Psychophysiology, 37 ~2000!, 43–54. Cambridge University Press. Printed in the USA.
Copyright © 2000 Society for Psychophysiological Research
Error monitoring during reward and avoidance learning
in high- and low-socialized individuals
ZIYA V. DIKMAN and JOHN J.B. ALLEN
Department of Psychology, University of Arizona, Tucson, USA
Abstract
The error-related negativity ~ERN! is a response-locked brain potential generated when individuals make mistakes
during simple decision-making tasks. In the present study, we examined ERN under conditions of reward and punishment, among participants who scored extremely low or high on the socialization scale of the California Psychological
Inventory ~CPI!. Participants completed a forced-choice task, and were rewarded for correct responses in half the trials,
and punished for incorrect responses in the remaining trials. A significant interaction between socialization ~SO! and
condition revealed that low-SO participants produced smaller ERNs during the punishment task than during the reward
task, whereas high-SO participants produced similar ERNs in both conditions. Reaction time and electromyogram data
essentially bolster the interpretation that the ERN effects reflect differences in error salience for high-SO and low-SO
participants, and are consistent with the avoidance-learning deficits seen in psychopathy.
Descriptors: Event-related brain potential ~ERP!, Error-related negativity, Socialization, Reward, Punishment
Cleckley ~1976! described that psychopaths had an incapacity to
learn from experience, a notion later refined and described as a
failure of avoidance learning ~e.g., Newman & Kosson, 1986!.
Although numerous studies have characterized performance deficits in psychopathy ~Newman, Patterson, & Kosson, 1987; Newman, Widom, & Nathan, 1985; Scerbo et al., 1990; Schmauk,
1970!, and many others have examined psychophysiological markers associated with psychopathy ~Arnett, Howland, Smith, & Newman, 1993; Jutai, Hare, & Connolly, 1987; Patrick, 1994; Raine &
Venables, 1987, 1988; Schmauk, 1970!, none have examined central nervous system correlates of error monitoring in this population. The process of error monitoring, an essential prerequisite for
adaptively altering behavior, can be investigated using a recently
characterized response-locked brain potential, the error-related negativity ~ERN; Falkenstein, Hohnsbein, Hoormann, & Blanke, 1991a,
1991b; Gehring, Goss, Coles, Meyer, & Donchin, 1993!. The present
study was therefore designed as an initial examination of the potential relationship between ERN amplitude and psychopathy, using
an analog study design involving low-socialized ~low-SO! individuals in lieu of psychopaths.
Behavioral Patterns in Psychopathy
One of the most studied and ecologically salient differences between psychopaths and nonpsychopaths is the psychopath’s apparent deficit in inhibiting punishable responses when performing a
task during which participants are rewarded for one type of response and punished for another ~Lykken, 1957; Newman & Kosson, 1986; Newman et al., 1985!. Psychopaths in these studies are
typically unable to inhibit punishable responses ~passive avoidance errors or errors of commission! as well as nonpsychopaths.
Psychopaths tend to produce more of the rewarded responses when
environmental cues indicate that the rewarded response is inappropriate. These deficits, however, are present only when the tasks
involve both rewarded and punished responses; when psychopaths
are given a task in which they are either punished or rewarded,
their performance is not significantly different than nonpsychopaths ~Newman & Kosson, 1986!. Psychopaths may also demonstrate avoidance learning ~AL! deficits only to specific types of
punishment—namely, electric shocks or being told that their response was wrong ~Schmauk, 1970!. Additionally, psychopathic
subjects in these experiments did not commit more errors of omission than controls, a finding that argues against the possibility that
psychopaths perform more poorly in general. Similar findings have
also been observed in younger samples ~Scerbo et al., 1990!.
Evidence from behavioral studies examining perseverative responses in psychopaths suggests that the aforementioned differences may be due to a deficit in response monitoring or the inability
to successfully utilize feedback in a timely fashion. Newman et al.
~1987! found that when psychopaths played a card game that involved gradually increasing odds of punishment, psychopaths performed more poorly than nonpsychopaths. This difference was
eliminated when they imposed a 5-s waiting period before allow-
Portions of these data were presented at the annual meeting of the
Cognitive Neuroscience Society, Boston, April, 1997, at the annual meeting of the Society for Psychophysiological Research, North Falmouth, MA,
October, 1997, and as part of a symposium on the ERN at the annual
meeting of the Society for Psychophysiological Research, Denver, CO,
September, 1998.
This research was supported, in part, by a grant from the McDonnellPew Program in Cognitive Neuroscience.
We thank Sohee Jun and Jamie Gray for their assistance in data collection.
Address reprint requests to: John J.B. Allen, Ph.D., Department of
Psychology, University of Arizona, P.O. Box 210068, Tucson, AZ 857210068, USA. E-mail: [email protected].
43
44
ing a response. These results were later extended to a group of
adolescents with undersocialized aggressive conduct disorder ~Shapiro, Quay, Hogan, & Schwartz, 1988!.
Other studies have also found evidence that psychopaths fail to
utilize feedback to alter their subsequent responses. It has been
demonstrated that psychopaths fail to slow down after making
errors in a task that involved punishment and reward, and that this
failure to slow down after punishment was associated with failure
to learn from the punishment ~Newman, 1987!.
Newman ~1987! conceptualized that the deficits apparent in
psychopaths are closely tied to the reaction of the psychopath ~or
uninhibited subject! to punishment. Uninhibited subjects fail to
learn from mistakes because their immediate reaction to the errors
they have made is to speed up on subsequent trials. This behavioral
pattern prevents strong associations from being formed between
environmental cues, responses to these cues, and their consequences, which ultimately results in a pattern in which the psychopath makes more approach responses. As mentioned above,
when a 5-s waiting period was interjected between feedback and
the following trial, psychopaths performance was equal to that of
nonpsychopaths ~Newman et al., 1987!. Thus, it may be the case
that psychopaths and other uninhibited subjects have a deficit in
their ability to attend to their reactions in complex situations, which
results in behavioral primacy of reward conditions found in the
experimental literature.
Psychophysiological Data
Reports by Raine and Venables ~1987, 1988! have examined physiological markers of attention: the P300 ~see Donchin, 1981! and
the contingent negative variation ~CNV; see Donchin, Ritter, &
McCallum, 1978!. Raine and Venables ~1987! found no differences in CNV or N1 amplitudes in a sample of noninstitutionalized
school boys who were defined as anti- or prosocial on the basis of
self-reports and behavioral ratings made by instructors. These authors did find differences in P300 amplitude produced to the warning ~S1! stimulus of their CNV paradigm between the two groups.
Whereas these groups were defined by a mean-split technique,
Raine and Venables were able to extend the original finding of
increased P300 amplitude to a group of psychopathic prisoners
~Raine & Venables, 1988!.
Raine and Venables ~1988! interpreted these results as a heightened ability of psychopaths ~or antisocials! to attend to events of
immediate interest. The interpretation of the data suggesting that
psychopaths may have an increased ability to attend to events of
interest is at odds with much of the behavioral literature, which
often characterizes psychopaths as having relatively short attention spans. For instance, Hare’s two-factor structure of psychopathy ~Hare et al., 1990! is replete with behaviors that seem to
indicate that psychopaths have difficulty sustaining attention, such
as proneness to boredom, poor behavior controls, lack of realistic long-term goals, impulsivity, and irresponsibility. One possible explanation for this seeming contradiction is offered by the
authors, citing Cleckley ~1976!; they noted that the tendency to
overfocus on immediate goals may result in a lack of wellplanned, long-term goals—consistent with most descriptions of
psychopaths.
Taken together, the evidence for deficits in AL paradigms and
differences in psychophysiological measures of attentional processes suggest that a closer examination of the attentional mechanisms specifically related to response monitoring in psychopaths
may clarify the relationship between these two systems. Particu-
Z.V. Dikman and J.J.B. Allen
larly well suited to such an investigation is a response-locked brain
potential described in the investigations of Falkenstein et al. ~1991a,
1991b! and of Gehring et al. ~1993!. Evidence from these studies
suggests that the response-locked ERN reflects the salience or
awareness of an error during a simple cognitive task. By recording
electroencephalographic ~EEG! activity during a simple forcedchoice reaction time ~RT! task and then time-locking EEG epochs
during which a participant made a mistake, an averaged responselocked potential is created ~as opposed to traditional stimuluslocked potentials!. Across trials during which participants made an
error that was subsequently corrected, a negative-going potential
was present that peaks approximately 70 ms after an overt response has been made ~Falkenstein et al., 1991a, 1991b; Gehring
et al., 1993!. This ERN was absent on correct trials.
A report by Scheffers, Coles, Bernstein, Gehring, and Donchin
~1996! addressed the issue of whether the ERN is a manifestation
of a system for detecting errors or a system for correcting and
compensating for errors. Evidence from Gehring et al.’s ~1993!
report could support either process, as ERN amplitude was related
to variables that assessed compensatory actions. This relationship,
however, could be explained if the output of compensatory mechanisms was related to the strength of error detection input. The
authors constructed a modified go0no-go task to determine whether
the ERN was produced on errors of action ~i.e., making a response
when no response was indicated!, reasoning that if a discernible
ERN was produced during these error trials, then the ERN must be
a manifestation of an error detection process as no compensatory
behavior can take place after an error of action. The data for this
question were unequivocal; the ERN produced during errors of
action was not statistically different from the ERN produced during errors of choice. Additionally, the data demonstrated no relationship between ERN amplitude and the force of the compensatory
responses on error trials involving choice reaction. Despite having
no possible mechanism to compensate for their mistakes, these
participants produced an ERN that was not related to force of
response. A later study compared the ERNs produced when participants used their feet to make responses with those produced
when they responded with their hands ~Holroyd, Dien, & Coles,
1998!. The mathematically created equivalent-dipole solutions for
the ERN converged to approximately the same source regardless of
response modality ~i.e., hands or feet!. The results of these investigations suggest strongly that the ERN is a manifestation of an
error detection process, or a reflection of error salience, and not a
manifestation of an error correction or compensation process related to a motor output.
Of theoretical import is the timing of the ERN. The potential
occurs at approximately 70 ms postresponse. This amount of time,
as Gehring et al. ~1993! argue, does not allow the ERN to be
simply a proprioceptive or externally driven processing step that
has been tacked on to the end of a response. Seventy milliseconds
is simply not enough time to encode the response that was made
and determine whether or not it was correct. More likely this
potential is being generated from a copy of the motor output made
at about the same time that this output is sent to the efferent
systems. Similar conclusions were reached by Falkenstein, Hohnsbein, and Hoormann ~1995! in their examination of a responselocked brain potential, which they labeled “NE”.
The present study used an AL paradigm similar to those described in the discussion of psychopathy above, with one important difference. Whereas those paradigms simultaneously
implemented punishments for incorrect responses and rewards for
correct responses, the present study separated AL trials and reward
Socialization and ERN amplitude
~REW! trials into two discrete blocks. This format was chosen
because errors under conditions of reward and punishment were
both of interest. In the paradigms typically used in psychopathy
research, rewards and punishments are simultaneous aspects of the
task; it would therefore be impossible to examine errors to the
rewards and punishments separately, because any error would result in the lack of reward and simultaneously the administration of
punishment. The present study therefore used blocked presentations of reward and punishment trials, in an attempt to most closely
approximate these previous studies, while allowing us to examine
separately errors involving lack of reward versus those involving
administration of punishment.
Before proceeding to a clinical study of psychopathy, we adopted
an analog investigation into the potential relationship between psychopathy and ERN amplitude. Promising results from such an
analog study might then justify the increased difficulty of studying
psychopaths, whether through recruiting noninstitutionalized psychopaths or obtaining data within the prison system ~see Widom,
1978!. Gough’s socialization ~SO! scale ~1994! of the California
Psychological Inventory ~CPI; Gough, 1957! provides a reliable,
efficient, oft-used measure of personality that is conceptually related to psychopathy as defined by Cleckley ~1976! and Hare et al.
~1990!. Originally labeled as a “delinquency” scale, the SO scale
contains many items that have high face validity for assessing
psychopathic-like behaviors and traits ~e.g. “I hardly ever get excited or thrilled,” “I often act on the spur of the moment without
stopping to think,” “I used to steal sometimes when I was a youngster”!. This relationship between psychopathy and socialization is
summarized by Kosson and Newman ~1989!, who suggested that
psychopaths may be a prototypical group “around which other
antisocial personalities are organized” ~p. 87!. Several empirical
studies have examined the relationship between psychopathy and
the SO scale. Gough ~1994! cited research by Cooney, Kadden,
and Litt ~1990!, who found a correlation ~r 5 20.36!1 between the
SO scale and Hare’s ~1980! Psychopathy Checklist ~PCL!. A correlation of similar strength was also obtained by Hare ~as cited in
Gough, 1994!. Kosson, Steuerwald, Newman, and Widom ~1994!
found statistically significant relationships between SO scale scores
and different measures of antisocial behavior, such as self-reported
stealing, vandalism, and drug use ~although they did not assess
psychopathy per se!.
Several investigators have used the SO scale experimentally
and found differences in low-SO samples that closely resemble
results from similar experiments with psychopaths. Kosson and
Newman ~1989! found attentional deficits in a divided attention
task. Several reports ~Raine & Venables, 1984; Waid, 1976; Waid
& Orne, 1982! have demonstrated that low socialized participants
were electrodermally hyporesponsive to a variety of stimuli. Deficits in the ability of low socialized students to inhibit wellestablished dominant responses were observed and then replicated
in PCL-selected psychopaths ~Howland, Kosson, Patterson, and
Newman, 1993!. Shapiro et al. ~1988! demonstrated behavioral
results in undersocialed aggressive conduct-disordered students,
similar to the pattern of reward dominance in psychopaths. Collectively, such results suggest that low-socialized individuals are a
reasonable population to study in lieu of psychopaths in an analog
study.
1
The negative correlation is a function of how these scales are scored.
Low scores on the SO scale are indicative of increased antisocial behavior,
whereas high scores on the PCL are indicative of increased psychopathy.
45
The Present Study
The review presented to this point has demonstrated that psychopaths and other impulsive groups ~e.g., low-socialized! behave
differently from normal subjects in conditions in which error monitoring or detection is influenced by conditions of reward and
punishment. Although studies exist using event-related potentials
to investigate information processing in psychopathy ~e.g., Raine
& Venables, 1987, 1988!, no studies have investigated responserelated potentials that are sensitive to error monitoring. Recent
studies, reviewed above, suggest that the response-locked ERN is
related to error detection. The present study therefore examined
this error potential in an analog study of psychopathy, under
conditions of both reward and avoidance learning. Given the welldocumented avoidance-learning deficits in psychopathy, we predicted that—for the low-socialized subjects ~the analog psychopath
group!—ERN amplitude would be smaller under conditions of
avoidance learning than under conditions of reward.
Method
Participants
During the screening of introductory-level psychology students,
2,244 participants completed the 54-item CPI SO scale, which was
administered during two semesters as part of a requirement for
research participation. All questionnaires were scored and ranked
by score, such that both the highest and the lowest SO scores were
ranked first, the second highest and lowest scores were both ranked
second, and so forth, creating a list of potential participants that
included both high- and low-socialized participants. Thus two groups
of subjects were created, a high-SO group and a low-SO group
serving as an analog for psychopaths. The low-SO group was
comprised of participants taken from the bottom 3% of all scores,
the high-SO group from the top 3% of all possible scores.
Once ranked, SO scale information was hidden from the research team, with researchers blind to group membership. Participants were contacted in order of their ranking and asked to
participate in a study of brain waves and learning for which they
would earn experimental credits necessary for completion of the
class, and could additionally earn up to $5.00. Experimenters attempted to remain blind to socialization status; however, extreme
differences in appearance, demeanor, and personality between the
members of the two groups rendered the attempt at blinding less
than entirely effective.
CPI SO group means for low-SO participants was 19.1 and for
high-SO participants was 48.0. A T test confirmed differences
between the two groups ~T 5 235.388, p , .001!.
Procedure
Upon arrival at the laboratory each participant was given a brief
tour and asked to read and sign a consent form. Participants were
then asked to complete several personality inventories and biographical information questionnaires, the results of which are not
reported here. While participant were completing these questionnaires, they were prepared for EEG recording.
After recording a short, resting baseline EEG, participants were
provided with instructions for the task, displayed both on the computer screen and read aloud by the experimenter. Following the
instructions, participants completed a short series of practice trials.
The task, a version of the Eriksen Flankers task ~Eriksen &
Eriksen, 1974!, consisted of identifying the middle character ~via
a button press! of a five-letter string of characters that was either
46
Z.V. Dikman and J.J.B. Allen
compatible or incompatible with the central letter. The letter strings
consisted of the letters “S” and0or “H” and four possible stimuli
were possible ~“SSSSS,” “HHHHH,” “SSHSS,” and “HHSHH”!.
Characters were displayed unmasked for 52 ms in a standard,
green font on a black background and subtended 1.6 degrees of
visual angle horizontally and 0.63 degrees vertically. During the
task a small fixation symbol ~*! was placed approximately 0.32
degrees below the center character of the letter string and was
present on the screen at all times except during feedback.
During one half of the trials, the subject was required to respond to a central “S” by pressing the button in one hand and to the
“H” with the other hand. Hand-letter assignment was switched
during a short rest period that occurred every 80 trials, and initial
hand-letter assignment was counterbalanced between participants.
Participants were instructed to correct themselves if they thought
they had made a mistake, and were allowed to respond ad lib for
approximately 1,000 ms following the stimulus offset. RTs and
accuracy were recorded on a computer and these data were merged
with the EEG offline.
Participants completed 640 trials in each of the two different
conditions described below. Initial order of these conditions was
randomized between participants. After completion of the first half
of the trials ~either AL or REW! a short break was offered, after
which they completed two short questionnaires and a 5-min eyetracking task. Upon completion of both tasks subjects were debriefed, paid, and given class credit.
AL. During this half of the experiment, participants were told
that they would receive a loud tone after any trial in which the final
response was incorrect, or when no response was made. The tone
was computer generated and played through the headphones at
approximately 95 dB for approximately 1,000 ms. Following correct trials, including those during which participants had selfcorrected, participants received no feedback.
REW. During this portion of the experiment, participants were
told that for each correct answer they would be credited with a
small amount of money, and that they could earn up to $5.00.2
After an incorrect response a message ~“NO $”! was flashed briefly
on the monitor. Following correct trials, including those during
which participants had self-corrected, participants received no
feedback.
When necessary, feedback was presented to participants 1,000 ms
after stimulus offset in both conditions, regardless of response
latency. Because error trials that were not self-corrected were never
included in the analyses ~see below!, this feedback stimulus did not
influence any of the waveforms presented and analyzed in this
report.
Thirty-four students ~16 low-SO and 18 high-SO! completed
all tasks. Technical difficulties with stimulus presentation required
that two participants’ data ~one high-SO and one low-SO! be dropped
from subsequent analysis. Two other participants ~high-SO! failed
to produced enough error trials ~n , 12! to create robust averagewaveforms for examination. Thus 15 participants were examined
for each group, and these data are reported here.
EEG Recording
Prior to the arrival of the participant at the laboratory, all hardware
necessary for completion of the experiment was calibrated. EEG
2
All participants were given $5.00 following the experiment regardless
of performance during this task.
was recorded from 25 scalp sites via a standard electrode cap. Eye
movements and blinks were recorded from three different electrooculographic ~EOG! sites on the face: two signals were obtained
from electrodes placed approximately 1–2 cm below the center of
fixation for each eye, and one channel from an electrode placed on
the nasion. All EEG and EOG channels were referenced to A1
online, and rereferenced offline to linked mastoids.
EMG recordings were obtained with a bipolar montage located
over muscle tissue approximately 3 cm proximally from the wrist
between the tendons of the flexor carpi radialis and supinator
longus muscles. This location allowed recording of a measurable
EMG signal when subjects depressed the response button. All EEG
and EOG impedances were less than 5 kV. EMG impedances were
less than 10 kV; however, two low-SO participants had excessive
EMG impedance artifacts, which required that their EMG data be
dropped from subsequent analysis.
EEG was amplified 20,000 times, and digitized at 256 Hz continuously during the experiment. EEG was filtered online at 0.01
and 100 Hz. EOG signals and EEG sites FP1 and FP2 were amplified 5,000 times.
The electrocardiogram ~EKG! from each participant was also
recorded. EKG signals were amplified 10,000 times and filtered at
0.1 Hz and 100 Hz. These data are not reported here.
After hookup and completion of all the questionnaires, each
participant was seated in a comfortable chair in a sound-attenuated,
darkened room approximately 90 cm from a computer monitor. At
this time participants were fitted with a pair of headphones to be
worn over the electrode cap. Participants were also instructed on
the use of the response buttons necessary for completion of the
experiment.
EEG Analysis
Continuous EEG files were inspected visually and artifacts were
deleted from the data file. EEG files were then filtered digitally
with a 15-Hz low pass ~96 dB0octave! filter. Eye blinks within the
continuous EEG file were corrected via a regression analysis ~Semlitsch, Anderer, Schuster, & Presslich, 1986!.
Continuous EEG files were combined with behavioral data, and
then segmented into 1,500-ms long epochs, beginning 500 ms
before each response. Each epoch was baseline-corrected by subtracting the average value of the EEG 50 ms before the response
from the entire epoch.
EEG epochs were sorted and averaged to create the main conditions for analyses. Two different waveforms were created for
both the AL and REW tasks. A waveform representing correct
trials ~COR! was created by taking only those trials during which
the participant made one correct response that did not take place
sooner than 200 ms.3 A waveform for self-corrected error trials
~ERR! was created by averaging only those trials during which the
subject made one response with each hand, the latter of which was
correct ~once again eliminating trials in which a response occurred
in less than 200 ms!.
ERN amplitudes were calculated by first subtracting COR waveforms from ERR waveforms separately for both conditions for all
participants. The negative-going peak amplitude in a 70-ms window centered at 70 ms postresponse at site Cz was then identified.
The average amplitude for a 50-ms window around this negative
peak was then calculated for all sites ~based on the latency at site
3
Any response made prior to the 200-ms stimulus onset was considered
to be too fast to reflect complete processing of the stimulus. This value was
used after referring to the literature on similar cognitive tasks.
Socialization and ERN amplitude
Cz only!. Finally, the average EEG in a 100-ms window immediately preceding the response at each site was subtracted from the
averaged negative peak. Three midline sites ~Fz, Cz, and Pz! were
chosen for analysis.
47
Table 1. Mean Accuracy and Reaction Times (ms)
for Avoidance Learning and Reward Tasks
by Socialization Group
Avoidance learning
EMG Analysis
EMG activity from each forearm was classified as representing an
accurate or inaccurate motor response, based on the button press
for that trial. Continuous EMG data were then segmented into
epochs from 2600 to 1875 ms with respect to the button press.
These data were baselined using a 300-ms long window beginning
600 ms before the button press. EMG epochs were then averaged
separately for correct and incorrect trials for both the AL and REW
conditions. Only EMG for correct presses and error presses was
examined. EMG from the inactive hand during correct trials and
from the correct hand during error trials was not examined. Total
EMG activity ~i.e., area in microvolts! in a 400-ms window beginning 100 ms before the response was then calculated from the
averaged waveforms for error and correct trials separately for the
AL and REW conditions.
RT-Corrected ERNs
In a detailed analysis of the ERN, Scheffers et al. ~1996! matched
a subset of correct trials to error trials by selecting only correct
trials with similar RTs to those of the error trials, allowing them to
control the confound that error trials often had different mean RTs
than correct trials. In an attempt to address this possibility we
examined a subset of correct trials by averaging only those correct
trials that fell within 650 ms of the mean RT for the error trials for
each condition separately. Results of this analysis are reported
below.4
Brain Electrical Source Analysis (BESA)
To facilitate a direct comparison between the ERN data presented
here and previous ERN research, and to explore possible anatomical correlates of the ERN, dipole modeling was performed. To
maximize spatial resolution, the original 25 scalp sites were included along with the three ocular sites and the two mastoid sites,
all referenced to the average activity of all 30 sites. The mathematically modeled dipole generators that were consistent with the
observed pattern of scalp activity were estimated using specialized
software ~Berg & Scherg, 1994!. Data were averaged to create four
files for analysis: high and low SO punishment, and high and low
SO reward across conditions and subjects. Data files to be modeled
consisted of subtraction waveforms ~correct trials from error trials!. Best-fit dipoles were modeled in a 50-ms window centered at
70-ms postresponse, which mirrored the peak measurements described above.
Results
Behavioral Performance
Accuracy and RT data are presented in Table 1. Performance differences in accuracy between high-SO and low-SO subjects in the
AL and REW conditions were tested in a 2 ~Socialization group! 3
2 ~Condition! analysis of variance ~ANOVA!. No main effects or
interactions were observed. Moreover, among the errors commit4
One high-SO participant’s data for the punishment condition was
unavailable as there were too few correct trials within 650 ms of the mean
RT for the error punishment trials.
Reward
Socialization a
% Correct
~6SD!
RT
~6SD!
% Correct
~6SD!
RT
~6SD!
High
Low
87.8 ~6.2!
87.8 ~6.9!
442 ~62!
441 ~55!
88.4 ~5.1!
86.9 ~7.6!
451 ~51!
425 ~53!
Note: RT 5 reaction time.
a
n 5 15 for both groups.
ted, participants in both groups were equally likely to self-correct
their errors, regardless of condition ~REW, AL!. Overall, 84% of
the errors committed were subsequently self-corrected.
ERN Data
Response-locked waveforms at midline sites are presented in Figure 1, and error trial 2 correct trial difference waveforms at Cz are
presented in Figure 2. Visible in these plots is a clear difference in
ERN amplitude between the AL and REW conditions at site Cz
within the low-SO group, whereas differences between these two
conditions were not readily apparent in the high-SO group.
To test the hypothesis that ERN amplitude would vary systematically as a function of group, condition, and accuracy, a mixedmodel, 2 3 2 3 2 3 3 ~Socialization group 3 Condition 3
Accuracy 3 Scalp site! ANOVA was computed. An interaction
between group, condition, and accuracy would indicate that low-SO
and high-SO participants differed in their pattern of ERN amplitude as a function of reward and punishment. When effects involving the repeated-measures factor of site—which has more than two
levels—were found to be significant, degrees of freedom were
adjusted using Greenhouse–Geisser ~1959! Epsilon correction, but
the original degrees of freedom are presented along with the Epsilon value.
As expected, ERN amplitude for incorrect trials was significantly more negative than for correct trials, as indicated by the
main effect of accuracy, F~1,28! 5 167.1, p , .001. ERN amplitude also varied significantly by site, F~2,56! 5 86.4, E 5 0.981,
p , .001, with site Cz producing the largest ERNs, followed by Fz,
and Pz, respectively. These factors also interacted significantly,
F~2,56! 5 55.6, p , .001, indicating that the ERN amplitude at
site Cz was more sensitive to errors than sites Fz and Pz.
Examining group differences, a significant Socialization group 3
Condition 3 Accuracy interaction, F~1,28! 5 6.1, p , .05 ~Figure 3!, was observed. A Duncan’s multiple range analysis of this
interaction indicated that whereas amplitude did not differ on correct trials between groups, low-SO participants generated significantly smaller ERNs during incorrect AL trials than during incorrect
REW trials ~ p , .05!. This analysis also highlighted betweengroups differences: low-SO participants also produced smaller ERNs
on incorrect AL trials than did high-SO participants on incorrect
REW trials ~ p , .05!. A trend also indicated that low-SO participants’ ERN amplitudes for errant AL trials were smaller than
high-SO participants ERNs for errant AL trials ~ p , .09!. A final
trend ~ p , .08! in the data was observed in which the low- and
high-SO groups differed in the amplitude of ERNs produced for
correct REW trials. As expected, all error trials were significantly
48
Z.V. Dikman and J.J.B. Allen
Figure 1. Response-locked waveforms at midline sites for correct and error trials under conditions of avoidance learning ~AL! and
reward ~REW! for high and low socialized subjects. Note the difference between REW and AL trials among low-socialized subjects
at site Cz.
more negative than all correct trials. No significant differences or
trends were observed between AL and REW conditions within the
high-SO group.
A four-way interaction between all factors nearly achieved statistical significance, F~2,56! 5 3.1, p 5 .053 ~Figure 4!. This
interaction approached significance as the effects of the significant
three-way interaction described above were most robust at site Cz
and were less apparent at sites Fz and Pz.
RT-Corrected ERNs
To address the possibility that faster RTs on error trials than on
correct trials contributed artifactually to the pattern of results ~see
Scheffers et al., 1996!, we examined RT-matched trials ~Figure 5!.
A mixed-model, 2 3 2 3 2 3 3 ~Socialization group 3 Condition 3
Accuracy 3 Site! ANOVA was computed that largely replicated the
previous analysis. Main effects for accuracy, F~1,27! 5 206.0, p ,
.001, and site, F~2,27! 5 70.2, p , .001, were observed, as was an
interaction between these two factors, F~2,54! 5 44.2, p , .001.
A three-way interaction similar to the one observed in non-RT-
corrected data was observed in this analysis in which socialization
group, condition, and accuracy interacted, F~1,27! 5 4.4, p , .05.
A follow-up Duncan’s multiple range test of this three-way
interaction revealed a pattern of results similar to the non-RT correct data. A trend was observed in which ERN amplitudes of errant
trials for low-SO group in the REW condition were more negative
than errant trials in the AL condition ~ p , .056!. A trend was also
observed for errant trial REW ERNs for the low-SO group to be
more negative than the ERNs for errant trials in the AL condition
for the high-SO group ~ p , .055!. This analysis also revealed a
significant difference between the ERNs produced by high-SO and
low-SO groups on correct trials during the REW condition ~ p ,
.05!; low-SO participants generated more positive potentials than
high-SO participants.
RT
To further examine the possibility that ERN amplitudes were merely
a reflection of RT, a 2 3 2 3 2 ~Socialization group 3 Condition 3
Accuracy! ANOVA was performed on RT data ~see Figure 6!. A
Socialization and ERN amplitude
Figure 2. Error 2 Correct waveforms for high-socialized ~high-SO! and low-socialized ~low-SO! groups by condition at scalp site Cz.
Solid lines represent avoidance learning ~AL! trials, and dashed lines represent rewarded ~REW! trials.
Figure 3. Significant three-way interaction between socialization, condition, and accuracy ~ p , .05!. Correct trial amplitudes are
displayed in the left panel, error trial mean amplitudes are displayed on the right. Error bars represent the standard error of the mean
for each data point separately. Note that the left and right panels are graphed with different minimum and maximum points, but utilize
the same scaling factor.
49
50
Z.V. Dikman and J.J.B. Allen
Figure 4. Nonsignificant four-way interaction ~ p , .06! between socialization group, condition, accuracy, and site. Correct trials are
displayed in the left panel, error trials in the right panel. Note that the left and right panels are graphed with different minimum and
maximum points, but utilize the same scaling factor. The figure demonstrates the focal nature of the potential, which is detected
maximally at site Cz.
Figure 5. Reaction time corrected waveforms ~error trials 2 correct trials! at site Cz. The correct trials used to construct these
waveforms only included those correct trials that fell within 650 ms of the mean RT for the error trials for each condition separately,
thus controlling for RT differences between error and correct trials.
Socialization and ERN amplitude
51
Table 2. Locations in Three-Dimensional Space and Residual
Variance of BESA-Modeled Dipole Generators
Coordinates
Condition
High-SO REW
Low-SO REW
High-SO AL
Low-SO AL
X
Y
Z
Residual
variance
~%!
0.0
0.7
22.4
21.9
13.9
12.6
13.1
11.1
33.0
27.4
29.6
26.1
10.7
9.6
13.5
19.2
Note: BESA 5 brain electrical source analysis; SO 5 socialized;
REW 5 rewarded; AL 5 avoidance learning.
Figure 6. Reaction times for the high-socialized ~high-SO! ~solid! and
low-SO ~shaded! groups as a function of condition ~avoidance learning
@AL#, and reward @REW#! and accuracy. A main effect for accuracy ~ p ,
.001! was observed in these data.
grated to the same location as the initial dipole. Table 2 provides
the modeled locations for the generators in three-dimensional ~X,
Y, Z! space, as well as the residual variance remaining following
the best-fit procedure.
Discussion
significant main effect for accuracy was observed in the data,
F~1,28! 5 186.5, p , .001. RT across SO groups and conditions
on error trials was approximately 63 ms faster than on correct
trials. Socialization interacted significantly with accuracy, F~1,28! 5
4.3, p , .05, revealing that low-SO participants had faster RTs on
error trials than high-SO participants. Finally, a trend ~ p , .08!
was apparent in the data that suggested RTs for both groups on AL
trials were nearly equivalent, whereas low-SO participants responded approximately 25 ms more quickly than high-SO participants on REW trials.
EMG Results
To examine the extent to which ERN amplitudes were directly
related to the amount of force that participants used to respond,
we examined EMG activity during responses made during the
two conditions ~Figure 7!. The microvolt values representing
total EMG activity were subjected to a 2 ~Socialization group! 3
2 ~Condition! 3 2 ~Accuracy! ANOVA. A main effect for accuracy was significant, F~1,26! 5 12.036, p , .01; as expected,
participants responded more forcefully on correct trials than on
incorrect trials.
An interaction between socialization and condition just missed
statistical significance, F~1,26! 53.91 , p 5.059. To examine this
interaction more closely a Duncan’s multiple range follow-up analysis was computed. Within the low-SO group responses tended to be
stronger during the REW condition than during the AL condition
~ p , .08!. The low-SO group’s force of response on rewarded
trials was greater than the high-SO groups response on rewarded
trials and punished trials ~ p , .01 and p , .05, respectively!.
BESA Results
Results of BESA modeling were consistent for all four conditions,
with each model producing a one-dipole solution in which the
generator was located in the medial frontal lobes. These solutions
were obtained by allowing a single dipole to find the optimal fit
~i.e., minimizing residual variance!. This one-dipole solution in the
medial frontal lobes was not dependent on the initial placement of
the dipole. Furthermore, when additional dipoles were added, the
initial solution remained unchanged, and the additional dipole mi-
The results of several analyses provide convergent evidence that
high-SO and low-SO participants differ in their error monitoring
under conditions of REW and AL. ERNs produced by low-SO
groups during AL conditions are smaller than their ERNs when
rewarded. Furthermore, between-subject trends suggest that the
ERNs produced by the low-SO group in the AL task also differed
from those produced by the high-SO group during either task.
The present data cannot be explained simply by a difference in
awareness of having committed errors, as only trials during which
subjects spontaneously corrected their errors were included in the
ERN averages; moreover, there is no reason to presume that these
spontaneous corrections necessarily require awareness at the time
the correction is made. Rather, the difference in ERN amplitude
between REW and AL conditions suggests that low-SO participants, during trials on which they were punished as opposed to
rewarded: ~1! found errors to be less salient; ~2! monitored their
errant responses less closely; or ~3! were less concerned about the
consequences of having made an error. These findings are consistent with previous research demonstrating AL deficits in low-SO
participants, and by extension psychopaths. These data also extend
previous research by identifying a central nervous system correlate
of this phenomenon.
Gehring et al. ~1993! posited that the anterior cingulate cortex
may be the locus for an ERN generator, based on evidence from
animal studies ~Gemba, Sasaki, & Brooks, 1986! and theoretical
human motor networks ~Goldberg, 1985!. The coordinates obtained in the present study compare favorably to data published
previously ~Miltner, Braun, & Coles, 1997!. The present models
were localized to positions that fell within the range observed by
Miltner et al. with the exception that the present models placed the
dipole source more posterior than those of previous studies. This
difference may be attributable to several factors. First, we used a
lower density of electrodes across the scalp ~especially over anterior regions!. Second, there may exist individual differences between the present sample and those in previous studies, as we
selected extreme groups and previous studies used unselected samples. Third, whereas we modeled only a single dipole over a limited time span, Miltner et al. modeled a longer time period with
two dipoles that would include not only the earlier-occurring ERN
52
Z.V. Dikman and J.J.B. Allen
Figure 7. Waveforms ~in microvolts! representing electromyogram ~EMG! responses on correct trials ~top panel! and error trials
~bottom panel!. A main effect of accuracy was observed ~ p , .01!.
~consistent with the present study! but also a later positivity that
has a more posterior distribution. If there is any overlap of this
later positivity with the earlier ERN activity, then this could have
the net effect of placing our single dipole more posteriorly than
obtained under the two-dipole solution of Miltner et al. Finally,
there are differences in the tasks used, as Miltner et al. utilized a
time-estimation task different from the task used in the present
study. In support of this last possibility, Miltner et al. published
dipole data taken from Dehaene, Posner, and Tucker ~1994!, who
implemented a choice RT task and reported BESA Y-coordinate
~anterior-posterior axis! values that fell closer to those found in the
present study.
Although dipole modeling does not prove conclusively that the
ERN is generated within the anterior cingulate cortex, Dehaene
et al. ~1994! argued that “the observed scalp negativity is so tightly
localized, however, as to support the idea that the generator is
located relatively closely underneath electrode FzS @which is located between scalp sites Fz and Cz#, within either the supplementary motor area ~SMA! or the anterior cingulate cortex” ~p. 304!.
It is also worth noting that the present data provide no evidence
that a different generator is operative as a function of subject status
~high or low SO! or task ~REW or AL!. Thus any differences
between high- and low-SO subjects do not appear to be the result
of wholly different processing strategies.
Additional evidence for the role of anterior cingulate cortex in
generation of the ERN can be found in a comprehensive review of
anterior cingulate functioning by Devinsky, Morell, and Vogt ~1995!.
A growing body of theory and evidence implicates medial frontal
lobe structures not only in the generation of the ERN, but also in
the behavioral patterns of psychopaths. Damasio ~1994! provided
a neuroanatomical model detailing the interface between emotion
and cognition, especially social cognition. Damasio’s Somatic
Socialization and ERN amplitude
53
Marker Hypothesis suggests that the anterior cingulate may be a
critical component of a system that uses affective responses to
guide decision-making processes.
Devinsky et al. ~1995! provided additional evidence; behaviors
observed in cases of cingulate lesions or cingulate-focused epilepsy bear a remarkable similarity to those of psychopaths, with
examples including disinhibition, aggressiveness, and lack of social restraint. Additionally, Devinsky et al. ~1995! cited evidence of
a reduced ability for cingulate-lesioned animals to learn in AL
paradigms. A recent neuropsychological investigation of psychopaths also found significant deficits in psychopathic prisoners on
several different tests thought to tap ventral frontal lobe functioning ~Lapierre, Braun, & Hodgins, 1995!. Also suggestive of a
relationship between cingulate dysfunction and psychopathy are
reports detailing autonomic hyporeactivity in psychopaths ~Arnett
et al., 1993; Patrick, 1994; Schmauk, 1970!. The anterior cingulate
cortex has long been considered one of several structures necessary for the full experience, display, and control of normal affect,
and is situated in an ideal location for the interface between emotion and cognition. When coupled with the clinical reports detailed above, the possible role of the anterior cingulate cortex in
the pathological presentation of psychopathy deserves further
investigation.
Methodological Issues and Limitations
The results of this experiment may be limited by the use of an
analog measure of psychopathy, which limits the generalizability
of the findings to the population of psychopaths. Importantly, however, the low-SO participants in this experiment were drawn from
a large pool of potential participants and their scores represent the
very lowest of all possible scores in this sample. When compared
with other published examinations of low-SO groups, this sample
falls well below the mean socialization levels for these studies
~e.g., Howland et al., 1993!. It is therefore likely that the low-SO
participants included in this study provide the strongest possible
analog group to psychopaths ~as defined by the PCL!. Furthermore, results from the EMG and RT data are consistent with previous findings in psychopaths. The low-SO participants responded
more forcefully and more quickly during conditions that promised
rewards than those that threatened aversive consequences.
The present results must also be interpreted in light of our
decision to select extreme groups, which means that the control
group is a highly socialized group. Little has been noted in the
literature about individuals who demonstrate extremely high scores
on the CPI SO scale. Whereas the between-group differences may
have been somewhat influenced by this choice of extreme groups,
the primary finding of interest is that, among low-SO individuals,
ERN amplitude differs in the predicted manner between conditions
of REW and AL. Moreover, the present pattern of results is remarkably consistent with what previous investigations have ob-
served: low-socialized or psychopathic groups differ in response to
reward and punishment, with performance deficits being observed
during punishment conditions.
Another potential concern is that this paradigm created overall
differences in the difficulty level of the REW and AL conditions,
and therefore created differences in the value of incentives provided in the REW condition and the punishments implemented in
the AL condition. Evidence from the present study, however, supports the notion that the incentives we used were similar in salience and motivation to the participants. For instance, ERNs
produced by the high-SO group during the REW and AL tasks did
not differ, suggesting that the two tasks demanded similar attentional and other cognitive resources, and that differences within the
low-SO group were not due to differences between the incentives
per se. Results from the analysis of accuracy, EMG, and RT data
also support this conclusion. No statistically significant differences
were observed within the accuracy data to suggest that differential
incentives between the conditions existed. It is therefore plausible
that the differences observed within the low-SO group are due to
factors related to their perceptions and motivations regarding the
tasks, and not differences between the tasks in terms of difficulty,
processing requirements, or overt performance.
Individual Differences and Measurement
The present results suggest that the ERN reflects processes beyond
those that determine response latency and error rates. Whereas
low-SO participants tended to respond more quickly and responded more forcefully on rewarded trials than on punished trials,
these measures did not interact with accuracy as did ERN amplitude. Although the RT and EMG measures together indicate that
the low-SO group favored the reward condition, they provide an
incomplete view that is complemented by the ERN data.
The results of these analyses, when combined with existing
evidence from recent experimental reports, suggest that ERN
amplitude reflects error detection ~cf. Gehring et al., 1993; Miltner et al., 1997; Scheffers et al., 1996!, and other factors that
influence the salience of such an error. In this respect, we agree
with Scheffers et al. ~1996! that “the ERN process can be influenced . . . by the implications of erroneous behavior in the context of specific task requirements” ~p. 52!. Furthermore, we would
add that individual differences modulate the ERN process by
providing a context for the evaluation of an error. The magnitude of such modulation can, at least in the present study, be
substantial; among low-SO participants, ERN difference-waveform
amplitude during REW trials was 33% larger than that during
AL trials. Although it remains to be determined whether similarly large effects are related to other individual difference variables, the ERN warrants investigation in a wider range of
experimental research involving individual differences, consciousness, attention, and neuropsychology.
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~Received June 5, 1998; Accepted March 23, 1999!