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The Journal of Neuroscience, December 1, 2001, 21(23):9430-9437
Functions of the Medial Frontal Cortex in the Processing of
Conflict and Errors
William J.
Gehring and
David E.
Fencsik
University of Michigan, Ann Arbor, Michigan 48109
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ABSTRACT |
A principal function of the medial frontal cortex, in particular
the anterior cingulate cortex (ACC), is to monitor action. The
error-related negativity (ERN, or NE), an
event-related brain potential, reflects medial frontal
action-monitoring processes. Specifically, the error-detection theory
of the ERN states that the ERN reflects ACC processing that is directly
related to detecting the error. This theory predicts that ERN and ACC
activity should increase directly with the dissimilarity of the error
from the correct response, with similarity defined with respect to the common movement features of the responses. In contrast, the
conflict-detection theory claims that ACC and ERN activity represent
the detection of response conflict. This theory predicts that the
activity should increase directly with the similarity of the error and
the correct response. To test these theories, we investigated the
effects of response similarity and conflict on the ERN, using a task
that involved hand and foot movements. ERN activity was largest under conditions of high response conflict, where the error was similar to
the correct response. This finding favors the conflict-detection theory
over the error-detection theory, although the ERN was not associated
with posterror slowing, as predicted by proponents of both theories.
Discrepancies between our results and those of past studies may stem
from the use in previous studies of four-finger response tasks which
are subject to unique physiological and biomechanical constraints. We
conclude that the ERN reflects medial frontal activity involved in the
detection or affective processing of response conflict.
Key words:
anterior cingulate; error-related negativity; response
conflict; error detection; event-related potential; action
monitoring
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INTRODUCTION |
A critical function of the human
brain is to monitor behavior and prevent undesirable actions. Evidence
suggests that the medial frontal cortex, particularly the anterior
cingulate cortex (ACC), is involved in this action monitoring (Bush et
al., 2000 ; Paus, 2001). Studies of the error-related negativity (ERN,
or NE), a medial frontal negative component of
the event-related brain potential, have contributed to this evidence.
The ERN occurs at approximately the same time as errors in reaction
time (RT) tasks (Falkenstein et al., 1991, 1995; Gehring et al., 1993,
1995; Miltner et al., 1997; Tucker et al., 1999). Animal studies,
source localization modeling, and functional magnetic resonance imaging (fMRI) suggest that the ERN is generated in the ACC (Gemba et al.,
1986; Dehaene et al., 1994; Kiehl et al., 2000).
Investigators have developed competing theories about the psychological
processes represented by medial frontal activity. According to one
theory, the ERN reflects a process associated with error detection
(Coles et al., 1995; Falkenstein et al., 1995). An opposing theory
asserts that the activity reflects the detection of response conflict
(Carter et al., 1998). Either type of processing will respond to
erroneous response activation, but error detection indicates which
response is incorrect, whereas conflict detection indicates only that
competing responses are present in the motor system. Computational
models and fMRI evidence support the plausibility of a
conflict-detection process (Carter et al., 1998; Botvinick et al.,
2001).
Our study focused on four-choice response tasks, where previous
results have putatively supported the error-detection theory (Bernstein
et al., 1995; Falkenstein et al., 1996). In these studies, participants
responded with the index or middle finger of either hand. A larger ERN
occurred when the error and the correct response were dissimilar
(different fingers on opposite sides of the body) than when they were
similar (adjacent or mirror-image fingers). Bernstein et al. (1995)
found that error rates were greatest and the ERN was smallest when the
error finger was adjacent to the correct-response finger, suggesting
that those errors were the most similar to the correct response. These
investigators concluded that the size of the ERN reflects how much the
error deviates from the correct response (Bernstein et al., 1995;
Falkenstein et al., 1996). If so, these results support the
error-detection theory over the conflict-detection theory, because the
ERN was smallest in the condition in which conflict was presumably greatest.
Nevertheless, because adjacent fingers interact at neural, muscular,
and biomechanical levels (Ohtsuki, 1981; Hager-Ross and Schieber,
2000), conclusions obtained with four-finger tasks may have limited
generality. Therefore, we used a hand-foot task (based on that of
Blythe, 1963) to eliminate the confounds inherent in adjacent-finger
responses. According to error-detection theory, the ERN should be
larger when the error and the correct response are dissimilar (e.g.,
left hand vs right foot). According to conflict-detection theory, the
ERN should be larger when the error and correct response are similar
(e.g., left hand vs left foot or left hand vs right hand).
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MATERIALS AND METHODS |
Participants. Six women and four men between the ages
of 19 and 22 years (mean = 20.7 years) participated in the study.
Participants received $6.00 per hour plus bonuses based on their
performance. All were right-handed, had normal or corrected-to-normal
vision, and normal color vision.
Apparatus and procedure. The stimuli were words presented on
a 15 inch NEC (Tokyo, Japan) Multisync computer monitor. At a viewing
distance of 60 cm, the letters subtended 1.5° of visual angle. A
fixation cross ("+") appeared 1° below the words. Stimuli (duration of 200 msec) were presented every 2400-2700 msec.
The task was a manual variant of the Stroop task (Macleod, 1991).
Stimulus words were the color names "blue," "green,"
"purple," "red," and "yellow." The words appeared on the
screen in one of those five colors. For each participant, four of those
colors were selected, and one was assigned to each of the four
responses. A Latin square was used to assign stimuli to responses, such
that each possible pair of colors was assigned to each response pair once in the group of 10 participants (Sheehe and Bross, 1961). Using
five colors in the Latin square created a design for 10 participants.
For each participant, one of the color words and its corresponding
color did not appear.
On each trial, a stimulus word and stimulus color were chosen at random
and presented to the participant. Participants were instructed to make
one of the four responses according to the color of the stimulus,
ignoring the stimulus word. Finger responses involved flexion of the
right or left index finger, pressing a 1 cm2 button on a response pad (P/N 1141;
Neuroscan, Inc., Sterling, VA). Foot responses involved plantar
flexions of the right or left foot, depressing a pedal (Bilbo
Innovations, Sunnyvale, CA) located on the floor.
Each participant completed two 4 hr sessions on separate days. At the
beginning of the first session, participants filled out consent forms,
a health and medication questionnaire, and a Beck depression inventory
(Beck, 1961). In each session, after electrode application,
participants were given two blocks of practice trials and then
completed 24 blocks, each consisting of 64 trials.
Electrophysiological recording. The electroencephalogram
(EEG) was recorded from 56 scalp electrode sites with tin electrodes embedded in a nylon mesh cap (Electro-Cap International, Eaton, OH).
The electrode locations consisted of Fp1, Fp2, AF3, AFz, AF4, F7, F5,
F3, F1, Fz, F2, F4, F6, F8, FT7, FC5, FC3, FC1, FCz, FC2, FC4, FC6,
FT8, T7, C5, C3, C1, Cz, C2, C4, C6, T8, TP7, CP3, CPz, CP4, TP8, P7,
P5, P3, P1, Pz, P2, P4, P6, P8, POz, O1, Oz, and O2 (American
Electroencephalographic Society, 1991). Six additional sites were
located at the midpoints of the following pairs: FCz-F1, FCz-F2,
Cz-FC1, Cz-FC2, CPZ-C1, and CPZ-C2. EEG data were recorded with a
left mastoid reference. An average mastoid reference was derived
off-line using right mastoid data. The electro-oculogram (EOG) was
recorded from tin electrodes above and below the left eye and external
to the outer canthus of each eye. A ground electrode was placed on the
forehead. A finger flexion electromyogram (EMG) was recorded from the
first dorsal interosseous muscle; a plantar flexion EMG was recorded
from the gastrocnemius muscle in the calf (Zipp, 1982). Impedances were
kept below 10 k . The EEG, EMG, and EOG were amplified by SYNAMPS
amplifiers (Neuroscan, Inc.). The EEG and EOG were recorded from DC to
100 Hz (half-amplitude cutoff). The EMG was recorded from 10 to 200 Hz
(half-amplitude cutoffs). The data were digitized at 1000 Hz.
Data reduction. After recording, the EMG data were digitally
high-pass filtered with a half-amplitude cutoff point at 20 Hz (24 dB/octave roll-off) and rectified. EEG and EMG data were then digitally
low-pass filtered with a half-amplitude cutoff point at 50 Hz (24 dB/octave roll-off) and reduced to a sampling rate of 200 Hz. The EEG
data were corrected for vertical and horizontal ocular movement
artifacts (Gratton et al., 1983). Statistical analyses used data
filtered at the 50 Hz cutoff, referenced to the average of the mastoid
electrodes. The data presented in the figures were filtered with a nine
point Chebyshev II low-pass filter (Matlab 5.3; Mathworks, Natick, MA)
with a half-amplitude cutoff at ~8 Hz.
For each trial on which a button press occurred, we determined the time
at which the onset of EMG activity occurred. The algorithm computed a
threshold consisting of twice the SD of the integrated EMG in the 500 msec preceding the stimulus. It then started at the moment of switch
closure and, working backward in time, found the first EMG data point
that fell below that threshold value. It continued searching backward
until the values stopped decreasing. That point was accepted as the EMG
onset point if it occurred after the stimulus but within an interval of
300 msec before the switch closure (Van Boxtel et al., 1993). On
average, the EMG onset preceded the switch closure by ~100 msec. To
maintain consistency with the previous studies, we did not attempt to
identify errors that were evident in the EMG but that did not result in
a switch closure (Bernstein et al., 1995; Falkenstein et al.,
1996).
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RESULTS |
Each participant completed ~3000 trials (range = 2911-3111) for a total of 30,070 observations. Participants maintained
an overall error rate of 10.1% on average (range = 7.5-12.5%),
resulting in 3047 observations on error trials. Errors were grouped
into three types, which varied in their similarity to the correct
response: ipsilateral, contralateral, and opposite (Blythe, 1963). In
this case, an ipsilateral error was executed on the correct side of the
body, with the incorrect limb. A contralateral error was executed on
the incorrect side of the body, with the correct limb. Opposite errors
were most dissimilar from the correct response, being responses in
which both the limb and the side of the body were incorrect. In the
analyses below that use these error categories, we perform two
orthogonal contrasts: the first tested for an effect of similarity by
comparing the two high-similarity conditions with the low-similarity condition (i.e., the mean of the contralateral and ipsilateral conditions vs the opposite condition). The second contrast compared the
two high-similarity conditions (i.e., contralateral vs ipsilateral).
Behavioral data
Averaged error rates and RTs separated by error type are shown in
Figure 1. We quantified the error rate as
the proportion of errors within each condition; for the statistical
analyses, we used the arc sine transform. As shown in Figure 1,
participants made more errors in the two high-similarity conditions
than in the low-similarity condition
[F(1,9) = 194.339; p < 0.000001; mean squared error (MSe) = 0.0014]. The difference
between the ipsilateral and contralateral conditions was marginally
significant (F(1,9) = 3.92;
p = 0.079; MSe = 0.0065). Identical contrasts on
the RT data indicated that RTs were longer on high-similarity trials
than on low-similarity trials (F(1,9) = 6.02; p = 0.037; MSe = 1853). RTs in the two
high-similarity conditions, however, did not differ from each other
(F(1,9) = 2.50; p = 0.15; MSe = 877).
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Figure 1.
Response conflict was greatest for error responses
that were similar to the correct response. Percentage of errors
(top) and mean RT (bottom) are shown as a
function of error type for all 10 participants.
Ipsilateral denotes errors on the same side of the body
as the correct response. Contralateral indicates an
error committed on the incorrect side of the body, using the correct
limb. Opposite indicates an error for which the limb and
side were both incorrect. Error bars are ±1 SE.
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To investigate individual differences in response strategy, we examined
the participants who showed a pronounced tendency to make more
ipsilateral than contralateral errors. This analysis was based on one
by Bernstein et al. (1995), who showed that the effects of response
similarity were most reliable in such participants. The rationale of
the analysis was that for individuals whose errors tended to be
ipsilateral, ipsilateral responses were most similar and confusable.
Accordingly, we calculated the ratio of ipsilateral errors to
contralateral errors for each participant. Participants whose ratio was
>1 made more errors in which the side was correct but the limb was
not; we refer to the error pattern in this group as
ipsilateral-prevalent. Those whose ratio was <1 made more errors in
which the limb was correct but the side was not; we refer to this error
pattern as contralateral-prevalent. Comparing each participant's
ratios from session 1 and session 2, we found that six participants
consistently showed the ipsilateral-prevalent pattern, two consistently
showed the contralateral-prevalent pattern, and two switched prevalence
patterns from one session to the other. Our analysis focused on the six
participants who showed a consistent ipsilateral-prevalent pattern.
Figure 2 shows the average error and RT
pattern from the ipsilateral-prevalent group. As one would expect,
there was a stronger tendency to make ipsilateral errors within the
ipsilateral group than in the overall analysis. The error rate in the
two high-similarity conditions was greater than in the low-similarity
condition (F(1,5) = 75.12;
p = 0.00034; MSe = 0.0022). Moreover, the error
rate in the ipsilateral condition was greater than in the contralateral condition (F(1,5) = 16.54;
p = 0.0097; MSe = 0.0035). The contrasts on RTs
indicated that RTs were longer in the two high-similarity conditions
than in the low-similarity condition
(F(1,5) = 10.54; p = 0.023; MSe = 914). The RTs on ipsilateral trials and contralateral trials did not differ (F(1,5) = 1.10;
p = 0.34; MSe = 1109).
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Figure 2.
A subgroup of six participants showing the
greatest tendency to make ipsilateral errors. Percentage of errors
(top) and mean RT (bottom) are shown as a
function of error type for participants showing more ipsilateral errors
than contralateral errors (ipsilateral-prevalent participants). Error
types are described in Figure 1. Error bars are ±1 SE.
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Our analysis of RT and error rates establishes that the participants,
in particular the subgroup with the ipsilateral-prevalent error
pattern, had a greater tendency to make errors and respond slowly when
the error and correct response were similar, with the greatest increase
in error rate occurring for ipsilateral errors.
The ERN
We derived the ERN by aligning the EEG records from each trial at
the point of EMG onset and averaging separately for correct and error
responses. Consistent with previous studies, the ERN appeared as a
distinct peak on error trials beginning ~50 msec after EMG onset and
peaking at ~165 msec. The scalp maximum of the ERN occurred at the
frontocentral electrode site FCz (Fig. 3). Little or no ERN was present on
correct trials.
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Figure 3.
Grand average ERN scalp topography and waveform.
Top, A topographic map of the ERN peak (error-correct
difference) at 155-165 msec after the onset of EMG activity.
Lighter colors represent regions of greater negativity;
isocontour lines represent increments of 1 µV.
Dots represent electrode locations. The view shows the
top of the head, with the nose pointing upward. The scalp maximum of
the ERN occurs at the FCz electrode, indicated by the
cross. Bottom, the grand average ERN
waveform at FCz. The ERN is evident as a negative-polarity peak at 165 msec after EMG onset.
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As we mentioned previously, the primary analysis of interest concerns
whether the ERN was larger for high-similarity errors than for
low-similarity errors, supporting conflict-detection theory, or had the
reverse pattern predicted by error-detection theory. To evaluate the
effect of error type on the ERN, we calculated the mean amplitude at
FCz for each error type in the epoch from 140 to 190 msec after EMG
onset, spanning the peak of the ERN, relative to a baseline interval of
0-50 msec after the onset of the EMG. Figure
4 shows that errors in the two
high-similarity categories were associated with a larger ERN than
errors in the low-similarity category
(F(1,9) = 5.02; p = 0.052; MSe = 5.93). Of the two high-similarity conditions, the
ipsilateral errors were associated with ERN amplitudes that were
greater than those of contralateral errors
(F(1,9) = 6.07; p = 0.034; MSe = 2.02).
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Figure 4.
The amplitude of the ERN was greatest in the
high-conflict, ipsilateral error condition. Error-trial ERN waveforms
for all 10 participants are plotted as a function of error type. EMG
onset occurs at time 0. The waveform is from the
frontocentral electrode FCz. Error types are described in Figure
1.
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Next we performed a separate analysis on the ipsilateral-prevalent
group, who had the greatest tendency to make ipsilateral errors. The
ERN data for those participants are shown in Figure 5. The results were qualitatively similar
to the overall analysis but appear more pronounced: ERN amplitudes in
the two high-similarity conditions were greater than those in the
low-similarity condition (F(1,5) = 8.05; p = 0.036; MSe = 6.17). The ipsilateral
error ERN was larger than the contralateral error ERN
(F(1,5) = 99.15; p = 0.00018; MSe = 0.19).
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Figure 5.
The effects of response conflict on the ERN
waveform were greatest for participants whose behavior was disrupted
most by response conflict. Error-trial ERN waveforms for
ipsilateral-prevalent participants on error trials are plotted as a
function of error type. EMG onset occurs at time 0. Error types are
described in Figure 1.
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To ensure that the preceding changes in ERN amplitude resulted from a
change in the activity of the cortical generator of the ERN, rather
than activity in some other brain region, we computed topographic maps
of the ERN effects. Specifically, we subtracted the opposite error
waveform from the ipsilateral waveform using the waveforms seen in
Figures 4 and 5. We computed topographic maps from the difference
waveforms in the 155-165 msec epoch, at the peak of the ERN. Figure
6 shows these maps. The frontocentral (FCz) scalp maximum seen in both maps confirms that the modulations in
ERN amplitude seen in Figures 4 and 5 result from a change in the
activity of the cortical generator of the ERN and not from some other
source.
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Figure 6.
Topographic maps confirm that the event-related
potential conflict effects resulted from changes in the cortical
generator of the ERN. Maps represent the difference between the
ipsilateral errors and opposite errors at 155-165 msec after the onset
of EMG activity. Lighter colors represent regions of
greater negativity; isocontour lines represent
increments of 1 µV. Dots represent electrode
locations. The view shows the top of the head, with the nose pointing
upward. The scalp maximum of the conflict effect occurs at the FCz
electrode, the site of the ERN, indicated by the cross.
Left, Map from all 10 participants;
right, map from the ipsilateral-prevalent group.
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The ERN and posterror behavior
In preliminary analyses, we determined that correct
responses after errors were slower than the overall mean RT, and so we explored the relationship between the ERN and posterror slowing. Proponents of error-detection and conflict-detection theories predict
that the degree of slowing on the posterror trial will be directly
related to the amount of ERN activity on the preceding error trial
(Coles et al., 1995; Botvinick et al., 2001). Figure 7 shows the average RTs for the trials
before and after an error (omitting sequences in which an error
occurred in the pre-error or posterror sequence). Participants'
responses became increasingly fast until an error occurred. The correct
response after the error was slower than the error and other correct
responses. We used paired t tests to compare the RTs in
these sequences with the mean of all correct RTs (Fig. 7). Three of the
four responses before the error were significantly faster than the mean
correct RT. The error itself was faster than the mean correct RT, and the correct response immediately after the error was slower than the
mean correct RT.
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Figure 7.
Reaction times on correct trials
(C) before and after the error
(E) show that participants increased their speed
before the error and then slowed responses after the error. Error bar
indicates mean correct reaction time for all trials except those that
occur immediately after errors. The reaction times in the
line represent four correct trials before the error
( 4) to four correct trials after the error (+4). Error bars are +1
SE. Values differing from the mean correct reaction time on the
left are indicated by asterisks
(t(9) = p < 0.05;
one-tailed t test). From left to
right, the t values are 2.25, 1.25, 3.12, 4.42, 3.02, 2.07, 0.65, 0.50, and 1.21.
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To determine whether the ERN was associated with this slowing, we
compared sequences in which slowing was clearly evident with those in
which little or no slowing occurred. We matched each error trial with
another error trial of the same type on the basis of RT. If no exact
match was available, the trial with the closest RT was accepted as a
match, unless that RT differed from the first error trial by >10 msec.
(For multiple exact or near matches, a match was drawn at random.) We
then sorted these two trial types according to which preceded the
longest RT on the subsequent correct trial. As shown in Figure
8, left, this procedure
created one set of trials that on average had a comparatively large
amount of slowing after the error and another set in which little or no
slowing was evident. As shown in Figure 8, right, the ERN on
error trials preceding fast correct responses was virtually identical
to that preceding slow correct responses (F = 0.00). This result was duplicated in a separate analysis focusing on the
ipsilateral-prevalent group (F = 0.00); both results
indicate that posterror slowing was not related to the preceding
ERN.
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Figure 8.
The degree of ERN activity was unrelated
to the amount of posterror slowing. Left, Trials that
were identified as associated with slowing after the error or with no
slowing, where the two sets of trials had equivalent error-trial
reaction times. Those trials formed the basis of the analysis on the
right, which compares the ERN that preceded posterror
slowing with the ERN that was not associated with slowing.
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DISCUSSION |
Our results disconfirm the predictions of the
error-detection theory of ERN-ACC function (Bernstein et al., 1995;
Falkenstein et al., 1996; Coles et al., 2001). The
response-similarity-dependent increase in ERN amplitude is more
consistent with the conflict-detection theory of the ERN and ACC
(Carter et al., 1998; Botvinick et al., 2001). The smallest degree of
response conflict (and the smallest ERN) was observed when the error
and the correct response were dissimilar. The data suggest that the
ipsilateral condition, showing the largest ERN, was also associated
with the greatest response conflict: ERN and behavioral effects were
most pronounced in those participants for whom the ipsilateral errors
were most similar to the correct response. This behavioral result
is consistent with Blythe's (1963) report that errors in a four-choice
hand-foot task were predominantly ipsilateral. Contralateral errors
were also associated with elevated reaction times, but the error rate for these errors was smaller than for the ipsilateral errors, suggesting that the contralateral errors were associated with an
intermediate level of conflict between that of the ipsilateral and
opposite errors.
The studies by Bernstein et al. (1995) and Falkenstein et al. (1996)
used a four-finger response task and found ERN results that were
essentially the opposite of our own. Nonetheless, four-finger tasks may
have limited generality. Finger movements cause a number of interfinger
interactions that can affect responses in a four-finger task. Movement
of one finger is accompanied by movements in nearby fingers (Hager-Ross
and Schieber, 2000). The pattern is complex, however, because isometric
flexion of one finger in gripping movements reduces the muscle activity
and strength of the adjacent finger (Ohtsuki, 1981). The interactions
between fingers may result from a number of biomechanical, muscular,
and neural factors (Ohtsuki, 1981; Hager-Ross and Schieber, 2000).
Control mechanisms should therefore be less sensitive to ipsilateral
movements in the four-finger task, in which coactivation is not a
reliable indicator of response conflict, than in the hand-foot task.
If sensitivity were reduced in this manner, then a decreased ERN in the
ipsilateral condition, as observed by Bernstein et al. (1995), would
result. In the Botvinick et al. (2001) model, such an adjustment might
involve a reduction in the mutual inhibition between units
corresponding to adjacent fingers.
Participants' responses gradually became faster until the error
occurred and then slowed after the error, consistent with the
hypothesis that participants adjusted their speed in response to the
error (Rabbitt and Rodgers, 1977; Laming, 1979) or to conflict (Botvinick et al., 2001). Our finding that posterror slowing was unrelated to the size of the preceding ERN contradicts the assertions of error-detection and conflict-detection theorists that ERN and ACC
activity should be related to posterror slowing (Gehring et al., 1993;
Coles et al., 1995; Botvinick et al., 2001). Nonetheless, a
dissociation between the ERN and posterror slowing may not be a
critical disconfirmation of either theory. Posterror slowing might not
be the strategic reaction to the error that some investigators have
claimed (Rabbitt and Rodgers, 1977; Botvinick et al., 2001). For
example, some posterror trials could represent a continuation of the
breakdown in processing that caused the error. Moreover, with short
intervals between trials, capacity limits related to processing the
error can interfere with processing on the posterror trial (Welford,
1979). Finally, even if the slowing were a strategic reaction, the ERN
might simply precede some other reaction to an error, such as
autonomic, verbal, or postural activity.
Our data suggest that it is more likely that conflict detection, rather
than error detection, is the computational source of the ERN. A few
investigators have claimed to disconfirm the conflict-detection theory
by showing a larger ERN in conditions in which the stimuli engender
response conflict than in conditions involving less confusable stimuli
(Falkenstein et al., 2000; Scheffers and Coles, 2000). Nonetheless, an
error in response to a conflict-laden stimulus will not necessarily
encounter more competition than an error in response to a simpler
stimulus. Conflict will be related not only to the stimulus but also to
motor activity from anticipatory guessing (Gratton et al., 1988),
corrective action (our unpublished observations), and even aspecific
fluctuations in readiness (Coles et al., 1985).
Another line of evidence against conflict-detection theory has
attempted to dissociate measures of conflict from the ERN. These
studies, however, depend on the assumption that the measures are
sensitive to all of the relevant conflict. Studies have used the
lateralized readiness potential (LRP) as an index of conflict and have
dissociated it from the ERN (Falkenstein et al., 2000; Luu et al.,
2000b). The LRP, however, is limited in its ability to measure
conflict: It measures the difference in activation between responses
and not the conflicting activation they share in common. EMG measures
of conflict are limited in a similar manner. Coles et al. (2001)
reported that the ERN on error trials was larger than on correct
trials, even for correct trials that had the same amount of agonist EMG
on each arm as the error trials, supporting the sensitivity of the ERN
to errors rather than conflict (M. G. H. Coles, personal
communication). Nevertheless, response conflict can be present with no
discernable agonist-muscle EMG activity (Gratton et al., 1988), and
on-line adjustments in EMG activity can reduce or reverse the effects
of conflict on the agonist-muscle EMG activity (Gordon and Ghez, 1987)
(W. J. Gehring and A. R. Willoughby, unpublished observations).
Moreover, EMG and LRP measure preresponse activity, yet computational
models of conflict indicate that the period after the error should be when conflict is maximal (Botvinick et al., 2001).
More difficult to reconcile with conflict-detection theory are studies
showing medial-frontal negative-polarity brain potentials similar to
the ERN in response to feedback stimuli that are temporally separate
from the error response (Miltner et al., 1997), stimuli that are
associated with negative affect (Tucker et al., 1999), and
stimuli signifying monetary losses (Gehring and Willoughby, unpublished
observations). ERN and ACC activity might therefore represent a
more general evaluative system, one that processes the motivational
significance of events including, but not limited to, errors and
conflict (Bush et al., 2000). Consistent with this perspective,
performance emphasizing accuracy over speed increases the size of the
ERN (Gehring et al., 1993; Falkenstein et al., 1995). Individual
differences support this perspective: ERN dysfunctions have been
reported in individuals who are high in negative affect and negative
emotionality (Luu et al., 2000a), individuals with obsessive-compulsive
disorder (Gehring et al., 2000), and individuals with symptoms of
psychopathy (Dikman and Allen, 2000). Also consistent with a broader
significance of the ERN is a recent report that the ERN is part of the
frontal midline  rhythm (Luu and Tucker, 2001), which is consistent
with the oscillatory appearance of the ERN in numerous articles
(Gehring et al., 1995, 2000).
Yet the question of how to integrate these findings with the notion of
conflict detection remains. One possibility is that processing conflict
and processing the motivational significance of errors engages distinct
parts of the ACC. The locus of error-related fMRI activation (Kiehl et
al., 2000; Menon et al., 2001) has thus far tended to lie anterior to
the locus of conflict-related activation (Carter et al., 1998). Another
possibility is that a broader theory of ACC function might be
necessary, one that encompasses the processing of conflict as well as
motivational significance. Botvinick et al. (2001) note that many
circumstances other than response conflict activate the ACC, and they
suggest that the conflict detection apparatus is part of an "early
warning system," a general system for determining when cognitive
control is needed to prevent negative outcomes.
Our suggestion is somewhat the reverse: that conflict, as defined by
Botvinick et al. (2001), is itself a notion that might unify various
"warning signal" functions of the ACC (including the function
represented by the ERN). The initial cortical line of defense against
negative events is a signal that something is amiss, without specifying
what is wrong. This information is provided by the energy [in the
sense proposed by Botvinick et al. (2001)] across multiple cognitive,
affective, and motor representations. Early detection of conflict among
these representations can generate fast aspecific interventions,
whereas slower mechanisms determine the causes of the problem. In its
function, then, conflict detection is very similar to the cyclical
redundancy check used in computer systems to ensure the integrity of
computer data transmissions: a computation that detects the need for
additional corrective computation and retransmission without specifying
exactly what went wrong. If the ERN is indeed a part of midline frontal
, it could represent part of a signal that ensures the consistency of parallel computations in distributed cortical and subcortical areas
(Luu and Tucker, 2001), becoming especially prominent on error trials,
when those computations conflict.
| |
FOOTNOTES |
Received May 21, 2001; revised Aug. 22, 2001; accepted Aug. 31, 2001.
This research was supported by National Institute of Mental Health
Grant MH55286-01 to W.J.G. and by a National Science Foundation Graduate Fellowship to D.E.F. We gratefully acknowledge the comments provided by David E. Meyer and Adrian Willoughby.
Correspondence should be addressed to William J. Gehring, Department of
Psychology, University of Michigan, 525 East University, Ann Arbor, MI
48109-1109. E-mail: wgehring{at}umich.edu.
| |
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ABSTRACT
INTRODUCTION
