Available via license: CC BY-ND 4.0
Content may be subject to copyright.
Title:
Common and distinct BOLD correlates of Simon and flanker conflicts which can(not)
be reduced to time-on-task effects
Authors:
Jakub Wojciechowskia,b,*, Katarzyna Jurewicza,c,*, Patrycja Dzianoka, Ingrida Antonovaa,
Katarzyna Palucha, Tomasz Wolakb, Ewa Kublika,#
aNencki Institute of Experimental Biology, Polish Academy of Sciences, 3 Pasteur St, 02-093
Warsaw, Poland
bBioimaging Research Center, Institute of Physiology and Pathology of Hearing,
10 Mochanckiego St, 02-042, Warsaw, Poland
cDepartment of Physiology, Faculty of Medicine and Health Sciences, McGill University,
3655 Promenade Sir-William-Osler, H3G 1Y6, Montreal, Quebec, Canada
* equal contribution; # corresponding author
Abstract:
The ability to identify and resolve conflicts between standard, well trained behaviors, and
behaviors required by the current context is an essential feature of cognitive control. To date,
no consensus has been reached on the brain mechanisms involved in exerting such control:
while some studies identified diverse patterns of activity across different conflicts, other
studies reported common resources across conflict tasks or even across simple tasks devoid
of conflict component. The latter reports attributed the entire activity observed in the
presence of conflict to longer time spent on the task (i.e. to the so-called time-on-task
effects). Here we used an extended Multi-Source Interference Task (MSIT) which combines
Simon and flanker types of interference to determine shared and conflict-specific
mechanisms of conflict resolution in fMRI, and their separability from the time-on-task
effects. Increases of activity in the dorsal attention network and decreases of activity in the
default mode network were largely shared across the tasks and scaled in parallel with
increasing reaction times. Importantly, activity in the sensory and sensorimotor cortices, as
well as in the posterior medial frontal cortex (pMFC)–a key region implicated in conflict
processing–could not be exhaustively explained by the time-on-task effects.
1
.CC-BY-ND 4.0 International licensemade available under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is
The copyright holder for this preprintthis version posted May 22, 2023. ; https://doi.org/10.1101/2023.05.19.541457doi: bioRxiv preprint
Highlights:
● Flanker and Simon conflicts activate and deactivate largely the same brain regions.
● Common activity in DAN and DMN is mostly explained by time-on-task effects.
● Conflict-specific activity emerges mostly in the sensory cortices.
● pMFC (incl. dACC and pre-SMA) shows both time-on-task and conflict-related
activity.
Keywords:
cognitive control, Eriksen flanker task, Simon task, time-on-task, response conflict,
fMRI
2
.CC-BY-ND 4.0 International licensemade available under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is
The copyright holder for this preprintthis version posted May 22, 2023. ; https://doi.org/10.1101/2023.05.19.541457doi: bioRxiv preprint
1. Introduction
A large part of our everyday behavior is guided by well-learned reactions and pre-existing
associations between stimuli and responses. However, a truly effective behavior depends on
the ability to identify and resolve conflicts between our standard reactions and those required
by the current context. The mechanisms of cognitive control that enable such flexible
behavior have frequently been studied with response conflict tasks, i.e. tasks which use
various input stimuli to simultaneously prompt different, competing responses. A popular
example of a paradigm which elicits response conflict is the so-called Simon task (Simon
1967), in which conflict occurs due to the spatial arrangement of stimuli and responses. In
the most classic form of the task, subjects respond to a feature of a stimulus (e.g., color)
presented to either the left or right of a central fixation, by pushing response buttons with
their left (e.g., for blue) or right (e.g., for red) hand. Performance is impaired when the side of
presentation does not match the side of response, (e.g., a red stimulus presented on the left
side). Another frequently used paradigm is the flanker task (Eriksen and Eriksen 1974), in
which conflict occurs due to a spatial cluttering of symbols. In its classic form, subjects have
to respond to the central letter, number or arrow, while ignoring the surrounding distractors.
Most pronounced interference is observed when distractors are associated with alternative
responses. These two tasks represent the examples of the stimulus-response (Simon) and
stimulus-stimulus (flanker) interference. While both eventually evoke the response conflict,
i.e., increased competition between behavioral alternatives, the origin of interference is
different in the two types of task. It stems from a direct mismatch between spatial properties
of the stimuli and response dimension in Simon; and begins already at the level of stimulus
interpretation in the flanker task (Egner et al. 2007).
Over the past few decades, research on cognitive control has tried to determine if different
conflicts are resolved through separate mechanisms that operate in a conflict-specific
manner or if there is a central resource for dealing with all kinds of conflicts. However,
previous imaging studies which compared conflict-induced activity across different tasks
brought rather divergent results. Several studies reported distinct regions involved in
resolving different forms of cognitive interference (Egner et al. 2007; van Veen and Carter
2005). Others found substantial differences and some overlap of activation between conflicts
(Fan et al. 2003; Frühholz et al. 2011; Liu et al. 2004; Wager et al. 2005; Wittfoth et al.
2006). There were also studies which found mostly overlapping mechanisms underlying
resolution of different conflicts (Peterson et al. 2002). Multiple factors could possibly account
for such heterogeneity of results. Compared tasks were often based on different types of
3
.CC-BY-ND 4.0 International licensemade available under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is
The copyright holder for this preprintthis version posted May 22, 2023. ; https://doi.org/10.1101/2023.05.19.541457doi: bioRxiv preprint
stimuli, making it more difficult to disentangle conflict-related effects from task-specific
processing of information. Also, the early studies were often severely underpowered which
might have led to either a decreased sensitivity for task-related activations or an increased
number of false positive findings (as demonstrated in Button et al. 2013; Ioannidis 2005;
Eklund et al. 2018; Poldrack et al. 2017; Szucs and Ioannidis 2020). The meta-analytic
approach indicated that some limited regions, including the posterior medial frontal cortex
(pMFC), activate consistently across different conflict tasks, while some consistent
between-task differences were also identified in fronto-parietal regions (Cieslik et al. 2015; Li
et al. 2017; Nee et al. 2007). Recently, however, the conflict-processing role of regions
commonly engaged by the conflict tasks has been questioned from another perspective. A
new important stand on the correlates of conflict processing, as observed in fMRI research,
came from the series of studies which investigated reaction time (RT)-related variability of
the blood-oxygen-level-dependent (BOLD) signal (Carp et al. 2010; Grinband et al. 2008;
Grinband et al. 2011; Weissman and Carp 2013; Yarkoni et al. 2009). It has been shown that
activity in a large set of regions, including pMFC, changes as a function of RT across various
experimental designs (Grinband et al. 2008; Yarkoni et al. 2009). In particular, even the most
simple task which does not require any selection (i.e. a simple button press in response to
stimulus appearance) demonstrates close correlation between the RT and the strength of
the blood-oxygenation-level-dependent (BOLD) response (Weissman and Carp 2013). In
response-conflict tasks, conflict conditions are always characterized by longer RT in
comparison to control, which does not entail interference. Thus, RT constitutes a
confounding factor, which may account for a large part of brain activity observed in these
tasks. While it is true that RTs are longer because of the occurrence of conflict, brain activity
associated with longer RTs may not necessarily reflect conflict-related processing. For
example, a slower response might be accompanied by a longer exposure to visual stimuli,
prolonged need for attention maintenance, increased need for task rehearsal etc., all of
which may contribute to the changes in the BOLD signal in different parts of the brain. The
question posed by the recent neuroimaging research was whether conflict-related activity
can be distinguished from the effects of the time-on-task as such.
To address this question Carp et al. 2010 used a Multi-Source Interference Task (MSIT)
which includes both Simon- and flanker-type interference, combined in a single condition to
maximize the strength of conflict and recruitment of the related brain resources (Bush et al.
2003). The study showed that RT-related variability of the signal in no-conflict trials of this
task sufficed to predict the entire activity observed in multi-source interference trials when
4
.CC-BY-ND 4.0 International licensemade available under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is
The copyright holder for this preprintthis version posted May 22, 2023. ; https://doi.org/10.1101/2023.05.19.541457doi: bioRxiv preprint
BOLD responses were appropriately scaled on the basis of RT. Another study showed that
even a paradigm with only one possible response (i.e. a simple button press at stimulus
appearance that does not require any perceptual or motor selection) can be successfully
used to model multi-source conflict activity in MSIT with the same scaling method
(Weissman and Carp 2013). Similarly, there was no difference in BOLD responses to MSIT
conflict and no-conflict conditions when trials from each category were subsampled to have
similar RT (Carp et al. 2010). The same results were obtained also with the Stroop task
(Grinband et al. 2011). As such, these studies strongly suggest that the resources recruited
by conflict tasks are shared across cognitive operations which influence RT, limiting their
interpretation in terms of conflict processing. The so-called time-on-task effects appeared
more closely tied to task maintenance or attention that may play a role in even the simplest
tasks.
The exhaustive role of RT-related effects in interference tasks is hard to reconcile with the
large number of reports of pMFC engagement in various aspects of conflict processing
(Ullsperger et al. 2014). It also remains at odds with findings based on electrophysiological
techniques, which show conflict-related encoding in the same regions that seem to reflect
pure time-on-task effects in fMRI data (Sheth et al. 2012; Ebitz et al. 2020). However, the
all-encompassing time-on-task effects are also perplexing on the grounds of fMRI research.
The time-on-task effects occur independently from the presence of conflict (i.e., across all
task conditions). Therefore, while regions commonly involved in response conflict may or
may not reflect the time-on-task effects, conflict-specific activity would need to diverge from
the RT-based predictions. If different types of conflicts have different sources of interference,
RT-related variability should not explain the entire signal that is related to processing of
conflict.
In our study, we wanted to reiterate the question about common and conflict-specific
mechanisms of conflict processing. We used MSIT, the paradigm previously adopted for
demonstrating the exhaustive role of time-on-task effects in BOLD responses within conflict
tasks, but in the extended version comprising also single conflict conditions. The use of a
single type of stimuli and the same instruction for probing Simon and flanker conflicts, in a
task performed by the same participants in a single session, minimized the role of
task-specific attributes. Importantly, common and conflict-specific responses for the
conditions of extended MSIT have recently been identified with magnetoencephalography
(MEG) (Wiesman and Wilson 2020; Wiesman et al. 2020), giving a hint that there are at
least partially separate mechanisms underlying resolution of these two conflicts. Thus, we
5
.CC-BY-ND 4.0 International licensemade available under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is
The copyright holder for this preprintthis version posted May 22, 2023. ; https://doi.org/10.1101/2023.05.19.541457doi: bioRxiv preprint
used a task that is ideally suited for contrasting different types of conflicts, as well as for
determining the contribution of time-on-task effects to clarify some of the longstanding
questions on the common and distinct mechanisms involved in the response-conflict
resolution.
2. Methods
2.1 Participants
Forty-one (22 female, 19 male) healthy young adults (mean age = 24.6 years, SD = 4.2)
participated in the study. One subject was excluded due to incomplete data. All subjects
were right-handed and had normal or corrected-to-normal vision. Subjects were recruited at
the University of Warsaw and via social media. Before taking part in the study, all subjects
gave their written informed consent for participation and were screened for any
contraindications to magnetic resonance imaging. The study was approved by the ethical
committee of the University of Nicolaus Copernicus in Toruń, Poland, and was conducted in
accordance with the Declaration of Helsinki.
2.2 Experimental procedure
The task used in the study was an extended version of the Multi-Source Interference Task
(MSIT; Bush et al. 2003). Originally, MSIT consisted of two conditions: no-conflict and
multi-source conflict trials. The latter combined effects of stimulus-response incongruence
(Simon effect) and stimulus-stimulus incongruence (flanker effect). In the extended version
which we use here, these two types of cognitive interference were also included separately,
with only one source of interference (Simon or flanker) present at a given trial.
The three digits (from the set 0, 1, 2 and 3) were presented in the center of the screen: two
of them were the same and one (digit 1, 2 or 3) was always different (Figure 1A). The stimuli
were presented on the dark screen (RGB: 48, 48, 48), the color of the stimulus was light
gray (RGB: 226, 226, 226). Subjects had to identify the unique digit (target) and indicate it by
responding with the appropriate finger of the right hand, i.e., with index finger for ”1”, middle
finger for ”2”, or ring finger for ”3”. In the no-conflict trials (00) the position of the unique digit
was congruent with the correct button choice and the target was flanked by zeros, which
were not mapped to any response (e.g., ”003”). In Simon-only condition (S0) the target was
flanked by zeros, but the position of the unique digit was incongruent with the spatial position
6
.CC-BY-ND 4.0 International licensemade available under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is
The copyright holder for this preprintthis version posted May 22, 2023. ; https://doi.org/10.1101/2023.05.19.541457doi: bioRxiv preprint
of the correct response (e.g., ”030”). Conversely, in flanker-only condition (F0), the unique
digit and correct response were spatially congruent, but the flankers were always different
from zero, i.e., entailed their own possible button-choices (e.g., ”223”). In the multi-source
interference condition (FS) both the position of the unique digit and response-mapping of the
flankers were incongruent with the correct response associated with the target (e.g., ”232”).
Trials were presented for 900 ms in event-related design with additional grouping into
mini-blocks of three or four consecutive trials (equal number) drawn from the same
condition, to further increase the power of contrasts between conditions. Each trial was
followed by 800-1300 ms inter-trial interval within and 2500-4400 ms between mini-blocks.
Three 10-second pauses presenting a fixation cross were inserted after each third of a run.
Order of stimuli was pseudo-random, such that there were no more than two consecutive
mini-blocks of the same condition type. On average, a single fMRI run of the task consisted
of approx. 100 trials (~25 per condition). Subjects underwent four acquisition runs, each
lasting 5:45 minutes.
The experimental protocol (stimulus delivery and response registration) was controlled by
the Presentation software (Neurobehavioral Systems Inc.). Stimuli were presented on
MR-compatible full HD display screen (NordicNeurolab Inc.) set behind the MR scanner. A
mirror system mounted on a head coil was used to project the visual stimuli to the subject.
Behavioral responses were collected using MR-compatible response pads (SMIT-Lab Co.).
Before entering MR and beginning the experiment, subjects underwent brief training (lasting
on average 2:20 minutes) of the task to familiarize them with the procedure.
Figure 1: MSIT conditions. A) Example stimuli in no-conflict (00), Simon (S0), flanker (F0) and
multi-source (FS) conflict conditions. In each case of the example, “3” is the correct response and
requires a button press with a third (ring) finger. B) Reaction time (for correct responses) and C) error
rate in the four MSIT conditions. White rectangles represent the mean, boxes span the 25th to 75th
percentile of the distribution with the median marked as a horizontal bar, crosses represent the
outliers.
7
.CC-BY-ND 4.0 International licensemade available under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is
The copyright holder for this preprintthis version posted May 22, 2023. ; https://doi.org/10.1101/2023.05.19.541457doi: bioRxiv preprint
2.3 Behavioral data analysis
Incorrect trials (wrong response, RT < 200 ms or RT > 1700 ms) were discarded from
reaction time and fMRI analysis. Mean RT for correct trials and error rate for 00, S0, F0 and
FS trials for each subject were entered in 1x4 repeated-measures model ANOVA. To check if
Simon and flanker have additive effects on behavior or interact with each other when present
simultaneously in multi-source conflict condition, we tested whether RT and accuracy
changes are equal in FS versus summed F0 and S0 conditions (i.e., whether FS - 00 = (F0 -
00) + (S0 - 00)). Pairwise comparisons applied Tukey’s HSD (honestly significant difference)
test and held the familywise error rate at 0.05. All statistical analyses were performed in the
MATLAB environment.
2.4 Image acquisition
Scanning was performed on a 3T Siemens Prisma MRI scanner (Siemens Medical Solutions
Inc.) using a 64-channel RF head coil. Structural MRI data were acquired with a T1-weighted
3D MP-Rage sequence (TR = 2.4 s TI = 1000 ms, TE = 2.74 ms, 8° flip angle, matrix
320x320, FOV = 256x256 mm, isometric voxel size 0.8 mm, 240 slices), lasting 6:52
minutes. Functional data were acquired using Multi-band (Siemens naming: Simultaneous
Multi-Slice) EPI sequence (TR = 750 ms, TE = 31ms, FA = 52°, FOV = 220x220 mm, matrix
= 110x110, 72 axial slices, isometric voxel size 2 mm, no interslice gap, Slice Accel. Factor =
8, In-Plane Accel. Factor = 1, IPAT = 8).
2.5 Image preprocessing
Preprocessing of the MRI data was performed using SPM12 (SPM; WellcomeTrust Centre
for Neuroimaging, London, UK), FSL (Smith et al. 2004) and in-house MATLAB code.
Multi-band EPI sequence provides an overall signal-to-noise increase of 40%, but introduces
image distortions due to the accumulation of phase errors in the k-space. To account for this
effect, all functional scans had an even number of runs with the opposite phase coding
direction (Anterior-Posterior and Posterior-Anterior). Spatial distortions were corrected with
the FSL topup() tool based on the average representation of the images in both directions.
Functional data was spatially realigned to the mean image, to which the structural image
was then co-registered. Segmentation and normalization to the common MNI space was
performed based on high-resolution structural images with resampling to 1 mm isometric
voxels. The obtained transformation parameters were applied to the functional volumes with
resampling to 2 mm isometric voxels. The normalized functional images were spatially
8
.CC-BY-ND 4.0 International licensemade available under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is
The copyright holder for this preprintthis version posted May 22, 2023. ; https://doi.org/10.1101/2023.05.19.541457doi: bioRxiv preprint
smoothed with a Gaussian kernel of full-width half-maximum (FWHM) of 5 mm, and
additionally high-pass filtered above 0.008 Hz (time constant of 128 sec).
2.6 Image analysis
2.6.1 Model estimation
Functional images were analyzed using the general linear model in SPM12. BOLD
responses evoked by each of the four types of trial included in MSIT (00, S0, F0, FS) were
modeled as separate regressors along with their parametric modulators, which reflect a
trial-by-trial RT-BOLD relationship within the given condition. Incorrect trials were modeled
as a separate regressor and discarded from subsequent analyses. The model also included
six motion-related nuisance regressors and one regressor for pauses. Whole-brain
group-level analyses used a voxel-level threshold of p < 0.001 for each side of comparison
(as per standard SPM analysis) and a cluster level threshold of pFWE = 0.05, unless stated
otherwise.
2.6.2 Assessment of activity induced by conflicts
To demonstrate which regions were engaged by different types of interference, we first
compared each of the conflict conditions to a no-conflict baseline (i.e., two sided contrasts
were defined: [S0 vs 00], [F0 vs 00] and [FS vs 00]). We treated the three conflict conditions
separately (and not within a full factorial design) for several reasons: (1) one of the main
objectives of this study was to test how RT-related variability explains conflict signals; RT is
monotonically increasing across task conditions, enabling RT-based predictions for each
type of conflict, (2) another objective of the study was to test conflict-specific effects
distinguishing Simon and flanker conflict processing; any interaction between Simon and
flanker effects would impact the estimates (main effects) of Simon- or flanker-related activity,
(3) previous imaging studies investigating the time-on-task effects in MSIT used a version of
task with multi-source conflict only; testing FS vs 00 contrast enables comparisons between
current and previous reports. However, for completeness, the interaction effects between the
conflicts were also tested in a separate contrast, which compared the summed changes of
the BOLD signal in Simon and flanker conditions with the changes observed in multi-source
conflict (i.e., testing whether FS - 00 = (F0 - 00) + (S0 - 00)), which can be transformed to
[(FS + 00) vs (S0 + F0)] contrast).
9
.CC-BY-ND 4.0 International licensemade available under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is
The copyright holder for this preprintthis version posted May 22, 2023. ; https://doi.org/10.1101/2023.05.19.541457doi: bioRxiv preprint
Additional analyses were performed to specify which of the observed effects were common
across Simon and flanker conditions, and which activations resulted from only one of the
conflicts. First, we performed conjunction analysis of Simon and flanker activations, [(S0 vs
00) ∩ (F0 vs 00)], to specify which regions engage both in Simon and in flanker conflicts.
Conjunction was based on (S0 vs 00) and (F0 vs 00) maps, which were thresholded at lower
than regular voxel-level threshold of p < 0.01 to include more regions engaged by the task,
even if they differed in activation strength. The resulting conjunction map was thresholded
again at a standard voxel-level threshold of p < 0.001. Second, we directly compared Simon
and flanker conditions [F0 vs S0] to reveal regions of maximum difference in the activity
evoked by these two conflicts. Third, to expose qualitative rather than quantitative
differences, we masked out the commonly modulated regions (i.e., conjunction result, [(S0
vs 00) ∩ (F0 vs 00)]) from the map of the two-sided contrast of Simon and flanker conditions
([F0 vs S0]). This operation allowed us to obtain regions specifically more active in flanker
than in Simon condition, expressed in positive contrast values, and specifically more active
in Simon than in flanker condition, expressed in negative contrast values.
2.6.3 Assessment of activity exceeding time-on-task effects
To test if activity observed in MSIT conditions is associated with the time-on-task or
conflict-related effects, we performed RT-regression analyses proposed in previous works
(Carp et al. 2010; Weissman and Carp 2013). First, we estimated the time-on-task effects in
Simon, flanker, and multi-source conflicts on the basis of their mean RT and GLM model
parameters obtained for no-conflict condition. Namely, we used the beta estimate of the
mean activity in no-conflict condition (00) and the beta estimate of the linear change of signal
with RT in no-conflict condition (parametric modulator of 00 trials, 00RT) to predict the level of
activity based on mean RT change in a conflict condition (R
T
Conflict) with respect to mean RT
in the no-conflict condition, R
T
00). Thus, for each voxel, activity was estimated according to
the following formula (separately for each conflict condition):
rtConflict = 00 + 00RT × (R
T
Conflict – R
T
00)
Next, for each of the conflict conditions we compared their activity maps and the
corresponding RT-predicted counterparts (i.e., two-sided contrasts were performed: [S0 vs
rtS0], [F0 vs rtF0] and [FS vs rtFS]). These contrasts showed regions of activity unexplained
by the time-on-task effects.
In parallel to analyses provided for entire Simon- and flanker-related activity, we performed
conjunction analysis of activity exceeding the time-on-task effects in Simon and flanker
10
.CC-BY-ND 4.0 International licensemade available under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is
The copyright holder for this preprintthis version posted May 22, 2023. ; https://doi.org/10.1101/2023.05.19.541457doi: bioRxiv preprint
conditions, [(S0 vs rtS0) ∩ (F0 vs rtF0)]. Again, for this conjunction analysis, we lowered the
significance threshold of the constituent maps to uncorrected voxel-level p = 0.01 to
disregard regional differences resulting merely from the strength of the effect. We also
directly compared the strength of Simon- and flanker-related activity unexplained by
time-on-task, [(F0 vs rtF0) vs (S0 vs rtS0)]. Lastly, to highlight spatially distinct regions
resulting from RT-regression in Simon and flanker conditions, we masked the difference of
activity exceeding time-on-task in the two conflict conditions [(F0 vs rtF0) vs (S0 vs rtS0)], by
their conjunction [(S0 vs rtS0) ∩ (F0 vs rtF0)].
2.6.4 ROI analysis
Lastly, we performed additional analyses in the pMFC, to verify the contribution of
time-on-task effects in this region, most often implied in conflict processing and selected for
similar analyses in previous studies (Carp et al. 2010; Weissman and Carp 2013). Spherical
regions of interest (ROI) with a 8 mm radius were centered at the peak of activity identified in
pMFC in the conflict vs no-conflict conditions (S0 vs 00, F0 vs 00 and FS vs 00). For most
direct comparison with previous studies, we also analyzed the observed and RT-predicted
signals evoked by multi-source conflict condition in ROIs centered at the peaks identified by
Carp et al. 2010 and Weissman and Carp 2013. Thus, all ROIs were selected orthogonally
to the time-on-task effects in question. To outline the dependencies between the observed
effect sizes and the study sample, we sub-sampled individual beta weights for the observed
and RT-predicted signal (N = 24, 1000 randomly drawn samples). This simulation illustrated
the chances of observing a “significant” result (p < 0.05) for the smaller study sample (as
used in the previous studies).
3. Results
3.1 Behavioral indices of conflict effects in MSIT
The analysis of RTs from correct trials (Figure 1B) showed that there were significant
differences between the task conditions (F(3,117) = 250.43, p < 0.001). Relative to
no-conflict trials (M = 625.13 ms, SD = 78.17), participants were significantly slower in the
Simon (M = 675.52 ms, SD = 81.16, p < 0.001), flanker (M = 774.17 ms, SD = 92.17, p <
0.001), and multi-source (M = 847.96 ms, SD = 119.64, p < 0.001) conflict conditions.
11
.CC-BY-ND 4.0 International licensemade available under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is
The copyright holder for this preprintthis version posted May 22, 2023. ; https://doi.org/10.1101/2023.05.19.541457doi: bioRxiv preprint
Furthermore, participants were significantly slower in flanker than in Simon condition (p <
0.001). The multi-source conflict also resulted in slower responses than in both Simon (p <
0.001) and flanker (p < 0.001) conditions.
Similarly, the analysis of error rate (Figure 1C) revealed significant differences between task
conditions (F(3,117) = 51.60, p < 0.001). Relative to no-conflict trials (M = 3.15%, SD =
4.21), participants had a significantly higher error rate in the Simon (M = 5.00%, SD = 4.83, p
= 0.003), flanker (M = 5.91%, SD = 5.00, p < 0.001), and multi-source (M = 12.22%, SD =
8.92, p < 0.001) conflict conditions. However, accuracy was not significantly different
between flanker and Simon trials (p = 0.276), while the multi-source condition resulted in
higher error rate than in either of the single conflicts (p < 0.001).
Previous studies suggested that Simon and flanker conflicts may interact with each other
when simultaneously present in multi-source condition (Wiesman and Wilson 2020). To
check if Simon and flanker have additive or interactive effects on behavioral performance,
we tested whether RT and error rate changes are equal in multi-source and the sum of
Simon and flanker conditions (i.e., whether FS - 00 = (F0 - 00) + (S0 - 00)). Indeed, RTs for
multi-source conflict were significantly longer than expected from the additive model (M diff =
23.39 ms, SD = 51.87, t(39) = 2.85, p = 0.007). Multi-source conflict trials also resulted in
significantly more errors (M diff = 4.47%, SD = 6.14, t(39) = 4.60, p < 0.001) than expected
from the sum of Simon and flanker-induced errors.
3.2 Brain activity in Simon, flanker and multi-source conflicts
In the first step, we identified brain regions involved in solving conflicts, separately for:
Simon, flanker and multi-source conditions (Figure 2, Suppl. Tables S1-S3). When
contrasted with no-conflict condition, each type of the conflict included in MSIT evoked
increased activity in the posterior medial frontal cortex (pMFC). Increased activity was also
observed in dorsolateral prefrontal cortex (dlPFC) and the core regions of dorsal attention
network (DAN): intraparietal sulci (IPS), frontal eye fields (FEF), and ventral premotor
regions (vPM). Activity in flanker and multi-source conditions showed broader spatial extent
and comprised clusters in higher-order visual cortex (lateral occipital complex, LOC), anterior
insulas (aIns), and subcortical regions: thalamus and caudate nuclei. The observed pattern
of activation was bilateral, however, activity in DAN was stronger in the left hemisphere. A
number of regions also deactivated bilaterally during interference trials. When compared to
no-conflict condition, each type of the conflict showed decreased activity in regions of the
default mode network (DMN): ventromedial (vmPFC), ventrolateral (vlPFC), and dorsomedial
12
.CC-BY-ND 4.0 International licensemade available under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is
The copyright holder for this preprintthis version posted May 22, 2023. ; https://doi.org/10.1101/2023.05.19.541457doi: bioRxiv preprint
prefrontal cortex (dmPFC); posterior sector of cingulate cortex with precuneus (Prec), as well
as in angular gyri (Ang) and anterior temporal regions (aTmp). Again, a decrease of activity
in flanker and multi-source conditions showed broader spatial extent and comprised clusters
in ventral occipitotemporal cortex (lingual gyri and hippocampal regions), cuneus, and
sensorimotor cortex (SM, central and precentral sulci and paracentral lobule).
Figure 2. Brain regions involved in the three types of conflicts induced by MSIT.
Contrasts between: A) Simon (S0) and no-conflict (00); B) flanker (F0) and no-conflict (00);
C) multi-source (FS) and no-conflict (00) conditions. Contrast maps are thresholded at
voxel-level p<0.001 and FWE-corrected (p<0.05) for cluster size. Labels written in white
indicate first occurrence (from subplot A to C), whereas gray indicates that the label was
already present in a previous subplot - such a presentation highlights the fact that no new
brain regions were involved in FS conditions, despite the overall stronger/broader effects.
For abbreviations, see the list at the end of the manuscript.
For completeness, we also checked if some of the hemodynamic responses reflected the
super-additive effects between Simon and flanker conflicts observed in behavioral
performance. To this aim we performed a contrast [(FS + 00) vs (S0 + F0)] which effectively
tests if the difference from no-conflict trials is the same for multi-source conflict as for the
sum of Simon and flanker conditions (i.e., is equivalent to testing if FS - 00 = (F0 - 00) + (S0
- 00)). There were no significant clusters of super- or sub-additive activity in multi-source
condition at the adopted significance level (i.e., cluster corrected at pFWE = 0.05).
3.3 Common and distinct regions involved by Simon and flanker conflicts
Conflict conditions included in the task showed substantial similarity of activation. To expose
regions which showed a response both to Simon and flanker conflicts we performed a
13
.CC-BY-ND 4.0 International licensemade available under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is
The copyright holder for this preprintthis version posted May 22, 2023. ; https://doi.org/10.1101/2023.05.19.541457doi: bioRxiv preprint
conjunction analysis of Simon and flanker-related differences from no-conflict conditions
(Figure 3A, Suppl. Table S4). Conjunction was based on uncorrected maps, which were cut
off at a lower voxel-level threshold of p < 0.01 (see Methods). The conjunction analysis
confirmed that regions of common activation included pMFC, bilateral dlPFC and DAN
nodes (IPS, FEF, vPM). Deactivation areas were also shown as largely common across
conflict conditions, comprising bilateral regions of the DMN (vlPFC, vmPFC, dmPFC, Ang
and aTemp), as well as the posterior part of the cingulate cortex and precuneus (Prec).
Figure 3. Common and distinct brain regions involved in resolving Simon and flanker
conflicts. A) Conjunction of flanker (F0) and Simon (S0) effects. B) Difference between
flanker- and Simon-related effects. C) Difference between flanker- and Simon-related effects
masked by their conjunction map. A conjunction map was created with the voxel-level
threshold p<0.01 used to encompass larger areas of common activity. Contrast maps in
subplots B and C are thresholded at voxel-level p<0.001 and FWE-corrected (p<0.05) for
cluster size.
To identify the regions which show significantly different activity in Simon and flanker
conflicts, we directly contrasted these two conditions. This comparison revealed that most of
the regions activated by both Simon and flanker processing were also significantly more
active in flanker than Simon conflicts (Figure 3B, Table S5).
Regions identified by conjunction analysis were subsequently excluded from the F0 vs S0
contrast map to expose activity, which may be deemed specific for particular conflict (Figure
3C, Suppl. Table S6). The procedure indicated two classes of residual, conflict-specific
activations: clusters directly adjacent to, or spatially separated from, the regions identified in
conjunction analysis. The former confirmed that flanker conflict recruited broader areas of
pMFC and DAN and deactivated broader areas of DMN. The latter proved pronounced
14
.CC-BY-ND 4.0 International licensemade available under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is
The copyright holder for this preprintthis version posted May 22, 2023. ; https://doi.org/10.1101/2023.05.19.541457doi: bioRxiv preprint
differences between Simon- and flanker-related activity in the visual areas. Higher-order
visual cortex (LOC) demonstrated significantly higher activity in the flanker compared to
Simon condition, while other areas, such as parts of lingual gyrus and cuneus, showed
significantly lower activity in flanker condition. The applied procedures also exposed
significantly lower flanker- than Simon-related activity in the angular gyri and the bilateral
supramarginal gyri (SMG). The latter effect was not earlier captured by contrasts with
no-conflict condition.
3.4 Activity exceeding the time-on-task effects in Simon, flanker and
multi-source conflicts
In line with the hypothesis that activations observed during conflict resolution reflects
time-on-task effects, activity observed in MSIT showed progression of intensity and extent
coinciding with increasing reaction time (i.e., Simon < flanker < multi-source conflict). To
de-confound activity related to conflict resolution from the effects of prolonged time spent on
task, we modeled the activity in Simon, flanker and multi-source conditions, as expected
solely on the basis of RT (see Methods 2.6.3) and compared them to observed activity.
Figure 4. Regions with activations/deactivations exceeding the time-on-task effects in
the three types of conflicts induced by MSIT. A) Contrast between the observed (S0) and
RT-predicted (rtS0) activity in Simon conflict; B) the observed (F0) and RT-predicted (rtF0)
activity in flanker conflict; C) the observed (FS) and RT-predicted (rtFS) activity in
multi-source conflict. Contrast maps are thresholded at voxel-level p<0.001 and
FWE-corrected (p<0.05) for cluster size.
Whole brain maps (Figure 4, Suppl. Tables S7-9) revealed that in the Simon conflict, activity
exceeding time-on-task effects was identified in left IPS. In flanker and multi-source conflicts
higher-than-predicted activity included the pMFC, and additionally the right occipital pole
15
.CC-BY-ND 4.0 International licensemade available under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is
The copyright holder for this preprintthis version posted May 22, 2023. ; https://doi.org/10.1101/2023.05.19.541457doi: bioRxiv preprint
(LOC) in flanker conflict. In addition to previously identified regions, comparison between the
observed and RT-predicted signal in flanker and multi-source conditions showed
higher-than-predicted activity in subcortical regions (caudate and putamen) and white matter.
Flanker and multi-source conditions also showed lower-than-predicted activity in visual
cortex (calcarine sulcus, cuneus, fusiform gyri), in regions which were not previously found
to engage in the task.
3.5 Common and distinct regions exceeding the time-on-task effects in Simon
and flanker conflicts
Finally, we repeated the conjunction analysis and direct comparison between Simon and
flanker conflicts, as well as the masking procedure for those signals that exceed the
time-on-task effects. The conjunction analysis confirmed that there were clusters of common
above-time-on-task activity in the pMFC (encompassing dorsal anterior cingulate cortex
[dACC] and pre-supplementary motor area [pre-SMA]) in both Simon and flanker conflicts. A
similar pattern was also present in right aIns and right IFG (Figure 5A, Suppl. Table S10). It
also confirmed common higher-than-predicted activity in right caudate and putamen, as well
as in white matter across conflict conditions.
The contrast analysis showed that activity in SMG, right sensorimotor cortex, occipital poles,
calcarine sulcus, cuneus and fusiform gyri, deviated from RT-predicted signal significantly
more in flanker than in Simon condition (Figure 5B, Suppl. Table S11). All of these regions
fell outside the conjunction map, as confirmed by the masking procedure (Figure 5C, Suppl.
Table S12).
Figure 5. Common and distinct regions exceeding the time-on-task effects in Simon
and flanker conflicts. A) Conjunction of activity exceeding the time-on-task effects in both
16
.CC-BY-ND 4.0 International licensemade available under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is
The copyright holder for this preprintthis version posted May 22, 2023. ; https://doi.org/10.1101/2023.05.19.541457doi: bioRxiv preprint
Simon and flanker conflict conditions. B) Difference between activity exceeding the
time-on-task effects in flanker and Simon conditions. C) Difference between activity
exceeding the time-on-task effects in flanker and Simon conditions, masked by their
conjunction. A conjunction map was created with the voxel-level threshold p<0.01 used to
encompass larger areas of common activity. Contrast maps in subplots B and C are
thresholded at voxel-level p<0.001 and FWE-corrected (p<0.05) for cluster size.
3.6 Time-on-task effects in pMFC
Additional analyses of pMFC ROIs enabled direct comparison with previous results
describing the contribution of time-on-task effects within this area (Carp et al. 2010;
Weissman and Carp 2013). These previous reports used a version of the task comprising
no-conflict and multi-source conflict conditions; thus, we compared observed and
RT-predicted signals evoked by multi-source condition (FS) within ROIs centered at the
pMFC coordinates reported in these studies ([-3, 18, 51], Carp et al. 2010; [-6, 12, 49],
Weissman and Carp, 2013; Figure 6A). We also analyzed the scope of the time-on-task
effect across all of the conflict conditions in ROIs defined within our study, in the respective
main contrast between the conflict and no-conflict condition ([-2, 8, 58], S0 > 00; [-4, 10, 54],
F0 > 00; [2, 8, 52], FS > 00; Figure 6A). The comparison between RT-predicted and
observed activity in the pMFC ROIs showed that conflict-related activity exceeded
time-on-task effects regardless of the task and specific coordinates employed for ROI
definition (Figure 6B): the signal was significantly higher than predicted in ROIs defined by
Carp et al. (2010) and Weissman and Carp (2013) for multi-source conflict, and, although
non-significantly, also within ROI defined in our study; single-conflicts introduced in our task
also evoked significantly higher activity than predicted on the basis of RT. Even though
multi-source condition evoked the highest conflict, it also seemed less reliable in proving the
difference between the expected and the observed responses (Figure 6B and C).
Multi-source conflict condition may be less powerful to capture differences in question than
uni-source conflicts if the latter evoke smaller but presumably less variable responses. We
thus performed an additional simulation to check how the observed variability in the strength
of the effect would translate into results of a smaller-sample study (N = 24; for comparison to
the previous studies, N = 21, Carp et al. 2010; N = 24, Weissman and Carp, 2013). Random
subsampling (n = 1000) showed that pairwise comparison between RT-predicted and
observed signals in multi-source condition had only ~13% chance of demonstrating the
result below the conventional significance threshold (Figure 6C) in pMFC ROI defined in our
data, and ≤ 68% chance in ROIs located at coordinates from previous studies. Interestingly,
17
.CC-BY-ND 4.0 International licensemade available under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is
The copyright holder for this preprintthis version posted May 22, 2023. ; https://doi.org/10.1101/2023.05.19.541457doi: bioRxiv preprint
in single conflict conditions this probability was much higher (>83%). Thus, our
complementary analysis confirms conflict-related signals in orthogonally defined pMFC
ROIs, but also suggests that single conflicts may provide more robust effects than
multi-source conflict condition.
Figure 6. Time-on-task effects in pMFC. A) pMFC ROIs centered at the peak activity
identified in multi-source conflict condition (FS > 00) in Carp et al. 2010 ([-3, 18, 51],
magenta) and Weissman and Carp, 2013 ([-6, 12, 49], yellow), and in each conflict condition
vs no-conflict in our study (S0 > 00, red, [-2, 8, 58]; F0 > 00, blue, [-4, 10, 54]; FS > 00,
green, [2, 8, 52]). B) Comparison of RT-predicted and actual increase of activity (arbitrary
units) in ROIs indicated in panel (A). Analysis on full group of N = 40, *** p < 0.001, ** p <
0.01, * p < 0.05. Outline colors code as in panel A, with grating marking real data and bars
filled with a color indicating predicted values. C) Probability of observing a particular p-value
when comparing RT-predicted and actual activity in pMFC ROIs indicated in panel A when
the sample size equals N = 24 (simulation based on the data obtained in the present study,
subsampled and tested 1000 times). Note that for N = 24 probability of reaching p = 0.05
18
.CC-BY-ND 4.0 International licensemade available under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is
The copyright holder for this preprintthis version posted May 22, 2023. ; https://doi.org/10.1101/2023.05.19.541457doi: bioRxiv preprint
threshold is higher (≥ 0.75) for single conflict conditions (S0, F0) and lower for FS condition
(< 0.6) at each of the tested ROI locations. Color code as in panels A and B.
4. Discussion
Our study sought to determine shared and distinct correlates of conflict processing evoked
by stimulus-response and stimulus-stimulus interference. The use of the extended MSIT
(Sheth et al. 2012), the cognitive interference task which incorporates the Simon
(stimulus-response) and flanker (stimulus-stimulus) conflicts, both separately and in
combination, allowed us to compare conflict-related activity within a uniform design and set
of stimuli. A number of regions showed common involvement in conflicts entailed by the
MSIT, mainly within the dorsal attention network and medial prefrontal regions. Extensive
common deactivation was also observed within the default mode network. In agreement with
previous studies, which noted divergent activity depending on the type of interference,
conflict-specific activity in the MSIT emerged mostly in the sensory/sensorimotor cortices
(Egner and Hirsch 2005; Egner et al. 2007). Importantly, we were able to show that a part of
the activity cannot be explained by time-on-task effects; neither within the regions engaged
by a single type of interference nor within those involved across different conflicts.
Our results reinforce the idea that cognitive conflict engages additional mechanisms of
cognitive control beyond those involved in less demanding, more automatized responses
(Shenhav et al. 2013). This conclusion has been questioned by a series of studies which did
not identify any BOLD activity over and above the effects explained by a longer time spent
on processing the task in the presence of conflict (Carp et al. 2010; Grinband et al. 2011;
Weissman and Carp 2013). These neuroimaging studies suggested that either there is no
specific modulation of activity attributable to conflict occurrence or such modulation cannot
be captured with the fMRI technique. In contrast, our findings support the coherence
between fMRI and other experimental techniques that demonstrate conflict-dependent
modulation (Ebitz and Platt 2015; Ebitz et al. 2020; Hanslmayr et al. 2008; Nigbur et al.
2011; Nigbur et al. 2012; Michelet et al. 2016).
4.1 Regions of common activity for both Simon- and flanker-type conflicts
Isolation of MSIT component conflicts revealed substantial similarity between Simon- and
flanker-induced activation maps. Notably, a major part of this activity clearly overlapped with
19
.CC-BY-ND 4.0 International licensemade available under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is
The copyright holder for this preprintthis version posted May 22, 2023. ; https://doi.org/10.1101/2023.05.19.541457doi: bioRxiv preprint
the large-scale intrinsic connectivity networks, i.e. networks stably identified with functional
connectivity methods (Krienen et al. 2014; Power et al. 2011; Yeo et al. 2011). Regions of
common activity for both Simon- and flanker-type conflicts were largely confined to the DAN
(bilateral FEF, vPM and IPS/superior parietal lobule) and pMFC (dACC and pre-SMA), a
major part of the cingulo-opercular network (CON). With regards to deactivation, we
observed a down-modulation of the DMN (dmPFC, bilateral angular gyri and anterior
temporal cortex). The regions of the DAN are known to coordinate action and spatial
relationship between the objects and the effectors (eyes/hands; Andersen and Buneo 2002;
Colby and Goldberg 1999). The DAN is also the main network implicated in attention, both in
terms of directing attention to a specific location and to specific properties of stimuli
(Corbetta and Shulman 2002; Squire et al. 2013). Increased activity clearly delineating the
DAN may reflect the spatial nature of both Simon and flanker tasks. Even though
stimulus-response mapping is congruent in flanker, both tasks rely on selection between
spatially distributed elements. The inherently spatial character of Simon and flanker tasks
can be explained via a comparison with the Stroop task, in which stimuli features (color and
meaning) are bound to the same object (word) and thus do not entail spatial processing
within the task execution (Stroop 1935). Thus, activity within the DAN likely reflects directing
attention to, and selecting between, the MSIT stimuli. Increased activity within pMFC has
been repetitively found in conflict tasks and in other contexts related to action selection such
as reward and error monitoring, reacting to surprise, regulating arousal etc. (Ebitz and
Hayden 2016; Kolling et al. 2016; Heilbronner and Hayden 2016; Ullsperger et al. 2014).
Most broadly, pMFC has been proposed to mediate the interaction between motivational
state and motor plans. From the network perspective, activity within CON has been shown to
increase in phase at the initiation and completion of a particular activity and remain at
elevated levels throughout the activity (Dosenbach et al. 2006). This suggested that CON
has an important contribution to the implementation and maintenance of a specific activity
rule. Much less is known about how deactivation of DMN may contribute to the execution of
effortful tasks. This is due to the bias within the neuroimaging studies that prevalently report
the one-sided contrasts (Cieslik et al. 2015, but see McDonald et al. 2017). However, we
hypothesize that increased activity in the DAN and decreased activity in the DMN reflect the
two sides of the same coin, supporting a maintained, elevated focus on external stimuli.
Anticorrelation of the DAN and pMFC on the one side and the DMN on the other has been
observed even in the absence of a task (Fox et al. 2005; He et al. 2007; Jurewicz et al.
2020). Work on the functional architecture of the brain suggest a hierarchy of the networks
that form the extrinsic and intrinsic systems: the former related to taking action in the
20
.CC-BY-ND 4.0 International licensemade available under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is
The copyright holder for this preprintthis version posted May 22, 2023. ; https://doi.org/10.1101/2023.05.19.541457doi: bioRxiv preprint
environment (encompassing the DAN), and the latter related to processes disengaged from
external stimulation (encompassing the DMN; Golland et al. 2008; Margulies et al. 2016).
Thus, the parallel increase and decrease of activity in large-scale networks involved in MSIT
is probably one of the basic mechanisms that orchestrate brain resources to effectively
support the visuomotor task and externally driven action.
Importantly, activations and deactivations in the DAN and the DMN were gaining area and
strength, in an order consistent with the progression of RTs (from Simon to flanker and
multi-source conflicts). The processes which closely correlate with the reaction times are
collectively referred to as the “time-on-task effects''. This set includes the mechanisms of
attention, movement preparation etc., and plays an essential role in execution of any task.
Examination of RT-related effects in our data revealed that indeed time-on-task effects could
explain most of DAN and DMN activations and deactivations, indicating that most of the
activity within these two networks is non-specific to conflict occurrence. (Activity exceeding
time-on-task effects was present in IPS in both Simon and flanker conflicts, but it was absent
from the conjunction of the two activity maps, indicating spatially non-overlapping
conflict-related activity in Simon and flanker. See the next section.) A notable exception
among the regions commonly activated by all the tasks was pMFC. Although some part of
the activity in pMFC was explained by the time-on-task, parts of pMFC exhibited an
up-modulation exceeding RT-based predictions (see also the Discussion section 4.4).
4.2 Conflict-specific activity
Conflict-specific effects were identified predominantly for the flanker task: there were
flanker-specific increases of the signal within the regions commonly involved in MSIT
conditions and flanker-specific activations as well as deactivations in other parts of the brain.
Most notably, flanker-type interference caused widespread increases and decreases of the
signal in several regions of the visual cortex. Occipital poles, extending to inferior and
superior occipital gyri and encompassing the lateral occipital complex (LOC) were activated
only by the flanker conflict. As predicted for the conflict-specific response, a part of the
activity within these regions could not be explained by simple time-on-task effects.
Involvement of these regions, known to take part in the analysis of letters, symbols, and
digits, likely reflected enhanced processing of MSIT stimuli in the presence of flanker
interference. At the same time, parts of lingual and fusiform gyri on the basal surface of the
brain, as well as parts of cuneus, were deactivated by flanker conflict. The retinotopic
mapping of this part of the visual cortex suggests that processing of the task-irrelevant
21
.CC-BY-ND 4.0 International licensemade available under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is
The copyright holder for this preprintthis version posted May 22, 2023. ; https://doi.org/10.1101/2023.05.19.541457doi: bioRxiv preprint
peripheral space may be suppressed during stimulus-stimulus conflict resolution (Wu et al.
2012; see Fan et al. 2007 for a similar observation). Calcarine sulcus, as well as parts of
fusiform gyri were not significantly up- or down-modulated when compared to no-conflict
condition, even though RT-related variability in these regions predicted an increase of the
signal with longer RT. Possibly, the activity in calcarine and fusiform gyri may have saturated
quickly, or such an increase was inhibited in conflict conditions. Together, a complex pattern
of flanker conflict-related changes in the visual cortex suggested a multifaceted
enhancement of the task-relevant information (cf. Wiesman and Wilson 2020). Additionally,
activity exceeding time-on-task effects was present in IPS in both Simon and flanker conflicts
in spatially non-overlapping regions. Our results support the view that response conflict in
flanker condition influences the whole path of information processing extending back to the
visual cortex over the period when the task is solved. This observation expands the idea of
“stimulus biasing”, discussed as a mechanism for conflict resolution (Botvinick et al. 2001;
Cohen et al. 1990; Egner and Hirsch 2005), which consists in excitatory biasing of
task-relevant feature. MSIT does not allow for anticipatory preparation of the relevant feature
or dimension of stimuli – a target can only be selected when the whole stimulus has been
integrated. Modulation of activity at different levels of the information processing pathway
points at the distributed nature of action selection (Cisek and Kalaska 2010). In this task,
modulation of the visual cortex appears to play an active role in decision-making and
response selection via top-down modulation and feedback.
Flanker-specific deactivation was also observed in some other regions, including SMG
(Figure 3C and Figure 5C). Subtractive logic of BOLD analysis makes it ambiguous whether
lower Simon-than flanker-related activity means a flanker-specific deactivation or a
Simon-specific activation (Figure 3C). However, RT-based comparisons help disambiguate
this observation: activity within SMG in flanker condition was lower than predicted on the
basis of RT and it was so to a greater extent than in Simon condition (Figure 5C). Thus,
modulation within SMG reflects another example of a flanker-related suppression of
hemodynamic response, rather than Simon-specific increase. Spatial localization of this
effect agrees with conflict-specific effects identified earlier in a similar comparison of Simon
and Stroop tasks (Egner et al. 2007). We may speculate that suppression in this region may
prevent premature responses in flanker conditions, enabling longer analysis of the stimuli in
this more demanding task (Simos et al. 2017). Lack of Simon-specific changes of activity
beyond conflict-dependent enhancement in IPS may be related to the fact that the mapping
between the spatial position of a target and the required response is computed within the
22
.CC-BY-ND 4.0 International licensemade available under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is
The copyright holder for this preprintthis version posted May 22, 2023. ; https://doi.org/10.1101/2023.05.19.541457doi: bioRxiv preprint
DAN. Consequently, stimulus-response mapping, targeted by Simon-type interference,
depends on the neural circuits that overlap with regions supporting response selection,
attention orienting and maintenance etc., recruited by all task conditions.
4.3 Interaction between Simon- and flanker-type conflicts
Resources shared between the tasks could also have been revealed in the analysis of the
interaction of Simon and flanker conflicts. Behavioral performance in multi-source
interference condition was worse than expected from adding the influences of Simon and
flanker interference. This result is consistent with previous findings on extended MSIT
(Wiesman and Wilson 2020) and other tasks combining Simon and flanker conflicts (Fan et
al. 2003; Frühholz et al. 2011; Hommel 1997; Mückschel et al. 2016) and may indicate
shared processing mechanisms between the tasks (Egner 2008). However, despite
superadditive RTs and error rates in multi-source condition, we did not observe any reliable
interactive effects in the BOLD signal. Extensive discussion of potential sources of diverging
behavioral and neural results in multi-source conflict task can be found in Rey-Mermet et al.
2019. One possible explanation is that the saturation of the hemodynamic response, which
was boosted in the highly demanding multi-source condition, left no space for superadditive
effects (Schei et al. 2011). It is also possible that the interaction influenced the dynamics, not
the overall magnitude of neuronal responses, and the BOLD signal could not detect such
differences. Indeed, some superadditive effects between Simon and flanker conflicts in
multi-source condition have been previously detected, and localized in the parietal and
premotor cortex, in MEG source-reconstructed signals of the selected oscillation bands
(Wiesman and Wilson 2020; Wiesman et al. 2020). The ERP analysis of the EEG signals
obtained within the same task (Dzianok et al. 2022) may help to resolve and explain
discrepancies between behavioral and fMRI results.
4.4 The scope of time-on-task effects
The question about the role of conflict in pMFC function is particularly vital as this region has
been frequently implicated in cognitive control and targeted in related research (Botvinick et
al. 1999; Shenhav et al. 2013; Ullsperger and von Cramon 2001). It is important to note that
while we found modulation of the BOLD activity attributable to cognitive conflict in pMFC,
that does not mean that we identified an abstract “conflict” signal, postulated in some
accounts on cognitive control (Shenhav et al. 2013). Our results are consistent with multiple
alternative views on the mechanisms of conflict resolution. While conflict-dependent signals
23
.CC-BY-ND 4.0 International licensemade available under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is
The copyright holder for this preprintthis version posted May 22, 2023. ; https://doi.org/10.1101/2023.05.19.541457doi: bioRxiv preprint
may relate to conflict monitoring, they may also result from multiple competing,
simultaneously available actions, or increased biasing of stimuli and/or responses (Cieslik et
al. 2015; Ebitz et al. 2020). The pMFC encompasses the pre-supplementary motor area
(pre-SMA) and anterior midcingulate cortex (aMCC) or, more often invoked, dorsal anterior
cingulate cortex (dACC). While pre-SMA has been more strongly associated with control of
motor output, e.g., selecting the appropriate response between different response
alternatives, aMCC has been primarily associated with enhancing behavioral plans relevant
to task demands against competing behavioral alternatives. However, both of these regions
have been found jointly involved in many tasks, and their clear functional dissociation is
rather difficult (Nachev et al. 2008). Similarly, in our task, we observed distributed activity in
both Simon and flanker conflicts spanning dACC and pre-SMA. In both tasks, the peak of
activity and conflict-dependent effects were localized predominantly in pre-SMA (cf. Beldzik
et al. 2015, Beldzik and Ullsperger 2023). This finding is similar to the previous report on the
mechanisms for resolving stimulus-stimulus and stimulus-response interference, which
showed the region of common activity in pre-SMA and stimulus-stimulus specific activity in
dACC (Li et al. 2017). However, on the basis of our results, we cannot conclude whether
Simon and flanker-related processing is localized to discrete neuronal populations in the
pMFC. These questions should be further addressed with other experimental techniques.
Indeed, a recent study using single-cell recordings in human dACC suggested that the
populations of cells significantly modulated by Simon and flanker conflicts were almost
entirely non-overlapping (Ebitz et al. 2020).
Significant differences obtained in our study between RT-predicted and conflict-induced
activations contrast with the results of similar analyses performed in previous research (Carp
et al. 2010; Grinband et al. 2011; Weissman and Carp 2013). Importantly, studies by Carp
and colleagues which showed that activity in pMFC is fully explained by time-on-task effects
also used MSIT, although in a classic version with only one, multi-source conflict condition.
Thus, divergent imaging outcomes cannot stem from task-specific features of neural
processing, but rather some experimental details that had a decisive impact upon the
results. Current and previous studies differed in multiple aspects of experimental settings,
such as methods of data acquisition, distribution and timing of the stimuli, number of
regressors etc. Although we cannot conclude which factor is crucial for explaining the
divergent results, we want to consider yet another factor–the power of statistical analyses,
as playing an important role here. A priori power analysis in neuroimaging research is
extremely difficult due to the huge variability of both experimental designs and cognitive
24
.CC-BY-ND 4.0 International licensemade available under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is
The copyright holder for this preprintthis version posted May 22, 2023. ; https://doi.org/10.1101/2023.05.19.541457doi: bioRxiv preprint
processes under study (Mumford 2012). However, a meta-research on the power of fMRI
analyses suggests that studies based on samples of around 20 subjects are only able to
capture relatively large effect sizes (Button et al. 2013; Poldrack et al. 2017). Our study was
based on a larger sample (n = 40), which almost doubled the samples used in previous
studies. An additional test performed for ROIs, located at the peaks of pMFC activity in
multi-source conflict condition reported by Carp et al. (2010) and by Weissman and Carp
(2013), performed for our full (n = 40) dataset, indicated significant overshoot of observed
signal over the time-on-task prediction. On the other hand, the predicted difference was not
large enough in FS condition to reach significance within the ROI indicated in our study.
When our sample was randomly limited to N = 24, probability that the comparison between
RT-predicted and actual activity was below the conventional significance threshold (p < 0.05)
was lower for all FS-based ROIs. The same analysis performed for S0 and F0 conditions
revealed considerably stronger conflict-specific effects. This suggests that a strong,
cumulative conflict may not always be the most apt to study the time-on-task vs.
conflict-related effects. Signs of conflict-related signals exceeding time-on-task effects were
already reported by Carp et al. (2010) in the whole brain analysis, in bilateral parietal
cortices. Within these regions, higher activity was found in multi-source conflict condition in
comparison to no-conflict condition when trials were subsampled to have matching RT.
Several brain regions were also shown to activate beyond the time-on-task effects in a
different conflict study, which tested oculomotor reactions in an anti-saccade task (Beldzik et
al. 2015) and Stroop and Simon tasks (preprint Beldzik & Ullsperger, 2023). Activated maps
included frontal and parietal areas as well as pMFC (in particular the pre-SMA), further
supporting our finding that these regions engage in different conflicts and show higher
activity than expected simply on the basis of RT.
4.5 Conclusions
Conflict-induced changes in brain activity were predominantly present in common regions of
DAN, pMFC and DMN regardless of the type of conflict; with their strength and extent
scaling from faster to slower-resolved tasks. Their coordinated activity suggested a joint
support for externally driven, visuo-spatial tasks, not limited to conflict resolution. However,
despite the uniform type of stimuli used for different conditions, conflict-specific activations
were also present in the sensory/sensorimotor cortices. Thus, sensory resources were
differentially recruited during various tasks, supporting the idea of distributed mechanisms
25
.CC-BY-ND 4.0 International licensemade available under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is
The copyright holder for this preprintthis version posted May 22, 2023. ; https://doi.org/10.1101/2023.05.19.541457doi: bioRxiv preprint
for resolving conflicts. Although time-on-task effects did explain a large part of observed
activations, the scope of this effect was not exhaustive, i.e., several regions exhibited the
activation above and below the levels predicted on the basis of RT. In particular, activity
within pMFC (encompassing dACC and pre-SMA) could not be reduced to the prolonged
time spent on task during resolution of different types of conflicts. Our study provides new
evidence that, with increased statistical power, conflict-related modulation of the BOLD
signal can be observed in regions associated with cognitive control. This insight helps to
explain conflicting findings in the field of cognitive control studies and validates neuroimaging
research on conflict processing, highlighting the need for the increased sensitivity for
capturing weaker–but critical–cognitive effects.
Declarations of Competing Interest
Authors declare that they have no conflict of interest.
CRediT authorship contribution statement
JW: Conceptualization, Investigation, Methodology, Data curation, Formal analysis,
Visualization, Writing (review & editing); KJ: Conceptualization, Methodology, Formal
analysis, Visualization, Writing (original draft), Writing (review & editing), Supervision; PD:
Conceptualization, Investigation, Data curation, Formal analysis, Writing (review & editing);
IA: Conceptualization, Writing (review & editing); KP: Conceptualization, Writing (review &
editing); TW: Conceptualization, Methodology, Writing (review & editing), Funding
acquisition, Supervision; EK: Conceptualization, Visualization, Writing (review & editing),
Funding acquisition, Supervision
26
.CC-BY-ND 4.0 International licensemade available under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is
The copyright holder for this preprintthis version posted May 22, 2023. ; https://doi.org/10.1101/2023.05.19.541457doi: bioRxiv preprint
Data and code availability
The unthresholded and unmasked group-level whole-brain results maps are available at
Neurovault repository: https://neurovault.org/collections/NTRDGLJW/
All analysis codes are available upon request.
Acknowledgements
This work was supported by the National Science Centre, Poland; grant number
2016/20/W/NZ4/00354. We thank Ms Alicja Dobrzykowska for her help in organizing MRI
data acquisition.
Abbreviations:
FEF - frontal eye field, IPS - intraparietal sulcus, vPM - ventral premotor cortex, Ang -
angular gyrus, aTmp - anterior temporal gyrus, pTmp - posterior temporal gyrus, LOC -
lateral occipital complex, aIns - anterior insula, Thal - thalamus, Caud - caudate, Ling -
lingual gyrus, Cun - cuneus, Prec - precuneus, Fus - fusiform gyrus, Hip - hippocampus,
Oper - operculum, pMFC - posterior medial frontal cortex, mPFC - medial prefrontal cortex,
dlPFC - dorsolateral prefrontal cortex, dmPFC - dorsomedial prefrontal cortex, vmPFC -
ventromedial prefrontal cortex, vlPFC - ventrolateral prefrontal cortex, SM - sensorimotor
cortex, SMA - supplementary motor area, pre-SMA - pre-supplementary sensorimotor area,
cal - calcarine sulcus, SMG - supra marginal gyrus, MCC - midcingulate cortex, aMCC -
anterior midcingulate cortex, dACC - dorsal anterior cingulate cortex, wm - white matter, Put
- putamen, DMN - default-mode network, DAN - dorsal attention network, MSIT -
multi-source interference task, FS - Simon and flanker effects combined in one condition, F0
- flanker effect condition, S0 - Simon effect condition, 00 - no-conflict condition
27
.CC-BY-ND 4.0 International licensemade available under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is
The copyright holder for this preprintthis version posted May 22, 2023. ; https://doi.org/10.1101/2023.05.19.541457doi: bioRxiv preprint
References
Andersen, R.A., Buneo, C.A., 2002. Intentional maps in posterior parietal cortex. Annu. Rev.
Neurosci. 25, 189–220.
Beldzik, E., Domagalik, A., Oginska, H., Marek, T., Fafrowicz, M., 2015. Brain Activations
Related to Saccadic Response Conflict are not Sensitive to Time on Task. Front. Hum.
Neurosci. 9, 664.
Beldzik, E., & Ullsperger, M. (2023). A thin line between conflict and reaction time effects on
EEG and fMRI brain signals. Preprint at bioRxiv (p. 2023.02.14.528515).
https://doi.org/10.1101/2023.02.14.528515
Botvinick, M.M., Braver, T.S., Barch, D.M., Carter, C.S., Cohen, J.D., 2001. Conflict
monitoring and cognitive control. Psychol. Rev. 108, 624–652.
Botvinick, M., Nystrom, L.E., Fissell, K., Carter, C.S., Cohen, J.D., 1999. Conflict monitoring
versus selection-for-action in anterior cingulate cortex. Nature 402, 179–181.
Bush, G., Shin, L.M., Holmes, J., Rosen, B.R., Vogt, B.A., 2003. The Multi-Source
Interference Task: validation study with fMRI in individual subjects. Mol. Psychiatry 8, 60–70.
Button, K.S., Ioannidis, J.P.A., Mokrysz, C., Nosek, B.A., Flint, J., Robinson, E.S.J., Munafò,
M.R., 2013. Power failure: why small sample size undermines the reliability of neuroscience.
Nat. Rev. Neurosci. 14, 365–376.
Carp, J., Kim, K., Taylor, S.F., Fitzgerald, K.D., Weissman, D.H., 2010. Conditional
Differences in Mean Reaction Time Explain Effects of Response Congruency, but not
Accuracy, on Posterior Medial Frontal Cortex Activity. Front. Hum. Neurosci. 4, 231.
Cieslik, E.C., Mueller, V.I., Eickhoff, C.R., Langner, R., Eickhoff, S.B., 2015. Three key
regions for supervisory attentional control: evidence from neuroimaging meta-analyses.
Neurosci. Biobehav. Rev. 48, 22–34.
Cisek, P., Kalaska, J.F., 2010. Neural mechanisms for interacting with a world full of action
choices. Annu. Rev. Neurosci. 33, 269–298.
Cohen, J.D., Dunbar, K., McClelland, J.L., 1990. On the control of automatic processes: a
parallel distributed processing account of the Stroop effect. Psychol. Rev. 97, 332–361.
Colby, C.L., Goldberg, M.E., 1999. Space and attention in parietal cortex. Annu. Rev.
Neurosci. 22, 319–349.
Corbetta, M., Shulman, G.L., 2002. Control of goal-directed and stimulus-driven attention in
the brain. Nat. Rev. Neurosci. 3, 201–215.
Dosenbach, N.U.F., Visscher, K.M., Palmer, E.D., Miezin, F.M., Wenger, K.K., Kang, H.C.,
Burgund, E.D., Grimes, A.L., Schlaggar, B.L., Petersen, S.E., 2006. A core system for the
implementation of task sets. Neuron 50, 799–812.
28
.CC-BY-ND 4.0 International licensemade available under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is
The copyright holder for this preprintthis version posted May 22, 2023. ; https://doi.org/10.1101/2023.05.19.541457doi: bioRxiv preprint
Dzianok, P., Antonova, I., Wojciechowski, J., Dreszer, J., Kublik, E., 2022. The
Nencki-Symfonia electroencephalography/event-related potential dataset: Multiple cognitive
tasks and resting-state data collected in a sample of healthy adults. Gigascience 11.
https://doi.org/10.1093/gigascience/giac015
Ebitz, R.B., Hayden, B.Y., 2016. Dorsal anterior cingulate: a Rorschach test for cognitive
neuroscience. Nat. Neurosci. 19, 1278–1279.
Ebitz, R.B., Platt, M.L., 2015. Neuronal activity in primate dorsal anterior cingulate cortex
signals task conflict and predicts adjustments in pupil-linked arousal. Neuron 85, 628–640.
Ebitz, R.B., Smith, E.H., Horga, G., Schevon, C.A., Yates, M.J., McKhann, G.M., Botvinick,
M.M., Sheth, S.A., Hayden, B.Y., 2020. Human dorsal anterior cingulate neurons signal
conflict by amplifying task-relevant information. bioRxiv.
https://doi.org/10.1101/2020.03.14.991745
Egner, T., 2008. Multiple conflict-driven control mechanisms in the human brain. Trends
Cogn. Sci. 12, 374–380.
Egner, T., Delano, M., Hirsch, J., 2007. Separate conflict-specific cognitive control
mechanisms in the human brain. Neuroimage 35, 940–948.
Egner, T., Hirsch, J., 2005. Cognitive control mechanisms resolve conflict through cortical
amplification of task-relevant information. Nat. Neurosci. 8, 1784–1790.
Eklund, A., Knutsson, H., Nichols, T.E., 2018. Cluster Failure Revisited: Impact of First Level
Design and Data Quality on Cluster False Positive Rates. arXiv [stat.AP].
Eriksen, B.A., Eriksen, C.W., 1974. Effects of noise letters upon the identification of a target
letter in a nonsearch task. Percept. Psychophys. 16, 143–149.
Fan, J., Flombaum, J.I., McCandliss, B.D., Thomas, K.M., Posner, M.I., 2003. Cognitive and
brain consequences of conflict. Neuroimage 18, 42–57.
Fan, J., Hof, P.R., Guise, K.G., Fossella, J.A., Posner, M.I., 2007. The Functional Integration
of the Anterior Cingulate Cortex during Conflict Processing. Cereb. Cortex 18, 796–805.
Fox, M.D., Snyder, A.Z., Vincent, J.L., Corbetta, M., Van Essen, D.C., Raichle, M.E., 2005.
The human brain is intrinsically organized into dynamic, anticorrelated functional networks.
Proc. Natl. Acad. Sci. U. S. A. 102, 9673–9678.
Frühholz, S., Godde, B., Finke, M., Herrmann, M., 2011. Spatio-temporal brain dynamics in a
combined stimulus-stimulus and stimulus-response conflict task. Neuroimage 54, 622–634.
Golland, Y., Golland, P., Bentin, S., Malach, R., 2008. Data-driven clustering reveals a
fundamental subdivision of the human cortex into two global systems. Neuropsychologia 46,
540–553.
Grinband, J., Savitskaya, J., Wager, T.D., Teichert, T., Ferrera, V.P., Hirsch, J., 2011. The
dorsal medial frontal cortex is sensitive to time on task, not response conflict or error
29
.CC-BY-ND 4.0 International licensemade available under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is
The copyright holder for this preprintthis version posted May 22, 2023. ; https://doi.org/10.1101/2023.05.19.541457doi: bioRxiv preprint
likelihood. Neuroimage 57, 303–311.
Grinband, J., Wager, T.D., Lindquist, M., Ferrera, V.P., Hirsch, J., 2008. Detection of
time-varying signals in event-related fMRI designs. Neuroimage 43, 509–520.
Hanslmayr, S., Pastötter, B., Bäuml, K.-H., Gruber, S., Wimber, M., Klimesch, W., 2008. The
electrophysiological dynamics of interference during the Stroop task. J. Cogn. Neurosci. 20,
215–225.
He, B.J., Snyder, A.Z., Vincent, J.L., Epstein, A., Shulman, G.L., Corbetta, M., 2007.
Breakdown of functional connectivity in frontoparietal networks underlies behavioral deficits
in spatial neglect. Neuron 53, 905–918.
Heilbronner, S.R., Hayden, B.Y., 2016. Dorsal Anterior Cingulate Cortex: A Bottom-Up View.
Annu. Rev. Neurosci. 39, 149–170.
Hommel, B., 1997. Interactions between stimulus-stimulus congruence and
stimulus-response compatibility. Psychol. Res. 59, 248–260.
Ioannidis, J.P.A., 2005. Why most published research findings are false. PLoS Med. 2, e124.
Jurewicz, K., Paluch, K., Wolak, T., Wróbel, A., 2020. Large-scale brain networks underlying
non-spatial attention updating: Towards understanding the function of the temporoparietal
junction. Cortex 133, 247–265.
Kolling, N., Behrens, T., Wittmann, M.K., Rushworth, M., 2016. Multiple signals in anterior
cingulate cortex. Curr. Opin. Neurobiol. 37, 36–43.
Krienen, F.M., Yeo, B.T.T., Buckner, R.L., 2014. Reconfigurable task-dependent functional
coupling modes cluster around a core functional architecture. Philos. Trans. R. Soc. Lond. B
Biol. Sci. 369. https://doi.org/10.1098/rstb.2013.0526
Li, Q., Yang, G., Li, Z., Qi, Y., Cole, M.W., Liu, X., 2017. Conflict detection and resolution rely
on a combination of common and distinct cognitive control networks. Neurosci. Biobehav.
Rev. 83, 123–131.
Liu, X., Banich, M.T., Jacobson, B.L., Tanabe, J.L., 2004. Common and distinct neural
substrates of attentional control in an integrated Simon and spatial Stroop task as assessed
by event-related fMRI. Neuroimage 22, 1097–1106.
Margulies, D.S., Ghosh, S.S., Goulas, A., Falkiewicz, M., Huntenburg, J.M., Langs, G.,
Bezgin, G., Eickhoff, S.B., Castellanos, F.X., Petrides, M., Jefferies, E., Smallwood, J., 2016.
Situating the default-mode network along a principal gradient of macroscale cortical
organization. Proc. Natl. Acad. Sci. U. S. A. 113, 12574–12579.
McDonald, A.R., Muraskin, J., Van Dam, N.T., Froehlich, C., Puccio, B., Pellman, J., Bauer,
C.C.C., Akeyson, A., Breland, M.M., Calhoun, V.D., Carter, S., Chang, T.P., Gessner, C.,
Gianonne, A., Giavasis, S., Glass, J., Homann, S., King, M., Kramer, M., Landis, D., Lieval,
A., Lisinski, J., Mackay-Brandt, A., Miller, B., Panek, L., Reed, H., Santiago, C., Schoell, E.,
Sinnig, R., Sital, M., Taverna, E., Tobe, R., Trautman, K., Varghese, B., Walden, L., Wang,
30
.CC-BY-ND 4.0 International licensemade available under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is
The copyright holder for this preprintthis version posted May 22, 2023. ; https://doi.org/10.1101/2023.05.19.541457doi: bioRxiv preprint
R., Waters, A.B., Wood, D.C., Castellanos, F.X., Leventhal, B., Colcombe, S.J., LaConte, S.,
Milham, M.P., Craddock, R.C., 2017. The real-time fMRI neurofeedback based stratification
of Default Network Regulation Neuroimaging data repository. Neuroimage 146, 157–170.
Michelet, T., Bioulac, B., Langbour, N., Goillandeau, M., Guehl, D., Burbaud, P., 2016.
Electrophysiological Correlates of a Versatile Executive Control System in the Monkey
Anterior Cingulate Cortex. Cereb. Cortex 26, 1684–1697.
Mückschel, M., Stock, A.-K., Dippel, G., Chmielewski, W., Beste, C., 2016. Interacting
sources of interference during sensorimotor integration processes. Neuroimage 125,
342–349.
Mumford, J.A., 2012. A power calculation guide for fMRI studies. Soc. Cogn. Affect.
Neurosci. 7, 738–742.
Nachev, P., Kennard, C., Husain, M., 2008. Functional role of the supplementary and
pre-supplementary motor areas. Nat. Rev. Neurosci. 9, 856–869.
Nee, D.E., Wager, T.D., Jonides, J., 2007. Interference resolution: insights from a
meta-analysis of neuroimaging tasks. Cogn. Affect. Behav. Neurosci. 7, 1–17.
Nigbur, R., Cohen, M.X., Ridderinkhof, K.R., Stürmer, B., 2012. Theta dynamics reveal
domain-specific control over stimulus and response conflict. J. Cogn. Neurosci. 24,
1264–1274.
Nigbur, R., Ivanova, G., Stürmer, B., 2011. Theta power as a marker for cognitive
interference. Clin. Neurophysiol. 122, 2185–2194.
Peterson, B.S., Kane, M.J., Alexander, G.M., Lacadie, C., Skudlarski, P., Leung, H.C., May,
J., Gore, J.C., 2002. An event-related functional MRI study comparing interference effects in
the Simon and Stroop tasks. Brain Res. Cogn. Brain Res. 13, 427–440.
Poldrack, R.A., Baker, C.I., Durnez, J., Gorgolewski, K.J., Matthews, P.M., Munafò, M.R.,
Nichols, T.E., Poline, J.-B., Vul, E., Yarkoni, T., 2017. Scanning the horizon: towards
transparent and reproducible neuroimaging research. Nat. Rev. Neurosci. 18, 115–126.
Power, J.D., Cohen, A.L., Nelson, S.M., Wig, G.S., Barnes, K.A., Church, J.A., Vogel, A.C.,
Laumann, T.O., Miezin, F.M., Schlaggar, B.L., Petersen, S.E., 2011. Functional network
organization of the human brain. Neuron 72, 665–678.
Rey-Mermet, A., Gade, M., Steinhauser, M., 2019. Sequential conflict resolution under
multiple concurrent conflicts: An ERP study. Neuroimage 188, 411–418.
Schei, J. L., Van Nortwick, A. S., Meighan, P. C., & Rector, D. M., 2011. Saturation
thresholds of evoked neural and hemodynamic responses in awake and asleep rats.
Dynamics and Fluctuations in Biomedical Photonics VIII, 7898, 243–250.
Shenhav, A., Botvinick, M.M., Cohen, J.D., 2013. The expected value of control: an
integrative theory of anterior cingulate cortex function. Neuron 79, 217–240.
31
.CC-BY-ND 4.0 International licensemade available under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is
The copyright holder for this preprintthis version posted May 22, 2023. ; https://doi.org/10.1101/2023.05.19.541457doi: bioRxiv preprint
Sheth, S.A., Mian, M.K., Patel, S.R., Asaad, W.F., Williams, Z.M., Dougherty, D.D., Bush, G.,
Eskandar, E.N., 2012. Human dorsal anterior cingulate cortex neurons mediate ongoing
behavioural adaptation. Nature 488, 218–221.
Simon, J.R., 1967. Choice reaction time as a function of auditory S-R correspondence, age
and sex. Ergonomics 10, 559–564.
Simos, P.G., Kavroulakis, E., Maris, T., Papadaki, E., Boursianis, T., Kalaitzakis, G., Savaki,
H.E., 2017. Neural foundations of overt and covert actions. Neuroimage 152, 482–496.
Smith, S.M., Jenkinson, M., Woolrich, M.W., Beckmann, C.F., Behrens, T.E.J.,
Johansen-Berg, H., Bannister, P.R., De Luca, M., Drobnjak, I., Flitney, D.E., Niazy, R.K.,
Saunders, J., Vickers, J., Zhang, Y., De Stefano, N., Brady, J.M., Matthews, P.M., 2004.
Advances in functional and structural MR image analysis and implementation as FSL.
Neuroimage 23 Suppl 1, S208–19.
Squire, R.F., Noudoost, B., Schafer, R.J., Moore, T., 2013. Prefrontal contributions to visual
selective attention. Annu. Rev. Neurosci. 36, 451–466.
Stroop, J.R., 1935. Studies of interference in serial verbal reactions. J. Exp. Psychol. 18,
643–662.
Szucs, D., Ioannidis, J.P., 2020. Sample size evolution in neuroimaging research: An
evaluation of highly-cited studies (1990-2012) and of latest practices (2017-2018) in
high-impact journals. Neuroimage 221, 117164.
Ullsperger, M., Fischer, A.G., Nigbur, R., Endrass, T., 2014. Neural mechanisms and
temporal dynamics of performance monitoring. Trends Cogn. Sci. 18, 259–267.
Ullsperger, M., von Cramon, D.Y., 2001. Subprocesses of performance monitoring: a
dissociation of error processing and response competition revealed by event-related fMRI
and ERPs. Neuroimage 14, 1387–1401.
van Veen, V., Carter, C.S., 2005. Separating semantic conflict and response conflict in the
Stroop task: a functional MRI study. Neuroimage 27, 497–504.
Wager, T.D., Sylvester, C.-Y.C., Lacey, S.C., Nee, D.E., Franklin, M., Jonides, J., 2005.
Common and unique components of response inhibition revealed by fMRI. Neuroimage 27,
323–340.
Weissman, D.H., Carp, J., 2013. The congruency effect in the posterior medial frontal cortex
is more consistent with time on task than with response conflict. PLoS One 8, e62405.
Wiesman, A.I., Koshy, S.M., Heinrichs-Graham, E., Wilson, T.W., 2020. Beta and gamma
oscillations index cognitive interference effects across a distributed motor network.
Neuroimage 213, 116747.
Wiesman, A.I., Wilson, T.W., 2020. Posterior Alpha and Gamma Oscillations Index Divergent
and Superadditive Effects of Cognitive Interference. Cereb. Cortex 30, 1931–1945.
32
.CC-BY-ND 4.0 International licensemade available under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is
The copyright holder for this preprintthis version posted May 22, 2023. ; https://doi.org/10.1101/2023.05.19.541457doi: bioRxiv preprint
Wittfoth, M., Buck, D., Fahle, M., Herrmann, M., 2006. Comparison of two Simon tasks:
neuronal correlates of conflict resolution based on coherent motion perception. Neuroimage
32, 921–929.
Wu, J., Yan, T., Zhang, Z., Jin, F., Guo, Q., 2012. Retinotopic mapping of the peripheral
visual field to human visual cortex by functional magnetic resonance imaging. Hum. Brain
Mapp. 33, 1727–1740.
Yarkoni, T., Barch, D.M., Gray, J.R., Conturo, T.E., Braver, T.S., 2009. BOLD correlates of
trial-by-trial reaction time variability in gray and white matter: a multi-study fMRI analysis.
PLoS One 4, e4257.
Yeo, B.T.T., Krienen, F.M., Sepulcre, J., Sabuncu, M.R., Lashkari, D., Hollinshead, M.,
Roffman, J.L., Smoller, J.W., Zöllei, L., Polimeni, J.R., Fischl, B., Liu, H., Buckner, R.L., 2011.
The organization of the human cerebral cortex estimated by intrinsic functional connectivity.
J. Neurophysiol. 106, 1125–1165.
33
.CC-BY-ND 4.0 International licensemade available under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is
The copyright holder for this preprintthis version posted May 22, 2023. ; https://doi.org/10.1101/2023.05.19.541457doi: bioRxiv preprint
