Parallel systems of error processing in the brain

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NeuroImage 22 (2004) 590–602

Parallel systems of error processing in the brain

Juliana Yordanova,a,b,* Michael Falkenstein,a Joachim Hohnsbein,a and Vasil Koleva,b

a Institute of Occupational Physiology, D-44139 Dortmund, Germanyb Institute of Physiology, Bulgarian Academy of Sciences, BG-1113 Sofia, Bulgaria

Received 19 August 2003; revised 20 January 2004; accepted 24 January 2004

Available online 14 April 2004

Major neurophysiological principles of performance monitoring are

not precisely known. It is a current debate in cognitive neuroscience if

an error-detection neural system is involved in behavioral control and

adaptation. Such a system should generate error-specific signals, but

their existence is questioned by observations that correct and incorrect

reactions may elicit similar neuroelectric potentials. A new approach

based on a time– frequency decomposition of event-related brain

potentials was applied to extract covert sub-components from the

classical error-related negativity (Ne) and correct-response-related

negativity (Nc) in humans. A unique error-specific sub-component

from the delta (1.5–3.5 Hz) frequency band was revealed only for Ne,

which was associated with error detection at the level of overall

performance monitoring. A sub-component from the theta frequency

band (4–8 Hz) was associated with motor response execution, but this

sub-component also differentiated error from correct reactions

indicating error detection at the level of movement monitoring. It is

demonstrated that error-specific signals do exist in the brain. More

importantly, error detection may occur in multiple functional systems

operating in parallel at different levels of behavioral control.

D 2004 Elsevier Inc. All rights reserved.

Keywords: Error-related negativity; Time– frequency analysis; Performance

monitoring; Wavelet transform; Executive control; Parallel systems; High-

resolution EEG

Introduction

To achieve a goal, humans have to be able to compare the

consequences of their behavior with the intended outcome of this

behavior. When a mismatch occurs between planned and actual

results of performance, that is, when an error is made, corrective

and adaptive actions are needed. Therefore, to enable error detec-

tion and correction, ongoing performance requires a continuous

monitoring in the brain.

Recently, a specific neuroelectric signal recorded at the scalp

has become an important tool for studying behavioural control and

adaptation in humans (Ullsperger and Falkenstein, in press). This is

1053-8119/$ - see front matter D 2004 Elsevier Inc. All rights reserved.

doi:10.1016/j.neuroimage.2004.01.040

* Corresponding author. Institute of Physiology, Bulgarian Academy of

Sciences, Acad. G. Bonchev str., bl. 23, BG-1113 Sofia, Bulgaria. Fax:

+359-2-979-37-49.

E-mail address: [email protected] (J. Yordanova).

Available online on ScienceDirect (www.sciencedirect.com.)

a negative-going potential with midline fronto-central maximum,

called error negativity (Ne, Falkenstein et al., 1990, 1991) or error-

related negativity (ERN, Gehring et al., 1993). The Ne phenom-

enon can be linked to the functioning of a performance monitoring

system in the brain because: (1) it emerges after incorrect reactions

leading to strategic behavioural re-adjustment, and (2) the sources

of Ne generation are thought to be localized in the medial frontal

brain regions (i.e., the anterior cingulate and the supplementary

motor area, Carter et al., 1998; Dehaene et al., 1994; Ullsperger

and von Cramon, 2001), known to play a major role in behaviour

and motor regulation (Posner and DiGirolamo, 1998).

However, current models for performance monitoring as related

to Ne differ with respect to the type and specificity of underlying

processes. According to the error-detection model, the Ne reflects

the activity of a genuine system in the medial frontal brain which

generates an error signal if a mismatch emerges between neural

representations of intended and actual movements (Bernstein et al.,

1995; Coles et al., 2001; Falkenstein et al., 1991, 2000; Holroyd et

al., 1998). The response-conflict model posits that the Ne is not

specific for errors and is generated during response competition

when two or more response programs are activated simultaneously

(Botvinick et al., 2001; Carter et al., 1998). The Ne has been

further conceptualized as related to the subjective evaluation of

action plans by the medial frontal lobe (Luu et al., 2000), to a

general system estimating the motivational value of various events,

among them errors and response conflict (Bush et al., 2000), or to

negative reinforcement learning mediated by the dopamine neuro-

transmitter system in the brain (Holroyd and Coles, 2002). Thus,

knowledge about the precise functional significance of Ne is

inconclusive. Yet, this knowledge is crucial with respect to major

principles of performance and action control.

A new perspective to understand these principles is provided by

the observation that correct motor responses can also elicit a

fronto-central negativity (correct-response-related negativity,

CRN, Falkenstein et al., 2000; Ford, 1999; Gehring and Knight,

2000; Luu et al., 2000; Vidal et al., 2000), termed here Nc. It is at

present a matter of discussion whether the Ne and Nc have the

same functional origin, with errors producing a larger negativity

than correct responses (Vidal et al., 2000). In this view, the frontal

negativity is not specific for errors and may be associated with a

process activated after both correct and incorrect responses. Alter-

natively, Nc has been suggested to appear on correct trials only

when subjects believe the response is an error (Scheffers and

J. Yordanova et al. / NeuroImage 22 (2004) 590–602 591

Coles, 2000). In this context, the Nc is generated by the subjective

experience of incorrect reactions and is virtually an Ne, which

would support the error-detection model. An integrative relation-

ship between Ne and Nc is also plausible according to which the

Ne may comprise of an Nc and an additional error-specific

activation superimposed on Nc (Falkenstein et al., 2000).

Concerning functional principles of performance regulation as

suggested by different models (Carter et al., 1998; Falkenstein et

al., 2000), the central question is whether an error-specific signal

exists in the brain. Hence, the relationship between Ne and Nc is of

special theoretical importance. In previous research, this issue has

been addressed by manipulating error rate and type in terms of

detectability, motivational value, and associations with conflicting

response tendencies. Here, a new conceptual and methodological

framework is proposed to clarify the relationship between Nc and

Ne. The approach is based on the following.

An electroencephalographic (EEG) signal can be described

precisely in three dimensions: (1) amplitude, (2) time, and (3)

frequency, although phase relationships should be also quantified

for a complete signal description (e.g., Basar, 1980, 1998; Freeman

and Rogers, 2002; Regan, 1989; Ruchkin, 1988). Typically,

response- and error-related brain potentials are analyzed in the

time domain. These analyses have shown that Nc and Ne do

manifest similarities in polarity, timing, and topographical distri-

bution (Vidal et al., 2000), implying common origins of correct and

incorrect response detection and evaluation. A classical time-

domain representation of ERPs reveals the timing of underlying

neural events. However, the frequency characteristics of those

events remain uncovered and no information can be obtained

about rhythmic or oscillatory events from various frequency bands

present in the signal (Basar, 1998). The inability of the time-

domain ERP representation to extract frequency characteristics of

the signal seems to be a serious disadvantage because EEG

activities from several frequency ranges (theta, alpha, beta, and

gamma) have been associated consistently with motor performance

(e.g., Babiloni et al., 2000; Feige et al., 2000; Pfurtscheller et al.,

2000; Urbano et al., 1998; Yordanova et al., 2000). These obser-

vations point to the possibility that multiple frequency-specific

components are generated during correct and error response

production. On the other hand, analysis only in the frequency

domain does not allow to know whether and how frequency

components are time-locked to correct or incorrect response

production. To enable an adequate comparison between Ne and

Nc, one task of this study was to characterize completely the Ne

and Nc signals by analysis in the time, frequency, and time–

frequency domain. Secondly, the major question of whether error-

specific neuroelectric signals are generated in the brain was

addressed. If multiple frequency components co-exist (Basar,

1980, 1998; Kolev et al., 1997; Yordanova et al., 2000) during

correct and error response production, they remain covert in time-

domain waveforms of Nc and Ne because of their full or partial

overlapping. Importantly, such covert components, if extracted

from a complex heterogeneous structure of Ne, may reflect error-

specific processes that are not activated after correct responses.

In the present study, to elucidate the relationship between the

Nc and Ne and seek for error-specific neuroelectric signals in the

brain, response-related potentials (RRPs) to correct and incorrect

reactions were elicited and analyzed in the time, frequency, and

time–frequency domains. Frequency analyses were carried out

with fast Fourier transform (FFT), and time–frequency analyses

were performed by means of wavelet transform (WT, Mallat, 1999;

Samar et al., 1999). For quantification and reliability of topography

analysis, spatial characteristics of averaged RRPs were enhanced

by calculating current source density (CSD, Babiloni et al., 1996;

Perrin et al., 1989).

Methods

Subjects

A total of 14 subjects from 19 to 27 years of age was studied

(mean, 23.5, SD = F2.8). All of them were healthy (no medica-

tion), with normal or corrected to normal vision. None of the

subjects reported about neurologic, psychiatric, chronic somatic, or

hearing problems in the past. Written informed consent was

obtained from each participant and the study was approved by

the local ethical committee. Due to low error rate and insufficient

number of error trials, data from four subjects were excluded from

analyses.

Task

A four-choice reaction task (CRT) was employed. Four stimu-

lus types represented by the letters A, E, I, and O were delivered

randomly with equal probability of 25% in separate experimental

blocks. A total of 200 stimuli was presented in each block, with n =

50 for each stimulus type. The letters A, E, I, and O had to be

responded with the left middle, left index, right index, and right

middle fingers, respectively. They were designated as four stimu-

lus-response (SR) types (SR1, SR2, SR3, and SR4). Response

force was measured by sensometric tensors while subjects pro-

duced a flexion with each of the four fingers.

Subjects performed the CRT in two modalities—auditory and

visual. Auditory stimuli (duration 300 ms, intensity 67 dB SPL)

were delivered via headphones binaurally, with similar envelopes of

the sound pressure waves formed for all stimuli. Visual stimuli with

the same duration were shown in the middle of a monitor (visual

angles 1j horizontal/1.5j vertical, intensity 50 cd/m2) placed 1.5 m

in front of the subject’s face. Interstimulus intervals varied random-

ly between 1440 and 2160 ms (mean 1800 ms). If the response was

slower than 700 ms after stimulus onset, a feedback tone was

delivered at 700 ms after stimulus onset. This tone had to be

avoided by the subjects by responding fast enough. Sequences of

auditory and visual series were counterbalanced across subjects.

Data recording

Data were recorded on three consecutive experimental days. In

the first recording day, data from one auditory and one visual CRT

blocks were collected. In the second and third experimental days,

data from four auditory and four visual blocks were collected. A

total of nine auditory and nine visual CRT blocks was performed

by each subject. Counterbalancing of experimental blocks across

subjects was done as best as possible. Data from all nine sessions

in each modality were used for analysis to enable the collection of

a sufficient number of error trials. An error was defined as a button

press with a wrong finger. To control for effects of the number of

single trials on average amplitudes, correct responses were ran-

domly selected for each subject so that their number was equal to

the individual number of error trials. Because error rate depended

on the SR type, being significantly higher for index-finger

J. Yordanova et al. / NeuroImage 22 (2004) 590–602592

responses (SR2 and SR3) than for middle-finger responses (SR1

and SR4), only SR2 and SR3 types were further analyzed. The

mean number of trials used for averaging was 22 (SE = F2). EEG

was recorded from 64 channels with Cz as reference, with

frequency limits of 0.1–70 Hz, and a sampling rate of 250 Hz.

Data processing

EEG traces were visually inspected for gross EOG and EMG

artifacts. Contaminated trials were discarded along with records

exceeding F50 AV. Next, slight horizontal and vertical eye move-

ments preserved in the accepted trials were corrected by means of a

linear regression method for EOG correction (Gratton et al., 1983).

Error and correct EEG trials were averaged separately for each

modality.

Both the Ne and Nc were clearly visible after response-related

averaging, indicating the presence of phase-locked components in

single sweeps. Therefore, analyses were performed for the aver-

aged response-triggered potentials to extract the time–frequency

components that were most stable and locked to the response.

Response-related potential

Response-related averaging was triggered when a threshold

level of 5 N in the mechanogram was reached. In this way,

incomplete responses were disregarded. This trigger for averaging

appears about 150–200 ms later than the actual EMG displacement

indexing the actual response initiation.

From a methodological point of view, the distortion of RRPs

caused by preceding or overlapping stimulus-related components is

to be accounted for. In a recent study, it was demonstrated by

means of adjusted filtering at the level of single sweeps that such

effects are substantial for the later post-response time epochs and

that they are relatively small for time windows preceding or

coinciding with response preparation and execution (Braun et al.,

2002). On the basis of these results, RRPs were not corrected for

stimulus-related components.

Current source density

To achieve a reference-free evaluation, all data analyses were

performed after calculation of CSD of the signals (Babiloni et al.,

1996; Nunez et al., 1997; Perrin et al., 1989; Vidal et al., 2000).

The CSD transform replaces the potential at each electrode with the

CSD, thus eliminating the reference potential. The algorithm

applies the spherical Laplace operator to the potential distribution

on the surface of the head. Because the potential distribution is

only known at the electrodes used, the procedure of spherical

spline interpolation is employed to calculate the continuous poten-

tial distribution. The exact mathematical procedure is explained in

detail in Perrin et al. (1989). When applied with dense electrode

arrays (48–256 electrodes, 64 in the present study), this procedure

provides excellent estimates of the bioelectric activity of the

cortical surface (Nunez and Pilgreen, 1991).

Time-domain analysis

The time-domain analysis epoch had a length of 1600 ms,

with the moment of response production being located in the

center of this epoch. Nc and Ne were identified as the most

negative peak of RRPs at FCz within 100 ms after correct and

error responses, respectively. Nc/Ne peak amplitude and latency

were measured in the averaged RRPs against a baseline of 800–

600 ms before the button press. This baseline was chosen to

avoid the cancellation of response-related activity occurring

shortly after stimulus delivery.

Frequency-domain analysis

Frequency-domain analysis was performed by analyzing aver-

age RRPs with FFT. To focus the computations to the period of

response execution, FFT was calculated for a 1600-ms epoch

centered at the time point of button pressing. Also, a Hanning

window was applied so that activity beyond 250 ms before and

after the response was strongly reduced.

Time–frequency decomposition

To achieve a reliable analysis of slow frequency components in

the time–frequency domain and avoid possible edge effects, 8192-

ms-long epochs were used to obtain averaged RRPs. The moment

of response execution was in the center of the analysis epoch.

To represent RRPs in the time–frequency domain, RRPs were

analyzed by means of a continuous wavelet transform (CWT,

Mallat, 1999; Samar et al., 1999). Time–frequency representations

were calculated by Morlet’s wavelets as described previously (e.g.,

Jensen et al., 2002; Tallon-Baudry et al., 1997). The analytical

presentation of Morlet’s wavelet w(t, f ) is:

wðt; f Þ ¼ Aexpð�t2=2r2t Þexpð2ipftÞ;

where t is time, f is frequency, A ¼ ðrt

ffiffiffi

pp

Þ�1=2, rt is the wavelet

duration, and i = �1.

For time–frequency plots, a ratio of f0/rf = 3.8 was used, where

f0 is the central frequency and rf is the width of the Gaussian shape

in the frequency domain. The choice of the ratio f0/rf was orientedto the expected slower phase-locked components present in the

RRPs, which had an effect on the shape of the Morlet wavelet and

decreased its decay. The analysis was performed in the frequency

range 0.1–12 Hz with a central frequency at 0.1 Hz intervals. For

different f0, time and frequency resolutions can be calculated as 2rtand 2rf, respectively (Tallon-Baudry et al., 1997). rt and rf are

related by the equation rt = 1/(2prf). For example, for f0 = 1 Hz,

2rt = 1200 ms and 2rf = 0.53 Hz; for f0 = 3 Hz, 2rt = 400 ms and

2rf = 1.58 Hz; for f0 = 5 Hz, 2rt =240 ms and 2rf = 2.63 Hz.

Relevant time–frequency (TF) components were extracted in

the time domain and analyzed. To focus specifically on Nc/Ne

events, amplitudes and latencies of the negative phases of frequen-

cy-specific TF components were measured within 150 ms before

and after the response onset with the same baseline (800–600 ms

before the response), and subjected to statistical evaluation.

Phase-locking

CSD-transformed single sweeps used to obtain average RRPs

were subjected to CWT analysis and extracted in the delta (1.5–3.5

Hz) and theta (4–8 Hz) frequency ranges. They were further

analyzed to assess the phase-locking (between-sweep synchroni-

zation) of these TF components independently of their amplitude.

For a quantitative evaluation of the phase-locking, a modification

of the single-sweep wave identification (SSWI) method was

applied, with methodological details on the procedure presented

elsewhere (Kolev and Yordanova, 1997; Yordanova and Kolev,

1998a,b). In sum, the method identifies extrema (minima and

maxima) in each single sweep and replaces them by codes of �1

or + 1, respectively, placed on the corresponding latency positions.

Then, a summation of the modified sweeps is performed across

trials, with the sum value at each time point assigned to a

J. Yordanova et al. / NeuroIm

histogram bar. The histogram is thereafter normalized by dividing

the bar values by the number of sweeps included in the analysis.

For statistical assessment of phase-locking, histogram bar values

were squared and the mean of the normalized squared histogram

was measured for a period of 800–600 ms before the response

(reference) and for a period of 0–100 ms after the response

coinciding with Ne/Nc development. Measures of the reference

period were subtracted from the measures of the Ne/Nc period. To

normalize distribution, the obtained values were log10 transformed

and subjected to statistical evaluation.

Fig. 1. Grand average RRPs to correct and error responses at FCz in two modaliti

for Nc and Ne, respectively. Correct and error-related RRPs are presented separatel

the topographic maps of correct and error responses.

Statistical Analysis

Nc and Ne measures were compared at the FCz electrode where

Nc and Ne were maximal. In the time domain, the measured

parameters were Nc/Ne base-to-peak amplitude and peak latency.

In the time–frequency domain, the measured parameters were

peak amplitudes and latencies of three TF components in the

sub-delta, delta, and theta frequency bands (see Results), whose

negative phases coincided with Nc/Ne. The stability of phase

synchronization with the response was evaluated at FCz for the

delta and theta TF components.

age 22 (2004) 590–602 593

es (auditory—upper panel, and visual—lower panel). Topography maps are

y for left-hand and right-hand responses. Note that the scaling is different for


Nc/Ne measures were assessed statistically by means of anal-

ysis of variance with repeated measures (ANOVA). There were

three within-subjects variables: Modality (auditory vs. visual) �Accuracy (correct vs. incorrect) � Response side [SR2 (left) vs.

SR3 (right)]. Behavioural measures (RT and error number) were

subjected to the same analysis. For the sub-delta component (<1

Hz), mean amplitudes were measured for consecutive 100-ms time

windows and subjected to the same analysis with an additional

within-subjects variable time-dynamics (six levels corresponding

to the consecutive time windows).

To assess statistically the topographical distribution, an array

of nine electrodes was selected to cover midline and bilateral pre-

motor, motor, and sensorimotor regions. They were combined to

form two electrode factors Region (pre-central—FC3, FCz, FC4;

central—C3, Cz, C4; and post-central—CP3, CPz, CP4) and

Laterality (midline—FCz, Cz, CPz, contralateral /FC3, C3, CP3

for right-hand responses; or FC4, C4, CP4 for left-hand

responses/, and ipsilateral /FC4, C4, CP4 for right-hand

responses; or FC3, C3, CP3 for left-hand responses/ to the

responding hand). The factors in the overall ANOVA were:

Modality � Accuracy � Response side � Region � Laterality.

Whenever significant interactions of Accuracy with Region,

Laterality, or with Region � Laterality were obtained, data were

normalized (McCarthy and Wood, 1985). The analyses were

repeated with the normalized data to confirm the significance of

interactions. Statistical results from post hoc multiple tests were

corrected by adjusting the alpha level according to the Bonferroni

procedure. In the results, only significant main effects and

interactions will be described.

Fig. 2. Power spectra of RRPs at FCz (left panel) and C4 (right panel) of

correct and error responses produced by left and right hands in two

modalities, auditory (upper panel) and visual (lower panel).

Results

Performance

All subjects performed the task adequately. Error rate was

similar for left- and right-hand responses (mean 7.3% and 8.0%,

respectively). Reaction times (RTs) did not differ significantly

(Accuracy, F(1/9) = 1.45, P = 0.26) between error (mean 473

ms, SE = F15 ms) and correct (mean 461 ms, SE = F10.6 ms)

responses. RTs to errors were similar for the two response sides

(left: mean 475 ms, SE = F16 ms; right: mean 473 ms, SE = F15

ms), whereas RTs to correct responses were longer (F(1/9) = 13.9,

P = 0.005) for the left (mean 475 ms, SE = F12.2 ms) than right

hand (mean 453 ms, SE = F9.9 ms), as also reflected by a

marginally significant Accuracy � Response side interaction

(F(1/9) = 3.9, P = 0.08).

Correct-response and error negativities in the time-domain

Fig. 1 illustrates the CSD waveforms of time-domain RRPs at

FCz and topography maps of fronto-central negativities. RRP

morphology was similar across modalities and responding hands.

It is also shown that: (1) a negative component was observed for

both correct and error RRPs at fronto-central electrodes. (2) This

negative component was basically characterized by a phasic wave.

In many cases, it was superimposed on a slow negative wave, as

seen clearly for auditory left-hand RRPs. (3) The topography maps

illustrate that error-related RRPs had a clear maximum at FCz. A

similar midline fronto-central dominance was found for correct

responses, with an additional tendency for a greater involvement of

contralateral regions. Because of its negative polarity, scalp topog-

raphy, and peak latencies of 44.7 ms (SE = F7.4 ms) for correct

responses and 51.6 ms (SE = F5.8 ms) for incorrect responses, the

negative component in correct and error RRPs was identified as Nc

and Ne, respectively.

Base-to-peak Ne amplitude was substantially larger than Nc

amplitude (Accuracy, F(1/9) = 21.29, P < 0.001), whereas peak

latencies of Ne and Nc did not differ significantly (Accuracy,

F(1/9) = 1.29, P > 0.2). There was a significant main effect of

the response side on latency values, because for both correct

and incorrect responses, the latency of the fronto-central nega-

tivity was longer for left vs. right finger presses (F(1/9) = 7.39,

P < 0.05).

Frequency content of Nc and Ne

Fig. 2 illustrates power spectra of averaged RRPs to auditory

and visual stimuli. At FCz (left panels), correct and error-related

RRPs manifested a most prominent spectral peak at very low (<1

Hz, sub-delta) frequencies. Furthermore, RRPs were characterized

by a spectral peak between 2 and 3 Hz (delta), and a subsequent


compound component between 3.5 and 8 Hz (theta). However,

delta and theta spectral components were markedly larger for error

than for correct responses.

Fig. 2 (right panels) illustrates RRPs at electrodes overlying

motor cortical areas. Because the patterns of results were similar

for C3 and C4 locations, only RRPs at C4 are depicted. It is

shown that spectral peaks at the cortex contralateral to the

responding hand (C4 for the left hand) were similar for correct

and error-related responses: Spectral peaks occurred at very low

frequency (below 1 Hz), and a prominent spectral peak was seen

in the theta frequency band (3–6 Hz). Notably, no spectral activity

in the theta frequency range was observed for ipsilateral (right-

hand) responses at C4, irrespective of their accuracy (correct or

error). Accordingly, at C3, spectral peaks in the theta frequency

range were observed only for right-hand responses (both correct

and error), but not for left-hand responses (not shown in the

figure).

Multiple time–frequency sub-components of Nc and Ne

Fig. 3 presents time–frequency decomposition plots of aver-

aged RRPs at FCz and illustrates that several distinct TF compo-

nents accompanied response production.

(1) A phase-locked component within the 0.3–1.2 Hz (sub-delta)

band was generated during both correct and error responses. In

the present experimental setup with a mean inter-stimulus

Fig. 3. Time-frequency decomposition plots of RRPs at FCz of correct (upper pane

modalities, auditory (left panel) and visual (right panel). The description of the fi

interval of 1.8 s, an analysis epoch of 8 s can cover at least four

trials. Although the response-related activity of neighboring

trials is not phase-locked in the averaged potentials, there is a

theoretical possibility that the sub-delta components in the

RRPs of interest are affected by very slow components

associated with these neighboring responses. This possibility

can be excluded, because as shown in Fig. 3, the sub-delta TF

component started as early as about 500 ms before and lasted

for at least 500 ms after the response. It was equally well

expressed for correct and incorrect responses. These observa-

tions can explain the occurrence of the slow negative shifts in

the time-domain RRPs (see Performance subsection) and of the

sub-delta spectral peaks in the frequency domain RRPs (see

Correct-response and error negativities in the time-domain

subsection).

(2) After errors, a prominent TF component was seen in the 1.5- to

8-Hz (delta and theta) frequency band. This TF component was

localized within 100 ms after error execution. Fig. 3 also

demonstrates that this component was markedly reduced after

correct responses in comparison to errors, being almost absent

for the delta (1.5–3.5 Hz) frequency range. These observations

provide evidence that the major power of the phasic Ne

component in time-domain RRPs comes from phase-locked

delta and theta EEG activity.

Fig. 4 depicts difference time–frequency plots computed by

subtracting correct from error-related time–frequency plots. (1)

l) and error (lower panel) responses produced by left and right hands in two

gure is in the text.

Fig. 4. Time-frequency decomposition plots of difference RRPs (Error RRP

minus Correct RRP) at FCz produced by left (left panel) and right (right

panel) hands in two modalities (auditory—upper panel, visual—lower

panel).


For each modality and response type, the most conspicuous

difference between correct and incorrect responses was focused

to frequencies between 1.5 and 3.5 Hz. This is fully in line with

observations from the frequency-domain analysis (Fig. 2) reveal-

ing that the major spectral difference between correct and error

RRPs at FCz was in the 2- to 3-Hz activity. (2) This major

correct vs. incorrect distinction was a negative component

localized within 100 ms after the response, which coincided

with Ne time localization.

Error-specific time–frequency components

To verify statistically these observations, multiresolution scales

(frequency-specific components) were extracted from the wavelet-

decomposed RRPs, evaluated quantitatively, and compared be-

tween correct and incorrect responses. Evaluations were done for

the waveforms of each scale (TF component). Three TF compo-

nents were analyzed: (1) Sub-delta, 0.3–1.2 Hz, to quantify the

slow negative shifts accompanying response production; (2) Delta

(1.5–3.5 Hz), to reflect the major frequency difference between

correct and error-related processes; and (3) Theta (4–8 Hz), to

evaluate response-related theta activity appearing after both correct

and error responses. Peak amplitude and latency were measured for

the negative phase of each of these three TF components coincid-

ing with Nc/Ne.

Fig. 5 presents the three response-related TF components and

illustrates graphically the statistical results. After adjusting the

alpha level to the number of tests performed (Bonferroni correction

for three tests), the difference between correct and error responses

could be accepted as significant for P V 0.016. There was a

significant main effect of response accuracy only for the delta TF

component magnitude (delta, F(1/9) = 15.02, P < 0.005; sub-delta,

F(1/9) = 2.04, P = 0.19; theta, F(1/9) = 6.02, P = 0.037)—Fig. 5b.

Also, negative theta phase latency depended on response accuracy

(F(1/9) = 13.28, P < 0.005), because it was significantly longer for

error (mean 50 ms, SE = F5.8 ms) than for correct responses

(mean 14 ms, SE = F6.9 ms).

No main effects of the response side were yielded for the

amplitudes of sub-delta, delta, and theta TF components. How-

ever, for the theta TF component, a significant Accuracy �Response side interaction was found (F(1/9) = 10.49, P <

0.01) resulting from a substantially larger incorrect vs. correct

theta amplitudes for left-hand responses, and a lack of incorrect

vs. correct difference for the right-hand responses (Fig. 5c).

Furthermore, negative theta phase latency significantly differen-

tiated right- from left-hand responses (F(1/9) = 11.45, P < 0.005)

by being longer for left-(mean 42 ms, SE = 4.6 ms) than for

right-hand responses (mean 22 ms, SE = 5.4 ms). Fig. 5d

demonstrates theta components at FCz and C4 and shows that

in addition to the enhancement at FCz, there was a strongly

lateralized distribution of the phase-locked theta TF component

over the hemisphere contralateral to the responding hand. In

accordance with the frequency domain finding (Fig. 2), this

lateralization was observed for both correct and error responses.

Notably, as seen in Fig. 5d, the theta oscillations over the

contralateral motor cortex were phase-shifted relative to midline

frontal–central theta oscillations.

Phase-locking

The difference between correct and error responses was accept-

ed as significant for P V 0.025 after the alpha level was adjusted to

the number of the two tests performed to assess the phase-locking.

There was a significant main effect of response accuracy only for

the delta TF component phase-locking (Accuracy, F(1/9) = 10.29,

P = 0.01) because delta oscillations were strongly synchronized to

errors, in contrast to correct responses. For the theta TF compo-

nent, errors also tended to produce better phase-locked oscillations

(Accuracy, F(1/9) = 3.88, P = 0.08), which appeared to be more

pronounced for the left hand responses (Accuracy � Response

side, F(1/9) = 3.54, P = 0.09), but these differences were not

significant.

Topography effects

To explore further the Ne/Nc distinctions associated with delta

and theta TF components, scalp topography was analyzed statis-

tically for the 1.5–3.5 and 4–8 Hz TF oscillations as carrying the

information delineating response accuracy.

Fig. 6 shows topography maps calculated for the negative

phase of the delta TF component occurring within 100 ms after

the response. It demonstrates that for each modality, the topo-

graphical distribution of this component substantially differed

between correct and incorrect responses. For correct RRPs, the

delta TF component was distributed mainly over the pre-motor

frontal cortex contralateral to the responding hand. In contrast, a

Fig. 5. Grand average time-frequency components of RRPs at FCz extracted from the (a) sub-delta (0.3–1.2 Hz), (b) delta (1.5–3.5 Hz), and (c) theta (4–8 Hz)

frequency ranges for correct and error responses produced by the left and right hands in two modalities, auditory (left panel) and visual (right panel).

Corresponding to the curves of different TF components, group means (FSE) are shown graphically to illustrate statistical results. (d) Theta (4–8 Hz)

components are shown additionally for a shorter time window at FCz and C4 to illustrate fronto-central and sensorimotor theta oscillations. Note the different

time scale for (d).


clear maximum at FCz was revealed for error-related delta TF

component.

In accordance with the difference topography maps, there was a

significant Accuracy � Region � Laterality interaction (F(4/36) =

11.34, P < 0.0001) reflecting the FCz maximum for errors, and the

contralateral dominance of the negative phase for correct

responses. This interaction was confirmed after the same analysis

was carried out with normalized data sets (F(4/36) = 15.86, P <

0.0001), in which accuracy-related amplitude differences were

eliminated and electrode differences were emphasized (McCarthy

and Wood, 1985). Further testing simple accuracy effects at each of

the nine electrodes demonstrated that only at FCz was the negative

delta amplitude significantly larger for incorrect responses (Accu-

racy, F(1/9) = 15.02, P <0.004; Bonferroni corrected alpha level, P

Fig. 6. Topography maps of the delta time– frequency component of grand

average RRPs of correct and error responses produced by the left and right

hands in two modalities, auditory (upper panel) and visual (lower panel).

E�C—difference maps (Error minus Correct). Note that the scaling is

different for different topographic maps.

Fig. 7. Topography maps of the sub-delta and theta time– frequency

components of grand average RRPs of correct and error responses produced

by the left hand in the auditory modality. Positive and negative polarities

are marked additionally on the corresponding areas of the maps for the theta

TF component. Note that the scaling is different for different topographic

maps.


= 0.006). Thus, the lack of laterality effects in the difference maps

(Fig. 6) is verified by the results from testing simple effects. These

differential topography patterns indicate that distinct neuroelectric

sources contribute to Ne and Nc. Thus, a specific neural substrate

is essentially activated only after errors but not after correct

responses to produce a midline fronto-central potential.

The topography maps of sub-delta and theta TF components

coinciding with Nc/Ne are presented in Fig. 7 and show that:

(1) The topographical distribution of the sub-delta TF component

did not differ between correct and error RRPs and was

characterized by a midline fronto-central maximum, indepen-

dent of response accuracy. Because the magnitude of the sub-

delta TF component was larger than that of delta and theta TF

components in the correct RRPs (Fig. 5), the sub-delta TF

component had the largest contribution to the topographical

distribution of the Nc in the time domain (Fig. 1). This can

explain the midline fronto-central dominance of Nc typically

observed in the time domain.

(2) For correct RRPs, the negative theta phase within Nc

manifested a preponderance over the fronto-central medial

areas, with areas contralateral to response manifesting positive

values (Fig. 7). For error RRPs, the theta TF component within

Ne was strongly focused to the fronto-central midline

electrode, which modified the distribution pattern, although

theta oscillations also had positive values over the hemisphere

contralateral to the response. Because, as demonstrated in Figs.

5d and 7, theta oscillations were shifted in phase for midline

and lateral electrodes, the topography of the theta TF

component associated with the Ne/Nc was analyzed by

calculating the mean theta power within 0–100 ms after the

response. This analysis of theta power at nine electrodes

yielded a significant main effect for Accuracy (F(1/9) = 32.86,

P < 0.001), with errors eliciting larger theta oscillations than

correct responses. Yet, consistent with amplitude analysis

reported above for the FCz electrode, a significant Accuracy �Response side � Laterality interaction (F(2/18) = 5.15, P =

0.02; re-analysis with normalized data, F(2/18) = 4.55, P =

0.03) revealed that the theta oscillations were larger to error

than correct responses at midline locations only when they

were elicited by a left finger response (Accuracy � Response

side for midline electrodes, F(1/9) = 4.86, P < 0.05). At

contralateral and ipsilateral cortices, errors were associated

with larger theta oscillations, independently of the response

side (Accuracy, contralateral, F(1/9) = 12.09, P < 0.01;

ipsilateral, F(1/9) = 16.69, P < 0.005). Overall, theta

oscillations during the Ne/Nc were largest at the hemisphere

contralateral to the responding hand, and were lowest at the

ipsilateral one (Laterality, F(2/18) = 18.56, P < 0.001), but for

the fronto-central region, a midline predominance was found

(Region � Laterality, F(4/36) = 9.1, P < 0.001).

These observations indicate that the neural substrate generating

the sub-delta TF components during Nc/Ne may be similar for

correct and error responses. As opposed to the sub-delta and

similar to the delta TF component, the theta TF component of

Ne/Nc appears to stem from different neuronal networks that are

specific for correct and error responses.

Although it is possible that TF components from higher

frequency bands (alpha, beta, gamma) may also have a specific

reactivity to accuracy or response side variables, they were not


analyzed in this study because of their limited contribution to Nc/

Ne power.

Discussion

Major results of the present study demonstrate that: (1) in a

choice-reaction task, both correct and error responses elicited

fronto-central negativities (Nc and Ne) with similar characteristics

in the time domain; (2) however, the frequency content of Nc and

Ne was different, because a unique component in the delta (1.5–

3.5 Hz) frequency band was extracted only from Ne and was

specific for errors; (3) an additional component in the theta

frequency band (4–8 Hz) existed in both Nc and Ne, although it

also differentiated error from correct reactions depending on the

movement side.

Correct responses elicit a fronto-central negativity

A central question for understanding performance monitoring

is whether errors generate unique brain responses. Error-specific

responses may be an index for the presence of an error detection

system in the brain comparing intended with actual performance.

The view of an error detection control mechanism has been

challenged by the observation of a fronto-central negativity after

correct reactions (Falkenstein et al., 2000; Ford, 1999; Gehring

and Knight, 2000; Vidal et al., 2000), and has been further

complicated by the fact that Nc is not consistently found (cf.

Ridderinkhof et al., 2002; Ullsperger and von Cramon, 2002). In

the present study, the application of a refined methodology

providing for the spatial enhancement of high-resolution ERPs

helped to evince that both correct and incorrect responses elicited

a negative wave with a similar scalp topography (Vidal et al.,

2000). Because the CSD enhances superficial sources of neuro-

electric activity, this result may seem to suggest that common

cortical networks are engaged in Nc and Ne generation, implying

a functional similarity (or even identity) of these negative

components and objecting the hypothesis of the existence of a

unique and specific error-processing system in the brain. Yet, as

will be discussed below, this interpretation is likely to be wrong,

because it ignores the complex multi-frequency composition of

the Ne and Nc.

Heterogeneous nature of fronto-central negativities

One major finding from frequency and time–frequency anal-

yses was that both the Nc and the Ne had a heterogeneous

structure composed of co-existing electrical events in different

frequency ranges. At least three separate response-synchronized

sub-components in the sub-delta, delta, and theta frequency bands

contributed simultaneously to the response-related components

Nc and/or Ne.

From a functional point of view, the critical question is if

these sub-components reflect separate independent neuronal

events or a unitary one. Firstly, the independence of these events

is supported by the fact that the components extracted with the

wavelet analysis have specific frequencies. More importantly,

each of these TF components had a specific functional reactivity

to response accuracy and response side. Also, the specificity of

functional reactivity was accompanied by differences in scalp

topography suggesting differential distributions of underlying

neural activations. Particularly, a sub-delta TF component (<1

Hz) with an anterior (fronto-central) distribution was common for

Nc/Ne and did not differ significantly between correct and error

responses. The fronto-central sub-delta activity overlapped with a

delta (1.5–3.5 Hz) component that emerged exclusively after

errors with a specific scalp topography. In contrast to delta, the

midline fronto-central synchronized theta TF components were

not highly specific for errors as they emerged independently of

response accuracy. Yet, unlike both sub-delta and delta activity,

theta TF components differentiated correct from incorrect

responses depending on response side, and their topographical

distribution strongly depended on both response side and accu-

racy. Furthermore, the synchronization with the response of the

midline fronto-central theta components did not reliably differen-

tiate correct from error responses, whereas the delta activity was

significantly more strongly phase-locked with error than with

correct responses. Such effects cannot be produced by multiple

co-existing TF sub-components belonging to a common electrical

source of variation. Thus, on the basis of methodological,

functional, and topographical criteria, sub-delta, delta, and theta

TF components of RRPs appear to be electrophysiological

correlates of separate neural events associated with functionally

specific systems.

Considering further the nature of these events, it is to be

emphasized that clear spectral peaks at around 2.5 Hz (delta)

and 4–8 Hz (theta) were observed for both correct and incorrect

RRPs. Distinct peaks in the EEG spectrum are an index for the

presence of oscillations with specific frequency (Jensen et al.,

2002; Kahana et al., 1999; Ruchkin, 1988; Yordanova and

Kolev, 1998a). Thus, a quantitative verification of the genera-

tion of oscillatory theta and delta activity during Nc/Ne is

presented.

Parallel systems of performance monitoring

The co-existing time–frequency components during Nc/Ne

appeared to be associated with distinct functional mechanisms

activated simultaneously and these activations from several fre-

quency channels emerged in parallel to subserve specific functions

during response/error production.

Preparatory activity

A basic finding from studies of motor-related processes is the

generation of a negative slow shift at central– frontal electrodes

before self-paced voluntary movements, known as the Bereit-

schafts potential (BP, Kornhuber and Deecke, 1965; Kristeva et

al., 1991). The BP has been associated with preparatory activity

from the supplementary motor area (SMA) involved in triggering

and maintaining functionally relevant motor programs (Deecke et

al., 1999). As the present analyses with long time epochs

demonstrate, a substantial negative shift from the sub-delta

frequency band emerges at about 500 ms before response

execution. Because of its polarity, timing, and scalp topography,

this slow negative Nc/Ne sub-component may be linked to the

BP (Kristeva et al., 1991). This account suggests that an ample

preparatory activation develops before both correct and incorrect

responses, because no differences were found here between the

force level of correct and incorrect response production which

might alter the motor-related potentials (Slobounov et al., 2002).

In addition, RTs to errors were not shorter than those to correct

responses, as could be expected within a fast guess explanation


for error occurrence. A possible reason is that in the four-choice

reaction task, subjects’ cognitive strategy may not rely on

stimulus prediction, and accordingly, wrong guess errors may

not be very probable. Rather, the errors can be induced by

attention fluctuations affecting the stimulus-response matching

in working memory. Hence, most errors in the four choice-

reaction task appear preceded by a central preparatory activity.

If the SMA is engaged in the elicitation of the BP as reflected by

the sub-delta TF component (Deecke et al., 1999), as well as in

the generation of Ne (Dehaene et al., 1994; Ullsperger and von

Cramon, 2001), certain interactions may occur within the SMA

between the mechanisms regulating motor preparation and action

monitoring. This issue may be an important aspect of future

research.

Error-specific signals: performance monitoring

A unique oscillatory component from the delta frequency range

(1.5–3.5 Hz) precisely focused to the medial fronto-central elec-

trode FCz, accounted for Ne expression on the scalp. This delta

sub-component was not pronounced in the Nc after correct

responses (Fig. 6). In contrast to Ne, the delta sub-component

after correct responses was distributed primarily over the contra-

lateral hemisphere, with central midline locations being also

involved. These results indicate that after errors, specific neural

sources generate a medial fronto-central electric response and these

sources are not functionally active after correct reactions. This

observation strongly argues against the assumption of the func-

tional identity of Ne and Nc. Instead, it shows that there exists a

specific medial system for performance monitoring operating in

the 2- to 3-Hz frequency channel that detects errors of overall

performance.

It is an interesting issue for further studies to establish whether

the fronto-medial error-specific delta oscillations are generated

primarily in sensorimotor choice-reaction tasks, or they may

characterize the error processing in general. In a previous study,

Ne and Nc have been described in terms of oscillations (Luu and

Tucker, 2001) after filtering out low-frequency (below 4 Hz) EEG

activity from response-locked averages. Accordingly, the contri-

bution of delta activity to Ne has not been quantitatively evaluated

by these authors. Whether and how the expression of error-specific

delta oscillations may depend on methodological (e.g., CSD vs.

monopolar signals, type of time–frequency decomposition, etc.) or

functional (type of task and error processing) factors remains to be

evaluated.

Parallel error-specific signals: Movement monitoring

In the study of Luu and Tucker (2001), the Ne/Nc signal

remaining after removing slow activity integrated theta and alpha

(4–12 Hz) frequencies. This signal was analyzed in the time

domain and was shown to be larger for errors. Although this

approach has not allowed to prove reliably that theta oscillations

are present in the RRPs or to study precisely their specific

reactivity, it has nonetheless helped to suggest that oscillatory

theta activity has an important functional role in action monitoring

(Luu and Tucker, 2001; Luu et al., 2000).

In line with these suggestions, the present study revealed

prominent theta oscillations at midline fronto-central and contra-

lateral motor cortex locations after both correct and error

responses. A similar distribution also can be observed in Figs.

2 and 3 in the study of Luu and Tucker (2001), in addition to

the sources of oscillations localized by these authors in both the

centromedial frontal cortex and bilateral sensorimotor cortices.

According to present findings, midline fronto-central theta ampli-

tudes associated with Nc and Ne did not generally differ

between correct and incorrect responses, but differentiated errors

in a response-side-specific way. Also, fronto-central theta oscil-

lations manifested sensitivity to the response hand (left or right),

because negative theta phase latency was significantly longer for

left hand responses. Moreover, topography analysis revealed that

prominent theta oscillations were enhanced over the motor

cortex contralateral to the response hand, and were substantially

suppressed over the ipsilateral motor cortex, which was true for

both correct and error responses (Figs. 2 and 5d). These

response side effects and the scalp distribution over the contra-

lateral motor areas strongly imply that synchronized theta

oscillations are functionally involved with movement production

and/or regulation.

Most importantly, error signals can be generated also in the

theta frequency channel. For midline fronto-central location, they

depend on movement side: Only for left-hand responses, did errors

enhance theta amplitude at FCz, which may be associated with

differences in the strength of neural motor representations due to

hand dominance (see RT results), or with different localizations of

motor representations of left- and right-hand responses. Over

contra- and ipsilateral fronto-central, central, and parieto-central

regions, errors produced larger theta oscillations than correct

responses. These findings indicate that theta oscillations are also

functionally associated with error monitoring, but mainly in

relation to movement execution. Thus, response-synchronized

theta oscillations may subserve a separate system of movement

monitoring, which can generate error signals at the level of central

motor regulation.

Conclusion

In a choice reaction task, two systems monitoring errors during

motor behaviour can be delineated: The first system (performance-

monitoring system) is specific for the detection of performance

errors and operates in the delta frequency range. The second

system (movement-monitoring system) is involved in the monitor-

ing of planned and executed motor programs and operates in the

theta frequency channel. Both systems can generate error signals.

The error signal of the performance-monitoring system may be

regarded as a highly specific index for overall incorrect perfor-

mance, whereas the error signal from the movement-monitoring

system may depend on the type and number of concurrently

activated motor programs. These results appear consistent with

both the error-detection model and the response-conflict model for

the generation of error negativity, implying that each of the models

has emphasized a specific aspect of the complex error signal. It is

concluded that error signaling is a function distributed at multiple

levels of information processing in the brain that are activated in

parallel.

Acknowledgments

Authors are thankful to Drs. I. Gutberlet and A. Hornecker for

helpful discussions and to J. Hoormann, L. Blanke, D. Winter and

C. Westedt for technical assistance. This work was supported by

the Deutsche Forschungsgemeinschaft (DFG), Germany (Grant


No. Ho 965-5/3) by the National Science Council of the Ministry

of Education and Science, Bulgaria (Grant No. L-1316), and by the

German Federal Institute of Occupational Safety and Health

(BauA).

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Parallel systems of error processing in the brain

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