Download Negative affect induced by derogatory verbal feedback modulates

doi:10.1093/scan/nsp015
SCAN (2009) 4, 227–237
Negative affect induced by derogatory verbal
feedback modulates the neural signature of
error detection
Daniel Wiswede,1,2 Thomas F. Münte,1,3 and Jascha Rüsseler1,3
1
3
Department of Neuropsychology, Otto-von Guericke Universität Magdeburg, 2Klinik für Psychosomatische Medizin, Universität Ulm, and
Center for Behavioral Brain Sciences, Magdeburg, Germany
The present study examines the influence of induced affective state on performance monitoring. The electroencephalogram was
recorded while human participants engaged in a speeded choice-reaction time task commonly used to examine performance
monitoring processes. Prior to the experiment, participants were randomly allocated to receive either encouraging or derogatory
feedback during task execution. Feedback was based on each participant’s reaction times. Affective state was assessed prior
and after the experiment with a state questionnaire. Although participants of both feedback groups loaded high on items
describing tiredness in the state questionnaire, only those with derogatory feedback loaded higher on negative state items
and lower on positive state items after completion of the experiment. The error-related negativity (ERN) as an index of performance monitoring was increased after derogatory feedback; this difference was not seen at the beginning of the experiment.
Negative state correlated significantly with ERN amplitude. The error positivity, a later component following errors, did not differ
between feedback groups. This study provides further evidence that changes in affective state influence how we monitor ongoing
behavior.
Keywords: event-related brain potentials (ERPs); error-related negativity (ERN); error positivity (Pe); performance monitoring; emotion;
feedback
INTRODUCTION
Many cognitive processes are modulated by affective state.
Positive affect improves performance in a variety of cognitive activities, among them memory (Lee and Sternthal,
1999), problem solving (Isen et al., 1987; Isen, 2001), executive attention (Ashby et al., 2002) and decision making (Isen,
2001). Dreisbach and Goschke describe positive mood as a
mediator between antagonistic constraints of cognitive control processes: Positive affect increases flexibility, but at the
cost of increased distractibility (Dreisbach, 2006; Dreisbach
and Goschke, 2004). Whereas it is well known that negative
affect as it occurs in depression significantly impairs various
aspects of cognition (Ravnkilde et al., 2002), laboratory studies on the impact of negative affect on cognitive performance are scarce. This is surprising, since negative affect
might be of special biological importance, because it accompanies environmental events which might be harmful or
threatening. Some research indicates that negative affect
promotes more careful, risk-averse and analytic behavioral
Received 28 June 2008; Accepted 2 April 2009
Advance Access publication 19 May 2009
We thank two anonymous reviewers for excellent suggestions. This article is supported by grants from the
DFG (MU1311/11-3, SFB 779-A5 to T.F.M.); also supported by the BMBF (contract 01GO0202). D.W. was a
fellow of the Hanse-Wissenschaftskolleg (Hanse Institute for Advanced Study) in Delmenhorst while writing
this article. Thanks to Nadine Strien and Peggy Tausche for support in data collection.
Correspondence should be addressed to Thomas F. Münte, MD, Department of Neuropsychology, Otto-vonGuericke University Magdeburg, Postfach 4120, 39016 Magdeburg, Germany.
E-mail: [email protected].
strategies (Park and Banaji, 2000; Bolte et al., 2003).
In general, it can be said that there is no simple relation in
that positive and negative affect show opposite effects on
behavior. Moreover, results for the impact of negative
affect on cognition are more equivocal (Mitchell and
Phillips, 2007).
In the present investigation, we are interested in the effect
of induced affect on performance monitoring. This might be
a particularly fruitful area of research as there are well
defined behavioral (e.g. post-error slowing) and neurophysiological markers available to characterize performance
monitoring. Moreover, a specific aspect of performance
monitoring, error detection, has been linked to the dopaminergic system, which is also thought to be amenable to
affective manipulations. A neurophysiological marker of performance monitoring is the error-related negativity (ERN),
a negative component in the event-related brain potential
emerging directly after error commission (Falkenstein
et al., 1991; Gehring et al., 1993). The ERN is observed
with a frontocentral distribution and a peak-latency between
50–100 ms after response execution. Electrophysiological
(Van Veen and Carter, 2002) and brain imaging techniques
(Ridderinkhof et al., 2004) point to sources in the anterior
cingulate cortex (ACC). Besides performance monitoring,
the ACC is involved in a multitude of other tasks (Bush
et al., 2000), among them the emotional evaluation of
events (Luu et al., 2000).
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SCAN (2009)
In line with this, the ERN has been shown to be changed
in patients with affective disorders. It is increased in patients
suffering from major depression (Chiu and Deldin, 2007;
Holmes and Pizzagalli, 2008), a condition associated with
negative affect, with medication having a moderating effect
on ERN amplitude (Schrijvers et al., 2008). The ERN is also
increased in subjects with obsessive compulsive disorder
(OCD) (Gehring et al., 2000; Hajcak and Simons, 2002;
Münte et al., 2008; but see also Nieuwenhuis et al., 2005,
for diverging results). Besides recurrent, unwanted thoughts
and rituals, OCD is characterized by a high level of negative
affect (Brown et al., 1998).
However, ERN-amplitude has also been shown to be
modulated by induced changes in affective state in healthy
subjects. Luu et al. (2000) were among the first relating trait
and state measures to ERN amplitude. They found that negative affect goes along with increased ERN amplitude at
the beginning of an experiment. An ERN relationship to
trait, but not to state variables like anger, tension and fatigue
has been reported by Tops et al. (2006). Although subjects
scored higher on the post-experimental state variables tiredness and anger, this was not correlated to ERN amplitude.
In addition, they did not find correlations for trait or state
variables when analyzing the difference ERPs, based on correct response ERPs subtracted from the erroneous response
ERPs (ERN). The authors interpret their findings of a
correlation between trait variables (behavioral shame, agreeableness) with ERN amplitude as reflecting higher task
engagement rather than changes in negative affect. The
ERN is also increased in subjects scoring high on a trait
scale for punishment sensitivity (Boksem et al., 2006a,
2006b), on a anxiety scale (Hajcak et al., 2003a) and on
negative affect scales (Hajcak et al., 2004).
Taken together, there is good evidence that a long-lasting
negative affect associated with psychiatric diseases or character traits goes along with an increased ERN amplitude.
However, how short-term affective state changes interact
with ERN amplitude is less clear. Findings of changed performance monitoring during affective state changes in
normal participants would be important, as they would be
able to explain performance alterations in related everyday
situations. One study (Moser et al., 2005) investigated the he
effects of fear induction on error processing and attentional
allocation. Whereas ERPs indicated reductions in attentional
allocation the ERN was not changed.
The ERN is typically followed by a component more positive for errors (error positivity, Pe). The Pe emerges around
200 ms after error commission and is slightly more posterior
than the ERN (Falkenstein et al., 2000). It has been suggested
that the Pe reflects post-error processing and is susceptible to
error salience (Falkenstein et al., 2000). The relationship
between Pe amplitude and state/trait measures is less clear.
Tops et al. (2006) found no relationship between state and
trait questionnaires and Pe amplitudes, whereas Hajcak and
colleagues (2004) reported a smaller Pe for anxious subjects.
D.Wiswede et al.
There are no Pe-differences between depressed patients
and controls (Chiu and Deldin, 2007; Holmes and
Pizzagalli, 2008).
The present study was conducted to further examine affective influences on performance monitoring. Subjects were
engaging in a speeded response task, while half of the subjects received derogative and half of the subjects received
encouraging feedback. Feedback about an individual’s performance has been shown to be an effective method to
induce changes in affective states, especially for negative
emotions (Westermann et al., 1996). Feedback was based
on individual reaction times and provided after completion
of 30 trials. State changes are assessed by a questionnaire
administered before and after the experiment. Importantly,
normal healthy participants, not pre-selected for trait negative affect, rather than extreme groups were exposed to the
two feedback conditions.
METHODS
Participants
Twenty-five right-handed women participated in the experiment. To keep comparability with previous research (Tops
et al., 2006; Wiswede, 2007; Wiswede et al., 2009), the present study gathered data from female subjects only. ERP data
were analyzed from 24 women (mean age 23.75 years, range
19–30 years). One subject was excluded, because it turned
out after the examination that she had taken psychoactive
medication. Subjects were allocated randomly to receive
either encouraging or derogatory feedback based on their
reaction times. There were 12 subjects in each feedback
group. All participants had normal or corrected to normal
vision, provided written informed consent according to the
Declaration of Helsinki (Br Med J 1991; 302: 1194), and
received either course credit or E 6.50 per hour following
completion of the experiment. The study protocol was
approved by the ethics committee of the University of
Magdeburg.
Stimuli and procedure
The experiment was based on the flanker task (Eriksen and
Eriksen, 1974). A trial consisted of the following sequence
(timing is provided in brackets): fixation cross (600–800 ms,
mean 700 ms), flanker stimulus until response. Flanker stimuli consisted of black capital letters (‘Courier new’ font)
H or S presented in front of a gray background (128, 128,
128 in RGB color space). A congruent flanker string was
either HHHHH or SSSSS; incongruent flanker strings were
SSHSS or HHSHH. Flankers were presented in random
order; there were 60% congruent and 40% incongruent
trials. They covered 2.18 of visual angle. Subjects were
asked to respond as fast and as correct as possible to the
central letter of the flanker string. They responded with a
left-hand key to the H and with a right-hand key to the S.
There were 10 blocks of 210 trials each. A feedback screen
was presented after every 30 trials. A set of 30 trials was
Affective modulation of performance monitoring
SCAN (2009)
Table 1 Feedback given in the two feedback conditions
Feedback algorithm
Feedback group
Level
encouraging
derogatory
‘gut’
‘sehr gut’
‘hervorragend’
‘no feedback’
‘no feedback’
‘no feedback’
‘no feedback’
‘no feedback’
‘no feedback’
‘nicht gut’
‘schlecht’
‘sehr schlecht’
Level
Level
Level
Level
Level
Level
1
2
3
1
2
3
RTN
RTN
RTN
RTN
RTN
RTN
up to 20 ms faster than RTN-1:
up to 40 ms faster than RTN-1:
more than 40 ms faster than RTN-1:
up to 20 ms slower than RTN-1:
up to 40 ms slower than RTN-1:
more than 40 ms slower than RTN-1:
RT, mean reaction time for a sub-block; N, last-performed sub-block. German feedback words are translated as: ‘gut’, ‘good’; ‘sehr gut’, ‘very good’; ‘hervorragend’,
‘brilliant’; ‘nicht gut’, ‘not good’; ‘schlecht’, ‘bad’; ‘sehr schlecht’, ‘very bad’.
defined as a sub-block. Thus, there were seven sub-blocks
per block; during the entire experiment, subjects received 70
feedbacks.
Subjects received feedback based on the mean reaction
time in the last-performed sub-block. Feedback was presented on the feedback screen at the end of each subblock. If the mean RT of the last-performed sub-block N
was faster than the mean RT of sub-block N-1, subjects in
the encouraging feedback group received positively worded
feedback on the screen, whereas subjects in the derogatory
feedback group received no feedback (‘continue with mouse
click’). If the mean RT of the current block was slower than
the mean RT in the sub-block N-1, subjects in the encouraging feedback group received no feedback (‘continue with
mouse click’), whereas subjects in the derogatory feedback
group received negatively worded feedback on the screen.
There were three levels of feedback for both groups, ranging
from mild (‘good’ vs ‘not good’) over moderate (‘very good’
vs ‘bad’) to strong (‘brillant’ vs ‘very bad’). The feedback
algorithm is described in Table 1.
Feedback words were provided in a sentence, for example
‘Your reaction time in the last sub-block was bad’. If no
feedback was given, the sentence ‘press the mouse button
to continue’ was shown. Each feedback screen was terminated by a button press. Therefore, subjects were permitted
to have a break if they omitted a button press after each
feedback screen. This feedback algorithm allowed individually tailored feedback, which represents the actual performance of the subject. Although this means that not every
subject received the same amount of mild, moderate and
strong feedback, the algorithm assured that there were no
large differences in feedback levels between both groups and
fast and slow responders, as was confirmed by a post hoc
analysis of the received feedback.
Questionnaire. Changes in ‘current feeling’ (German:
‘Momentanes Befinden’) were assessed with a questionnaire
‘Aktuelle Stimmungsskala’ (ASTS) administered before and
after the experiment. The ASTS (Dalbert, 1992) is a shortened version of the ‘profile of mood state’ scale (POMS)
(McNair et al., 1971). Non-English questionnaires derived
from the POMS have previously been used to assess state
229
changes in a similar performance monitoring experiment
(Tops et al., 2006). The ASTS consists of 19 German adjectives, and subjects are required to estimate how well an
adjective represents their current feelings on a scale ranging
from 7 (very strong) to 1 (not at all). The items are summarized on five different scales, representing anger, sadness,
hopelessness, positive mood and tiredness.
Data recording and analysis. Recordings were conducted in an electrically shielded recording chamber
equipped with a Neuroscan EEG amplifier. Participants
were seated in a comfortable chair at a distance of 80 cm
to the screen. Stimuli were presented on a 19 in. analog
monitor. Chamber illumination was slightly dimmed.
The electroencephalogram (EEG) was recorded from 29
positions including all 19 standard locations of the 10/20
system with tin electrodes mounted in an elastic cap relative
to a reference electrode placed on the tip of the nose. Eye
movements were recorded with electrodes affixed to the
right and left external canthi [horizontal electrooculogram
(hEOG), bipolar recording] and at the left and right orbital
ridges [vertical electrooculogram (vEOG), bipolar recording]. Impedances of all electrodes were kept below 10 kV.
Biosignals were amplified with a band-pass from 0.05 to
30 Hz and stored with a digitization rate of 250 Hz. Prior
to ERP data analysis, all trials containing eye artifacts were
corrected using blind component separation (Joyce et al.,
2004). Artifacts on recording channels were rejected based
on individual peak-to-peak amplitude criteria using a special
purpose program with individual thresholds between 50 and
100 mV. ERPs were averaged for epochs of 1024 ms starting
100 ms prior to response execution. All ERP figures and all
ERP statistics are based on unfiltered data (except bandpass
from 0.05 to 30 Hz during recording).
ERPs were generated relative to a 100 ms pre-response
baseline and referenced to an electrode placed on the tip
of the nose. Consistent with previous research (Hajcak
et al., 2005), only responses given within 200–800 ms after
the flanker stimulus onset were included in data analysis.
Statistical analysis was based on the within factor correctness
(correct vs erroneous responses) and the group factor feedback (receiving either positive or derogatory feedback). The
ERN was examined at its topographical maximum (averaged
amplitude of electrodes FC1, FC2, Fz, Cz) and was quantified
by a mean amplitude measure in a time window 0 to 80 ms
after the erroneous (ERN) or the correct response. The later
positivity following erroneous responses will be referred to as
Pe. To keep results comparable to previous research (Hajcak
et al., 2004), the Pe was defined as mean amplitude in a
200–400 ms time window. The Pe has been shown to be
somewhat more posterior than the ERN (Falkenstein et al.,
2000). Consistent with previous research (Tops et al., 2006),
it will be analyzed on electrode Pz.
Correlations between questionnaire scores and ERP
amplitudes were computed based on difference waves, the
response-locked ERPs following correct responses were
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SCAN (2009)
subtracted from response-locked erroneous responses. The
difference wave will be referred to as ERN and Pe; the
same time window as for ERN and Pe analysis were applied
to ERN and Pe.
Behavioral responses (RT and error rates) were obtained
and subjected to ANOVA statistics. In addition, post-error
slowing (PES) was assessed. PES refers to the finding that
responses directly following an erroneous response (posterror trials) are slower compared to trials that follow correct
responses (post-correct trials; Hajcak et al., 2003b, 2004;
Rabbitt, 1981, 2002). However, since responses for erroneous
trials are usually faster than for correct trials, this effect could
be caused by regression toward the mean. As fast responses
are relatively rare, it is more likely that a fast response is
succeeded by a slower response. Since errors are usually
faster than correct responses, it is more likely that a relatively
slow correct response follows. To distinguish between posterror effects caused by regression towards the mean from
‘pure’ error-induced RT slowing, a subset of correct trials
was selected that matched the erroneous trials in terms of
reaction time and total number (see Hajcak et al., 2003a,
2003b for a similar procedure). Thus, the selected correct
trials belong to the faster responses among all of the correct
trials. Reaction times of correct trials given directly after
those response-matched correct trials (post-correct trials)
and response times of correct responses given directly after
an erroneous response (post-error trials) provide the basis
for the computation of PES. However, one could argue that
the above-described analysis of PES might include also
responses which are correct just by chance, since premature
responses or guesses are correct in 50% of the cases. In
addition, one reviewer noted that there is a compatibility
bias in errors vs hits (more congruent hits, more incongruent
errors), which should be controlled for in PES analysis. Thus,
for an additional analysis, a subset of congruent and incongruent correct trials was selected that matched the number of
incongruent and congruent erroneous trials. As above, analysis was based on reaction time of succeeding correct trials.
Mood changes were analyzed with ANOVA based on the
within subject factors mood (five levels, indicating the ASTS
scales anger, sadness, hopelessness, positive mood and tiredness), PrePost (two levels, ASTS prior and after the experiment) and the group factor feedback.
RESULTS
Questionnaire
After the experiment, there were differences in current mood
state, as indicated by a significant mood PrePost group
interaction [F (4,88) ¼ 6.3; P < 0.001, see Figure 1 and
Table 2]. To get detailed information about the nature of
mood changes induced by feedback procedure, statistics
were conducted separately for each mood scale and feedback
group. There were no differences between pre- and postexperimental ASTS scales in the encouraging feedback
group, except on the scale tiredness, indicating that
D.Wiswede et al.
Fig. 1 Results of the ASTS questionnaire; feedback group is coded by line thickness.
Upper part: scale level, as used for ANOVA. Items summarized according to the ASTSmanual. Asterisks indicate significant differences between pre- and post-test,
P < 0.001. Lower part: item-level. ASTS-Scores are based on difference postexperimental ASTS minus pre-experimental ASTS results; positive values indicate
that people loaded higher on this item after completion of the EEG experiment.
Table 2 Results of the ASTS questionnaire; mean values for all five scales,
see also Figure 1
Sadness
Hopelessness
Anger
Positive mood
Tiredness
Both groups
Encouraging
Derogatory
Pre
Post
Pre
Post
Pre
Post
1.5
1.3
1.3
4.0
3.1
2.0
1.3
1.7
3.2
4.3
1.6
1.3
1.1
4.1
2.9
1.7
1.2
1.2
3.7
4.1
1.3
1.3
1.4
3.9
3.2
2.3
1.4
2.1
2.7
4.4
Affective modulation of performance monitoring
participants felt somewhat tired after experiment completion
(paired sample t-test, Bonferroni-correction for multiple
comparisons, t (anger, sadness, hopelessness, positive
mood) < 0.04; t (tiredness) ¼ 4.4; P < 0.001). The derogatory feedback group showed post-experimental mood
changes on the scales anger (t ¼ 3.2; P < 0.008), sadness
(t ¼ 3.5; P < 0.005) and positive mood (t ¼ 5.2;
P < 0.001). They were also more tired after the experiment
(t ¼ 4.4; P < 0.001). To summarize, the encouraging feedback group did not feel differently on scales indicating current positive and negative mood after the experiment. The
derogatory feedback group showed increased negative and
decreased positive mood after experiment completion.
Behavioral data
Reaction times. RT data were analyzed by ANOVA with
factors correctness, congruency and feedback. Subjects were
faster in giving erroneous compared to correct responses
[correctness: F(1,22) ¼ 89.4, P < 0.001]. Also, responses were
faster for congruent than for incongruent flanker stimuli
[congruency: F(1,22) ¼ 76.2, P < 0.001]. The congruency
effect was weaker for erroneous responses [congruency correctness interaction; F(1,22) ¼ 39.00; P < 0.001] and
there was a tendency for subjects receiving derogatory feedback to respond faster [feedback F(1,22) ¼ 3.16; P < 0.089].
When separate ANOVAs were computed for correct and
erroneous responses, we obtained for the correct responses
a main effect of congruency [F(1,22) ¼ 212.5; P < 0.001] with
the effects of feedback and the interaction between congruency feedback were non-significant. For the erroneous
responses a marginally significant main effect of congruency
was obtained [F(1,22) ¼ 4.1; P < 0.055, feedback and interaction ns). See Figure 2 and table 3.
As feedback might affect behavior differently over the
course of the session, the experiment was subdivided into
10 blocks, each containing seven sub-blocks of 210 trials.
As shown in Figure 2, the derogatory feedback group was
slightly but not significantly faster after receiving the initial
seven feedback screens (first block); with this trend continuing during the entire experiment [ANOVA, as above, with
additional factor block (10 levels); correctness block:
F(9,198) ¼ 3.55;
P < 0.001,
feedback:
F(1,22) ¼ 3.45;
P < 0.077]. Separate tests conducted for each block revealed
group differences for erroneous responses in blocks 3, 6, 9
and 10 [t(22) > 2.09; P < 0.048].
Error rates. A similar ANOVA design (factors congruency, feedback) was applied to the error rates, which was
found to be increased for incongruent flanker stimuli but
was not modulated by the feedback manipulation [congruency: F(1,22) ¼ 95.7; P < 0.001, feedback and interaction
ns; encouraging/derogatory feedback, congruent 6.5/7.7%,
incongruent 17.8/20.7%]. Dividing the experiment into 10
blocks did not change this pattern [block: F(9,198) ¼ 61.7,
P < 0.001; no significant effect of feedback or interaction].
SCAN (2009)
231
Post-error slowing, response-matched data. Correct
responses were faster when given after a correct trial than
when given after an erroneous trial. This effect was consistently seen in both feedback groups (post correct trials, derogatory/encouraging feedback: 371 ms/392 ms; post-error
trials, derogatory/encouraging feedback: 407 ms/431 ms).
In an ANOVA with response (post-correct vs post-error)
and feedback as factors a main effect of the former was
obtained [F(1,22) ¼ 76.4, P < 0.001; main effect of feedback
and response feedback interaction ns).
Post-error slowing, congruency-matched data. Equalizing the number of congruent and incongruent hits and
errors did not change the PES pattern. Reaction time
increased after erroneous responses; this effect was seen in
both feedback groups (post-correct trials, derogatory/
encouraging feedback: 378 ms/405 ms; post-error trials,
derogatory/encouraging feedback: 407 ms/431 ms; response
(post-correct vs post-error) F(1,22) ¼ 29.8, P < 0.001; main
effect of feedback and response feedback interaction ns).
Response-locked ERPs
A typical ERN was seen for error trials compared to correct
trials (Figure 3A). Visual inspection suggested that the ERN
was more pronounced in the derogatory feedback group.
This was corroborated by the statistical analysis (ANOVA
with correctness and feedback as factors; correctness
F(1,22) ¼ 61.97; P < 0.001; correctness feedback: F(1,22) ¼
9.38; P < 0.01). When separate tests were conducted for correct and erroneous responses, a reliable effect emerged for
the error trials [t (22) ¼ 2.13; P < 0.04] but not for the correct responses [t (22) ¼ 1.49; ns, see Table 4 for amplitudes
in microvolts). Thus, derogatory feedback resulted in a more
negative ERN, but did not influence ERPs to correct
responses.
However, correct responses were given more slowly than
erroneous responses and they vastly outnumber erroneous
trials. To ensure that the null finding for correct responses is
not due to these factors, an analysis was conducted including
only a subset of correct trials, which matched the erroneous
trials in terms of reaction time and total number. This
analysis did not change the pattern described above
[t-test on response-matched correct trials, t(22) ¼ 1.2;
P < 0.23; ns). See Hajcak et al. (2003a, 2004) for a similar
procedure.1
To exclude the possibility that group differences in ERN
amplitude were present prior to mood induction, data sets
were split into two parts: the first part comprised the first
10% errors committed at the beginning of the experiment
and the corresponding correct trials, the second part
1 One reviewer suggested to analyze flanker congruency effects as well. We reanalyzed response-locked ERP
data, including the factor congruency. This did not change the general pattern (congruency, congruency correctness or congruency feedback ns).
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SCAN (2009)
D.Wiswede et al.
Fig. 2 Behavior data. Top row: reaction times divided by flanker congruency. Middle and bottom row: reaction times and error rates with the experiment divided into 10 blocks,
each containing 210 trials. Error bars indicate 1 standard error.
contained the remainder of the data.2 A clear ERN was present in both experimental parts which was modulated
by feedback in the second but not in the first part
2 One could argue that it would be better to split the experiment before subjects received the first
feedback. However, this was not possible here because none of the subjects committed enough errors during
the first 30 trials to generate meaningful ERPs. Errors were not distributed equally across the experiment;
some subjects performed better at the beginning, others at the end, with a slight tendency for increased error
rates during the course of the experiment. Thus, splitting the experiment after a fixed set of feedback screens
would include too few errors in subjects performing well at the beginning of the experiment. Thus, splitting
the experiment after 10% of the errors assured a sufficient amount of errors in the ERP. However, this
splitting procedure caused a different number of feedback screens included in the first 10% of errors. Average
number of feedback screens given during the first 10% of errors: 8.9 out of 70; min 2, max 15; no differences
between feedback groups.
(ANOVA, Factors part, correctness, feedback, interaction
part correctness feedback, F(1,22) ¼ 4.2; P < 0.05). This
three-way interaction was decomposed by assessing feedback
effects separately for the correct and erroneous response
ERPs, and separately for first and second parts. Only the
comparison for erroneous responses in the second part was
significant [t (22) ¼ 2.3; P < 0.03]. See Figure 3B, amplitudes
in microvolts are given in Table 4.
Correlation of mood scales with ERN amplitude. ERN
amplitudes were correlated with the ASTS questionnaire
scores (difference post-experimental minus pre-experimental
ASTS, see Figure 4). There was a significant negative
Affective modulation of performance monitoring
SCAN (2009)
233
correlation for all scales indicating negative states (anger:
r ¼ 0.435; P < 0.034, sadness: r ¼ 0.469; P < 0.02, hopelessness: r ¼ 0.55; P < 0.001, average of all negative scales:
r ¼ 0.56; P < 0.004) and a positive correlation for the positive mood scale (r ¼ 0.57; P < .004). The tiredness scale did
not correlate significantly with ERN amplitude (r ¼ 0.18;
ns). Importantly, the positive mood scale did not correlate
significantly with sadness and hopelessness. Thus, having
more negative or less positive feelings after the experiment
was associated with greater ERN amplitude.
Pe analysis. There was a clear difference between erroneous and correct responses in the Pe time window
(200–400 ms, electrode Pz). This effect was analyzed by
ANOVA (factors correctness, feedback). Importantly, no
main effect of feedback was obtained [F(1,22) ¼ 2.3; ns),
whereas the effect of congruency was significant
[F(1,22) ¼ 79.6; P < 0.001; interaction ns; mean amplitudes:
correct, encouraging/derogative feedback: 5.3/3.2 mV;
error, encouraging/derogative 9.2/11.3 mV). One could
argue that the Pe amplitude might be influenced by differences already emerging in the ERN time window. Thus, the
same analysis as above was conducted with amplitudes
defined as (mean amplitude 200–400 ms) minus (mean
amplitude 0–80 ms), which is the same as setting the baseline
to the ERN time window. This analysis resulted in the same
conclusion; feedback did not influence Pe amplitude [correctness: F(1,22) ¼ 99.4; P < 0.001; feedback F(1,22) ¼ 2.1;
P < 0.16, interaction ns; correct, encouraging/derogative
9.6/9.4 mV; error, encouraging/derogative 5.6/9.2 mV).
Correlation of mood scales with Pe amplitude. There
was no significant correlation between Pe amplitude with
any of the mood scales.
Table 3 Reaction times in milliseconds
Table 4 ERN amplitudes in microvolts
Correctness
Correct
Error
Congruency
Congruent
Incongruent
Congruent
Incongruent
Group
Group
Derogatory
Encouraging
367
405
308
319
395
430
343
345
Derogatory
Encouraging
Correctness
Correct
Error
Correct
Error
Amplitude
5.0
1.1
3.4
0.8
divided into two parts
10%
90%
3.1
2.4
1.8
2.2
5.4
0.9
3.7
1.1
Fig. 3 (a) Response-locked ERPS on midline electrodes and difference waves (ERN; error minus correct). (b) Response-locked ERPs for the beginning of the experiment (first
ten percent of errors) and the remaining experiment.
234
SCAN (2009)
D.Wiswede et al.
Fig. 4 Relationship between ERN amplitude (error minus correct, mean of electrodes Fz, CZ, FC1, FC2 in a time window 0–80 ms after response) and scales for negative (left
site) and positive (right site) state. State measures are based on the difference pre-experimental ASTS minus post-experimental ASTS questionnaire. Numbers on the group
symbols identify individual subjects.
DISCUSSION
The present study provides evidence that induced negative
affect is associated with greater amplitude of the ERN, a key
marker of human performance monitoring. Affective state
measured via a questionnaire administered prior and after
the experiment demonstrated that negative but not positive
emotions were amplified by feedback on task performance.
Behavioral data showed a tendency towards a faster, but
somewhat more erroneous response strategy when receiving
derogatory feedback. The ERN modulation emerged only
after receiving encouraging or derogatory feedback and was
correlated to the degree of increased negative or decreased
positive affective state.
Whereas negative affect was successfully induced by giving
derogatory feedback, encouraging feedback did not elevate
positive feelings. Indeed, even after receiving permanent
encouraging feedback, subjects felt slightly more negative.
Although this might appear surprising at first glance, it is
in line with a meta analysis showing that common methods
of mood induction, among them performance feedback, are
much weaker in inducing positive relative to negative states
(Westermann et al., 1996). Together with the increased
tiredness caused by a demanding task, this might explain
why subjects did not feel more positive even after repeated
encouraging feedback.
Although the effect was not significant across all blocks,
subjects in the derogatory feedback condition were
somewhat faster on errors at the cost of a slightly increased
error rate. Thus, there was a slight tendency in the derogatory feedback group to emphasize speed over accuracy. It is
unlikely, however, that this explains the ERN differences,
because previous research showed that the ERN is smaller
rather than larger when speed is emphasized over accuracy
(Gehring et al., 1993; Falkenstein et al., 2000). Likewise,
a more impulsive strategy with premature responses leads
to a decrease rather than increase of ERN amplitude
(Pailing et al., 2002). Thus, it is unlikely that changes in
response strategies rather than changes in affective state
contributed to the ERN increase.
In addition to negative affect, derogatory feedback might
also lead to increased motivation which in turn might have
influenced ERN amplitude.3 A study by Boksem et al.
(2006a, 2006b) manipulated motivation but not affect and
might thus be revealing. In an extended Simon-Task, motivation was increased by providing extra-money for the
participants exhibiting the best performance. Those who
became faster at the cost of increased error rates did not
show changes in ERN amplitude, whereas subjects who
increased accuracy at the cost of performing more slowly
3 Whereas the two feedback schemes were designed to be symmetrical, it is conceivable that the impact of
the two feedback modes on participants’ motivation to change their behavior might have been different. For
example, the word carrying ‘mild’ feedback in the encouraging and derogatory conditions were ‘good’ and
‘not good’, respectively. The incentive to improve performance might thus have been higher in the derogatory
group. Also, the derogatory feedback might be considered to carry more important information than the
encouraging feedback.
Affective modulation of performance monitoring
increased ERN amplitude. On the behavioral level, this pattern resembles our derogatory (faster RT, higher error rate)
and encouraging feedback (longer RT, lower error rate)
group, respectively, although we note, again, that the differences between groups were non-significant for both, errorrates and reaction times. Moreover, the ERN in the current
study is higher for subjects with faster reaction times (derogatory feedback group), whereas it was lower in the Boksem
et al. (2006a, 2006b) study. This argues against a motivational explanation of the ERN changes observed in the
present study.
Tops and colleagues (2006) reported ERN modification by
trait variables. The present study extends those findings in
that we included an experimental procedure to induce shortterm state changes. In contrast to Tops et al. (2006), we
found that an ERN increase goes along with less positive
or more negative states, even when the correlation is based
on ERN values. Differences in general emotional states
might explain the divergent finding, since it is reasonable
to assume that subjects in Tops et al. felt less negative after
the experiment compared to the derogatory feedback subjects of the present study. Thus, our data show that besides
task engagement and traits, the ERN also reflects short-term
changes in negative affect.
Taken together, the present results are best explained by a
modulation of the ERN by negative affect. Whereas the present results demonstrate the role of short term changes of
affect, previous investigations have shown an ERN increase
with long-term changes of affect, e.g. with depression (Chiu
and Deldin, 2007; Holmes and Pizzagalli, 2008), obsessive
compulsive disorder (Gehring et al., 2000; Hajcak and
Simons, 2002), and increased trait anxiety (Hajcak et al.,
2003a, 2004). In addition, ultra-short affective changes
might also influence the ERN as demonstrated by trial by
trial amplitude changes as a function of whether a flanker
stimulus was preceded by a emotionally negative or positive
picture (Wiswede et al., 2009).
Here, we report increased negativity after erroneous, but
not after correct responses. Moreover, derogative feedback
appears to result in a more positive component following
hits, but this effect was not confirmed statistically. This is in
contrast to previous research, which described an increased
negativity also after correct responses in subjects high in trait
negative affect (Hajcak et al., 2003a, 2004). This has been
interpreted as ‘increased engagement of the responsemonitoring system, evident on both correct and erroneous
trials (Hajcak et al., 2004, p.196)’. Increased negativity after
erroneous and correct responses was also reported when the
flanker stimulus was preceded by an unpleasant picture
(Wiswede et al., 2009), but not in subjects with depression
relative to control subjects (Holmes and Pizzagalli, 2008).
Thus, our null finding is hard to integrate into existing findings and needs further replication.
What could be the mechanism by which induced
negative affect influences the ERN as the neural index of
SCAN (2009)
235
performance monitoring. Both, the ERN and affective
changes have been associated with the neuromodulatory
transmitter dopamine. For the ERN, Holroyd and Coles
(2002) propose that performance monitoring is integrated
into a system for reinforcement learning and that the ERN is
generated by comparing the given response with a prediction
provided by the basal ganglia. Error commission causes a
negative reinforcement learning signal, which is coded by
a phasic decrease in dopaminergic activity of mesencephalic
structures. This, in turn, results in decreased disinhibition
of the ACC and increased ERN amplitude. Several pieces of
evidence support this notion including (i) a decrease of the
ERN in Parkinson’s disease in which dopaminergic neurotransmission is compromised (Falkenstein et al., 2001;
Willemsen et al., 2008), (ii) a modulation of ERN amplitude
by dopaminergic polymorphisms (Krämer et al., 2007),
(iii) the presence of an ERN-like response in intracranial
recordings from the Nucleus accumbens, a key dopaminergic
structure (Münte et al., 2008), (iv) a decrease of the ERN
after the administration of dopamine receptor blocking
drugs (Zirnheld et al., 2004; de Bruijn et al., 2006) and
(v) an increase of the ERN-amplitude after dopaminergic
agents (de Bruijn et al., 2004). On the other hand, changes
in affect have been proposed to lead to changes in dopaminergic tone in the same structures even though the evidence
for a role of increased dopamine in positive affect has been
much better substantiated (Ashby et al., 1999) than a possible decrease of dopamine as underlying negative affect (but
see, for example, Lieberman, 2006). Affect-related tonic and
error-related phasic changes in dopaminergic activity might
thus interact to yield an increased ERN in the derogatory
feedback condition. On the basis of our current results, we
can not rule out the involvement of other neurotransmitter
systems, however. For example, negative affect may lead to
a higher arousal level mediated by noradrenaline. Moreover,
an increase of noradrenergic tone induced by the presynaptic
alpha-2 antagonist yohimbine has been shown to increase
the ERN (Riba et al., 2005).
The present study did not find affect-related differences in
the Pe time window, neither for correct nor for incorrect
trials. In contrast to that, Hajcak et al. (2004) report that
trait negative affect goes along with a general amplitude
decrease around 200–400 ms, seen for correct and for incorrect trials. They suggest that subjects high in negative affect
might be less aware of their errors or might find their errors
less salient. However, we regard this interpretation as counterintuitive, since literature describes a relationship between
anxiety and perfectionism, which is accompanied by overt
critical self-evaluation (Kawamura et al., 2001; Flett et al.,
1994). Thus, errors should be more rather than less salient to
subjects high in negative affect. In addition, earlier research
(Hajcak et al., 2003b) showed that error trials with smaller
Pe are associated with decrease in post-error slowing. If there
is a relationship between negative affect, decreased Pe amplitude and error salience, one would also expect decreased
236
SCAN (2009)
post error slowing in subjects high in negative affect. This
was not the case in Hajcak et al. (2004). Taken together, we
suggest that there is no direct relationship to trait nor state
negative affect and Pe amplitude. The differences between
the present findings and Hajcak et al. (2004) might also be
methodological. It is conceivable that Pe differences are still
influenced by effects seen in the ERN time window.
Additional Pe analysis based on Pe amplitude minus ERN
amplitude, as done in the current study, might abolish group
differences. In addition, visual inspection of the Pe group
differences in Hajcak et al. (2004) reveal that they were
almost absent on parietal electrode Pz. Since the Pe tends
to have a more posterior distribution than the ERN
(Falkenstein et al., 2000, Tops et al., 2006), analysis on
more parietal electrodes could integrate contradicting findings. Changed Pe amplitudes have also not been found in
subjects with depression (Chiu and Deldin, 2007; Holmes
and Pizzagalli, 2008), unless not treated with benzodiazepines (Schrijvers et al., 2008).
To summarize, our findings show a robust influence of
induced short term negative affect on the amplitude of the
ERN. Altered action monitoring processes might therefore
underlie affect-related changes in performance.
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