Nobody Is Perfect: ERP Effects Prior to Performance
Errors in Musicians Indicate Fast Monitoring Processes
Clemens Maidhof1*, Martina Rieger1, Wolfgang Prinz1, Stefan Koelsch1,2
1Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany, 2Department of Psychology, University of Sussex, Brighton, United Kingdom
Background: One central question in the context of motor control and action monitoring is at what point in time errors can
be detected. Previous electrophysiological studies investigating this issue focused on brain potentials elicited after
erroneous responses, mainly in simple speeded response tasks. In the present study, we investigated brain potentials before
the commission of errors in a natural and complex situation.
Methodology/Principal Findings: Expert pianists bimanually played scales and patterns while the electroencephalogram
(EEG) was recorded. Event-related potentials (ERPs) were computed for correct and incorrect performances. Results revealed
differences already 100 ms prior to the onset of a note (i.e., prior to auditory feedback). We further observed that erroneous
keystrokes were delayed in time and pressed more slowly.
Conclusions: Our data reveal neural mechanisms in musicians that are able to detect errors prior to the execution of
erroneous movements. The underlying mechanism probably relies on predictive control processes that compare the
predicted outcome of an action with the action goal.
Citation: Maidhof C, Rieger M, Prinz W, Koelsch S (2009) Nobody Is Perfect: ERP Effects Prior to Performance Errors in Musicians Indicate Fast Monitoring
Processes. PLoS ONE 4(4): e5032. doi:10.1371/journal.pone.0005032
Editor: Naomi Rogers, University of Sydney, Australia
Received July 24, 2008; Accepted February 17, 2009; Published April 1, 2009
Copyright: ? 2009 Maidhof et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This study was supported in part by a grant from the German Research Foundation (Deutsche Forschungsgemeinschaft) awarded to S.K. (KO 2266/4-1).
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: email@example.com
Musical performance is a highly complex and demanding
challenge for the human brain [1–3]. For example, a pianist
playing a Beethoven sonata has to retrieve from memory which
notes have to be played, and in which order this has to be done.
Then, the corresponding motor programs have to be activated in
order to execute the right movements at the right time with the
right intensity. Last but not least, the pianist permanently has to
monitor and evaluate the effects of the executed actions for
correctness. Importantly, all the processes are constantly overlap-
ping in time. Even though the pianist tries to avoid errors like
hitting the wrong key, such errors nevertheless occasionally occur.
One question that arises in the context of any kind of motor
expertise (in our case piano playing) is at what point in time errors
are actually detected by the sensorimotor system. More specifi-
cally, in the present study we investigated whether errors are
detected before a movement is fully executed.
In the motor control literature, it is assumed that fast movement
sequences are controlled without external feedback, because the
delays of sensory feedback are too long to have an impact on
performance (for a review, see ). Accordingly, studies in the
music domain showed that auditory feedback is not a prerequisite
for a successful performance ([5–7], for a review, see ). These
studies found that the complete absence of feedback has mostly no
effects on piano performance (whereas specific alterations of
auditory feedback can profoundly disrupt performance, see [5–
7,9]). Hence, it seems possible that monitoring mechanisms in
pianists can operate without auditory feedback, i.e. without the
perception of an auditory action-effect.
Furthermore, a behavioral study tried to investigate whether
motor experts can detect errors before the movement is completed
. That study found that incorrect responses of expert typists
were less forceful than correct responses. However, it is not clear
whether this effect reflects error-specific processing or results from
less activation of the incorrect response (see e.g. ). In addition,
no real-time correlate of electrical brain activity (e.g., EEG) was
recorded. Recording EEG is a technique particularly suited to
investigate the time course of cognitive processes on a fine-grained
time-scale, as for example the time an error is detected.
EEG-studies on error processing (for reviews, see [12–14])
isolated a component of the event-related potential (ERP)
appearing shortly after participants commit an error in a variety
of speeded response tasks (termed the error-related negativity,
ERN or Ne [15,16]). The ERN/Ne typically peaks around 50–
100 ms after incorrect responses, regardless of the modality in
which the stimulus is presented, and regardless of the modality in
which the response is made.
Although the ERN/Ne typically appears after the commission
of errors, a recent study  found increased negativities before
participants committed errors in a speech production task.
Participants were presented with sequences of word pairs with
identical initial phonemes (e.g., ‘‘ball doze’’, ‘‘bash door’’, ‘‘bean
deck’’). Every few trials, a word pair was marked for overt
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articulation. Importantly, in 10% of the sequences the initial
phonemes of the last word pair were exchanged (e.g. ‘‘darn bore’’).
When participants are required to vocalize those last word pairs,
they are likely to commit errors (e.g. ‘‘barn dore’’), because two
competing speech plans are activated and interfere with each
other. This study  found an increased negativity after the
presentation of the last word pair, and a second negativity after the
presentation of the vocalization prompt. However, it remained
unclear when exactly participants started to produce speech, and
hence the timing of this error response is not evident.
Furthermore, participants saw in each trial the stimuli that
induced conflict and hence the speech errors. Therefore, the
observed ERP effect might have reflected the resolution of conflict
in erroneous trials, rather than the detection of an upcoming error.
Thus, neural correlates of error detection prior to error execution
have remained elusive.
In the present study we investigated expert pianists performing
from memory while we recorded the EEG. That is, we investigated
highly trained experts committing errors in a complex situation, in
which participants did not react to external conflict-inducing
stimuli. We compared the brain potentials before and after correct
and incorrect keystrokes. More specifically, we hypothesized that
differences in the ERP pattern of correct and incorrect keystrokes
would occur even before the completion of the movement.
Ten highly trained pianists (6 female; mean age 24.3 years,
SD=2.8 years) took part in the study. Participants had on average
15.5 years of formal piano training (SD=4.5 years) and were
students at the music conservatory in Leipzig. All participants were
right-handed according to the Edinburgh Handedness Inventory
 (mean laterality quotient: 90.5, SD=11.2) and gave informed
written consent prior to the experiment. The study was approved
by the local ethics committee of the University of Leipzig, and
conducted in accordance with the Declaration of Helsinki.
Material and Apparatus
The stimuli consisted of major scales and two similar scale-like
patterns in two voices (see Figure 1). In each of 24 experimental
blocks, the stimuli had to be produced in different major keys in
one of the following two orders: C-Major/E-Major/D-Major/F#-
Major, or G-Major/B-Major/A-Major (in case of scales, these
sequences were repeated). The order of blocks was randomized
with the constraints that no identical stimulus type (scale, pattern
A, pattern B) occurred in direct succession and that stimuli with
the same order of major keys occurred maximally two times in
The instructed tempo for the scales was 144 beats per minute
(bpm) and for the patterns 69 bpm, i.e. each note event (=two
simultaneous notes) in scales should be produced every 104 ms
and in patterns every 217 ms. Randomly between every 40th to
60th produced note, the auditory feedback of a single note was
manipulated by lowering the pitch of one note by one semitone.
The results of that manipulation will be reported elsewhere.
The pianists performed on a Yamaha digital piano (Clavinova
CLP 130), and listened to their performances via AKG 240 studio
headphones at comfortable listening levels (approximately 65 dB,
dependent on the velocity of a keypress). All tones had the
standard MIDI (Musical Instrument Digital Interface) piano
timbre generated by a Roland JV-2080 synthesizer (Hamamatsu,
In the first part of the experiment (ca. 20 min), pianists listened
to prerecorded versions of the sequences, which were presented in
the same order as the pianists were later required to perform them.
Following a practice period with the notation in front of them,
participants were blindfolded (to exclude visual feedback and to
increase the task difficulty) and instructed to reproduce these
stimuli bimanually (parallel in octaves) in the same tempo as they
heard them before, i.e. stimuli should be reproduced from
memory. If they were not able to perform in the same tempo,
they chose their fastest possible tempo. They were informed about
the feedback manipulations, and instructed to continue playing, in
the event of a feedback manipulation as well as a mistake. When
required, participants could rest between two blocks. Before each
block, an acoustic instruction was played, informing the
participants which scales or patterns they had to produce in the
following block. Each performance session lasted approximately
1.5–2 h, and pianists were paid for their participation.
Data Recording and Analysis
Testing was carried out in an acoustically and electrically
shielded EEG cabin. Musical data were processed in MIDI format
with a modified version of the open source program ‘‘FTAP’’
[19,20]. To synchronize musical and electrophysiological data, this
program sent trigger signals concurrently with every 5th keypress
(and concurrently with the feedback manipulations) to the EEG
acquisition computer. For offline analyses, the MIDI information
(including timing information, keypress velocities, and pitch) was
saved on a hard disk.
The EEG was recorded with 60 Ag/AgCl scalp electrodes
placed according to the extended 10–20 system (FP1, FP2, AF7,
AF3, AFZ, AF4, AF8, F9, F7, F5, F3, FZ, F4, F6, F8, F10, FT9,
FT7, FC5, FC3, FCZ, FC4, FC6, FT8, FT10, A1, T7, C5, C3,
CZ, C4, C6, T8, A2, TP9, TP7, CP5, CP3, CPZ, CP4, CP6, TP8,
TP10, P9, P7, P5, P3, PZ, P4, P6, P8, P10, PO7, PO3, POZ, PO4,
PO8, O1, OZ, O2), referenced to the electrode at the left mastoid.
The ground electrode was placed on the sternum. The horizontal
electrooculogram (EOGH) was recorded bipolarly from electrodes
Figure 1. Examples of the stimulus material. A) Pattern A in C-
Major. B) Pattern B in C-Major and C) a diatonic scale in C-Major.
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placed on the outer left and right canthus and the vertical EOG
(EOGV) from electrodes placed below and above the left eye.
Impedance was kept below 5 kV. EEG signals were digitized with
a sampling frequency of 500 Hz.
After data acquisition, EEG data were downsampled to 250 Hz
to reduce the data size and re-referenced to the arithmetical mean
of both mastoid electrodes. We then performed an independent
component analysis (ICA) with standard parameters for artifact
removal as implemented in EEGLAB 4.51 (Swartz Center for
Computational Neurosciences, La Jolla, CA; http://www.sccn.
ucsd.edu/eeglab ). After calculating the independent compo-
nents, artifactual components due to eye movements and blinks
were selected based on the following criteria: a component was
considered to be artifactual if its topography showed peak activity
only over the horizontal or vertical eye electrodes, if it showed a
smoothly decreasing power spectrum (which is typical for eye
movement artifacts, see ), and if the component’s activity
contributed mainly to the raw EEG signal recorded by the
horizontal and vertical eye electrodes. The artifactual components
were subtracted from the EEG data, and then the EEG data were
filtered with a 0.25–25 Hz bandpass, finite impulse response filter.
Subsequently, an automatic rejection procedure was applied: Eye
artifacts (which could have still been present after the ICA
rejection procedure) were rejected whenever the standard
deviation within a 200 ms window centered around each sampling
point exceeded 25 mV in the EOG. Artifacts caused by drifts and
body movements were eliminated by rejecting sampling points
whenever the standard deviation exceeded 25 mV at any electrode
either within a 200, or within a 800 ms gliding window.
Performance errors were defined as playing an incorrect key
with one hand while pressing the correct key with the other hand.
Errors were manually identified off-line. Epochs containing other
types of errors like omissions or incorrect keypresses with both
hands simultaneously were discarded (on average, there were only
18 trials per participant containing the latter type of error). Only
errors that were preceded by a 1 s period of error-free
performance (and free of feedback manipulations) were analyzed.
Errors were identified separately for the scales and the patterns to
take into consideration that the different tempi of both types of
stimuli possibly influenced ERP effects. On average, there were
only 9 error trials during the performance of the scales, which is
insufficient to obtain a reasonable signal-to-noise ratio. Therefore,
these data were discarded and we will thus only report the data of
the performances of the patterns.
Subsequent to the rejection and filtering procedures, event-
related potentials were computed for incorrect (M=62, SD=37)
andcorrect (M=682, SD=187) keypressesfor2000 mstime-locked
to the onset of the tones (1000 ms before the onset and 1000 ms
after the onset). The baseline was set from 1000 ms to 800 ms
before the onset of the tone. For the computation of the signal-to-
noise ratio (SNR),we estimatedthe signal power by determining the
highest amplitude in the ERPs between -800 ms and +1000 ms.
The noise power was estimated by the standard deviation in the
baseline time interval, i.e. between 21000 and 2800 ms. The SNR
averaged across all participants was 11.1 (SD=5.2).
For statistical analysis, mean ERP amplitude values were
calculated for two regions of interest (ROIs) over the midline of
the scalp: one anterior with electrodes AFZ, FZ, FCZ, and CZ,
and one posterior with electrodes CPZ, PZ, POZ, and OZ. ERPs
were statistically analyzed by repeated measures analyses of
variance (ANOVAs) with the factors Keypress (correct, incorrect)
and AntPos (anterior, posterior). Time windows for statistical
analyses of ERP data were chosen based on visual inspection of the
grand average and centered around the maximum of the
differences between correct and incorrect performed notes. The
resulting time windows were 2150 to 280 ms (i.e. before the note
onset) and 240 to 320 ms (after note onset).
For the behavioral data, we analyzed the MIDI velocities (i.e., the
speed at which pianists pressed a key, measured on a scale ranging
from 0 to 127; MIDI velocity corresponds to the loudness of the
producedtone) of incorrectnotes,simultaneouscorrect notes(played
by the other hand), and correct notes when there was no error in
the onset of an erroneous note and the onset of the previous note
(played by the same hand), between the onset of the simultaneously
played correct note and the previous correct note (played by the
same hand), and between the onset of successive correct notes (i.e.
when there was no error in either hand). Whenever the IOI
exceeded1000 ms,thisIOIwasdiscarded.The(signed valuesofthe)
asynchronies of keypresses were calculated between errors and the
simultaneous correct notes, and between two simultaneous correct
notes. All behavioral data were statistically analyzed using repeated
measures ANOVAs and paired samples t-tests.
Pianists pressed incorrect and correct keys with different MIDI
velocities. An ANOVA with factor condition (incorrect keypress,
simultaneous correct keypress, correct keypress when no error was
(F(2,18)=15.18, p,.0001). Contrasts indicated that participants
pressed incorrect keys with a lower velocity (M=59, SD=8) than
the simultaneous correct keypresses (M=63, SD=7; p=.003) and
keypresses when there was no error present (M=64, SD=7,
p,.0001). There was no difference between simultaneous correct
keypresses (when an error was present in the other hand) and
keypresses when there was no error present (p=.4). This pattern of
results indicates that the lower velocity of the erroneous keypress did
not influence the simultaneous correct keypress of the other hand.
Pianists produced correct and incorrect keypresses with different
IOIs. An ANOVA with factor condition (IOIs between incorrect
keypress and the previous keypress, IOIs between simultaneous
correct keypress and the previous correct keypress, IOIs between
two successive correct keypresses) showed a main effect of
condition (F(2,18)=21.22, p=.001). Contrasts revealed that there
was no difference between IOIs between incorrect keypress and
SD=106 ms) and IOIs between the simultaneous correct keypress
and the previous correct keypress by the same hand (M=404 ms,
SD=109 ms; p=.24). However, IOIs between incorrect keypress
and the previous keypress were prolonged compared to the IOIs
between successive correct keypresses when there was no error
present (M=367 ms, SD=89 ms; p=.001), indicating that the
upcoming error slowed down the keypresses (pre-error slowing).
Note that the overall tempo (i.e., the IOIs between correct notes) is
slower than initially instructed. This is based on the fact that
participants could choose their own (fastest possible) tempo
whenever they were not able to perform in the instructed tempo,
resulting in a slower mean performance speed.
The asynchronies between two simultaneous correct notes
(M=22 ms, SD=5 ms) and between an incorrect and a
simultaneous correct note (M=24 ms, SD=9 ms) did not
significantly differ from each other (t(9)=2.71, p=.5).
Figure 2.A shows the grand-averaged waveforms time-locked to
the onset of keypresses. Compared to correct keypresses, incorrect
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keypresses elicited an increased negativity before a wrong key was
actually pressed down. The difference was maximal around
100 ms before the onset of the keypresses and showed a central
distribution (see Figure 2.B). An ANOVA for a time window
ranging from 2150 to 280 ms (i.e., before note onset) with factors
Keypress and AntPos indicated a significant main effect of Note
(F(1,9)=8.3, p=.018), but no interaction between Keypress and
AntPos (F,1). The pre-error negativity was followed by a later
positive deflection with an amplitude maximum at around 280 ms
after the onset of an incorrect note. This potential showed a
fronto-central scalp topography (see Figure 2.A and 2.B). An
ANOVA for a time window from 240 ms to 320 ms with factors
Keypress and AntPos revealed a main effect of Keypress
(F(1,9)=9.14, p=.014) and an interaction between factors Key-
press and AntPos (F(1,9)=6.8, p=.028), indicating that amplitude
values were larger over frontal leads than over parietal leads.
Note that IOIs were prolonged before incorrect keypresses and
that incorrect keys were pressed with lower velocities. Hence, the
ERP difference occurring before the keypress might be due to
motor-related processes, such as adjusting the force of the muscles
involved in the movement, rather than cognitive processes
underlying error monitoring. Such motor-related processes are
expected to be lateralized [22,23], whereas cognitive processes of
error processing do not show hemispheric differences (for reviews,
see [12–14]). To dissociate between a motor and a cognitive
explanation, we tested the lateralization of the ERP difference
between correct and incorrect keypresses: The ERPs were
analyzed separately for left-hand and right-hand errors, with the
assumption that motor-related processes of left-hand errors would
be reflected in potentials over right-hemispheric motor areas, and
Potential maps of ERPs of left-hand errors compared to correct
notes (averaged across both hands) are shown in Figure 3.A
(difference potential: correct notes subtracted from left-hand
errors). The analogous comparison for the right-hand errors is
shown in Figure 3.B (correct notes subtracted from right-hand
errors). For this analysis, three participants were excluded due to
the small number of trials (,10). An ANOVA performed on these
difference potentials with factors Hand (left, right), and Hemi-
sphere (left ROI including FC3, FC5, C3, and C5 vs. right ROI
including FC4, FC6, C4, and C6) showed no effect of Hand
(F(1,6),1, p=.78), reflecting that the amplitude of ERP effects did
Figure 2. ERP results and scalp distributions of correct and incorrect piano performances. A) Grand-average ERPs elicited by correctly and
incorrectly performed keypresses. The arrow indicates the note onset and thus the onset of the auditory feedback. The grey areas show the time
windows chosen for statistical analyses for electrodes that were included in the ROIs. Analysis revealed an early increased negative potential prior to
the onset of the note (termed pre-error negativity) and a subsequent positive deflection, resembling the early Error positivity (Pe) or the P3a. B) shows
the scalp distributions for the difference potentials for correct keypresses subtracted from incorrect keypresses.
Figure 3. Scalp maps of the difference potentials of left and
right-hand errors. A) shows the difference potential for correct
keypresses subtracted from left-hand errors and B) the difference
potential for correct keypresses subtracted from right-hand errors.
Correct keypresses are averaged across both hands.
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not differ between left- and right-hand errors, and no interaction
between factors Hand and Hemisphere (F(1,6),1, p=.88),
reflecting that potentials elicited by the errors were not lateralized.
Brain potentials elicited by correct and incorrect keypresses of
expert pianists differed already 100 ms before keypresses were fully
executed, and thus prior to the onsets of erroneous tones (pre-error
negativity). The early detection of errors is also observable at the
behavioral level: IOIs before erroneous keypresses were pro-
longed, and erroneous keypresses were executed more slowly.
However, the asynchronies between the hands did not increase in
erroneous trials. 280 ms after erroneous keypresses a frontocentral
positive potential was observed. In the following we will first
discuss processes occurring before errors are committed and then
turn to the processes occurring after errors are committed.
We assume that the ERPs elicited by incorrect performances
reflect neural mechanisms that detect errors before they are
actually committed, and before auditory feedback is available.
Given the speed of movement sequences in the present study
(about 3 keypresses with each hand per second), we suggest that
internal forward models predicting the sensory consequences of
actions [24–28] are the basis for detecting the errors even before
they were fully executed: Monitoring of fast movements, whose
control cannot wait for sensory feedback, has to rely mainly on
predictive (feedforward) mechanisms that compare internal action
goals with the predicted consequences of planned movements.
Studies investigating the activity of neurons in the primary
motor cortex (M1) of non-human primates showed that the latency
between the first activity in M1 and movement onset is variable
and can range up to several hundred milliseconds [29–32], but the
typical assumed latency is around 100 to 150 ms (e.g. ). At the
same time as the motor command is sent from M1 to the
periphery, an efference copy (or corollary discharge) is created in
brain structures also involved in the generation of the movement.
The efference copy is, however, not used to generate the ongoing
motor activity, but can be used to predict the outcome of the
motor command [24–28] (information of efference copies interact
at several levels of the central nervous system, and often modulate
sensory processing; for reviews, see [34,35]). The predicted
outcome can be compared to the intended outcome, and an error
signal is generated whenever there is a mismatch between
intended goal and predicted consequence. The error signal can,
in turn, modulate the motor command .
Accordingly, we assume that the mismatch between the
predicted consequence of a planned keypress and the associated
internal action goal, as detected by a feedforward control
mechanism, is reflected in the pre-error negativity. From the
present data we cannot conclude during which part of the
movement (planning, initiation, early stages of execution) this
feedforward control mechanism exactly occurs. However, it is
important to note that a detection mechanism seems to operate
before the pianists receive auditory feedback of their errors, i.e.
before pianists perceive the auditory results of their actions.
The modulation of the motor command by the error signal of
the feedforward mechanism might have resulted in the prolonged
IOIs before and the slower velocities of incorrect keypresses,
probably reflecting an attempt to avoid the error. In contrast to
what one might have assumed, IOIs were not only prolonged for
the hand that pressed the incorrect key, but IOIs were also
prolonged for the other hand that pressed simultaneously the
correct key. This is presumably due to bimanual coupling: studies
show that bimanual movements begin and end at similar times,
even when they have different parameters (e.g. amplitudes) and
movement times differ when the respective movements are
performed in isolation by one hand [36–38]. Our task required
tight bimanual coupling of the hands in terms of the timing.
Correspondingly, asynchronies between the hands did not differ
when an error was present or not.
One could argue that the pre-error negativity might reflect an
error during memory retrieval and, thus, an even earlier stage than
motor control or error monitoring. It is assumed that serial-
ordering errors (i.e. notes that are intended at another location in
the sequence) reflect the current activation of this erroneous
element in memory [39,40]. However, because pianists in our
study performed the same tones in parallel with both hands (one
octave apart), errors reflecting false memory retrieval should occur
in both hands, instead of only in one. Because we only analyzed
errors committed by one hand, it is unlikely that the pre-error
negativity reflects false retrieval from memory. Moreover, one
could also argue that the ERP difference before the note onsets
might be due to motor-related processes. Motor execution
processes are, however, expected to elicit lateralized EEG
potentials [22,23], which is not consistent with our data: The
separate analysis of left-hand and right-hand errors did not reveal
any lateralization effect. Therefore, it is unlikely that the ERP
difference reflects simply motor-related processes, but rather
processes operating at a higher cognitive level, associated with
monitoring or control. Finally, one could reason that the increased
negativity before incorrectly played notes reflects a process that
actually results in the production of an error. For instance, a recent
study  showed that lapses in preparatory attention networks
can lead to production errors. In that study the amplitude of the
Contingent Negative Variation (CNV), a brain potential indexing
preparatory attention, was decreased before stimulus presentation
when an erroneous response occurred. Therefore, if lapses in
preparatory attention were responsible for the errors in our study,
one would have expected a similar decrease in ERP amplitude.
However, ERPs elicited before incorrect performances had larger
(negative) amplitude values than those elicited before correct
performances, rendering such an explanation unlikely. Further, we
think that the observed ERP difference in our study occurred too
late to reflect lapses in attention. Considering the delay of activity
in M1 to movement onset (presumably around 100 to 150 ms),
lapses of attention should be observable before that time (as it was
reported in ), i.e. several hundred milliseconds before the
button press. Thus, the fact that an increased negativity (instead of
a decreased negative amplitude) was observed, in combination
with the observed timing of the effect (around 100 ms before
movement completion) renders it improbable that lapses in
preparatory attention can account for the present findings. A
similar explanation for the present results might be a temporal
disengagement of the action monitoring system. Two other studies
[42,43] found that trials preceding erroneous trials (in Eriksen
flanker and Stroop tasks) showed an enhanced positivity
(compared to trials preceding correct trials), thereby ‘foreshadow-
ing’ errors in future trials. This effect (termed the Error-preceding
Positivity, EPP) is thought to reflect ‘‘transient deficiencies in the
functioning of the monitor system prior to actual execution of an
error’’ . These deficits may be associated with failures to
activate adaptive control processes, resulting in occasional future
errors. Because we observed no enhanced positivity before
production errors, it is unlikely that a disengagement of the action
monitoring system is reflected in the observed ERP effect.
The expertise of our participants and the characteristics of our
task might explain why we did not observe an ERN (a potential
frequently observed following the commission of errors, see [12–
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14] for reviews) or an EPP component: In contrast to most studies
investigating error processing (mostly in simple speeded response
tasks, including the aforementioned studies [41–43]) our partici-
pants did not react to external stimuli according to pre-defined
arbitrary rules. Instead, they had to select the appropriate motor
commands according to internal goals that they formed on the
basis of instructions and the musical knowledge stored in their
long-term memories. In addition, the present experimental
situation reflects a task for which musicians are highly trained,
compared to the button press responses to stimuli presented in
standard error processing paradigms. Consequently, the error
could be detected earlier than in choice reaction tasks. Incorrect
notes also violated the regularity of the sequences and thus
represented auditory oddballs, which are known to elicit a
mismatch negativity (MMN; for a review, see ). However,
no MMN was visible in the ERPs, perhaps because it was
overlapped by the positive potential emerging in a similar latency
range (see below). Note that the magnitude of the ERPs (around
3 mV) was rather small compared to the amplitude of ERPs
elicited in standard error processing paradigms [12–14]. This is
probably due to the complexity of our task, involving a range of
interacting cognitive processes (e.g., memory retrieval, motor
planning, performance monitoring etc., see Introduction). In
addition, the simultaneous processing of input from different
sensory systems (auditory, tactile, somatosensory) might have
influenced the magnitude of the ERPs.
The fronto-central positive potential (emerging around 200 ms
and) peaking around 280 ms after incorrect keypresses strongly
resembles the Error Positivity (Pe), a potential frequently observed
following the ERN in studies of error processing (for reviews, see
[45,46]). Although the functional significance of the Pe has
remained rather unclear, three hypotheses about the Pe have
emerged: The affective-processing hypothesis [45,47] suggests that
the Pe reflects affective processing of the committed error or its
consequences. According to the behavior-adaption hypothesis
, the Pe reflects the adaptation of response strategy after an
error has been perceived, involving remedial performance
adjustments following errors. The error-awareness hypothesis
[49,50] proposes that the Pe reflects the conscious recognition of
a committed error. There is only little evidence in favor of the first
two hypotheses, whereas there are some empirical data supporting
the error-awareness hypothesis (e.g. [46,50,51]). Another way of
addressing the question about the functional significance of this
potential is to consider its similarities to the P300 component,
which has led to the suggestion that the Pe could reflect a P3b
associated with the motivational significance of an error (for a
review on the P300, see ). The Pe, however, can be
decomposed into an early and a late component, very similar to
the distinction between P3a (indexing the involuntarily attention
switch to novel and deviant stimuli, e.g. [53,54]) and P3b (taken to
reflect memory updating operations after task-relevant stimuli, e.g.
[55,56], but see also ). However, there are no studies directly
comparing the early Pe with the P3a and the late Pe with the P3b,
and therefore it remains unclear whether the early Pe reflects
similar processes as the P3a. Based on previous studies
[50,51,55,58] we suggest that the positive deflection observed in
the present study most likely reflects an early Pe or a P3a. Whether
this potential is related to later processing stages of tactile and/or
auditory feedback of the error, or simply due to the processing of
an oddball stimulus (leading to an involuntary reallocation of
attention) remains to be clarified. One way to address this would
be to investigate performance errors committed in the absence of
auditory feedback: if these errors also elicit the positivity, this
potential cannot reflect auditory novelty processing.
In conclusion, the method of investigating motor experts in a
natural context, accompanied with on-line measures of electrical
brain activity (like EEG), can help to answer crucial questions in
the domain of motor control and action monitoring. The
occurrence of a pre-error negativity indicates that an early error
detection mechanism operates in pianists even before an erroneous
movement is fully executed. Our data also show that the early
detection of errors influences movement execution, resulting in
pre-error slowing of both hands and in keypresses with reduced
velocity of the erroneous hand only. We assume that the
underlying process is the detection of a mismatch between a
predicted sensory consequence of an action and the intended
action goal. Thus, our results reveal neural mechanisms that are
able to detect errors prior to the execution of erroneous
The authors would like to thank Sylvia Stasch for help in data acquisition,
and Sebastian Jentschke and Daniela Sammler for help in data analysis.
Conceived and designed the experiments: CM MR SK. Performed the
experiments: CM. Analyzed the data: CM. Wrote the paper: CM MR WP
1. Mu ¨nte TF, Altenmu ¨ller E, Ja ¨ncke L (2002) The musician’s brain as a model of
neuroplasticity. Nat Rev Neurosci 3: 473–478.
2. Palmer C (2005) Sequence Memory in Music Performance. Curr Dir Psychol Sci
3. Zatorre RJ, Chen JL, Penhune VB (2007) When the brain plays music: auditory-
motor interactions in music perception and production. Nat Rev Neurosci 8:
4. Kawato M (1999) Internal models for motor control and trajectory planning.
Curr Opin Neurobiol 9: 718–727.
5. Finney SA (1997) Auditory Feedback and Musical Keyboard Performance.
Music Percept 15: 153–174.
6. Finney SA, Palmer C (2003) Auditory feedback and memory for music
performance: Sound evidence for an encoding effect. Mem Cognit 31: 51–
7. Pfordresher PQ (2005) Auditory feedback in music performance: the role of
melodic structure and musical skill. J Exp Psychol Hum Percept Perform 31:
8. Pfordresher PQ (2006) Coordination of perception and action in music
performance. Advances in Cognitive Psychology 2: 183–198.
9. Pfordresher PQ (2003) Auditory feedback in music performance: Evidence for a
dissociation of sequencing and timing. J Exp Psychol Hum Percept Perform 29:
10. Rabbitt PM (1978) Detection of errors by skilled typists. Ergonomics 21:
11. Gehring WJ, Coles MG, Meyer DE, Donchin E (1995) A brain potential
manifestation of error-related processing. In: Karmos G, Molna ´r M, Cse ´pe V,
Czigler I, Desmedt JE, eds. Perspectives of event-related potentials research.
Amsterdam: Elsevier. pp 267–272.
12. Botvinick MM, Braver TS, Barch DM, Carter CS, Cohen JD (2001) Conflict
monitoring and cognitive control. Psychol Rev 108: 624–652.
13. Yeung N, Cohen JD, Botvinick MM (2004) The neural basis of error detection:
conflict monitoring and the error-related negativity. Psychol Rev 111: 931–959.
14. van Veen V, Carter CS (2006) Error detection, correction, and prevention in the
brain: a brief review of data and theories. Clin EEG Neurosci 37: 330–335.
15. Falkenstein M, Hohnsbein J, Hoormann J, Blanke L (1990) Effects of errors in
choice reaction tasks on the ERP under focused and divided attention. In:
Brunia CHM, Gaillard AWK, Kok A, eds. Psychophysiological Brain Research.
Tilburg, The Netherlands: Tilburg University Press. pp 192–195.
16. Gehring WJ, Goss B, Coles MG, Meyer DE, Donchin E (1993) A Neural System
For Error Detection And Compensation. Psychol Sci 4: 385–390.
17. Mo ¨ller J, Jansma BM, Rodriguez-Fornells A, Mu ¨nte TF (2007) What the Brain
Does before the Tongue Slips. Cereb Cortex 17: 1173–1178.
18. Oldfield RC (1971) The assessment and analysis of handedness: The Edinburgh
Inventory. Neuropsychologia 9: 97–113.
Performance Errors Musicians
PLoS ONE | www.plosone.org6 April 2009 | Volume 4 | Issue 4 | e5032
19. Finney SA (2001a) FTAP: a Linux-based program for tapping and music Download full-text
experiments. Behav Res Methods Instrum Comput 33: 65–72.
20. Finney SA (2001b) Real-time data collection in Linux: a case study. Behav Res
Methods Instrum Comput 33: 167–173.
21. Delorme A, Makeig S (2004) EEGLAB: an open source toolbox for analysis of
single-trial EEG dynamics including independent component analysis. J Neurosci
Methods 134: 9–21.
22. Shibasaki H, Barrett G, Halliday E, Halliday AM (1980) Components of the
movement-related cortical potential and their scalp topography. Electroence-
phalogr Clin Neurophysiol 49: 213–226.
23. Colebatch JG (2007) Bereitschaftspotential and movement-related potentials:
origin, significance, and application in disorders of human movement. Mov
Disord 22: 601–610.
24. Wolpert DM, Ghahramani Z, Jordan MI (1995) An internal model for
sensorimotor integration. Science 269: 1880–1882.
25. Miall RC, Wolpert DM (1996) Forward models for physiological motor control.
Neural Netw 9: 1265–1279.
26. Wolpert DM, Miall RC, Kawato M (1998) Internal models in the cerebellum.
Trends Cogn Sci 2: 338–347.
27. Desmurget M, Grafton S (2000) Forward modeling allows feedback control for
fast reaching movements. Trends Cogn Sci 4: 423–431.
28. Wolpert DM, Ghahramani Z (2000) Computational principles of movement
neuroscience. Nat Neurosci 3 Suppl: 1212–1217.
29. Evarts EV (1974) Precentral and postcentral cortical activity in association with
visually triggered movement. J Neurophysiol 37: 373–381.
30. Porter R, Lewis MM (1975) Relationship of neuronal discharges in the
precentral gyrus of monkeys to the performance of arm movements. Brain Res
31. Thach WT (1978) Correlation of neuronal discharge with pattern and force of
muscular activity, joint position, and direction of intended next movement in
motor cortex and cerebellum. J Neurophysiol 41: 654–676.
32. Holdefer RN, Miller LE (2002) Primary motor cortical neurons encode
functional muscle synergies. Exp Brain Res 146: 233–243.
33. Hatsopoulos NG, Xu Q, Amit Y (2007) Encoding of movement fragments in the
motor cortex. J Neurosci 27: 5105–5114.
34. Poulet JFA, Hedwig B (2006) New insights into corollary discharges mediated by
identified neural pathways. Trends Neurosci 30: 14–21.
35. Crapse TB, Sommer MA (2008) Corollary discharge circuits in the primate
brain. Curr Opin Neurobiol 18: 1–6.
36. Marteniuk RG, MacKenzie CL, Baba DM (1984) Bimanual Movement
Control: Information Processing and Interaction Effects. Q J Exp Psychol
37. Spijkers W, Heuer H, Kleinsorge T, van der Loo H (1997) Preparation of
bimanual movements with same and different amplitudes: specification
interference as revealed by reaction time. Acta Psychol 96: 207–227.
38. Swinnen SP, Wenderoth N (2004) Two hands, one brain: cognitive neuroscience
of bimanual skill. Trends Cogn Sci 8: 18–25.
39. Palmer C, van de Sande C (1993) Units of knowledge in music performance.
J Exp Psychol Learn Mem Cogn 19: 457–470.
40. Palmer C, Pfordresher PQ (2003) Incremental planning in sequence production.
Psychol Rev 110: 683–712.
41. Padilla ML, Wood RA, Hale LA, Knight RT (2006) Lapses in a Prefrontal-
Extrastriate Preparatory Attention Network Predict Mistakes. J Cogn Neurosci
42. Ridderinkhof KR, Nieuwenhuis S, Bashore T (2003) Errors are foreshadowed in
brain potentials associated with action monitoring in cingulate cortex in humans.
Neurosci Lett 348: 1–4.
43. Hajcak G, Nieuwenhuis S, Ridderinkhof KR, Simons RF (2005) Error-
preceding brain activity: Robustness, temporal dynamics, and boundary
conditions. Biol Psychol 70: 67–78.
44. Na ¨a ¨ta ¨nen R, Paavilainen P, Rinne T, Alho K (2007) The mismatch negativity
(MMN) in basic research of central auditory processing: a review. Clin
Neurophysiol 118: 2544–2590.
45. Falkenstein M, Hoormann J, Christ S, Hohnsbein J (2000) ERP components on
reaction errors and their functional significance: a tutorial. Biol Psychol 51:
46. Overbeek TJM, Nieuwenhuis S, Ridderinkhof KR (2005) Dissociable Compo-
nents of Error Processing. On the Functional Significance of the Pe Vis-a-vis the
ERN/Ne. J Psychophysiology 19: 319–329.
47. van Boxtel GJM, van der Molen MW, Jennings JR (2005) Differential
Involvement of the Anterior Cingulate Cortex in Performance Monitoring
During a Stop-Signal Task. J Neurophysiol 19: 1–10.
48. Hajcak G, McDonald N, Simons RF (2003) To err is autonomic: Error-related
brain potentials, ANS activity, and post-error compensatory behavior.
Psychophysiology 40: 895–903.
49. Kaiser J, Barker R, Haenschel C, Baldeweg T, Gruzelier JH (1997) Hypnosis
and event-related potential correlates of error processing in a stroop-type
paradigm: a test of the frontal hypothesis. Int J Psychophysiol 27: 215–222.
50. Nieuwenhuis S, Ridderinkhof KR, Blom J, Band GP, Kok A (2001) Error-
related brain potentials are differentially related to awareness of response errors:
Evidence from an antisaccade task. Psychophysiology 38: 752–760.
51. Endrass T, Reuter B, Kathmann N (2007) ERP correlates of conscious error
recognition: aware and unaware errors in an antisaccade task. Eur J Neurosci
52. Polich J (2007) Updating P300: An integrative theory of P3a and P3b. Clin
Neurophysiol 118: 2128–2148.
53. Escera C, Alho K, Winkler I, Na ¨a ¨ta ¨nen R (1998) Neural Mechanisms of
Involuntary Attention to Acoustic Novelty and Change. J Cogn Neurosci 10:
54. Escera C, Corral MJ (2007) Role of Mismatch Negativity and novelty-P3 in
involuntary auditory attention. J Psychophysiol 21: 251–264.
55. Courchesne E, Hillyard SA, Galambos R (1975) Stimulus novelty, task relevance
and the visual evoked potential in man. Electroencephalogr Clin Neurophysiol
56. Knight R (1996) Contribution of human hippocampal region to novelty
detection. Nature 383: 256–259.
57. Verleger R (2008) P3b: Towards some decision about memory. Clin
Neurophysiol 119: 968–970.
58. van Veen V, Carter CS (2002) The timing of action-monitoring processes in the
anterior cingulate cortex. J Cogn Neurosci 14: 593–602.
Performance Errors Musicians
PLoS ONE | www.plosone.org7 April 2009 | Volume 4 | Issue 4 | e5032