. Addition-
ally, in this left amygdala region, no effects of trial type were
observed in the ANOVA contrasting AY and BX trials. Together,
these results confirm that the rostral ACC and amygdala did not
show any selective emotion conflict effects, and the small left
amygdala region that was identified showed a pattern of activation
that was very similar to other regions within cognitive control
network, i.e., sensitivity to both to the presence of task conflict and
to emotional processing, but no preferential response to emotional
conflict (e.g., these factors did not interact with one another).
With the present study, we examined neural activity associated
with emotional versus non-emotional conflict using a novel
paradigm: the emotional AX-CPT. This paradigm capitalized
on the use of controlled facial expressions as a response modality to
generate S-R incompatibility that was either emotional or non-
emotional in nature. The examination of brain activity associated
with the processing of these two forms of S-R incompatibility helps
clarify the extent to which emotional conflict relies on neural
circuitry common to that associated with more traditionally
studied forms of cognitive conflict. Specifically, the current
findings suggest that both emotional and non-emotional conflict
commonly engage a number of brain regions associated with
cognitive control, including the dorsal ACC and lateral PFC, as
well as certain areas implicated in both emotional processing and
cognitive control, such as bilateral anterior insula. Additionally,
most of these common regions demonstrated higher activity when
Figure 2. Cortical areas sensitive to the High . . Low conflict contrast. These areas fall within the CCN and REW masks and were identified as
showing significant (AY + BX) . (AX + BY) activation, collapsed across Emotion and Neutral conditions.
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processing emotional (versus non-emotional) conflict; in contrast,
we observed no conflict-sensitive regions where the non-emotional
task elicited greater activity than the emotional task.
Our findings are in line with several other studies examining the
neural basis of emotional versus non-emotional conflict. Processing
of both kinds of conflict may rely on cognitive control-related
brain areas [17,49]. In particular, mechanisms underlying both
emotional and non-emotional conflict detection have been
localized to the dorsal ACC [15,16,50]. However, the present
results are inconsistent with the older hypothesis that rostral and
dorsal subdivisions of the ACC are devoted to processing
emotional and cognitive conflict, respectively . In particular,
although we observed robust conflict-related activation in the
dorsal ACC in both Emotion and Neutral conditions, no such
patterns were observed in the rostral ACC, even when focusing
exclusively on Emotion conflict.
The absence of emotion-specific conflict regions in the ACC
during task processing may be surprising from the perspective of
classic theoretical distinctions, but is actually relatively consistent
with the prior literature. As discussed previously, original variants of
the emotion Stroop actually target emotional distraction or even
non-affective variables, and as such may not be appropriate for the
study of emotional conflict, as suggested by recent conceptual
analyses. More recentstudies that utilizeconflict-based variants
of the emotional Stroop and related tasks have been equivocal as to
whether rostral ACC is either engaged, or associated with the
detection (rather than resolution) of emotional conflict [15,16].
Additionally, in one recent study rostral ACC activity during the
emotion-conflict Stroop was dependent on the trait anxiety level of
participants . Thus, the current study adds to prior literature in
suggesting that caution is warranted regarding whether the rostral
ACC should in fact be associated with emotion conflict processing
per se. Instead, further investigation of this region is needed, that
focus on examining potential alternative accounts such as emotional
distraction, conflict resolution, and individual trait anxiety.
In contrast to the pattern in the rostral ACC, there were
significant effects of emotion on activation in a number of regions
associated with cognitive control functions, including the dorsal
ACC and lateral PFC. Interestingly, these effects were observed as
significant condition (Emotion . Neutral) and conflict (Conflict .
No Conflict) effects, without a significant condition 6 conflict
interaction. In other words, the emotion effects were additive to
conflict, rather than interactive, which suggests two independent
mechanisms. At first glance, this pattern seems somewhat counter-
intuitive, since the presence of affectively-valenced content did not
selectively modulate the magnitude of the conflict effect, but
instead increased activation equivalently on both high and low-
conflict trials. Nevertheless, the pattern may actually be fairly
consistent with interpretations regarding the nature of emotional
conflict and control.
Table 1. Activity in areas defined by task conflict (high.low conflict) within anatomically defined ROIs.
(mm3) ROIZBA Area
Time effect in
High vs. Low
in AY vs. BX
Time effect in
AY vs. BX Trial
0, 11, 48 7722 CCN3.63 32Dorsal ACC***
41, 28, 351512 CCN 2.889R DLPFC ***
244, 8, 33 5589CCN3.489L IFJ***
45, 5, 32 2673 CCN2.93 9 R IFJ***
249, 13, 32457 CCN3.20 47L IFG*
28, 0, 547155 CCN4.158 R superior
228, 21, 55 7938CCN3.838L superior
18, 260, 4323031CCN 4.837 R precuneus*
237, 252, 406534 CCN4.24 40L IPL**
10, 212, 43915 CCN3.88---- R thalamus*
29, 211, 61215 CCN2.89---- L thalamus*
231, 267, 2452052 CCN3.08----L cerebellum
32, 260, 2441215 CCN 3.07----R cerebellum
33, 262, 226 999CCN 4.27---- R cerebellum***
39, 20, 03996 EMO4.83 47/13 R anterior insula*
236, 17, 03051 EMO4.0247/13L anterior insula**
216, 5, 221161 EMO3.36----L putamen**
8, 217, 2101026 EMO3.12----R DA midbrain
26, 218, 2101242 EMO3.35----L DA midbrain
216, 21, 211324EMO2.84---- L amygdala**
1Significant effects of interest within these areas (condition*time interactions within high vs. low conflict contrast and AY vs. BX trials contrast; trial*time interactions
within AY vs. BX trials contrast) are marked by asterisks in their respective columns.
2Abbreviations: ROI=region of interest; CCN=cognitive control network; EMO=emotion/reward network; BA=Brodmann area; IFG=inferior frontal gyrus; IFJ=inferior
frontal junction; DLPFC=dorsolateral prefrontal cortex; ACC=anterior cingulate cortex; IPL=inferior parietal lobule; DA=dopaminergic.; FEF=frontal eye fields.
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Figure 3. Timecourses illustrating High . . Low Conflict and Emotion . . Neutral effects. Representative regions demonstrating both a high .
lowconflictandEmotion.Neutralpattern(duetoacondition*timeinteraction),but noconflict*condition interaction:(A)dorsalACC;(B)leftamygdala.
Figure 4. Timecourse illustrating BX . . AY trial-type effect. Representative region in right lateral PFC exhibiting BX . AY activity early, and
comparable levels of activity in both trial types later in the timecourse.
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In particular, a key feature of the Emotional AX-CPT is that
in the Emotion condition, there should be a relatively automatic,
but task-irrelevant, subjective emotional reaction to the affective
content present in the probe. This emotional reaction is task-
irrelevant because correct response selection requires consider-
ation only of the cognitive classification of the probe as having
positive or negative content (and in integrating this information
with cue classification). Indeed, the subjective emotional response
to the probe, which may automatically trigger a tendency to
activate the associated facial expression, can lead to an additional
source of response uncertainty. For example, if viewing a
negatively valenced probe stimulus triggers a tendency to make
a frown expression (or likewise, if viewing a positively valenced
probe stimulus triggers a tendency to smile), confusion can be
generated regarding whether this ‘‘expression tendency’’ is
appropriate for the current trial (i.e., correct on AX but incorrect
on BX trials). Under such circumstances, from a cognitive
control perspective, the optimal task strategy would be to
suppress any subjective emotional responses that might be
experienced in order to reduce response uncertainty. Because
such task-irrelevant emotional response tendencies can occur on
all trials in the Emotion condition, there would be generally
higher cognitive control demands in this condition relative to
In addition to the additive effects of emotion and conflict
observed in regions associated with cognitive control, this same
pattern was also present in the left amygdala, at least under an
adjusted statistical threshold. The amygdala has typically been
thought of as an emotion processing region whose activity, in
conflict, distraction, and regulation paradigms, will reflect the
emotional valence of stimuli, rather than tracking cognitive control
demands [16,51,52]. However, prior findings of amygdala activity
associated with increased cognitive control have also been
repeatedly observed, although they typically receive less attention
in the literature. For example, in one study increased amygdala
activation was associated with improved behavioral performance
during working memory, selectively under high-load conditions
. This finding, and others [54,55,56], supports alternative
theoretical views of amygdala function, in which this regions is
postulated to mediate general vigilance/goal-relevance-detection
processes that contribute to enhanced cognitive performance as
well as processing of emotional demands [57,58]. The pattern of
left amygdala activation in the present task – associated with both
emotion and conflict-processing – might be better characterized by
such an explanation, especially considering that emotional
information must be evaluated for valence, while at the same
time suppressing subjective emotional responses, in order to
optimally perform the task.
Beyond the main effects of condition and conflict, a number of
regions also exhibited distinct patterns of activity as a function of
the type of interference present. As in the original AX-CPT, the
emotional AX-CPT involves non-target trials eliciting conflict via
two different forms of interference: AY trials, where interference
is cue-based and relatively top-down in nature, and BX trials,
where interference is probe-based and relatively bottom-up in
nature. A number of frontal and parietal regions associated with
cognitive control demonstrated significant trial effects in the
present study, primarily because of BX . AY activity early in the
trial (with comparable activity levels late in the trial). Previous
studies of the AX-CPT have observed similar patterns of
activation within the lateral PFC and other regions, demonstrat-
ing the robustness of the effect [46,47]. The pattern of activity is
typically interpreted as reflecting the higher degree of interfer-
ence expectancy associated with B-cues (i.e., associated with non-
target responses) relative to A-cues (i.e., associated with target
responses), and thus increased demands for proactive cognitive
control . The current study extends this finding by
demonstrating that this interference expectancy effects can be
exhibited during emotional as well as non-emotional AX-CPT
conditions. As such, the current results support the general notion
that participants utilize the same types of proactive control
strategies even when experiencing high demands for such control
as a result of emotional conflict.
The emotional AX-CPT paradigm presented in the present
study, and the use of emotional facial expressions as a response
modality more generally, have the potential to provide a more
naturalistic technique from which to probe emotional conflict,
relative to the previous laboratory paradigms that have been used.
Facial expressions have direct, automatic associations with
different emotional experiences ; thus, they potentially provide
a performance measure that is a more sensitive index of both trial-
by-trial fluctuations and individual differences in emotional
processing. In the present study we were not able to obtain
behavioural performance measures due to technical difficulties,
but future studies capitalizing on this technique should explore this
possibility (e.g., via simultaneous EMG and fMRI recordings).
Additionally, using facial expressions as responses permits
elicitation of conflict via S-R interference, which is a robust form
of interference that has nevertheless been understudied (relative to
S-S interference) in the domain of emotion. The utilization of
facial expressions as a response modality provides a potential
means to probe emotional conflict via S-R interference in other
paradigms as well, such as the Stroop adaptations utilized by
Egner and colleagues [16,22]. For example, in Stroop conditions
that require participants to make facial expressions to semantically
associated words (e.g., ‘‘smile’’, ‘‘frown’’) while ignoring irrelevant
but superimposed affectively-valenced pictures, it would be
possible to manipulate congruency in an analogous manner to
that examined here.
One of the advantages of developing adaptations of the Stroop
and related paradigms (e.g., Flankers, Simon) that include facial
expressions as a response modality is that it would permit
exploration of experimental manipulations not easily implemented
in the AX-CPT. In particular, conflict-related shifts in control state
(e.g., conflict adaptation or resolution effects) have been profitably
examined through manipulation and examination of trial-by-trial
sequential effects , changes in relative trial frequencies ,
and other similar effects. As a means of eliciting emotional conflict
in a naturalistic, ecologically valid manner, the S-R incompatibil-
ity elicited through facial expression-based responding has the
potential to be exploited in a similar variety of experimental
manipulations, contributing to our knowledge of the behavioural
and neural mechanisms underlying emotional conflict processing.
It is our hope that this technique may provide one direction by
which investigations of emotional conflict may approach the rigor
and sophistication of similar research within the more traditional
realm of cognitive control.
EMG in the Emotion AX-CPT, from Chiew & Braver (2010), as a
(a) Error rates and (b) response onset times measured via
regions of interest (ROIs) used to mask the neuroimaging data.
Centres of mass for cognitive control network (CCN)
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(EMO) regions of interest (ROIs) used to mask the neuroimaging
Coordinates for hand-drawn emotion/reward-related
conflict) within anatomically defined ROIs with discernable errors
eliminated, and comparable areas with all trials included (from
Activity in areas defined by task conflict (high . low
Conceived and designed the experiments: KSC TSB. Performed the
experiments: KSC. Analyzed the data: KSC TSB. Wrote the paper: KSC
1. Eastwood JD, Smilek D, Merikle PM (2001) Differential attentional guidance by
unattended faces expressing positive and negative emotion. Percept Psychophys
2. Ohman A, Flykt A, Esteves F (2001) Emotion drives attention: detecting the
snake in the grass. J Exp Psychol Gen 130: 466–478.
3. Botvinick MM, Braver TS, Barch DM, Carter CS, Cohen JD (2001) Conflict
monitoring and cognitive control. Psychol Rev 108: 624–652.
4. Stroop JR (1935) Studies of interference in serial verbal reactions. J Exp Psychol
Gen 18: 643–662.
5. Kornblum S, Hasbroucq T, Osman A (1990) Dimensional overlap: cognitive
basis for stimulus-response compatibility–a model and taxonomy. Psychol Rev
6. Braver TS, Ruge H (2007) Neural mechanisms of cognitive control in cued task-
switching: rules, representations, and preparation. In: Bunge SA, Wallis JD, eds.
Neuroscience of rule-guided behavior. Oxford: Oxford University Press. pp
7. Cole MW, Schneider W (2007) The cognitive control network: Integrated
cortical regions with dissociable functions. Neuroimage 37: 343–360.
8. Owen AM, McMillan KM, Laird AR, Bullmore E (2005) N-back working
memory paradigm: a meta-analysis of normative functional neuroimaging
studies. Hum Brain Mapp 25: 46–59.
9. Wager TD, Smith EE (2003) Neuroimaging studies of working memory: a meta-
analysis. Cogn Affect Behav Neurosci 3: 255–274.
10. Botvinick MM, Cohen JD, Carter CS (2004) Conflict monitoring and anterior
cingulate cortex: an update. Trends Cogn Sci 8: 539–546.
11. Kerns JG, Cohen JD, MacDonald AW, 3rd, Cho RY, Stenger VA, et al. (2004)
Anterior cingulate conflict monitoring and adjustments in control. Science 303:
12. Carter CS, van Veen V (2007) Anterior cingulate cortex and conflict detection:
an update of theory and data. Cogn Affect Behav Neurosci 7: 367–379.
13. Carter CS, Braver TS, Barch DM, Botvinick MM, Noll D, et al. (1998) Anterior
cingulate cortex, error detection, and the online monitoring of performance.
Science 280: 747–749.
14. Buhle J, Wager T, Smith EE (2010) Using the Stroop task to study emotion
regulation. In: Hassin R, Ochsner K, Trope Y, eds. Self Control in Society,
Mind, and Brain. Oxford: Oxford University Press. pp 93–113.
15. Haas BW, Omura K, Constable RT, Canli T (2006) Interference produced by
emotional conflict associated with anterior cingulate activation. Cogn Affect
Behav Neurosci 6: 152–156.
16. Egner T, Etkin A, Gale S, Hirsch J (2008) Dissociable neural systems resolve
conflict from emotional versus nonemotional distracters. Cereb Cortex 18:
17. Ochsner KN, Hughes B, Robertson ER, Cooper JC, Gabrieli JD (2008) Neural
Systems Supporting the Control of Affective and Cognitive Conflicts. J Cogn
18. Lee TW, Dolan RJ, Critchley HD (2008) Controlling emotional expression:
behavioral and neural correlates of nonimitative emotional responses. Cereb
Cortex 18: 104–113.
19. Mathews A, MacLeod C (1985) Selective processing of threat cues in anxiety
states. Behav Res Ther 23: 563–569.
20. Larsen RJ, Mercer KA, Balota DA (2006) Lexical characteristics of words used
in emotional Stroop experiments. Emotion 6: 62–72.
21. Algom D, Chajut E, Lev S (2004) A rational look at the emotional stroop
phenomenon: a generic slowdown, not a stroop effect. J Exp Psychol Gen 133:
22. Etkin A, Egner T, Peraza DM, Kandel ER, Hirsch J (2006) Resolving emotional
conflict: a role for the rostral anterior cingulate cortex in modulating activity in
the amygdala. Neuron 51: 871–882.
23. Krug MK, Carter CS (2010) Adding fear to conflict: a general purpose cognitive
control network is modulated by trait anxiety. Cogn Affect Behav Neurosci 10:
24. Dimberg U, Thunberg M (1998) Rapid facial reactions to emotional facial
expressions. Scandinavian Journal of Psychology 39: 39–45.
25. Chiew KS, Braver TS (2010) Exploring emotional and cognitive conflict using
speeded voluntary facial expressions. Emotion 10: 842–854.
26. Cohen JD, Braver TS, O’Reilly RC (1996) A computational approach to
prefrontal cortex, cognitive control and schizophrenia: recent developments
and current challenges. Philos Trans R Soc Lond B Biol Sci 351: 1515–
27. Braver TS, Barch DM, Cohen JD (2002) The role of the prefrontal cortex in
normal and disordered cognitive control: a cognitive neuroscience perspective.
In: Stuss DT, Knight RT, eds. Principles of frontal lobe function. Oxford:
Oxford University Press. pp 428–447.
28. Braver TS, Paxton JL, Locke HS, Barch DM (2009) Flexible neural mechanisms
of cognitive control within human prefrontal cortex. Proc Natl Acad Sci U S A
29. Lang PJ, Bradley MM, Cuthbert BN (1999) International affective picture
system (IAPS): technical manual and affective ratings. Gainesville: University of
Florida, Center for Research in Psychophysiology.
30. Visscher KM, Miezin FM, Kelly JE, Buckner RL, Donaldson DI, et al. (2003)
Mixed blocked/event-related designs separate transient and sustained activity in
fMRI. Neuroimage 19: 1694–1708.
31. Talairaich J, Tournoux P (1988) Co-planar stereotaxic atlas of the human brain:
3-dimensional proportional system: an approach to cerebral imaging. New York:
32. Ahsan RL, Allom R, Gousias IS, Habib H, Turkheimer FE, et al. (2007)
Volumes, spatial extents and a probabilistic atlas of the human basal ganglia and
thalamus. Neuroimage 38: 261–270.
33. Jensen J, Smith AJ, Willeit M, Crawley AP, Mikulis DJ, et al. (2007) Separate
brain regions code for salience vs. valence during reward prediction in humans.
Hum Brain Mapp 28: 294–302.
34. Kable JW, Glimcher PW (2007) The neural correlates of subjective value during
intertemporal choice. Nat Neurosci 10: 1625–1633.
35. Knutson B, Adams CM, Fong GW, Hommer D (2001) Anticipation of
increasing monetary reward selectively recruits nucleus accumbens. J Neurosci
36. Knutson B, Fong GW, Bennett SM, Adams CM, Hommer D (2003) A region of
mesial prefrontal cortex tracks monetarily rewarding outcomes: characterization
with rapid event-related fMRI. Neuroimage 18: 263–272.
37. Kringelbach ML, Rolls ET (2004) The functional neuroanatomy of the human
orbitofrontal cortex: evidence from neuroimaging and neuropsychology. Prog
Neurobiol 72: 341–372.
38. Nitschke JB, Dixon GE, Sarinopoulos I, Short SJ, Cohen JD, et al. (2006)
Altering expectancy dampens neural response to aversive taste in primary taste
cortex. Nat Neurosci 9: 435–442.
39. O’Doherty J, Critchley H, Deichmann R, Dolan RJ (2003) Dissociating valence
of outcome from behavioral control in human orbital and ventral prefrontal
cortices. J Neurosci 23: 7931–7939.
40. O’Doherty J, Dayan P, Schultz J, Deichmann R, Friston K, et al. (2004)
Dissociable roles of ventral and dorsal striatum in instrumental conditioning.
Science 304: 452–454.
41. Whalen PJ, Bush G, McNally RJ, Wilhelm S, McInerney SC, et al. (1998) The
emotional counting Stroop paradigm: a functional magnetic resonance imaging
probe of the anterior cingulate affective division. Biol Psychiatry 44: 1219–1228.
42. McAvoy MP, Ollinger JM, Buckner RL (2001) Cluster size thresholds for
assessment of significant activation in fMRI. Neuroimage 13: S198.
43. Forman SD, Cohen JD, Fitzgerald M, Eddy WF, Mintun MA, et al. (1995)
Improved assessment of significant activation in functional magnetic resonance
imaging (fMRI): use of a cluster-size threshold. Magn Reson Med 33: 636–
44. Ward DB (2000) Simultaneous inference for MRI data. http://afni.nimh.nih.
45. Bush G, Luu P, Posner MI (2000) Cognitive and emotional influences in anterior
cingulate cortex. Trends Cogn Sci 4: 215–222.
46. MacDonald AW, 3rd, Carter CS (2003) Event-related FMRI study of context
processing in dorsolateral prefrontal cortex of patients with schizophrenia.
J Abnorm Psychol 112: 689–697.
47. Paxton JL, Barch DM, Racine CA, Braver TS (2008) Cognitive control, goal
maintenance, and prefrontal function in healthy aging. Cereb Cortex 18:
48. Sohn MH, Albert MV, Jung K, Carter CS, Anderson JR (2007) Anticipation of
conflict monitoring in the anterior cingulate cortex and the prefrontal cortex.
Proc Natl Acad Sci U S A 104: 10330–10334.
Neural Circuitry of Emotional & Cognitive Conflict
PLoS ONE | www.plosone.org10March 2011 | Volume 6 | Issue 3 | e17635
49. Compton RJ, Banich MT, Mohanty A, Milham MP, Herrington J, et al. (2003)
Paying attention to emotion: an fMRI investigation of cognitive and emotional
stroop tasks. Cogn Affect Behav Neurosci 3: 81–96.
50. Wittfoth M, Schroder C, Schardt DM, Dengler R, Heinze HJ, et al. On
emotional conflict: interference resolution of happy and angry prosody reveals
valence-specific effects. Cereb Cortex 20: 383–392.
51. Dolcos F, McCarthy G (2006) Brain systems mediating cognitive interference by
emotional distraction. J Neurosci 26: 2072–2079.
52. Ochsner KN, Bunge SA, Gross JJ, Gabrieli JD (2002) Rethinking feelings: an
FMRI study of the cognitive regulation of emotion. J Cogn Neurosci 14:
53. Schaefer A, Braver TS, Reynolds JR, Burgess GC, Yarkoni T, et al. (2006)
Individual differences in amygdala activity predict response speed during
working memory. J Neurosci 26: 10120–10128.
54. Holland PC, Gallagher M (1999) Amygdala circuitry in attentional and
representational processes. Trends Cogn Sci 3: 65–73.
55. Holland PC, Gallagher M (2006) Different roles for amygdala central nucleus
and substantia innominata in the surprise-induced enhancement of learning.
J Neurosci 26: 3791–3797.
56. Ousdal OT, Jensen J, Server A, Hariri AR, Nakstad PH, et al. (2008) The
human amygdala is involved in general behavioral relevance detection: evidence
from an event-related functional magnetic resonance imaging Go-NoGo task.
Neuroscience 156: 450–455.
57. Davis M, Whalen PJ (2001) The amygdala: vigilance and emotion. Mol
Psychiatry 6: 13–34.
58. Sander D, Grafman J, Zalla T (2003) The human amygdala: an evolved system
for relevance detection. Rev Neurosci 14: 303–316.
59. Cacioppo JT, Petty RE, Losch ME, Kim HS (1986) Electromyographic activity
over facial muscle regions can differentiate the valence and intensity of affective
reactions. J Pers Soc Psychol 50: 260–268.
60. Gratton G, Coles MG, Donchin E (1992) Optimizing the use of information:
strategic control of activation of responses. J Exp Psychol Gen 121: 480–506.
61. Carter CS, Macdonald AM, Botvinick M, Ross LL, Stenger VA, et al. (2000)
Parsing executive processes: strategic vs. evaluative functions of the anterior
cingulate cortex. Proc Natl Acad Sci U S A 97: 1944–1948.
Neural Circuitry of Emotional & Cognitive Conflict
PLoS ONE | www.plosone.org 11March 2011 | Volume 6 | Issue 3 | e17635