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The psychophysiology of error and feedback processing in attention deficit hyperactivitydisorder and autistic spectrum disorderGroen, Yvonne
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THE PSYCHOPHYSIOLOGY OF
ERROR AND FEEDBACK PROCESSING IN
ATTENTION DEFICIT HYPERACTIVITY DISORDER
AND
AUTISTIC SPECTRUM DISORDER
YVONNE GROEN
This work was financially supported by: the Graduate School of Behavioral and Cognitive
Neurosciences (BCN) and the Protestants Christelijke Kinderuitzending (PCK).
COVER ILLUSTRATION: Wilbert van der Steen, printed with permission.
COVER DESIGN: Yvonne Groen
PRINT: Grafimedia (Facilitair bedrijf), Groningen
© Y. Groen, Groningen, 2009
THE PSYCHOPHYSIOLOGY OF ERROR AND FEEDBACK PROCESSING IN
ATTENTION DEFICIT HYPERACTIVITY DISORDER
AND AUTISTIC SPECTRUM DISORDER
Proefschrift
ter verkrijging van het doctoraat in de
Medische Wetenschappen
aan de Rijksuniversiteit Groningen
op gezag van de
Rector Magnificus, dr. F. Zwarts,
in het openbaar te verdedigen op
woensdag 9 september 2009
om 16.15 uur
door Yvonne Groen
geboren op 27 december 1979
te Ruinerwold
Promotor: Prof. dr. R.B. Minderaa
Copromotores: Dr. M. Althaus
Dr. L.J.M. Mulder
Dr. A.A. Wijers
Beoordelingscommissie: Prof. dr. J.A. den Boer
Prof. dr. J.L. Kenemans
Prof. dr. M.W. van der Molen
ISBN (printed edition): 978-90-367-3914-6
ISBN (digital edition): 978-90-367-3915-3
HET KLEINE STOUTE JONGETJE
Er was een heel klein jongetje
dat dolgraag klappen kreeg
hij gooide daarom vaak zijn melk
in ‘t gootsteenkastje leeg
en als hij stout was kon je dat
in Kudelstaart zelfs horen
de kletsen voor zijn billen
en de draaien om zijn oren
Hij vond het prachtig
als zijn vader harde petsen gaf
en zelfs voor zijn verjaardag
vroeg hij altijd weer om straf
Tot er iemand kwam die zei:
dáár gaan we iets aan doen
voor straf krijgt hij geen klappen meer
maar elke dag een zoen
en als hij héél vervelend is
dan geven we een feest
en sinds díe tijd is ’t jongetje
nog nooit zo lief geweest!
UIT:
Marianne Busser en Rond Schröder (2005). HET GROTE VERSJESBOEK. Van Holema &
Warendorf, Houten
- Voor papa -
CONTENTS
ABBREVIATIONS 9
CHAPTER 1 11
GENERAL INTRODUCTION
CHAPTER 2 31
PHYSIOLOGICAL CORRELATES OF LEARNING BY PERFORMANCE FEEDBACK IN CHILDREN: A
STUDY OF EEG EVENT-RELATED POTENTIALS AND EVOKED HEART RATE
CHAPTER 3 65
ERROR AND FEEDBACK PROCESSING IN CHILDREN WITH ADHD AND CHILDREN WITH AUTISTIC
SPECTRUM DISORDER: AN EEG EVENT-RELATED POTENTIAL STUDY
CHAPTER 4 105
EVOKED HEART RATE ANALYSES OF ERROR AND FEEDBACK SENSITIVITY IN ADHD AND
AUTISTIC SPECTRUM DISORDER
CHAPTER 5 133
DIFFERENTIAL EFFECTS OF 5-HTTLPR AND DRD2/ANKK1 POLYMORPHISMS ON
ELECTROCORTICAL MEASURES OF ERROR AND FEEDBACK PROCESSING IN CHILDREN
CHAPTER 6 165
GENERAL DISCUSSION
REFERENCES 185
NEDERLANDSE SAMENVATTING 211
DANKWOORD 225
CURRICULUM VITAE 229
ABBREVIATIONS
9
ABBREVIATIONS
ACC Anterior Cingulate Cortex (d = dorsal, r = rostral)
ADHD Attention Deficit Hyperactivity Disorder
ANS Autonomic Nervous System
ASD Autistic Spectrum Disorder
BAS Behavioural Activation System
BIS Behavioural Inhibition System
CBCL Child Behavioural Checklist
CSBQ Children’s Social Behaviour Questionnaire
CTRS-R Conners’ Teacher Rating Scale- Revised
DISC-IV Diagnostic Interview Schedule for Children-IV
ECG ElectroCardioGram
EEG ElectroEncephaloGram
EF Executive Function
EHR Evoked Heart Rate
ERP Event-Related Potential
ERN Error-Related Negativity
FMRI functional Magnetic Resonance Imaging
HFA High Functioning Autism
HR Heart Rate
LC Locus Coereleus
ABBREVIATIONS
10
LPP Late Positive Potential
MPH Methylphenidate
NAC Nucleus Accumbens
NE Noradrenaline
NTS Nucleus Tractus Solitarius
IBI Inter Beat Interval
RT Reaction Time
SCQ Social Communication Questionnaire
SCR Skin Conductance Response
SD Standard Deviation
SE Standard Error
SPN Stimulus Preceding Negativity
TD Typically Developing
TOM Theory of Mind
PDD (NOS) Pervasive Developmental Disorder (Not Otherwise Specified)
PE error Positivity
PMFC prefrontal Medial Frontal Cortex
PFC Prefrontal Cortex
WISC-III Wechsler Intelligence Scale for Children -III
CHAPTER 1
GENERAL INTRODUCTION
GENERAL INTRODUCTION
12
STUDY OBJECTIVES
The main question of this thesis is whether children with the developmental disorders
Attention Deficit Hyperactivity Disorder (ADHD) or Autism Spectrum Disorder (ASD)
have deficits in error and feedback processing and whether they can be discriminated
from each other in some aspects of this processing. In the past two decades
psychophysiological research has largely extended our knowledge of cortical and
autonomic correlates of error and feedback processing in healthy adults, which helps us
to understand specific cognitive control or executive functioning processes.
Psychophysiological measurements may, therefore, be useful in exploring differences in
specific aspects of these cognitive control or executive functioning processes. To this
end both electrocortical and autonomic measures were obtained while children with
ADHD or ASD, as well as a typically developing (TD) children, performed cognitive
tasks in which feedback on their performance was manipulated.
However, psychophysiological research on error and feedback processing in children is
scarce, although recently more and more developmental studies have been published.
Moreover, the relation between electrocortical and autonomic measures of error and
feedback processing is an under-exposed subject in the literature. A first subquestion of
this thesis is, therefore, (how) do electrocortical and autonomic measures of error and
feedback processing relate?
The mainstay of ADHD treatment is stimulant medication, mostly Methylphenidate
(Mph), which markedly and rapidly reduces the overt clinical manifestations of the
syndrome. A second subquestion of this thesis is whether Mph intake in children with
ADHD influences the psychophysiology of error and feedback processing.
Finally, a third subquestion is whether specific genetic factors influence the
psychophysiology of error and feedback processing. To this end subgroups were formed
within the whole tested sample of typically developing (TD) children and children with
developmental disorders, based on common functional polymorphisms of two genes,
involved in serotonergic and dopaminergic neurotransmission respectively. The variants
of these genes have been linked to specific personality traits that have independently
from each other been suggested to affect reinforcement-controlled behaviour. This
research approach may increase the understanding of natural variations in the
CHAPTER 1
13
psychophysiology of error and feedback processing, herewith crossing the borders of
psychopathological phenotypes.
ERROR AND FEEDBACK PROCESSING
RELEVANCE
A great deal of our acquired behaviour is learnt by attending to feedback on our actions.
From birth we are continuously confronted with feedback on our behaviour; as a baby
we are praised when we show new behaviour and as we grow a little older we are
continuously told what and what not to do by our parents, teachers, peers and other
people. Feedback comes to us in different forms; it ranges from a frown or a smile to
words of refusal/approval and from tangible rewards or punishments (e.g. money or
candy) to more neutral signs that inform us whether we performed correctly or not
(knowledge of results). Although feedback is all around us in different forms, we
become more and more independent from external feedback by learning from it.
As a child we may rely heavily on feedback from our environment, but as a young adult
we grow out to be self-regulatory: most situations around us are well-known and we
know what kind of behaviour suits which (social) situation. In well-known situations we
will automatically show appropriate and well-adapted (social) behaviour, but when
things go wrong or in changing and unknown situations we must overrule our automatic
behaviour and we need to increase cognitive control. In these instances it is thus of great
importance that we continuously monitor our behaviour/performance and environment,
for the purpose of detecting the need for increased cognitive control and for adjustment
of behaviour. This executive function (EF) ability is called performance monitoring.
This thesis focuses on the monitoring of events that signal the need for increased
cognitive control: the commission of error responses and the receipt of negative
feedback. The continuous evaluation whether current behaviour is adequate and
successful, is the key to appropriately determining and implementing behavioural
adjustments. The detection of error responses may allow subjects to alter their response
strategy, e.g. by adjusting the speed-accuracy trade-off, while the receipt of negative
feedback may be used to leave the currently used inappropriate stimulus-response
coupling and shift to one that results in positive feedback.
GENERAL INTRODUCTION
14
NEUROBIOLOGY
The past two decades of research on performance monitoring and error processing have
largely increased the understanding of the involved neural mechanisms. Based on
functional neuroimaging, electrophysiological, lesion and intracranial recording studies,
the Anterior Cingulate Cortex (ACC), along with connected prefrontal structures, has
been indicated as one of the main brain areas involved in the monitoring of
unfavourable outcomes (e.g. negative feedback), error responses, response conflict, and
decision uncertainty (Ridderinkhof, Ullsperger, Crone, & Nieuwenhuis, 2004; Taylor,
Stern, & Gehring, 2007). The ACC can be functionally divided into a dorsal region
(dACC) that connects with (parts of) the basal ganglia, e.g. striatum, and is involved in
motor and cognitive processes, and a rostral region (rACC) that interacts with
paralimbic and limbic regions, such as the amygdala and insula, and mediates more
emotional processes (Bush, Luu, & Posner, 2000; Phillips, Drevets, Rauch, & Lane,
2003). Both regions of the ACC show increased activation in response to errors (Carter
et al., 1998; Kiehl, Liddle, & Hopfinger, 2000) and have been identified as potential
neuronal sources of error-related Event-Related Potentials (Dehaene, Posner, & Tucker,
1994b; Luu, Tucker, Derryberry, Reed, & Poulsen, 2003; van Veen & Carter, 2002;
Mathalon, Whitfield, & Ford, 2003; Miltner et al., 2003). A recent review by Taylor and
colleagues (2007) states that both the dACC/ prefrontal Medial Frontal Cortex (pMFC)
and rACC/lateral Prefrontal Cortex (PFC) are convincingly involved in error
processing.
According to a recent theory by Holroyd and Coles (2002) the role of the dACC in error
and feedback processing may be explained in terms of a common functional and
neurobiological mechanism that codes events according to the reinforcement learning
principle (Schultz, 2000). Following error commission the dACC implements error-
based reinforcement learning using phasic dopaminergic signals from the striatum and
mesencephalic dopamine system. Phasic increases of DA activity in the basal ganglia
code for events that are unexpectedly better than expected, while phasic decreases code
for events that are suddenly worse than expected. As these phasic changes in DA
activity are conveyed to the dACC, these reward and error signals can be used to
identify and select appropriate behaviours and thereby improving performance (Holroyd
& Coles, 2002). Error-related activity of the rACC has been proposed to reflect
appraisal of the affective or motivational significance of errors (Luu et al., 2003; Taylor
CHAPTER 1
15
et al., 2007; van Veen & Carter, 2002). The rACC likely fulfils this role in conjunction
with the insula and amygdala, as these structures are densely interconnected with the
rACC (Van Hoesen, Morecraft, & Vogt, 1993) and become increasingly active during
error processing (Menon, Adleman, White, Glover, & Reiss, 2001; Taylor et al., 2007;
Brazdil et al., 2002; Garavan, Ross, Murphy, Roche, & Stein, 2002).
PSYCHOPHYSIOLOGY
Since the early nineties ElectroEncephalogram (EEG) Event-Related Potential (ERP)
studies in humans have identified several electrocortical components reflecting error
and feedback processing. Moreover, heart rate (HR) has also been found sensitive to
performance monitoring activity. As these psychophysiological measures are the central
point of this thesis they are briefly introduced here.
The error-related EEG component that has received most attention in performance
monitoring literature is the Error-Related Negativity (ERN: Gehring, Coles, Meyer, &
Donchin, 1990; Gehring, Goss, Coles, Meyer, & Donchin, 1993; Ne: Falkenstein,
Hohnsbein, Hoormann, & Blanke, 1991). A similar component occurs when negative
feedback is processed: the feedback ERN (Medial Frontal Negativity: Gehring &
Willoughby, 2002; Feedback ERN: Holroyd & Coles, 2002; Feedback Related
Negativity: Müller, Möller, Rodriguez-Fornells, & Münte, 2005; Holroyd & Coles,
2002; Holroyd, Larsen, & Cohen, 2004a). These components reflect the first warning
signal that ongoing behaviour is no longer appropriate and that increased cognitive
control is needed (Holroyd & Coles, 2002; Nieuwenhuis, Ridderinkhof, Blow, Band, &
Kok, 2001; Brown & Braver, 2005). Source localisation studies point to the ACC as the
main neuronal source of the ERN (Taylor et al., 2007). Both components may represent
a phasic decrease of dopaminergic firing to the dACC (Holroyd & Coles, 2002).
Further error processing may be reflected by the error Positivity (Pe), which is a
positive-going potential that follows the ERN (Falkenstein et al., 1991; Davies,
Segalowitz, Dywan, & Pailing, 2001). Some studies indicate that the amplitude of the
Pe, but not the ERN, covaries with awareness of the error (see for a review: Overbeek,
Nieuwenhuis, & Ridderinkhof, 2005) and that the Pe, but not the ERN, is associated
with the strategic slowing of response time after errors (post error slowing) (Hajcak,
McDonald, & Simons, 2003b; Nieuwenhuis et al., 2001). Several authors have noted
similarities between the Pe and the stimulus-related P3 (Davies et al., 2001; Leuthold &
GENERAL INTRODUCTION
16
Sommer, 1999a; O'Connell et al., 2007; Overbeek et al., 2005; Jonkman, Van Melis,
Kemner, & Markus, 2007). As the P3 has been linked to phasic responses of the locus
coeruleus-noradrenaline (LC-NE) system (Nieuwenhuis, Aston-Jones, & Cohen, 2005),
conscious error processing may, therefore, be associated with increased phasic activity
of the noradrenergic system.
Regarding feedback processing, successive to the feedback ERN a P3 is elicited
(Miltner, Braun, & Coles, 1997), which may reflect the processing of relevant
information that can be used to modify future behaviour (Müller et al., 2005). Literature
is inconsistent as to whether the feedback P3 amplitude is larger for positive or negative
feedback, but in general the P3 is known to increase when (1) the subjective probability
of the stimulus is low, (2) the motivational significance of the stimulus is high and (3)
the amount of attention paid to the stimulus is high (for a review see: Nieuwenhuis et
al., 2005). Another relevant component that has been designated as the affective
counterpart of the classical P3 is the Late Positive Potential (LPP). The LPP is elicited
by highly arousing pleasant and unpleasant pictures compared to neutral pictures and is
thought to reflect increased attention to affective-motivational stimuli (Cuthbert,
Schupp, Bradley, Birbaumer, & Lang, 2000b; Hajcak & Olvet, 2008; Hajcak, Moser, &
Simons, 2006; Schupp et al., 2000b). It has been hypothesised that this component
reflects facilitated or amplified stimulus processing resulting from amygdala-activity
(Hajcak et al., 2006; Bradley et al., 2003a).
The prefeedback Stimulus Preceding Negativity (SPN) has also been described in
relation to feedback monitoring. The prefeedback SPN is a negative-going slow wave
that has been associated with the anticipation of the affective motivational value of
feedback stimuli (for an overview see: Böcker, Baas, Kenemans, & Verbaten, 2001).
The prefeedback SPN is, for example, larger in preparation of rewarding feedback
opposed to non-rewarding feedback and larger in preparation of informative opposed to
uninformative feedback (Kotani, Hiraku, Suda, & Aihara, 2001; Chwilla & Brunia,
1991). The insular cortex, which is intimately connected with the limbic system, has
repeatedly been suggested to be one of the main neural generators of the prefeedback
SPN (Böcker, Brunia, & Van den Berg-Lenssen, 1994; Brunia, De Jong, Van den Berg-
Lenssen, & Paans, 2000; Tsukamoto et al., 2006). The prefeedback SPN amplitude
CHAPTER 1
17
may, therefore, be the reflection of the subject’s motivational involvement in the task
(Bastiaansen, Böcker, & Brunia, 2002).
Performance monitoring processes are also reflected by autonomic measures. First of
all, heart rate (HR) decelerates briefly in anticipation and preparation of upcoming
feedback stimuli (see the review by: Jennings & Van der Molen, 2002). This concerns
brief beat-to-beat increases in the time between heartbeats (Inter Beat Intervals: IBIs)
that can be observed by selecting IBI times around feedback stimuli and computing
averages of the resulting IBI patterns across feedback conditions (for example positive
and negative feedback). The resulting pattern of IBIs is called Evoked Heart Rate
(EHR). Whereas positive feedback immediately elicits an acceleratory recovery at
feedback onset, negative feedback elicits a prolonged or enhanced EHR deceleration
(Crone et al., 2003c; Somsen, Van der Molen, Jennings, & Van Beek, 2000; Van der
Veen, Van der Molen, Crone, & Jennings, 2004). Similar enhanced EHR decelerations
are also elicited by error responses (Crone, Somsen, Zanolie, & Van der Molen, 2006;
Hajcak et al., 2003b; Hajcak et al., 2003b).
DEVELOPMENTAL CHANGES
Developmental psychophysiological studies in children and adolescents have
established that the ability of performance monitoring grows with age. As this thesis
concerns 10-to 12-year-old children, the impact of typical development on the relevant
psychophysiological measures will be shortly addressed here.
The ERN amplitude in school-aged children is substantially smaller than in young
adults and its amplitude develops throughout the second decade of life (Davies,
Segalowitz, & Gavin, 2004; Hogan, Vargha-Khadem, Kirkham, & Baldeweg, 2005;
Santesso, Segalowitz, & Schmidt, 2006). The Pe seems to follow a different
developmental trajectory; two studies have indicated that school-aged children show Pe
amplitudes similar to young adults (Davies et al., 2004; Santesso et al., 2006). With
regard to EHR measures of error processing, a developmental study by Crone and
colleagues (2006) showed that 8- to 10-year-old children show no EHR deceleration
after error responses, while 12- to 14-year-old children and 16- to 18-year-old
adolescents do. This suggests that with increasing age children become able to online
monitor their behaviour. Moreover, another developmental EHR study has indicated
that preadolescents do not process feedback information as efficient as adults (Crone,
GENERAL INTRODUCTION
18
Jennings, & Van der Molen, 2004). While 12-year-old children and adults showed
differentiated EHR responses to different types of feedback stimuli, 8- to-10-year-old
children showed undifferentiated EHR responses. These developmental findings on
error and feedback processing can be related to the relatively slow maturation until early
adulthood of the frontal lobes in general (Stuss, 1992) and the ACC in particular
(Cunningham, Bhattacharyya, & Benes, 2002; Eshel, Nelson, Blair, Pine, & Ernst,
2007; Davies et al., 2004; Santesso et al., 2006).
(HOW) DO ELECTROCORTICAL AND AUTONOMIC CORRELATES
OF ERROR AND FEEDBACK PROCESSING RELATE?
Heart rate deceleration in response to performance feedback has been suggested to be a
reflection of the same error monitoring system that is at the basis of the ERN (Somsen
et al., 2000; Jennings & Van der Molen, 2002; Crone et al., 2003c). This suggestion is
supported by findings of shared functional characteristics on the one hand, and by
findings of a shared neural substrate on the other. It is, for example, quite well
established that the dorsal ACC, which is involved in the generation of the ERN, also
forms part of a system that generates changes in autonomic state during effortful
cognitive processing (for a review see: Critchley, 2005). A study by Hajcak and
colleagues (2003b), however, failed to find a significant correlation between error-
related heart rate deceleration and the ERN. These authors, however, did report on a
positive correlation between the Pe amplitude and subsequent skin conductance
response activity and suggested that the Pe triggers the subsequent autonomic nervous
system (ANS) activity. They concluded that the full range of performance monitoring
processes may rely on the interplay of centrally generated signals, affecting both
decision-making systems in the brain and peripheral changes in body state (Hajcak et
al., 2003b).
The first subquestion this thesis deals with, is whether different error- and feedback-
related ERP components are interrelated with simultaneously measured heart rate
responses to those events. Answers were sought by both investigating their functional
characteristics during a feedback-based learning task and by directly computing
correlations between the ERP components and EHR responses in a sample of typically
developing preadolescent children.
CHAPTER 1
19
CAN ADHD AND ASD BE DISCRIMINATED ON THE
PSYCHOPHYSIOLOGY OF ERROR AND FEEDBACK PROCESSING?
ADHD, ASD AND THEIR OVERLAP
ADHD is a disorder characterised by a persistent pattern of inattention and/or
hyperactivity-impulsivity that is more frequent and severe than is typically observed in
individuals at a similar level of development. ADHD is regarded as one of the most
common psychiatric disorders of childhood and has been estimated to affect 3%-7% of
school-aged children worldwide (American Psychiatric Association, 2000). The
symptoms must be present during at least six months and some impairment must have
been present before the age of 7 years. Moreover, some impairment from the symptoms
must be present in at least two settings (e.g. at home and at school). Three subtypes of
ADHD are distinguished in the DSM-IV-TR: the predominantly inattentive, the
predominantly hyperactive/impulsive and the combined type. The latter is by far the
most common.
Children with ADHD have numerous difficulties in both structured situations, such as
the classroom, and unstructured situations, such as the playground, that impair the
affected individuals and disturb their fellow humans. The hyperactive and impulsive
symptoms are the most outstanding characteristic of children with ADHD, finding
expression in shouting out replies, interrupting others, being reckless and accident-
prone. Less outstanding, however not less impairing, are the inattention symptoms,
which are manifested by, for example, difficulties with attending to instructions in
academic and social situations and being poorly organized and forgetful. ADHD is
designated as a heterogeneous disorder, because the symptoms vary both within and
between individuals. Within individuals the ADHD behaviour may be rather context-
dependent, for example a child with ADHD may be distractible and inattentive in the
classroom, but restless and impulsive at home. Between individuals there is large
variability in symptom presentation, severity and comorbid conditions.
Autistic Disorder is defined by the early onset of a ‘triad of deficits’ (Wing & Gould,
1979): impaired development in social interaction and communication and a markedly
restricted repertoire of activity and interests (American Psychiatric Association, 2000).
The most characteristic aspects of individuals with Autistic Disorder concern gross and
sustained impairment in reciprocal social interaction and the ability to form and
GENERAL INTRODUCTION
20
maintain relationships (Tanguay, Robertson, & Derrick, 1998). Abnormalities in verbal
and nonverbal communication concern difficulties in carrying on conversations and
social chat, its most distinctive feature being its lack of, or unusual, social quality
(Jarrold, Boucher, & Russell, 1997). Individuals with Autistic Disorder often show a
delay in, or a total lack of, the development of spoken language and often use
stereotyped, repetitive or idiosyncratic language. Finally, individuals with Autistic
Disorder also show restrictive, repetitive and stereotyped patterns of behaviour, interests
and activities. This often concerns unusual preoccupations and circumscribed interests
that are abnormal in intensity or focus, adherence to non-functional routines or rituals
and/or stereotyped movements and activities.
The umbrella term Autistic Spectrum Disorders (ASDs) is used to cover a broader range
of autistic-like disorders. It includes individuals showing ‘atypical autism’ that do not
meet the criteria for Autistic Disorder because of late age onset, atypical
symptomatology, or subthreshold symptomatology, or all of these. These individuals are
classified as Pervasive Developmental Disorder Not Otherwise Specified (PDDNOS).
No positive criteria have been formulated for this disorder, although the diagnosis
requires severe and pervasive impairment of Autistic Disorder symptoms (American
Psychiatric Association, 2000). Prevalence rates of ASD depend on the definition of the
disorder, but estimates that also include PDDNOS range from 30 to 60 cases per 10.000
individuals (Fombonne, Zakarian, Bennett, Meng, & Lean-Heywood, 2006; Rutter,
2005). This thesis describes children that had been diagnosed as having PDDNOS, who
will be referred to as children with ASD.
Although ADHD and ASD are described as clearly distinct disorders, in clinical
practice it often appears difficult to discriminate between the two (Clark, Feehan,
Tinline, & Vostanis, 1999; Jensen, Larrieu, & Mack, 1997). Phenomenological studies
report that many children with ADHD also have ASD symptoms and vice versa (see for
a review: Nijmeijer et al., 2008). Many children with ADHD show inadequate social
behaviours that are crucial for their prognosis (Greene et al., 1996; Greene, Biederman,
Faraone, Sienna, & GarciaJetton, 1997). These children are characterised by a limited
repertoire of social responses and a lack of comprehension of the impact of their actions
on others (Nijmeijer et al., 2008). The most frequently reported ASD symptoms in
children with ADHD are impairments in social interaction and a lack of awareness of
CHAPTER 1
21
feelings and thoughts of others (Buitelaar, Van der Wees, Swaab-Barneveld, & Van der
Gaag, 1999; Santosh & Mijovic, 2004; Clark et al., 1999; Nijmeijer et al., 2008). The
other way around, children with ASD often display symptoms of ADHD (Ghaziuddin,
Weidmer-Mikhail, & Ghaziuddin, 1998; Goldstein & Schwebach, 2004; Keen & Ward,
2004; Lee & Hinshaw, 2006; Yoshida & Uchiyama, 2004; Nijmeijer et al., 2008). Some
children with ASD have for instance been found to score as high as children with
ADHD on hyperactivity and acting out behaviour and have been found to even fulfil all
criteria for the diagnosis of ADHD (Jensen et al., 1997; Frazier et al., 2006).
Moreover, both ADHD and ASD have been related to executive functioning deficits
(Barkley, 1997; Pennington & Ozonoff, 1996; Russell, 1997). Intact Executive
Functions (EFs) enable individuals to show goal-directed behaviour that is flexibly
adapted to the environment. EF deficits may, therefore, hamper children with ADHD
and ASD in self-regulatory capabilities in everyday life. There is, however, an ongoing
debate on the type of EF profile that is specific for either disorder (Sergeant, Geurts, &
Oosterlaan, 2002; Geurts, Vertie, Oosterlaan, Roeyers, & Sergeant, 2004; Happé,
Booth, Charlton, & Hughes, 2006; Ozonoff & Jensen, 1999). Studies directly
comparing the performance of children with ADHD and children with ASD on
neuropsychological tasks tapping distinct domains of executive functioning, have
suggested that children with ADHD show greater deficits in response inhibition, while
children with ASD show marked deficits in planning, flexibility and response
selection/monitoring (Ozonoff & Jensen, 1999; Geurts et al., 2004; Happé et al., 2006,
but see for a different finding Nyden, Gillberg, Hjelmquist, & Heiman, 1999). Although
neuropsychological tasks may be closely related to complex tasks in everyday life and,
therefore, have large ecological validity, one major limitation of their use is that the
performance measures reflect the outcome of multiple underlying component processes.
The main question of this thesis with respect to these issues is whether ASD and ADHD
show distinct deficits in component processes of EF, specifically in the area of
monitoring errors and feedback. The use of psychophysiological measures allows for
separating specific cognitive control processes and, consequently for making inferences
about their underlying neurobiological sources.
GENERAL INTRODUCTION
22
ERROR AND FEEDBACK PROCESSING IN ADHD
The firstly stated symptom of inattention in the DSM-IV, and subject of this thesis, is
that a child with ADHD ‘often fails to give close attention to details or makes careless
mistakes in schoolwork, work, or other activities’ (American Psychiatric Association,
2000, p. 92). Children with ADHD seem to have difficulties in interrupting their actions
and in adjusting incorrect or maladaptive responses, which finally results in the
commission of careless errors. This suggests that error and feedback processing deficits
are inherent to ADHD.
Influential comprehensive models of ADHD have advocated that disinhibition is central
to the disorder, and distinguishes it from other disorders (Barkley, 1997; Quay, 1988a;
Quay, 1988b). The inhibitory deficits result in a failure to delay responding and can be
regarded as a cognitive deficit, i.e. a deficit of EFs. Other comprehensive models of
ADHD suggest that the disorder is characterised by an altered motivational style
(Haenlein & Caul, 1987; Douglas & Parry, 1994) or at least by the interplay of
cognitive and motivational deficits (Sonuga-Barke, 2002; Sergeant, 2000; Sagvolden,
Johansen, Aase, & Russell, 2005a). Motivational deficits in ADHD may be expressed
by a deficient sensitivity to reinforcement, including aberrant reward and/or punishment
sensitivity and decreased sensitivity, or aversion, to delay of reward (Haenlein & Caul,
1987; Quay, 1988a; Quay, 1988b; Douglas & Parry, 1994; Sergeant, 2000; Sonuga-
Barke, 2002; Sagvolden et al., 2005a; Rapport, Tucker, Dupaul, Merlo, & Stoner, 1986;
Carlson, Mann, & Alexander, 2000; Carlson & Tamm, 2000). Although literature on
motivational deficits in ADHD mainly concerns reward-related processes, subjects with
ADHD have also been suggested to show diminished sensitivity to negative feedback,
such as punishment and absence of reward (Carlson et al., 2000; Carlson & Tamm,
2000; Douglas & Parry, 1994; Quay, 1988a; Quay, 1988b).
Neurobiological animal models of ADHD have linked the meso-limbic dopamine
pathways that are associated with the reward circuit in the brain to the motivational
deficits in ADHD (Sagvolden et al., 2005a; Sonuga-Barke, 2002). Meso-cortical
dopamine pathways on the other hand have been linked to the deficient inhibitory
control, i.e. the cognitive deficits in ADHD (Sagvolden et al., 2005a; Sonuga-Barke,
2002). Although the distinction between cognitive and motivational deficits is
theoretically useful, they are linked functionally and neurobiologically (Nigg, 2001).
CHAPTER 1
23
Both theories point to interconnected neural systems of the basal ganglia and prefrontal
cortex. An important structure, serving as a ‘bridge’ between lower brain systems, like
the basal ganglia and the limbic system, and the prefrontal cortex is the ACC. This
structure is suggested to be involved in both ‘hot’ (motivational, affective, emotional)
and ‘cool’ (cognitive) regulation processes (Bush et al., 2000). The ACC is suggested to
be involved in the processing of both errors and feedback (Taylor et al., 2007).
Investigating electrocortical responses during error and feedback processing may,
therefore, provide insight into regulation processes that integrate cognitive and
motivational explanations of ADHD.
The majority of performance studies on feedback processing in ADHD have revealed
that feedback on the performance of children with ADHD has a positive effect on their
task performance and self-reported motivation, this effect being more prominent than in
TD children (see for a review: Luman, Oosterlaan, & Sergeant, 2005). However,
children with ADHD may have problems in keeping optimal performance when they
have to rely solely on their intrinsic motivation (Douglas & Parry, 1994; Sergeant,
2000; Luman et al., 2005).
Few studies have investigated psychophysiological measures of error and feedback
processing in ADHD. One ERP study in ADHD children suggests an initial enhanced
sensitivity to negative feedback (enhanced feedback ERN), but diminished further
evaluation of feedback information (decreased later positivity) (Van Meel, Oosterlaan,
Heslenfeld, & Sergeant, 2005b). Unpublished work by Van Meel, Heslenfeld,
Oosterlaan, Luman & Sergeant (2005) showed that children with ADHD anticipate
feedback stimuli to a lesser extent in comparison to TD children (decreased prefeedback
SPN). EHR studies point to a diminished physiological sensitivity to feedback stimuli in
general (Luman, Oosterlaan, Hyde, Van Meel, & Sergeant, 2007; Luman, Oosterlaan, &
Sergeant, 2008; Crone, Jennings, & Van der Molen, 2003a) and a diminished
discrimination between positive and negative feedback in particular (Crone et al.,
2003a). Regarding ERP studies on the processing of error responses, the findings on
ERN amplitude in children with ADHD vary widely (Liotti, Pliszka, Perez, Kothmann,
& Woldorff, 2005a; Van Meel, Heslenfeld, Oosterlaan, & Sergeant, 2007; Jonkman et
al., 2007; Wiersema, Van der Meere, & Roeyers, 2005; Burgio-Murphy et al., 2007). To
date the Pe amplitude is fairly consistently found to be reduced in children with ADHD
GENERAL INTRODUCTION
24
(Jonkman et al., 2007; Overtoom et al., 2002a; Wiersema et al., 2005, but see for a
different finding Burgio-Murphy and colleagues, 2007).
ERROR AND FEEDBACK PROCESSING IN ASD
One of the most influential comprehensive theories of the social difficulties in Autistic
Disorder is the Theory of Mind (ToM) deficit hypothesis (Baron-Cohen, Leslie, & Frith,
1985; Frith, 1989). This theory states that autistic individuals suffer from ‘mind-
blindness’, disabling them in understanding other people’s beliefs and desires, and in
using this knowledge for predicting the behaviour of others. The ability to adequately
ascribe mental states to others is also referred to as ‘mindreading’ or ‘mentalising’
(Frith & Frith, 2001). Although it has been demonstrated that ToM deficits are not
specific to Autistic Disorder, the ToM theory has been accepted as important to the
understanding of its social deficits (see for a review: Happé, 1994).
Another influential theory proposes that deficits in the EFs underlie many of the key
characteristics of autism (Pennington & Ozonoff, 1996; Russell, 1997; see for a review:
Hill, 2004). Since the emergence of the executive dysfunction theory of autism, ToM
deficits in Autistic Disorder have been explained by EF deficits. It has been argued that
the development of executive functions allows the child’s ToM to develop and that
performance on ToM tasks can even be reduced to executive function ability (see for a
review: Hill, 2004). By reviewing the EF deficit theory of Autistic Disorder, Hill (2004)
concludes that EF deficits may next to non-social characteristics, such as rigidity and
perseveration, explain the social characteristics of the disorder as well. However, she
also stresses the need for clearer EF profiles, which can be fulfilled by ‘fractionating’
the executive system and its dysfunction in autism. Investigating component processes
of error and feedback processing in ASD may, thus, gain insight into specific EF
deficits in this disorder.
Recently, the ACC has been shown to become active when normal subjects either
attribute mental states to themselves or others (Frith & Frith, 2001; Amodio & Frith,
2006; Mundy, 2003). Various other social cognition tasks, involving self-knowledge or
person perception activate the (r)ACC as well (see for a meta-analysis: Amodio & Frith,
2006). In line with the profound difficulties of subjects with Autistic Disorder on the
performance of mentalising tasks, several neuroimaging studies have found support for
a hypofunctional ACC (Haznedar et al., 2000; Ohnishi et al., 2000; Gomot et al., 2006).
CHAPTER 1
25
Two of these studies, moreover, report that ACC activity is negatively associated with
symptom presentation in autism (Haznedar et al., 2000; Ohnishi et al., 2000). The ACC
may thus, next to error and feedback monitoring, also be involved in the processing of
high level abstract representations that play a major role in social cognition. Deficits in
ACC functioning may hamper subjects with ASD in (1) monitoring errors and feedback
and, accordingly, in flexibly adapting to changing environments, and (2) attributing
mental states to themselves or others, and, accordingly, in developing social adequate
behaviour.
Some performance studies have found evidence for an error correction impairment in
ASD. Russell and Jarrold (1998) found that autistic children have a deficit in the
correction of error responses, both when they are provided with visual feedback about
their errors and when they have to detect their errors themselves. Bogte and coll
eagues (2007), moreover, showed absent post error slowing in a group of adult subjects
with Autistic Disorder, whereas the control group substantially adjusted their reaction
time after errors. Performance studies on feedback processing revealed that children
with ASD perform worse than TD children when receiving social feedback, but not with
non-social feedback (e.g. sensory or tangible) (Garretson, Fein, & Waterhouse, 1990;
Dawson et al., 2002; Ingersoll, Schreibman, & Tran, 2003). One study by Althaus and
colleagues, however, showed that children with ASD have more difficulties than TD
children in keeping up performance in a sustained attention task despite the provided
performance feedback (Althaus, De Sonneville, Minderaa, Hensen, & Til, 1996).
To date only one ERP study has investigated performance monitoring ability in ASD by
Henderson and colleagues (Henderson et al., 2006). This study could not reveal overall
differences in ERN amplitude between ASD and TD children. Larger ERN amplitudes
in the ASD group, however, were predictive of a smaller impairment in social
interaction as well as of less internalising problems.
Given the overlap in problem behaviour between the two disorders in clinical practice
as well as overlap in some EF deficits in both disorders, it is useful to directly compare
children with both disorders on component processes of EFs. Psychophysiological
measures may be a tool for ‘fractionating’ the executive system and refining research
into EF deficits in these disorders (see: Hill, 2004). To date psychophysiological
GENERAL INTRODUCTION
26
research on error and feedback processing in ADHD is rather inconsistent and research
in this topic in ASD is scarce. Therefore, one of the main questions of this thesis is
whether children with ADHD and children with ASD can be discriminated on the
psychophysiology of error and feedback processing.
DOES METHYLPHENIDATE STIMULATE ERROR AND FEEDBACK
PROCESSING IN ADHD?
The mainstay of ADHD treatment is the prescription of low dose stimulant medication,
such as Methylphenidate (Mph; Ritalin®) and dexamphetamine. Non-pharmacologic
psychosocial therapies, such as behavioural and cognitive-behavioural therapy, are not
as effective as stimulants in reducing the core ADHD symptoms (The MTA
Cooperative Group, 1999; see for a recent meta-analysis: Van der Oord, Prins,
Oosterlaan, & Emmelkamp, 2008). Numerous placebo-controlled randomized studies
have given evidence that stimulant medication markedly and rapidly reduces the overt
clinical symptoms of ADHD such as restlessness, inattentiveness and impulsiveness
(see for meta-analyses: Miller, 1999; Jadad et al., 1999). The effect on
neuropsychological measures is also evident (although less robust as the effect on overt
symptoms). Stimulants have for example been shown to increase task accuracy and
focussed attention in search tasks and decrease impulsive responses in subjects with
ADHD (Douglas, Barr, Desilets, & Sherman, 1995; Tannock, Schachar, & Logan,
1995; Brumaghim & Klorman, 1998). These effects of low dose Mph on EFs are the
result of its stimulating effect on prefrontal catecholamine neurotransmission, especially
dopamine and noradrenaline (Arnsten, 2006; Pliszka, 2005; Seeman & Madras, 1998).
Regarding error processing, Mph is found to increase remedial action after error
commission: RT slowing after error trials increases in children with AD(H)D (De
Sonneville, Njiokiktjien, & Bos, 1994a; Krusch et al., 1996b). In accordance with this
finding, one small placebo-controlled study found that Mph normalises the Pe
amplitude in children with ADHD (Jonkman et al., 2007).
Given that Mph stimulates prefrontal catecholamine neurotransmission, which is also
involved in error and feedback processing, and some evidence of improved error
processing in Mph-treated children with ADHD, the second subquestion of this thesis is
whether Mph stimulates the psychophysiological responses to errors and feedback in
children with ADHD.
CHAPTER 1
27
DO SPECIFIC GENETIC FACTORS INFLUENCE THE
PSYCHOPHYSIOLOGY OF ERROR AND FEEDBACK PROCESSING?
Behavioural genetic studies provide strong evidence that psychiatric disorders have a
substantial genetic component (Sanders, Duan, & Gejman, 2004). However, due to the
large heterogeneity and complexity of psychiatric phenotypes it is (1) difficult to
pinpoint specific genes that contribute to psychiatric syndromes as well as (2) to link
specific genes to behaviour (Faraone et al., 2005). Endophenotypes, or phenotypes that
are more closely linked to the neurobiological substrate of a disorder, offer the potential
to address these two issues simultaneously (Freedman, Adler, & Leonard, 1999).
Abnormal functioning performance monitoring mechanisms may underlie cognitive and
behavioural deficits across a range of disorders and personalities. As a consequence,
psychophysiological measures of error and feedback processing may serve as
endophenotypes for genetic studies of psychopathology.
The third subquestion of this thesis is whether specific genetic factors influence the
psychophysiology of error and feedback processing, herewith making a start in
elucidating the genetics of performance monitoring. Answers are sought by
investigating the relationship between polymorphisms of two genes and several ERP
components related to error and feedback processing in a, with respect to
psychopathology, heterogeneous sample of children. In specific, common
polymorphisms of two genes, the serotonin transporter (5-HTTLPR) gene and the D2
dopamine receptor (DRD2/ANKK1) gene, are investigated that have in common that
they have both been associated with a predisposition to alcoholism (Wu et al., 2008).
Although the field needs expansion, several studies indicate that ERP components of
error processing are influenced by genetic factors. Recently, a twin study has indicated
that individual differences in the response-locked ERN and Pe for example, are highly
heritable (Anokhin, Golosheykin, & Heath, 2008). One study by Falgatter and
colleagues explored the association between the ERN/Pe and the common
polymorphisms of the 5-HTTLPR gene (Fallgatter et al., 2004). These authors reported
an enhanced ERN amplitude, and a trend in the same directions for the Pe, in carriers of
the low-activity short variant of this polymorphism compared to carriers of the long
variant. Individuals carrying the short variant have repeatedly been suggested to be
prone to anxiety-related personality traits (Brown & Hariri, 2006; Jacob et al., 2004;
GENERAL INTRODUCTION
28
Sen, Burmeister, & Ghosh, 2004), to show augmented neural processing of aversive
stimuli (Canli et al., 2005), and greater sensitivity to stimuli associated with punishment
(Finger et al., 2007). With regard to the common polymorphisms of the DRD2 gene to
our best knowledge no studies have directly investigated error- or feedback-related
ERPs. The DRD2 Taq1A1 allele has been related to the Reward Deficiency Syndrome,
pointing to an inefficiency in the acquired reward system. Carriers of this allele may,
therefore, be less sensitive to positive feedback than noncarriers.
Next to making a start to elucidate genetic factors influencing error and feedback
processing, the adopted research strategy may also provide insight into the natural
variations in error and feedback processing style. Previous research has for example
shown that individuals with different personality types exhibit different
electrophysiological responses to errors. Leaving the circumscribed psychopathological
phenotypes may thus increase the understanding of natural, genetically determined,
variations in error and feedback processing.
OUTLINE OF THIS THESIS
CHAPTER 2 describes electrocortical (ERP) and autonomic (EHR) measures of error and
feedback processing in 10- to 12-year-old TD children and the dynamics of these
measures during feedback-based learning. This chapter provides insight into the
component processes of normal error and feedback processing in children and,
moreover, provides insight into the relationship between cortical and autonomic
measures of performance monitoring. This chapter serves as a basis for CHAPTER 3, in
which the same electrocortical measurements are applied in the comparison of children
with ADHD and children with ASD. It focuses on the dissociation of these two
developmental disorders on performance monitoring ability and provides more detail on
the (dysfunctional) neurobiological basis of the performance monitoring components.
CHAPTER 4 describes autonomic (EHR) responsiveness to feedback stimuli in nearly
identical samples as described in CHAPTER 3. In this chapter, a paradigm was adopted in
which three different feedback approaches were administered to the children (neutral,
reward and punishment). This chapter also aims at dissociating children with ADHD
from children with ASD, but focuses on the autonomic sensitivity to feedback.
CHAPTER 5 describes variations in electrocortical (ERP) measures of error and feedback
processing due to two common functional polymorphisms of respectively the serotonin
CHAPTER 1
29
transporter gene (5-HTTLPR) and the D2 dopamine receptor gene (DRD2). This
chapter aims at elucidating the genetic basis of component processes of performance
monitoring. This research approach is helpful for the identification of endophenotypes
of psychopathology and may, therefore, eventually increase our understanding of the
genetic basis of psychiatric diseases. Finally, CHAPTER 6 presents a summary of the
main findings, the general conclusions and discusses possible implications for further
research and clinical practice.
GENERAL INTRODUCTION
30
CHAPTER 2
PHYSIOLOGICAL CORRELATES OF LEARNING BY
PERFORMANCE FEEDBACK IN CHILDREN: A STUDY OF EEG
EVENT-RELATED POTENTIALS AND EVOKED HEART RATE.
YVONNE GROEN
ALBERTUS A. WIJERS
LAMBERTUS J. M. MULDER
RUUD B. MINDERAA
MONIKA ALTHAUS
The study described in this chapter has been published in Biological Psychology, 76,
174-187, 2007.
CHAPTER 2
32
ABSTRACT
In this study we measured Event-Related Potentials (ERPs) and evoked heart rate
(EHR) to investigate performance monitoring in 10- to 12-year-old children. The
children received feedback on their performance while conducting a probabilistic
learning task. Error-related ERP components time-locked to the response increased in
amplitude when the children had learned the task, whereas the feedback-locked
components decreased. Concerning EHR, there was a general reduction in feedback-
related heart rate deceleration when the children had learned. Moreover, a prolonged
heart rate deceleration was observed at error feedback onset in comparison to positive
feedback, which shifted in timing when the task progressed. Together, the ERP and
EHR-measures suggest a shift from external to internal monitoring when the children
are learning by performance feedback. The data suggest that error- and feedback-related
EHR deceleration is a reflection of the same error monitoring system that is responsible
for the emergence of the Error-Related Negativity.
CHAPTER 2
33
INTRODUCTION
OBJECTIVE
Adults are capable of adapting and optimising their behaviour by making use of
feedback information from the environment, or by comparing the action at hand to an
internal representation of the intended action. These abilities are called external and
internal performance monitoring respectively (Müller et al., 2005). Since the early
nineties, performance monitoring has been thoroughly investigated by means of Event-
Related Potentials (ERPs) computed from the electroencephalogram (EEG) related to
errors and negative performance feedback (Gehring et al., 1990; Falkenstein et al.,
1991; Miltner et al., 1997). As these events signal failure or a decreased probability of
receiving rewards, they require increased performance control. These
psychophysiological measures have given information on the component processes and
underlying brain mechanisms of performance monitoring. Moreover, since 2000,
evoked heart rate (EHR) has also been described to reflect the processing of errors and
negative performance feedback (Somsen et al., 2000; Crone et al., 2003c; Hajcak et al.,
2003b). The control of Autonomic Nervous System (ANS) activity has, therefore, been
suggested to form an integral part of the performance monitoring ability (Hajcak et al.,
2003b).
Psychophysiological research on performance monitoring has mainly focussed on
adults. Studying the underlying mechanisms of this ability in children, however, is also
valuable because the development of performance monitoring in childhood is
considered essential for the emergence of self-regulated behaviours and emotions in
later life (Kopp, 1982; Rothbart & Bates, 1998). In the present study we combined
ERPs and EHR to investigate performance monitoring in a group of 10- to 12-year-old
normally developing children while they performed a probabilistic learning task. In this
task the children learned stimulus-response combinations by making use of performance
feedback that was contingent to their responses. In one half of the stimulus
presentations the children could actually use the feedback to learn the correct
combinations, whereas in the other half they could not, i.e. the feedback was either
informative or uninformative. This paradigm originates from Holroyd & Coles (2002)
and allows us to examine measures of both internal and external performance
monitoring as learning progresses throughout the course of a block of trials. Identifying
ERP and EHR correlates of these processes in a group of normally developing children
CHAPTER 2
34
may allow for later comparisons with children showing different types of
psychopathology.
ERP COMPONENTS OF PERFORMANCE MONITORING
The two most investigated performance monitoring ERP components have been the
response-locked Error-Related Negativity (ERN: Gehring et al., 1990; Gehring et al.,
1993; Ne: Falkenstein et al., 1991) and the feedback ERN (Medial Frontal Negativity:
Gehring & Willoughby, 2002; Feedback ERN: Holroyd & Coles, 2002; Feedback
Related Negativity: Müller et al., 2005). The ERN is thought to be a reflection of a
mismatch between actual and intended actions or goals (Ridderinkhof et al., 2004). Both
components show a frontocentral scalp distribution. The response-locked ERN typically
occurs between 40 to 100 ms after the commission of an incorrect response and the
Feedback ERN occurs approximately 250 ms after negative feedback onset (Miltner et
al., 1997). The sources of both components are found clustered in and around the rostral
zone of the Anterior Cingulate Cortex (ACC; Ridderinkhof et al., 2004; Holroyd et al.,
2004b). The response- and feedback-locked ERN have been proposed to represent the
activity of one and the same error detection system within the midbrain dopamine
system (Holroyd & Coles, 2002).
Holroyd and Coles (2002) showed that in a probabilistic learning task, the response- and
feedback-locked ERN are interdependent. The amplitude of the response-locked ERN
increased as the learning task proceeded, while the feedback ERN decreased. This
implies that while progressively learning the correct stimulus-response combination, the
subjects rely less on the performance feedback and more on their own representation of
what the response should be; they detect their errors even before feedback onset. In a
condition where the feedback was unrelated to the subjects’ performance, i.e. the
feedback was uninformative, both components decreased in amplitude at the end of the
task. This suggests that the subjects cease monitoring both the feedback and their own
responses when they discover that a correct stimulus-response combination can not be
learned.
Besides the response-locked ERN, another error-related response-locked component has
been described. Successive to the ERN an error Positivity (Pe) can be distinguished,
peaking approximately 200-400 ms after onset of an incorrect response with a
maximum on parietal electrode sites (Falkenstein et al., 1991; Davies et al., 2001). The
CHAPTER 2
35
functional significance of the Pe is less clear than that of the ERN, but it is probably a
reflection of the awareness of the committed error (Kaiser, Barker, Haenschel,
Baldeweg, & Gruzelier, 1997; Nieuwenhuis et al., 2001; Overbeek et al., 2005) and
subsequent remedial action (Hajcak et al., 2003b). Complementary, the Pe has been
suggested to be a P3 response to internal detection of errors (Miltner et al., 1997; Davies
et al., 2001; Overbeek et al., 2005). The traditional stimulus-locked P3 is sensitive to
motivationally significant events like novel, task-relevant and highly deviant stimuli
(Nieuwenhuis et al., 2005). In case of error detection, the error is the motivationally
salient stimulus itself.
With respect to feedback-related potentials, two other performance monitoring
components have often been described in literature. Successive to the feedback ERN a
P3 response is elicited by feedback stimuli, which shows a parietal maximum (Miltner
et al., 1997). The feedback-locked P3 has been interpreted as a reflection of the
processing of relevant information about past events that can be used to modify future
behaviour (Müller et al., 2005). The findings with regard to this component have,
however, been inconsistent: some studies report enlarged P3 components to negative
feedback as compared to positive feedback (Chwilla & Brunia, 1991; Yeung, Holroyd,
& Cohen, 2005), whereas others report the opposite (Holroyd et al., 2004a; Hajcak,
Holroyd, Moser, & Simons, 2005).
Besides the feedback-induced components, an anticipatory component to feedback
stimuli has also been described. This prefeedback Stimulus Preceding Negativity (SPN)
has preponderance over the right hemisphere and is, in case of visual feedback, maximal
just before feedback onset at occipital-parietal electrode positions (Brunia & Damen,
1988; Brunia & Van Boxtel, 2004). This component has been found to be sensitive to
the affective-motivational properties of the anticipated stimuli. The prefeedback SPN is,
for example, larger in reward conditions than in nonreward conditions and larger when
expecting unpleasant as opposed to neutral feedback (Kotani et al., 2001). It is also
larger in anticipation of informative feedback compared to uninformative feedback
(Chwilla & Brunia, 1991). Bastiaansen, Böcker & Brunia (2002) suggested that the
amplitude of the SPN is dependent on the subject’s motivation or effort to perform the
task accurately. Support for the involvement of the prefeedback SPN in emotional and
motivational processing comes from brain imaging studies (see for an overview: Böcker
CHAPTER 2
36
et al., 2001). The insular cortex, known to be involved in motivational and reward
processing, has repeatedly been suggested to be one of the major neural generators of
the prefeedback SPN (Böcker et al., 1994; Brunia et al., 2000; Tsukamoto et al., 2006).
AUTONOMIC NERVOUS SYSTEM CORRELATES OF PERFORMANCE MONITORING
Since the early seventies a deceleration of heart rate has been described in anticipation
of upcoming sensory information or action (Lacey & Lacey, 1974). Preparatory heart
rate deceleration has since then been firmly established in EHR studies (for a review
see: Jennings & Van der Molen, 2002). Recently, EHR has also been found to reflect
both internal and external performance monitoring activity. More specifically, negative
performance feedback has been described to elicit a prolonged heart rate deceleration,
whereas positive feedback immediately elicits an acceleratory recovery (Somsen et al.,
2000; Crone et al., 2003c). A similar prolonged heart rate deceleration has also been
described to occur after error responses (Hajcak et al., 2003b). Hajcak, McDonald and
Simons (2003) found that incorrect responses in a two-choice reaction time task were
not only associated with the characteristic ERN-Pe complex, but also with greater heart
rate deceleration and larger Skin Conductance Responses (SCRs). These authors found
that SCR was correlated with the Pe, and that both SCR and Pe were correlated with a
measure of compensatory behaviour after error commission, i.e. post-error slowing. The
authors argued that ANS activity forms an integral part of performance monitoring and
stated that subjects ‘not only know that they erred, they feel it’ (Hajcak et al., 2003b, p.
901).
Heart rate deceleration in response to performance feedback has been suggested to be a
reflection of the same error monitoring system that is responsible for the ERN (Somsen
et al., 2000; Crone et al., 2003c). This suggestion is supported by findings of shared
functional characteristics on the one hand, and by findings of a shared neural substrate
on the other. Firstly, both the feedback ERN and feedback-related heart rate
deceleration are only elicited by performance feedback that carries informative value for
the adjustment of future performance (Holroyd & Coles, 2002; Crone et al., 2004) and
not by feedback that is uninformative to the subject (Crone et al., 2003c; Holroyd &
Coles, 2002). Secondly, like the ERN (Ridderinkhof et al., 2004) feedback-related heart
rate deceleration is related to remedial action. For example good performers showed a
CHAPTER 2
37
greater deceleration after negative feedback than bad performers (Somsen et al., 2000)
and greater deceleration has been found in reaction to negative feedback stimuli that are
followed by correctly adjusted trials than to those followed by incorrectly adjusted trials
(Van der Veen et al., 2004). Lastly, the neural generator of the ERN, i.e. the ACC, also
forms part of a system that is involved in the central modulation of ANS activity during
volitional behaviours and in the subsequent reception of ANS signals from the
periphery (Critchley et al., 2003; Critchley, 2005).
EXPECTATIONS
Developmental psychophysiological studies in children and adolescents have
established that performance monitoring ability grows with age. The ability to internally
monitor behaviour, as measured by the ERN amplitude, continues to develop
throughout the second decade of life (Davies et al., 2004; Hogan et al., 2005; Santesso
et al., 2006). In line with the ERP findings in children, developmental EHR studies also
report an increase in error-related cardiac deceleration (Crone et al., 2006) and in
cardiac deceleration to performance feedback (Crone et al., 2004) from childhood to
adulthood. Although performance monitoring processes in the group of 10- to 12-year-
old children of the present study may not have reached adult levels yet, we expect that
the underlying mechanisms of this ability are similar to those in adults.
We, therefore, expect that internal response monitoring will gradually increase with
learning the task, while at the same time external feedback monitoring will decrease. In
line with Holroyd and Coles (2002) we expect this to be reflected by an increased
response-locked ERN and a decreased feedback-locked ERN in the second section
compared to the first section of the task. Extending this rationale to the other
performance monitoring components, we expect the response-locked Pe to increase with
learning, while the feedback P3 and prefeedback SPN may decrease. In the
uninformative condition, we expect all performance monitoring components to be
smaller than in the informative condition, because the children will have soon found out
that no stimulus-response combination can be learned in this condition. We further
expect the EHR responses to parallel the performance monitoring components by
showing a decrease in the feedback-related deceleration to informative negative
feedback during the second compared to the first task section. In the uninformative
condition the feedback-related deceleration to negative feedback is expected to be
CHAPTER 2
38
smaller than in the informative condition. Finally, we will explore the relationship
between the electrocortical performance monitoring components and EHR deceleration,
because heart rate deceleration to performance feedback has been suggested to be a
reflection of the same error monitoring system that is responsible for the generation of
the ERN (Somsen et al., 2000; Crone et al., 2003c).
METHODS
SUBJECTS
Eighteen children (12 boys and 6 girls) were recruited from primary schools in
Groningen and by advertisement in the newsletter of the University Medical Centre in
Groningen. The children were 10- to 12- years old and had full scale IQ scores ranging
from below average (88) to gifted (122), with a mean of 103 (SD 9,5), as measured by
the Wechsler Intelligence Scale for Children-III (WISC-III). On a self-report list for
handedness (Van Strien, 2003) 14 children reported to be strongly right-handed. The
other four children were classified as ambidexter, but still had a tendency for right-
handedness. The children were healthy and had normal or corrected to normal vision.
They had no clinical form of psychopathology, as measured by the Child Behavioural
CheckList filled out by the parents (CBCL: Achenbach & Rescorla, 2001). None of the
children scored within the clinical range of this list. Written informed consent was
obtained from the parents of all children and the 12-year-old children themselves before
they entered the experiment. The study was approved by the Medical Ethical Committee
of the University Medical Center Groningen.
TASK
FEEDBACK CONDITIONS
A probabilistic learning task (Holroyd & Coles, 2002) was used, which had been
adopted in a curtailed form from Crone et al. (2004). In this task the children were
asked to discover the correct stimulus-response combinations by making use of
performance feedback. They performed nine blocks, each containing 96 stimulus
presentations (trials). Within every block four new coloured pictures (A, B, C and D)
were randomly presented 24 times to the child. The pictures (Microsoft Clipart ®)
belonged to the categories ‘animals’, ‘fruits’, ‘music’ and ‘sports’. Each stimulus set of
four pictures contained one picture from every category, but independently of the
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39
category each picture was assigned to one of four feedback conditions (see Table 1). In
the informative feedback condition the type of feedback, i.e. positive and negative
feedback (valence), was associated with the subject’s response. Pressing the left key to
picture A, resulted in positive feedback whereas pressing the right key resulted in
negative feedback. For picture B this coupling was opposite: pressing the left key to
picture B, resulted in negative feedback while pressing the right resulted in positive
feedback. In this condition, the amount of trials in the negative and positive feedback
condition was dependent on the error rate of the subject (see Table 1). In the
uninformative feedback condition the valence of the feedback stimulus was independent
of the response. The valence for picture C was always positive and the valence for
picture D was always negative. The number of trials in this condition was, therefore,
equal for both feedback valences (24 trials). Note that by random presentation of the
stimuli, the feedback conditions (both informational value and feedback valence) were
randomly distributed too. To study the learning process, each block was cut into
sections. These were quartiles for the performance measures, whereas for the
physiological measures halves were chosen in order to retain enough error trials. The
sections were then averaged across the nine blocks.
The stimulus presentation in the task was machine-paced. However, to take into account
individual differences in response speed, an individual deadline time was computed for
every subject. This individual deadline time (mean reaction time +10%) was determined
in a deadline determination block, which preceded the nine experimental blocks, but
followed a short practice block. In all blocks the children were emphasised to win as
many points as they could, but they were ignorant of the feedback conditions. The
children lost two points when they failed to respond within the deadline time and won
or lost one point in case of respectively positive and negative feedback. Because of the
punishment for late reactions the children were forced to respond quickly. At every new
task block they started with 52 points, which could maximally add up to 100 points.
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40
TRIAL STRUCTURE
Every block started with the presentation of a new set of 4 stimuli. Each trial started
with the presentation of one stimulus from the stimulus set. As soon as the stimulus
appeared, the children had to press the left or the right key. The presentation time of the
stimulus was equal to the duration of the individual deadline and not terminated by the
response. After a fixation cross shown for 1000 ms, the feedback stimulus appeared on
the screen for 1500 ms. The feedback valence was symbolised as follows: a green
square indicated positive feedback, a red square negative feedback and a black square
late reactions. The trial was closed by a variable Intertrial Interval (ITI), which could be
500, 750 or 1000 ms. In the experimental phase the children received a total of 864
stimulus presentations. See Figure 1 for a schematic representation of the trial structure.
TABLE 1. Distribution of feedback conditions within one task block. Four pictures that were
repeatedly presented in every task block were either coupled to informative feedback (A and B) or to uninformative feedback (C and D). The ratio of positive and negative feedback in the
informative condition depended on the individual error rate, whereas this ratio was stable in the
uninformative condition.
FIGURE 1. Time course of a single trial. Within one task block each trial started with the presentation of one out of four stimuli. The feedback stimulus appeared 1000 ms after stimulus off-set and stayed
on the screen for 1500 ms. The next trial started after a variable Inter Trial Interval (ITI) of 500, 750
or 1000 ms.
Stimulus Fixation cross
+
Feedback ITI
+
Stimulus
Individualdeadline 1000 ms 1500 ms 500/750/1000 ms
Time
Informational value Picture Valence # Trials
Informative (48 trials) A Positive = left key 24 - error rate
Negative = right key error rate
B Positive = right key 24 - error rate
Negative = left key error rate
Uninformative (48 trials) C Positive = left and right key 24
D Negative = left and right key 24
Task block consisting of 96 trials
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41
PROCEDURE
The children were seated on a comfortable chair in front of a computer screen in a room
that was separated from a control room by a one-way screen. After a standardised
instruction the children performed a short practice block consisting of 24 trials, which
was followed by the deadline block consisting of 96 trials. After application of the
electrodes the children performed the nine experimental blocks (each lasting between 6
and 7 minutes). After five experimental blocks there was a break of 20 minutes. At the
end of the experiment the children received a present (a toy), independently of their
scores.
PERFORMANCE MEASURES
The probabilistic learning task was built and presented by means of E-Prime (version
1.1, Psychological Software Tools). Key type (left or right), reaction time (RT) and
accuracy of the response were recorded for every trial. To investigate the process of
learning in the informative feedback condition three performance measures were
computed for all quartiles: RTs, individual standard deviations (SDs) of RTs and
percentage of correct responses. To investigate response strategies in the uninformative
feedback condition the percentage of ‘key changes’ was computed. This was the
percentage of trials that the children switched from one key to the other in response to
stimuli that were coupled to uninformative feedback (i.e. stimulus C or D).
EEG AND COMPUTATION OF ERPS
The EEG was recorded using a lycra stretch cap (Electro-Cap Center BV) with 21
electrodes, placed according to the 10-20 system (O1, Oz, O2, P3, P5, P7, Pz, P4, P6,
P8, C3, Cz, C4, F3, Fz, F4, F7, F8, FP1, FPz en FP2). Vertical and horizontal eye
movements were recorded with electrodes respectively above and next to the left eye.
For all channels Ag-AgCl electrodes were used and impedances were kept below 10
kΩ. Using the REFA-40 system (TMS International B.V.), all channels were amplified
with filters respectively set at a time constant of 1 second and a cut-off frequency of 130
Hz (low pass). The data from all channels were recorded with a sampling rate of 500 Hz
using Portilab (version 1.10, TMS International B.V.). Using BrainVision (version 1.05,
Brain Products), the signals were off-line filtered with a 0.25 Hz high pass and 30 Hz
low pass filter, and referenced to the left ear electrode.
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To investigate the ERN and Pe, EEG segments were cut around the children’s responses
ranging from 500 ms before to 800 ms after response onset, with the first 200 ms
serving as a baseline. This was done for both response types, i.e. correct and incorrect
responses. In the uninformative feedback condition the response actually could neither
be correct nor incorrect. For communicative purposes we will use the term ‘correct’ for
responses preceding always positive feedback and ‘incorrect’ for responses preceding
always negative feedback. Segments for investigating prefeedback and feedback-
induced ERPs were cut separately, in order to keep the number of rejected segments due
to artefacts as low as possible. For the prefeedback SPN the segments ranged from 1000
ms before to 200 ms after feedback onset, with the first 200 ms of the segment serving
as a baseline. For the feedback ERN and feedback P3, segments ranged from -200 ms to
1000 ms after feedback onset, with the first 200 ms serving as a baseline. All segments
were scanned for artefacts. Segments with very high or low activity and/or spikes and/or
drift due to large eye-movements, head or body movements, or equipment failure were
removed before the analyses. Segments with eye blinks were kept and corrected,
adopting the Gratton & Coles procedure (Gratton, Coles, & Donchin, 1983). For every
child the segments were then averaged separately for all electrode positions, all
feedback conditions, and the task sections.
ELECTROCARDIOGRAM AND COMPUTATION OF EHR RESPONSES
The electrocardiogram (ECG) was recorded using two Ag-AgCl electrodes that were
placed at the right side of the thorax between the collarbone and the sternum and at the
left side between the two lower ribs. The ECG was also recorded with a sampling rate
of 500 Hz. R-peaks were detected online using Portilab (version 1.10, Twente Medical
Systems). To include only validly recorded interbeat intervals (IBIs), the IBIs were
corrected for artefacts using Carspan (version 1.15). In this program for analysing
cardiovascular data, a procedure was adopted in which intervals that deviated more than
four SDs from a running mean of 60 seconds were set as possible artefacts. Using a
linear interpolation algorithm, corrections were then made in case a set of additional
criteria, related to increased variability due to the artefact, was met (for a more detailed
description, see Mulder, 1992). Finally, all data were visually inspected in order to
check for adequate corrections.
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43
In order to analyse EHR in response to feedback stimuli, sequential IBIs were extracted
from the R-peak series. In accordance with Somsen and colleagues (2000) five
sequential IBIs around the feedback stimuli were selected. IBI0 was the interval in
which the feedback was presented, which was followed by two successive intervals:
IBI1 and IBI2. The other two intervals were those preceding the feedback stimulus: IBI-
2 and IBI-1, with IBI-2 serving as the baseline interval.
DATA ANALYSES
Performance measures were analysed by means of a repeated measures ANOVA with
task section (quartile 1 to 4) as the within subject variable. This was done for the mean
percentage of correct responses, mean RT and individual SDs of RTs in the informative
condition and the percentage of key changes in the uninformative condition. Repeated
contrasts for quartile were computed to investigate changes from quartile to quartile.
With regard to the statistical analyses of the response-locked and feedback-induced ERP
components, mean amplitude values were computed for successive intervals. For
relatively short-lasting components, i.e. the ERN and early feedback-induced
components, intervals of 20 ms (10 sample points) were chosen, whereas for the
relatively long lasting components, i.e. the Pe and feedback P3, intervals of 50 ms (25
sample points) were chosen. The electrode positions of interest were Fz, Cz and Pz, as
the ERN and feedback ERN have been described to have a midline frontocentral
topography (Falkenstein et al., 1991; Gehring et al., 1993) and the Pe a more
widespread centroparietal topography (Falkenstein et al., 1991; Davies et al., 2001). On
all successive intervals repeated measures ANOVAs were conducted by applying a
3*2*2*2 design, with the within subject variables electrode position (Pz vs. Cz vs. Fz),
condition (informative vs. uninformative), valence (positive vs. negative) in case of
feedback-locked segments or response type (correct vs. incorrect) in case of response-
locked segments, and section (first vs. second section). Because analyses were
performed for multiple successive intervals there was an increasing risk of capitalisation
on chance. Effects were, therefore, only reported if two or more consecutive intervals
were significant. For significant intervals the minimum and maximum F-values (Fmin
and Fmax, respectively) with the smallest levels of significance are reported.
Following literature describing the prefeedback SPN, we chose to only analyse the
mean amplitude value of the 200 ms preceding the feedback presentation. The
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44
electrodes of interest for this component were the left and right central, frontal and
parietal electrode sites, because feedback manipulations have been shown to modulate
the SPN on these electrode positions (Chwilla & Brunia, 1991; Kotani et al., 2001). A
repeated measures ANOVA was conducted by applying a 4*2*2*2*2 design on each
interval, with the within subject variables electrode position (F3/4 vs. F7/8 vs. C3/4 vs.
P3/4), hemisphere (left vs. right), condition (informative vs. uninformative), valence
(positive vs. negative), and section (first vs. second section).
For all ERP analyses significant interactions were further specified by applying the
same design to the separate levels of the involved factors. For instance, when there was
an interaction with electrode position, analyses were separated for the electrode
positions and when there was an interaction with condition, analyses were separated for
the conditions.
With respect to the EHR measures a repeated measures ANOVA was conducted with a
5*2*2*2 design, with the within subject variables sequence (IBI-2 vs. IBI-1 vs. IBI0 vs.
IBI1 vs. IBI2), condition (informative vs. uninformative), valence (positive vs.
negative) and section (first section, second section). For the EHR analyses significant
interactions, or interactions with medium or large effect size, with condition or section
were further specified by applying the design to the separate levels of these factors.
Repeated contrasts were computed for sequence to investigate changes between
successive cardiac cycles.
To account for possible violations of the sphericity assumption for factors with more
than two levels Greenhouse-Geisser adjusted p-values and the epsilon correction factor
are reported together with the unadjusted degrees of freedom and F-values. For all
analyses the partial eta squared effect sizes are reported (Stevens, 2002).
In order to investigate associations between the ERP and EHR measures of performance
monitoring we investigated correlations among these measures. A description of the
measures entering these analyses is given in the results section (3.4), because the choice
of these measures could only be made post hoc.
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45
RESULTS
PERFORMANCE MEASURES
With a mean individual deadline time of 775 ms, the children achieved a score of 74 out
of 100 points on average. They were late in 5% of the trials and these trials were
excluded from further analyses. In the informative condition the children reacted faster
than in the uninformative condition (488 ms vs. 517 ms; F(1, 17) = 73.2, p < .001, η2 =
.81). In the informative condition they were faster on incorrect trials than on correct
trials (465 ms vs. 512 ms; F(1, 17) = 62.1, p < .000, η2 = .79), whereas in the
uninformative condition they were faster on ‘correct’ trials (i.e. those followed by
always positive feedback) than on ‘incorrect’ trials (i.e. those followed by always
negative feedback) (495 ms vs. 538 ms; F(1, 17) = 44.3, p < .001, η2 = .72).
INFORMATIVE CONDITION
As can be seen in Figure 2A, the children became more accurate in the informative
condition as the learning task proceeded. There was a significant effect of quartile (F(3,
51) = 86.6, p < .001, η2 = .84, ε = .52). The children increased in accuracy until the third
quartile and stabilised thereafter. This is reflected by significant contrasts for quartile (1
vs. 2: F(1, 17) = 173.7, p < .001, η2 = .91; 2 vs. 3: F(1, 17) = 7.6, p < .05, η2 = .31; 3 vs.
4: F(1, 17) = 3.0, p = .10, η2 = .15). The individual SDs of RTs decreased across all four
quartiles (see Figure 2C). This is reflected by an effect of quartile (F(3, 51) = 21.4, p <
.001, η2 = .56, ε = .67) and significant contrasts for all successive quartiles (1 vs. 2: F(1,
17) = 18.4, p < .001, η2 = .52; 2 vs. 3: F(1, 17) = 7.3, p < .05, η2 = .30; 3 vs. 4: F(1, 17)
= 5.3, p < .05, η2 = .24). As can be seen in Figure 2B, there was no learning effect for
the mean RTs, which is reflected by absence of an effect of quartile (F(3, 51) = 1.1, p >
.05, η2 = .06, ε = .47).
UNINFORMATIVE CONDITION
Even though in the uninformative feedback condition the children could not learn a
stimulus-response combination, they could have adjusted their response strategies
during the task. To discover these strategies in this condition, the proportion of ‘key
changes’ was computed for stimuli that were always followed by positive feedback as
well as for stimuli that were always followed by negative feedback. The children
changed to the other key in the negative feedback condition in 34,1% of the trials,
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46
whereas in the positive feedback condition they only changed in 11,5% of the trials (see
Figure 2D). These percentages differed significantly (F(1, 51) = 48.5, p < .001, η2 =
.74). Moreover, the children showed less changes of keys as the learning task
proceeded, which was reflected by an effect of quartile (F(1, 51) = 10.9, p < .001, η2 =
.39, ε = .75). This learning effect was caused by a strong decrease of change trials from
the first to the second quartile and was equal for the uninformative positive and
uninformative negative condition. This is reflected by a significant contrast for quartile
1 vs. 2 (F(1, 17) = 26.5, p <.001, η2 = .61) and no interaction of quartile by condition
(F(3, 51) = 1.5, p > .05, η2 = .08, ε = .68).
FIGURE 2. Performance measures for four successive task sections. Depicted are the percentage of
accurate responses, mean reaction time (RT) and individual standard deviations (SDs) of RTs in the informative condition (A, B, C respectively) and the percentage of key changes in uninformative
condition (D).
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47
ERPS
As already mentioned in the methods section, the number of included segments in the
ERP averages varied across conditions (see Table 1). For this reason the number of
included trials in the response-locked ERP averages will be shortly addressed here. The
smallest number of segments occurred in the informative condition in the second
section for incorrect responses, because in this condition the number of errors was
lowest. The mean number of segments in this condition was 20, with a minimum of 8
and a maximum of 46. Although for one child the average was based on only 8 trials,
inspection of this individual average showed a clear ERN-Pe complex. The mean
number of segments for correct responses in this condition was 157, with a minimum of
59 and a maximum of 185. For the uninformative condition the number of included
trials was larger. In the second section for instance a mean of 82 ‘incorrect’ trial
segments were included, with a minimum of 28 trials and a maximum of 100 trials. For
‘correct’ trial segments this was 88 trials, with a minimum of 38 and a maximum of
104.
RESPONSE-LOCKED POTENTIALS
ERN
In the informative condition the course of the ERP before and just after the response
appeared to be more negative for incorrect responses than for correct responses (see the
upper part of Figure 3). The timing of this effect, however, differed among electrode
positions, which is reflected by an interaction of electrode position by response type
from -300 to -140 ms (Fmin(2, 34) = 5.0, p < .05, η2 = .23, ε = .80; Fmax(2, 34) = 15.6, p
< .001, η2 = .86, ε = .87) and from -20 ms to 80 ms (Fmin(2, 34) = 6.8, p < .05, η2 = .12,
ε = .94; Fmax(2, 34) = 12.4, p < .001, η2 = .42, ε = .83). At Pz the difference emerged
from 260 ms before response onset, which already disappeared at response onset
(response type: Fmin(1, 17) = 4.7, p < .05, η2 = .22; Fmax(1, 17) = 14.5, p < .001, η2 =
.46). At Cz and Fz the effect of response type was present from respectively -160 ms to
60 ms (Fmin(1, 17) = 5.0, p < .05, η2 = .22; Fmax(1, 17) = 31.5, p < .001, η2 = .46) and -
120 ms to 60 ms (Fmin (1,17) = 4,7, p < .05, η2 = .21; Fmax (1,17) = 29,3, p < .001, η2 =
.63). Because at Cz and Fz the differences in amplitude at the time of the response were
largest, further analyses were conducted at these electrode positions. The scalp
distribution of the observed negativity was similar to the frontocentral scalp distribution
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48
of the ERN described in literature, whereas the timing was remarkably early. In the
uninformative condition the course of the ERP before and just after the response was
opposite as compared to the informative condition (see the lower part of Figure 3). At
Fz the signal was more negative for ‘correct’ responses as compared to the ‘incorrect’
responses from -240 to 100 ms (response type: Fmin(1, 17) = 10.1, p < .01, η2 = .65;
Fmax(1, 17) = 32,0, p < .001, η2 = .37). This difference in the effect between the
informative and uninformative condition at Fz is expressed by an interaction of
condition by response type from -180 ms to 100 ms (Fmin(1, 17) = 5.6, p < .05, η2 = .24;
Fmax(1, 17) = 57.9, p < .001, η2 = .77).
In the informative condition the ERN at Cz and Fz was larger in the second section of
the task than in the first section (see the upper part of Figure 3). This learning effect is
reflected by an interaction of response type and section from –120 to 20 ms at Cz
(Fmin(1, 17) = 4.4, p = .05, η2 = .21; Fmax(1, 17) = 7.0, p < .05, η2 = .29) and from -100
ms to 60 ms at Fz (Fmin(1, 17) = 6.5, p < .05, η2 = .28; Fmax(1, 17) = 14.4, p < .001, η2 =
.46). The difference between correct and incorrect responses in the uninformative
condition also increased from the first to the second section of the task (see the lower
part of Figure 3). In this condition a short-lasting interaction of response type by section
is present from -140 ms to -80 ms at Fz (Fmin(1, 17) = 4.9, p < .05, η2 = .22; Fmax(1, 17)
= 8.7, p < .01, η2 = .34). This condition-dependent learning effect is reflected by a
significant three-way interaction of condition by response type by section from -140 ms
to 60 ms (Fmin(1, 17) = 6.5, p < .05, η2 = .28; Fmax(1, 17) = 15.7, p < .001, η2 = .48).
Pe
In the informative condition the ERP for incorrect responses roughly after response
onset was more positive than the ERP for correct responses (see the upper part of Figure
3). This effect was both larger and earlier at Cz and Pz compared to Fz, as is reflected
by an electrode by response type interaction in the interval of 100 ms to 650 ms (Fmin(2,
34) = 4.0, p < .05, η2 = .19, ε = .64; Fmax(2, 34) = 27.5, p < .001, η2 = .62, ε = .76). At Fz
there only was a short-lasting effect of response type from 300 ms to 400 ms (Fmin(1,
17) = 5.4, p < .05, η2 = .24; Fmax(1, 17) = 6.0, p < .05, η2 = .26). On Cz and Pz, however,
the effects of response type appeared to be present from respectively 150 ms to 600 ms
(Fmin(1, 17) = 8.0, p < .05, η2 = .32; Fmax(1, 17) = 38.4, p < .001, η2 = .70) and 100 ms to
600 ms (Fmin(1, 17) = 11.2, p < .01, η2 = .40; Fmax(1, 17) = 190.0, p < .001, η2 = .92).
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49
This positivity in response to incorrect responses has a timing and scalp distribution that
is similar to the Pe described in literature. The Pe was much larger in the informative
condition than in the uninformative condition (compare the upper and lower part of
Figure 3 at Pz), which is reflected by an interaction of condition and response type at Pz
from 100 ms to 650 ms (Fmin(1, 17) = 5.5, p < .05, η2 = .25; Fmax(1, 17) = 117.9, p <
.001, η2 = .91). Although this effect is hardly visible in Figure 3, a short-lasting Pe
could be observed in the uninformative condition at Pz from 200 ms to 400 ms (Fmin(1,
17) = 5.1, p < .05, η2 = .23; Fmax(1, 17) = 13.4, p < .05, η2 = .44).
FIGURE 3. Response-locked ERPs. ERP waveforms time-locked to the response (0 ms) are depicted at
Fz, Cz and Pz for both the informative condition and the uninformative condition. For both the first and
second section of the task separate waveforms are shown for correct and incorrect responses.
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50
In the informative condition, the Pe increased from the first section of the task to the
second. This learning effect ranged from 200 ms to 350 ms at Cz (Fmin(1, 17) = 6.3, p <
.05, η2 = .27; Fmax(1, 17) = 6.8, p < .05, η2 = .29) and from 100 ms to 400 ms at Pz
(Fmin(1, 17) = 5.7, p < .05, η2 = .25; Fmax(1, 17) = 16.6, p < .001, η2 = .49). Although
hardly visible, a short-lasting learning effect, in the same direction as in the informative
condition, was also observed in the uninformative condition from 250 ms to 400 ms at
Pz (Fmin(1, 17) = 4.8, p < .05, η2 = .22; Fmax(1, 17) = 10.1, p < .01, η2 = .37).
FEEDBACK-INDUCED POTENTIALS
N1
As can be seen in Figure 4 at the electrode positions Fz and Cz, the feedback-induced
ERPs are initially characterised by a negative deflection with a peak latency of
approximately 100 ms (N1). Only at Fz, this component appeared to be significantly
larger in response to negative feedback compared to positive feedback. This is
expressed by a significant effect of valence from 100 ms to 140 ms (Fmin(1, 17) = 7.2,
p < .05, η2 = .30; Fmax(1, 17) = 7.8, p < .05, η2 = .32). This early effect of feedback
valence, however, appeared to be independent of the informational value of the
feedback, as is expressed by the absence of an interaction with condition in this interval.
Therefore, we cannot exclude that this valence effect is due to perceptual stimulus
characteristics and we will not go further into this matter.
P2A AND P3
Successive to the N1 at Fz and Cz, a positive peak could be observed at about 185 ms
after feedback onset, which was maximal at frontocentral electrode positions (see
Figure 4). This frontocentral component may be described as the P2a (anterior P2) or
Frontal Selection Positivity (Potts, Martin, Burton, & Montague, 2006a; Potts, 2004b;
Potts et al., 2006a). A second positive waveform could be observed at about 300 ms,
which showed a more centroparietal scalp distribution. This component may be
described as the feedback P3 (Miltner et al., 1997).
In the informative condition both components had larger amplitudes for negative than
for positive feedback. For the P2a this emerged as an effect of valence, which was
maximal at Cz from 160 ms to 240 ms (Fmin(1, 17) = 5.9, p < .05, η2 = .26; Fmax(1, 17) =
16.7, p < .001, η2 = .50), and for the P3 at Pz from 250 ms to 800 ms (Fmin(1, 17) = 5.1,
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51
p < .05, η2 = .23; Fmax(1, 17) = 14.3, p < .001, η2 = .46). The interval from 500 ms to
550 ms of the P3, however, showed marginal significance (F(1, 17) = 3.6, p = .08, η2 =
.18). In the uninformative condition no significant valence effect for the P2a is
observed, whereas for the P3 there is a short-lasting valence effect in the interval from
250 ms to 350 ms (Fmin(1, 17) = 7.2, p < .05, η2 = .30; Fmax(1, 17) = 7.4, p < .05, η2 =
.30). A significant interaction between valence and condition from 250 ms to 400 ms
confirmed that the valence effect of the P3 in the uninformative condition was smaller
than the effect in the informative condition (Fmin(1, 17) = 5.3, p < .05, η2 = .24; Fmax(1,
17) = 10.3, p < .01, η2 = .38). As Figure 4 suggests, the difference in valence effect for
both the P2a and P3 between the two conditions could be solely explained by a
difference in the amplitude for negative feedback. For the P2a this is confirmed by an
effect of condition at Cz from 140 ms to 240 ms for negative feedback (Fmin(1, 17) =
4.6, p < .05, η2 = .21; Fmax(1, 17) = 12.2, p < .01, η2 = .42) and the absence of this effect
for positive feedback (Fmin(1, 17) = 1.4, p > .05, η2 = .08; Fmax(1, 17) = 2.2, p > .05, η2 =
.11). For the P3 at Pz there is an effect of condition for negative feedback from 250 ms
to 400 ms (Fmin(1, 17) = 5.4, p < .05, η2 = .24; Fmax(1, 17) = 8.7, p < .01, η2 = .34), but
not for positive feedback (Fmin(1, 17) = 0.04, p > .05, η2 = .00; Fmax(1, 17) = 1.0, p > .05,
η2 = .06).
The amplitudes of both the P2a and P3 decreased from the first section to the second,
independent of condition and valence. The effect of section for the P2a was present at
Cz from 140 ms to 200 ms (Fmin(1, 17) = 5.9, p < .05, η2 = .21; Fmax(1, 17) = 10.1, p <
.01, η2 = .30) and for the P3 at Pz from 250 ms to 750 ms (Fmin(1, 17) = 4.6, p < .05, η2
= .13; Fmax(1, 17) = 13.1, p < .01, η2 = .17, with, however, marginal significance for the
interval of 400 ms to 450 ms F(1, 17) = 4.1, p = .06, η2 = .12). For both components
neither valence nor condition interacted with section.
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52
ANTICIPATORY FEEDBACK POTENTIALS
PREFEEDBACK SPN
As can be seen in Figure 5, a negative slow wave developed in the interval preceding
the feedback stimuli, especially in preparation of negative feedback. For both the
informative and the uninformative condition, this slow wave had preponderance over
the right hemisphere and showed a wide centroparietal distribution. This is confirmed
by a significant effect of hemisphere (F(1, 17) = 17.4, p < .01, η2 = .51) in the tested
interval of 200 ms preceding feedback onset for all included electrode positions and
absence of an interaction of hemisphere and condition. This slow wave has been
described in literature as the prefeedback SPN (Brunia & Damen, 1988; Brunia & Van
Boxtel, 2004).
The course of this prefeedback SPN was negative for upcoming negative feedback but
even positive for upcoming positive feedback. The difference between positive and
FIGURE 4. Feedback-induced ERPs. Feedback-induced ERP waveforms time-locked to feedback onset
(0 ms) are depicted at Fz, Cz and Pz for both the informative condition and the uninformative condition.
For both the first and second section of the task separate waveforms are shown for positive and negative feedback.
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negative feedback was largest for parietal electrode positions. This is expressed by an
overall effect of valence (F(1, 17) = 42.0, p < .001, η2 = .71) and an interaction of
electrode by valence (F(3, 51) = 8.0, p <.001, η2 = .32, ε = .75). The valence effect was
largest at parietal electrode positions (F(1, 17) = 124.2, p < .001, η2 = .88) and,
therefore, P3 and P4 are depicted in Figure 5. The amplitude difference for feedback
valence was larger in the informative condition than in the uninformative condition at
centroparietal electrode positions, as is reflected by an interaction of valence by
condition (F(1, 17) = 4.8, p < .05, η2 = .22) and an interaction of electrode by valence by
condition (F(3, 51) = 3.4, p < .05, η2 = .17, ε = .79). The interactions of condition by
valence for C3/C4 and P3/P4 are respectively (F(1, 17) = 6.6, p < .05, η2 = .28) and
(F(1, 17) = 8.8, p < .01, η2 = .34).
FIGURE 5. Prefeedback ERPs. Prefeedback ERP waveforms time-locked to feedback onset (0 ms) are
depicted at P3 and P4 for both the informative condition and the uninformative condition. For both the first and second section of the task separate waveforms are shown for positive and negative feedback.
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An overall effect of section could be observed for the prefeedback SPN. Its amplitude
was larger in the first section of the learning task than in the second, independent of
feedback valence, electrode position or feedback condition. This is reflected by an effect
of section (F(1, 17) = 26.3, p < .001, η2 = .61) and the absence of any interaction with
section.
EHR
As can be seen in Figure 6A, IBIs were longer in response to negative feedback
compared to positive feedback in the informative as well as in the uninformative
condition. This is reflected by an overall effect of valence (F(1, 17) = 14.3, p < .01, η2 =
.46). In both the informative and uninformative condition the general evoked heart rate
pattern was characterised by a deceleration prior to feedback and acceleration after
feedback onset (IBI0). In the informative condition the deceleration to negative
feedback was, however, prolonged for one additional cardiac cycle, delaying the
acceleration until IBI1. This deviant pattern is expressed by a significant interaction of
sequence, condition and valence (F(4, 68) = 3.6, p < .05, η2 = .17).
During the second section there was a general reduction in feedback-related heart rate
deceleration for both the informative and uninformative condition, i.e. IBIs were shorter
in the second section than in the first (see Figure 6B). This is reflected by an overall
effect of section (F(1,17) = 13.3, p < .01, η2 = .44). Moreover, when splitting the
analyses for the two task sections the typical prolonged heart rate deceleration to
informative negative feedback could only be observed in the first section. In the second
section, heart rate started accelerating again at IBI0. This acceleration in the second
section was, however, preceded by an enhanced deceleration at IBI-1 for negative
feedback. This pattern is expressed by a trend to significance with a large effect size for
the interaction of section by valence by sequence in the informative condition (F(4, 68)
= 3.0, p = .07, η2 = .15). Analyses per section with repeated contrasts for the factor
sequence could indeed reveal a significant interaction of sequence by valence for only
IBI-1 vs. IBI0 in the first section (F(1, 17) = 7.0, p < .05, η2 = .29) and for both IBI-2
vs. IBI-1 and in the second section (IBI-2 vs. IBI-1: F(1, 17) = 5.0, p < .05, η2 = .23;
IBI-1 vs. IBI0: F(1, 17) = 5.4, p < .05, η2 = .24).
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In contrast to the informative condition the increased IBIs in response to uninformative
negative feedback compared to positive feedback disappeared from the first to the
second section. Although in this condition the overall interaction of valence by section
did not reach significance, but showed medium effect size (F(1, 17) = 1.9, p = .19, η2 =
.10), no effect of valence could be observed in the second section of the task (F(1, 17) =
0.8, p > .05, η2 = .05), whereas it could in the first section (F(1, 17) = 10.7, p < .01, η2 =
.39).
FIGURE 6. EHR responses. Baseline corrected Interbeat Interval (IBI) changes in response to feedback
stimuli. IBI0 is the IBI at which the feedback was presented. Separate values are given for positive and negative feedback for the informative and uninformative feedback condition, both combined (A) and
separated (B) for the two task sections.
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TABLE 2. Correlations among EHR measures, ERP measures and accuracy in the informative
condition. Regarding the EHR and ERP measures, logarithmically transformed (ln) absolute difference values between correct and incorrect responses or positive and negative feedback were
used.
IBI-1 IBI0 IBI1 Accuracy
ERN (Fz) .264 .149 -.023 -.059
ERN (Cz) .576* .638** .573* .092
Pe (Cz) -.175 -.274 -.284 -.030
Pe (Pz) -.120 -.156 -.228 -.185
SPN (C4) .184 .131 .057 .395
SPN (P4) .130 .145 .154 .471*
P2a (Cz) -.005 .296 .286 .339
P3 (Pz) .165 .150 -.027 .401
Accuracy .135 .219 .366 -
**p < .01; *p < .05
CORRELATIONS AMONG MEASURES
In order to study correlations between the EHR and ERP measures of performance
monitoring, absolute values were computed of the difference between correct and
incorrect responses, for the response-locked measures, and of the difference between
positive and negative feedback, for the feedback-locked measures. The values were
computed such that they were positive and could, therefore, be interpreted as a measure
of the magnitude of the involved component or deceleration. Correlations were only
computed for the informative condition, because in this condition most performance
monitoring activity was present. Because of the large between subjects variation in the
difference values of both the ERP and EHR measures, these were logarithmically
transformed (ln) to approximate normal distribution.
Table 2 summarises the resulting correlations both between the EHR and ERP
difference values and between these physiological measures and accuracy. Significant
positive correlations were found between the ERN at Cz and the IBI difference values at
all selected IBI times, implying that larger response-locked ERN amplitudes go with
larger EHR decelerations. The only other significant correlation emerged between
accuracy and the SPN difference value at P4, implying that larger prefeedback
differences between positive and negative feedback go with larger performance
accuracy. Inspection of the scatterplots indicated that outliers could not explain the
significant correlations.
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DISCUSSION
The main objective of this study was to investigate psychophysiological correlates of
performance monitoring in children as learning proceeds throughout the course of a
learning task. The employed probabilistic learning task has proven to create the aimed
learning environment, because the children increased in accuracy and decreased in
response variability as the task proceeded. Even when the children received
uninformative feedback on their responses, they seemed to be actively engaged in
finding the right stimulus-response combinations. This was reflected by a higher
proportion of key changes in response to especially uninformative negative feedback,
i.e. the children tried the other key when the former key turned out to be wrong.
Strategic response selection in this condition may explain why the children were slower
on trials that were followed by uninformative negative feedback than on trials followed
by uninformative positive feedback.
With regard to the ERP measures of performance monitoring, the response- and
feedback-related components deviated in some aspects from what has previously been
described in adults and children. In the next section, the components elicited by the
probabilistic learning task are described first, before entering the discussion on
psychophysiological correlates of learning by performance feedback.
ERP COMPONENTS ELICITED BY THE PROBABILISTIC LEARNING TASK IN 10- TO 12-YEAR-OLD CHILDREN
When the ERN is regarded as a difference potential of incorrect and correct responses,
the children elicited a clear response-locked ERN on error trials, which showed a
similar frontocentral scalp distribution to the ERN reported in adults and children.
When, however, focussing on the typical ERN waveform, with its peak dipping below
baseline, the distribution was more frontal. The peak latency was maximal around
response onset, hence much earlier than the usually observed latency of 40 to 100 ms
after response onset in adults (Gehring et al., 1990; Falkenstein et al., 1991). Although
the observed ERN peak latency in the present study also occurs early compared to
previous reports in children, it must be emphasised that preresponse differences
between correct and error trials have actually been observed in children, although they
were not explicitly described in the text. This for example holds for the study by Davies
and colleagues (2004) in children from 7 to 12 years of age (Davies et al., 2004, p. 362,
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58
Figure 3) as well as for the study by Santesso and colleagues (2006) in 10-year-old
children (Santesso et al., 2006, p. 478, Figure 1). These early differences may be due to
altered stimulus evaluation on error trials but also to early error-related processing in
children. Unfortunately, these processes cannot be separated in the present design,
because of the overlap in timing of stimulus and response processing. An explanation
for the deviant peak latency of the ERN may be that the present probabilistic learning
task produces a different type of errors than commonly used tasks do, like for example
flanker tasks. In the probabilistic learning task the correct stimulus-response
combinations are not defined in the task instructions, which may cause uncertainty
about the desired response. As the presence of the ERN depends on the subject’s ability
to correctly represent the desired action (Dehaene, Posner, & Tucker, 1994a), this may
have played a role in the timing of the peak latency of the ERN.
In contrast to what has been consistently reported in adults (Miltner et al., 1997;
Gehring & Willoughby, 2002; Holroyd & Coles, 2002) and in one study with children
(Van Meel et al., 2005b), no typical feedback ERN was observed after negative
feedback onset. As the feedback ERN is proposed to be the reflection of an outcome
prediction error (Holroyd & Coles, 2002), the most obvious reason for the absence of a
feedback ERN is that the feedback in the present task was too predictable. However,
given that a prominent feedback ERN is elicited in conditions in which feedback
outcome is highly predictable (up to 75 % predictability; Hajcak et al., 2005) and in
similar trial-and-error learning paradigms in adults (Holroyd & Coles, 2002; Müller et
al., 2005), the absence of the feedback ERN may have been caused by another factor.
This may be that the motivational salience of the feedback stimuli in the present study
was not large enough for the children. The win and loss of points were indicated by
abstract symbols, i.e. a green and red square, and were unrelated to the eventual reward
retrieval, i.e. a present at the end of the experiment. In a study where feedback stimuli
indicated the win and loss of money, as symbolised by a picture of a treasure and a
bomb respectively, a feedback ERN was elicited by negative feedback in a group of 8-
to 12-year-old children (Van Meel et al., 2005b). Feedback stimuli may, therefore, have
to be more appealing to children for eliciting a feedback ERN compared to adults.
Instead of a feedback ERN, two positive peaks could be observed after both positive
and negative feedback onset: the P2a and P3. Both components were enlarged in
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59
response to negative feedback compared to positive feedback, but literature suggests
that they have a differential functional significance. The frontal P2a component has only
recently been described to be elicited by performance feedback, but in contrast to the
present study only in response to rewarding feedback stimuli (Potts et al., 2006a). The
P2a has, however, been suggested to have a similar source as the feedback ERN of
Medial Frontal Negativity, suggesting that both components are associated with
midbrain dopamine activity to the medial frontal cortex (Potts et al., 2006a). The
enhanced P2a in response to negative feedback in the present study may be interpreted
as a general reaction to motivationally salient stimuli. For example, in selective
attention paradigms the P2a to attended stimuli increases as task relevance of the stimuli
increases (for an overview see: Potts, 2004b). Similarly, an increased frontal P2a is also
observed in response to cues indicating that the upcoming stimulus requires enhanced
processing effort (Falkenstein, Hoormann, Hohnsbein, & Kleinsorge, 2003a).
While the enlarged P2a to negative feedback is explained in terms of motivational
salience, the enlarged P3 to negative feedback may be explained in terms of context-
updating and updating of working memory (Donchin & Coles, 1988). The findings of
enlarged P3 amplitudes to negative feedback compared to positive feedback for
example parallel the findings of ERP experiments adopting computerised Wisconsin
Card Sorting Tasks (WCST). In these type of tasks cards must be sorted according to an
initially unknown sorting rule, but this sorting rule changes unpredictably from one
series of cards to the other; in other words the task-set shifted (Barceló, Periáñez, &
Knight, 2002; Kopp, Tabeling, Moschner, & Wessel, 2006; Watson, Azizian, &
Squires, 2006). In these studies, strongly enlarged P3 components are elicited by
feedback cues that signal the unpredictable shifts in task-set (Barceló et al., 2002), and
are therefore suggested to reflect the updating of task rules from long-term memory as a
preparation for the next trial (Donchin & Coles, 1988; Barceló et al., 2002). In some
sense the probabilistic learning task in our study is similar to the WCST in that the
feedback tells the subject whether the current stimulus-response combination is correct
or whether the task-set has to be updated, legitimising the context-updating explanation
of the enlarged P3 amplitude.
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PHYSIOLOGICAL CORRELATES OF LEARNING BY PERFORMANCE FEEDBACK
Notwithstanding its early latency the response-related ERN amplitude increased as the
children had learned the task. This finding is in agreement with that of Holroyd & Coles
(2002) and implies an increased error evaluation with learning progression. Moreover,
not only the ERN, but also the Pe in our study increased in amplitude when learning
proceeded. This suggests that in addition to an increased error evaluation, error
awareness (Overbeek et al., 2005) increases too. Together, these phenomena imply an
increasing internal performance monitoring activity as stimulus-response combinations
have been learned. At the same time Holroyd and Coles (2002) observed a diminished
feedback ERN, suggesting that the subjects relied less and less on external feedback as
learning proceeded. Because in the present study no typical feedback ERN was
observed, this finding could not be replicated. Instead, the feedback-related prefeedback
SPN, P2a and P3 did decrease in amplitude as the task went on, regardless of the type of
feedback (positive or negative) or the informational value of the feedback (informative
or uninformative). As the prefeedback SPN is suggested to reflect the anticipation of the
affective-motivational value of feedback (Bastiaansen et al., 2002), the diminished SPN
during the second section may reflect that the feedback was of less affective-
motivational value for the children when they had learned the task. Indeed, the
diminished P2a on the one hand suggests that the feedback stimuli became less
motivationally salient to the children and the diminished P3 on the other that they were
evaluated less intensively for modifying future behaviour. Together, the response-and
feedback-related ERPs suggest a shift from external to internal monitoring as learning
proceeds throughout a probabilistic learning task.
Not only the ERPs, but also the EHR responses reflected performance monitoring
processes. As expected, an initial deceleration of heart rate before feedback onset was
observed, suggesting that, as previously proposed by Jennings and Van der Molen
(2002), the children were preparing for upcoming input. In line with other findings in
adults and children (Crone et al., 2003c; Crone et al., 2004; Crone et al., 2006), this
deceleration continued for one cardiac cycle only in response to informative negative
feedback. Just like the ERN, this prolonged deceleration to informative negative
feedback is thought to reflect a mismatch between intended actions and actual
performance outcomes (Somsen et al., 2000; Crone et al., 2003c; Crone et al., 2004). In
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the uninformative feedback condition no such prolonged deceleration was elicited, but
in this condition heart rate discriminated between negative and positive feedback only
in the first section of the task and not in the second section. This suggests that the
children had learned to recognise the uninformative character of the negative feedback
in the second section of the task. In contrast, Crone and colleagues (2004) reported that
children’s EHR kept discriminating between uninformative negative and positive
feedback, whereas adults’ EHR did not. They concluded that children continued to
extract meaning from the fake feedback throughout the task. However, they did not
conduct their analyses for separate task sections. The present results suggest, that the
children in the Crone and colleagues study may eventually have also recognised the
uninformative character of the feedback stimuli, but that they would have needed more
trials to do so than adults.
Consistent with the work by Crone and colleagues (2003c; 2004) we observed an
enhanced deceleration prior to informative negative feedback. Analyses of separate task
sections revealed that only in the first section of the task the typical prolonged
deceleration was elicited by informative negative feedback. This prolonged deceleration
was absent in the second section, but instead the deceleration prior to feedback (at IBI-
1) was enhanced. This pattern suggests that the children had learned to predict the
outcome of the trial prior to feedback onset in the second section of the task and that the
deceleration may have been elicited by the incorrect response preceding the feedback
stimulus (cf. Crone et al., 2004). This is likely, because enhanced heart rate
decelerations following error responses have been reported previously (Hajcak et al.,
2003b; Crone et al., 2006). In addition to the shift in timing of the heart rate
deceleration to informative negative feedback, there was a general reduction in
feedback-related heart rate deceleration for both informative and uninformative
feedback from the first to the second section. Convergent with the ERP measures the
EHR measures, therefore, suggest a shift from external to internal monitoring as
learning proceeds throughout a probabilistic learning task.
Our study provides further evidence for prolonged heart rate decelerations to negative
feedback being a reflection of the same error monitoring system that is responsible for
the emergence of the ERN. First of all, the functional characteristics of the prolonged
heart rate deceleration parallel the functional characteristics of the ERN; both being
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elicited only in conditions where performance feedback carries informative value and
both reflecting a shift from external to internal monitoring in a probabilistic learning
task. Moreover, the magnitude of the response-locked ERN amplitude was positively
related to the feedback-related heart rate deceleration. This finding differs from that of
Hajcak and colleagues (2003b) who failed to find a significant correlation between
error-related heart rate deceleration and the ERN. These authors, on the other hand,
reported a positive correlation between the Pe and subsequent SCR activity, which is
interpreted as a measure of sympathetic ANS activity (Critchley, Elliott, Mathias, &
Dolan, 2000). In the light of Damasio’s somatic marker hypothesis (Damasio, 1994)
they suggested that the Pe triggers the subsequent ANS activity. They conclude that the
full range of performance monitoring processes may rely on the interplay of centrally
generated signals, affecting both decision-making systems in the brain and peripheral
changes in body state (Hajcak et al., 2003b). Within this framework we suggest that the
vagally modulated (Somsen, Jennings, & Van der Molen, 2004) heart rate deceleration
to errors and negative feedback also serves as a somatic marker of erring. It is quite well
established that the dorsal ACC is involved in generating changes in autonomic state
during effortful cognitive processing (for a review see: Critchley, 2005) and, moreover,
that the source of the ERN is adjacent to this area (Ridderinkhof et al., 2004). The error
monitoring system may, therefore, provide for ANS warnings signals when events are
worse than expected (cf. Jennings & Van der Molen, 2002).
But how can these error- and feedback-related peripheral changes, or somatic markers,
eventually benefit cognitive processing? Peripheral changes in heart rate and
bloodpressure are immediately fed back to the brain. The primary subcortical relay
station for peripheral feedback, the Nucleus Tractus Solitarius (NTS), has strong
projections to the Locus Coeruleus (LC), which is the primary source nucleus of
noradrenaline in the brain (Berntson, Sarter, & Cacioppo, 2003; Berridge &
Waterhouse, 2003; Althaus et al., 2004). The LC- noradrenaline system in its turn is
strongly involved in the regulation of the individual’s state of alertness and the
facilitation of sensory information processing, but also in learning processes by
facilitating the formation of novel synaptic connections in the brain (Berridge &
Waterhouse, 2003). Stimulation of the NTS-LC system, for example, has repeatedly
been found to enhance memory consolidation (for an overview see: McGaugh &
Roozendaal, 2002). Via the NTS-LC feedback-loop of peripheral changes, EHR
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63
responses due to error and feedback processing may, therefore, have a functional impact
on the quality of information processing and learning.
In conclusion, incorrect responses elicited error-related potentials (ERN, Pe) in 10- to
12-year-old children, which as expected increased as learning proceeded in a
probabilistic learning task. Even though no feedback ERN could be observed, other
feedback-related potentials (prefeedback SPN, P2a and feedback P3) in their turn
decreased with task progression, suggesting that the children relied less and less on
feedback. The feedback-related EHR responses paralleled the electrocortical results; on
the one hand there was a general reduction in feedback-related heart rate deceleration
with task progression and on the other there was a shift in timing of the enhanced heart
rate deceleration to informative negative feedback with task progression. Both the ERPs
and the EHR responses, therefore, imply that external monitoring is gradually replaced
by internal monitoring as learning proceeds. Moreover, our results provide further
evidence for feedback-related heart rate deceleration being a reflection of the same error
monitoring system that is responsible for the ERN. For the emergence of self-regulatory
and socially adaptive behaviour, the full range of performance monitoring processes, i.e.
both cortical and autonomic, may be a prerequisite. The set of output measures
reflecting internal and external monitoring processes in this study may be a valuable
tool to reveal performance monitoring deficits in neurodevelopmental disorders.
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CHAPTER 3
ERROR AND FEEDBACK PROCESSING IN CHILDREN WITH ADHD
AND CHILDREN WITH AUTISTIC SPECTRUM DISORDER:
AN EEG EVENT-RELATED POTENTIAL STUDY
YVONNE GROEN
ALBERTUS A. WIJERS
LAMBERTUS J.M. MULDER
BRENDA WAGGEVELD
RUUD B. MINDERAA
MONIKA ALTHAUS
The study described in this chapter has been published in Clinical Neurophysiology,
119, 2476-2493, 2008.
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66
ABSTRACT
Objective: Performance monitoring was investigated in typically developing (TD)
children, children with Autistic Spectrum Disorder (ASD), and Methylphenidate (Mph)-
treated and medication-free children with Attention Deficit Hyperactivity Disorder
(ADHD). Methods: Subjects performed a feedback-based learning task. Event-Related
Potentials (ERPs) time-locked to responses and feedback were derived from the EEG.
Results: Compared to the TD and ASD group, the medication-free ADHD group
showed a decreased response-locked Error-Related Negativity (ERN) and error
Positivity (Pe), particularly as learning progressed throughout the task. Compared to the
medication-free ADHD group, the Methylphenidate-treated group showed a normalised
Pe. All clinical groups showed or tended to show a decreased feedback-locked late
positive potential to negative feedback. Conclusions: The ERPs suggest that
medication-free children with ADHD, but not children with ASD, have a diminished
capacity to monitor their error responses when they are learning by performance
feedback. This capacity partially ‘normalises’ in Mph-treated children with ADHD.
Both children with ADHD and ASD are suggested being compromised in affective
feedback processing. Significance: This study shows that measuring ERPs of error and
feedback processing is a useful method for (1) dissociating ADHD from ASD and (2)
elucidating medication effects in ADHD on component processes of performance
monitoring.
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INTRODUCTION
OBJECTIVE
Although Attention Deficit Hyperactivity Disorder (ADHD) and Autistic Spectrum
Disorder (ASD) are described as clearly distinct syndromes in the DSM-IV-TR
(American Psychiatric Association, 2000), in clinical practice it often appears difficult
to discriminate between the two disorders (Clark et al., 1999; Jensen et al., 1997).
Phenomenological studies report that many children with ADHD also have ASD
symptoms and vice versa (see for a review: Nijmeijer et al., 2008) and there is an
increasing body of research suggesting genetic overlap between the two disorders
(Smalley, Loo, Yang, & Cantor, 2005; Ronald, Simonoff, Kuntsi, Asherson, & Plomin,
2008). Moreover, both ADHD and ASD have been related to executive functioning
(EF) deficits (Geurts et al., 2004; Ozonoff & Jensen, 1999; Happé et al., 2006),
although there is an ongoing discussion on the type of EF profile that is specific for
each disorder. The present study uses electrocortical measures to investigate specific
aspects of EF processes in children with ASD, Methylphenidate-treated and medication-
free children with ADHD and a group of typically developing (TD) children. This
approach may allow for discriminating children with ASD and ADHD on specific EF
processes, as well as for investigating effects of the first-choice treatment of ADHD on
these processes.
The EF ability targeted in the present study concerns performance monitoring; the
ability to continuously monitor whether action goals have been reached in order to
optimise future behaviour (Stuss, Shallice, & Alexander, 1995). This ability can be
investigated by extracting Event-Related Potentials (ERPs) from the
electroencephalogram (EEG) that are time-locked to responses and feedback stimuli,
reflecting internal and external monitoring processes respectively (Gehring et al., 1990;
Müller et al., 2005; Falkenstein et al., 1991; Miltner et al., 1997). The children
performed a probabilistic learning task, in which they were required to learn stimulus-
response combinations by making use of performance feedback. An earlier study, which
included the present group of TD children demonstrated that while learning progresses
throughout the task, feedback-locked ERP-components (prefeedback Stimulus
Preceding Negativity, P2a and P3) decrease, while the response-locked ERP-
components (Error-Related Negativity and error Positivity) increase. This reflects that
during learning children become less dependent on feedback stimuli, while depending
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68
more and more on their internal monitoring system, i.e. they shift from an external
mode of performance monitoring to an internal mode (Groen et al., 2007).
Although ERP research does not allow for direct interpretations in terms of deficient
brain structures and neurotransmitter systems, indirect inferences can be made thanks to
the large body of fundamental research on this topic. In the next two sections a set of
response and feedback monitoring components is described, that may be used for
dissociating ADHD from ASD and for studying effects of Mph intake in children with
ADHD.
RESPONSE MONITORING IN ADHD AND ASD
The Error-Related Negativity (ERN) is a negative-going waveform peaking just after an
error response or negative feedback stimulus (Gehring et al., 1990; Miltner et al., 1997;
Falkenstein et al., 1991). This component is thought to reflect a mismatch between
actual and intended actions or goals and, therefore, occurs in response to unfavourable
outcomes, response errors, response conflict and decision uncertainty (Ridderinkhof et
al., 2004). Its neuronal source has been localised in the Anterior Cingulate Cortec
(ACC) (Taylor et al., 2007). The ERN is hypothesised to reflect phasic ACC activity in
response to reinforcement signals from the mesencephalic dopamine system that serves
as a trigger for further processing of the event and further deliberate compensatory
behaviour (Holroyd & Coles, 2002). Further conscious error processing is thought to be
reflected by the error Positivity (Pe), which is a positive-going potential following the
ERN. Contrary to the ERN, this component does not emerge on trials where the subject
is unaware of his committed error (Overbeek et al., 2005; Nieuwenhuis et al., 2001;
O'Connell et al., 2007). Several studies have suggested that the Pe is a P3(b) response to
the processing of errors (Leuthold & Sommer, 1999b; Davies et al., 2001; Overbeek et
al., 2005; O'Connell et al., 2007). A recent theoretical framework has proposed that the
P3 reflects a phasic response of the locus coeruleus-noradreneline (LC-NE) system to
the outcome of internal decision-making (Nieuwenhuis et al., 2005). Therefore,
Overbeek and colleagues (2005) suggest that error awareness, as reflected by an
enlarged Pe amplitude, is associated with increased phasic noradrenergic activity of the
LC-NE system.
Findings on the ERN amplitude in ADHD are inconsistent. Two studies have found
reduced ERN amplitudes in children with ADHD compared to TD children, suggesting
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69
that they have a deficit in monitoring ongoing behaviour (Liotti, Pliszka, Perez,
Kothmann, & Woldorff, 2005b; Van Meel et al., 2007). Wiersema and colleagues
(2005) as well as Jonkman and colleagues (2007), however, could not reveal differences
in ERN amplitude between children with ADHD and TD children. Burgio-Murphy and
colleagues (2007), finally, reported an enlarged ERN amplitude in children with ADHD
(combined type) and suggest that they are more emotionally reactive. The Pe is fairly
consistently found to be decreased in children with ADHD, suggesting that they become
less aware of their committed errors (Overtoom et al., 2002b; Wiersema et al., 2005;
Jonkman et al., 2007; but see: Burgio-Murphy et al., 2007). Reduced Pe amplitudes in
ADHD are in accordance with findings of reduced post error compensatory behaviour,
i.e. the strategic reaction time (RT) slowing after the commission of errors (Sergeant &
Van der Meere, 1988b; Schachar et al., 2004a; Wiersema et al., 2005). Reduced error
awareness may thus hamper children with ADHD in adequately adapting their
behaviour and consequently in learning from their mistakes.
Methylphenidate (Mph) is a stimulant that is widely used for the treatment of ADHD
symptoms and is known to block the re-uptake of both dopamine and noradrenaline,
thereby enhancing their extracellular release (Seeman & Madras, 1998; Pliszka, 2005).
Although sample sizes were small, a recent placebo-controlled study revealed that Mph
improves error processing in children with ADHD (Jonkman et al., 2007). In this study
children with ADHD treated with Mph showed a normalised error-related Pe amplitude.
This finding is in line with some performance studies, showing that Mph increases post
error slowing in children with AD(H)D (Krusch et al., 1996b; De Sonneville,
Njiokiktjien, & Bos, 1994b). In contrast to studies showing that stimulants like Mph
enhance response-locked ERN amplitudes in healthy adults (De Bruijn, Hulstijn,
Verkes, Ruigt, & Sabbe, 2004; De Bruijn, Hulstijn, Verkes, Ruigt, & Sabbe, 2005),
Jonkman and colleagues, however, did not find a modulating effect of Mph on the ERN
in children with ADHD. This suggests that Mph improves conscious error processing
but not error detection in ADHD.
Concerning ASD, several neuroimaging studies have found support for a
hypofunctional ACC in autism (Haznedar et al., 2000; Ohnishi et al., 2000; Gomot et
al., 2006), with two of them reporting that ACC activity is negatively associated with
symptom presentation in autism (Haznedar et al., 2000; Ohnishi et al., 2000). There is
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also evidence that ‘mentalising’ tasks which are difficult for subjects with ASD, like
joint attention and Theory of Mind tasks, recruit brain areas that are overlapping with
brain areas involved in the generation of the ERN (Amodio & Frith, 2006; Frith & Frith,
2001; Mundy, 2003). Henderson and colleagues (2006) were the first and only authors
to date, who conducted an electrophysiological study on performance monitoring in
children diagnosed with ASD. They could, however, not reveal overall differences in
ERN amplitude between the ASD and TD group, but found that within the ASD group
larger ERN amplitudes were predictive of a smaller impairment in social interaction as
well as of decreased internalising problems. The authors suggest that a response
monitoring deficit may not be a core feature of ASD, but that a measure like the ERN
might serve as ‘a bio-behavioural marker of cognitive processes that moderate the
development of children with autism’ (p. 106, Henderson et al., 2006).
Performance studies have suggested deficits in error correction in autism. Russell and
Jarrold (1998), for example, found that autistic children were more likely to fail
correcting errors than controls, both when they were provided with visual feedback
about their errors (external monitoring) and when they had to detect their errors
themselves (internal monitoring). Bogte and colleagues (2007), moreover, found that a
group of adult autistic subjects showed no post error slowing, whereas a control group
did. These studies suggest decreased error awareness in autism, predicting decreased Pe
amplitudes.
FEEDBACK MONITORING IN ADHD AND ASD
ERP research regarding feedback processing, has predominantly focussed on the
feedback ERN (Miltner et al., 1997; Müller et al., 2005). However, in our previous
study, which included the same group of TD children performing the present learning
task, we could not identify this component. Instead, a P2a, P3 and later occurring
positivity were elicited, all of them being increased to negative opposed to positive
feedback (Groen et al., 2007). Another study examining feedback-related ERPs in
children with ADHD (Van Meel et al., 2005b) also described such early frontal
positivity (but also a clear feedback ERN), which was enlarged in response to stimuli
indicating loss. Compared to TD children, children with ADHD showed a reduced P2a
amplitude to both positive and negative feedback stimuli, suggesting that the early
discrimination or categorisation of motivationally relevant stimuli is compromised in
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these children (Van Meel et al., 2005b). Additionally, children with ADHD showed a
decreased late positivity (after 450 ms) to negative feedback stimuli indicating loss.
This latter finding had been interpreted as a deficit in the affective evaluation of
feedback signals and altered evaluation of future consequences in children with ADHD
(Van Meel et al., 2005b).
Another study submitted by Van Meel and colleagues (in preparation) investigated the
anticipation of feedback stimuli in children with ADHD. The authors observed a
prefeedback Stimulus Preceding Negativity (SPN), a negative-going slow wave that has
been associated with the anticipation of the affective motivational value of feedback
stimuli (for an overview see: Böcker et al., 2001). Compared to TD children, children
with ADHD showed decreased prefeedback SPN amplitudes (Van Meel, Heslenfeld,
Oosterlaan, Luman, & Sergeant, submitted). This is in line with repeated findings of
decreased amplitudes of a similar negative slow wave in anticipation of target stimuli in
ADHD, the Contingent Negative Variation (see for a review: Barry, Johnstone, &
Clarke, 2003). Diminished negative slow waves in anticipation of upcoming task-
relevant information in ADHD may be interpreted as deficient preparatory control
processes that are due to diminished motivational involvement in task situations
(Sergeant & Van der Meere, 1988b).
Regarding autism, there is no literature available on performance monitoring
components other than the response-locked ERN. ERP research on autism has mainly
focussed on perceptual and attentional processing and has generally yielded inconsistent
findings because of methodological problems (see for a review: Kemner & Van
Engeland, 2006). Several performance studies have, however, investigated the
differential sensitivity to social versus non-social reward and feedback in autistic
children. These studies all show that, compared to TD children, autistic children are less
sensitive to social feedback, e.g. smiling or words of appreciation, while they show no
deficient sensitivity to non-social feedback, e.g. money or sensory feedback (Garretson
et al., 1990; Dawson et al., 2002; Ingersoll et al., 2003). Yet, other studies did suggest
an impairment in sensitivity to non-social feedback (Althaus et al., 1996) and reward
(Dawson, Osterling, Rinaldi, Carver, & McPartland, 2001). The present study may
contribute to the scarce literature on feedback sensitivity in ASD by measuring
feedback-related ERPs in age and intelligence matched groups of children.
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EXPECTATIONS
Both children with ADHD and ASD are hypothesised to both show smaller response
and feedback-related monitoring components than age and intelligence matched TD
children. The inclusion of a group of children with ADHD who took their normal dose
of methylphenidate at the time of the experiment, moreover, allows for studying the
effect of stimulant medication in children with ADHD on performance monitoring. In
agreement with a recently published study by Jonkman and colleagues (2007), we
expect that Mph selectively influences response monitoring components in children
with ADHD, with a stimulating effect on especially the Pe. Concerning feedback
monitoring, Mph-treated children with ADHD may also show larger components than
the medication-free children with ADHD and may, therefore, be more similar to TD
children.
METHODS
SUBJECTS
The study included 72 10-to-12-year old children who belonging to four experimental
groups: a typically developing (TD) group (n = 18), a medication-free ADHD group (n
= 18), a Methylphenidate (Mph)-treated ADHD group (n = 17) and an ASD group (n =
19). The TD children were recruited from primary schools in the city of Groningen and
by advertisement in the newsletter of the University Medical Centre in Groningen
(UMCG). The Child Behavioural Checklist (CBCL: Achenbach & Rescorla, 2001) was
filled out by the parents of all children to assess a wide range of childhood
psychopathology. None of the TD children scored within the clinical range of the total
problem scale of this list, suggesting that they were free from clinical behaviour
problems. The TD children, moreover, scored significantly lower on a parental
questionnaire measuring social dysfunction: the Children’s Social Behaviour
Questionnaire (CSBQ: Hartman, Luteijn, Serra, & Minderaa, 2006). See Table 1 for a
summary of all group characteristics.
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ADHD and ASD had been diagnosed by independent well-trained child psychiatrists of
our Department of Child- and Adolescent Psychiatry, according to the diagnostic
criteria of the DSM-IV-TR (American Psychiatric Association, 2000). Regarding
ADHD, only children with the combined type were included, which required
pervasiveness (at home and at school) of both inattentive symptoms and hyperactive-
impulsive symptoms observed during at least six months. Some of the symptoms caused
impairment before age 7 years. Regarding the ASD group, the children showed serious
and pervasive disabilities in the development of social and communicative skills, and
presence of stereotype interests and behaviour. These symptoms, however, did not meet
TABLE 1. Group characteristics.
Measures chi square
Handedness (left/ambidexter/right) .16 _
Gender (male/female) .29 _
Mph intake in past year (on/off) <.001 TD,ASD***<ADHD<ADHD Mph*
Measures Mean SD Mean SD Mean SD Mean SD p value
Age (years) 11,4 0,9 11,4 0,9 11,4 0,8 11,6 0,8 .88 _
Total IQ 103 9,5 102 10,2 99 11,3 100 13,0 .61 _
Verbal IQ 107 10,4 102 12,3 99 12,7 102 10,4 .29 _
Performal IQ 97 12,8 102 11,1 98 12,3 97 16,4 .61 _
Social Communication Questionnaire (SCQ)
Total _ 20,5 4,2 7,2 3,9 5,0 3,1 <.001 ADHD Mph, ADHD< ASD***
Social interaction _ 8,6 2,8 3,0 2,1 0,9 1,3 <.001 ADHD < ADHD Mph*;
ADHD, ADHD Mph < ASD***
Communication _ 6,4 1,9 2,7 1,5 2,5 1,4 <.001 ADHD Mph, ADHD< ASD***
Repetitive and Stereotype Behaviour _ 4,2 1,5 1,0 1,0 1,4 1,3 <.001 ADHD Mph, ADHD< ASD***
Children's Social Behaviour Questionnaire (CSBQ)
Total 7,2 7,8 47,7 13,6 31,6 14,0 28,8 9,9 <.001 TD***<ADHD Mph, ADHD< ASD**
Diagnostic Interview Schedule for Children (DISC) ADHD section
Attentional Problems _ 7,1 5,0 11,6 4,6 14,0 3,6 <.001 ASD**<ADHD Mph, ADHD
Hyperactive Impulsive Behaviour _ 3,1 3,5 12,0 4,0 13,8 4,3 <.001 ASD***<ADHD Mph, ADHD
Conners Teacher Rating Scale- Revised (CTRS-R)
Oppositional _ 50,4 7,8 60,6 11,3 58,5 13,4 <.05 ASD*< ADHD Mph
Inattentive/Cognitive Problems _ 52,7 11,0 53,0 6,5 58,3 13,7 .24
Hyperactivity-Impulsivity _ 53,2 6,3 64,4 10,7 64,9 14,3 <.01 ASD*< ADHD Mph; ASD**<
ADHD
Anxious/Shy _ 68,1 13,2 59,8 11,3 67,5 13,1 .10
Perfectionism _ 55,6 11,8 54,6 12,6 54,2 8,9 .93
Social Problems _ 70,0 14,9 57,0 8,5 60,8 14,6 <.05 ASD*>ADHD Mph
ADHD index _ 55,3 10,7 60,8 8,5 64,6 15,0 .06 ASD< ADHD
Child Behavioural Checklist (CBCL)
Total Problems 14,8 11,5 52,6 23,3 50,2 27,9 59,6 20,0 <.001 TD***< ADHD Mph, ADHD, ASD
Ratio: Clinical/ Not clinical 0/18 10/9 7/10 11/7
Internalizing Problems 4,3 4,4 15,1 8,5 8,9 8,7 11,4 8,0 <.01 TD*<ADHD, ASD
Ratio: Clinical/ Not clinical 1/18 11/8 3/14 8/10
Externalizing Problems 3,5 3,5 11,0 10,6 15,9 8,9 16,9 7,2 <.001 TD**< ADHD Mph, ADHD, ASD
Ratio: Clinical/ Not clinical 0/18 5/14 8/9 8/10
Bonferroni corrected post hoc
analyses
TD
n = 18
ADHD Mph
n = 17
ADHD
n = 18
ASD
n = 19
Ratio Ratio RatioRatio
0/4/14
12/6
0/18
* = p < .05; ** = p < .01; *** = p < .001
1/1/16
16/2
14/4
1/3/15
15/4
1/18
1/3/13
16/1
17/0
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the criteria for a full-blown Autistic or Asperger Disorder because of late age onset,
atypical symptomatology, or subthreshold symptomatology, or all of these and were
consequently diagnosed as having Pervasive Developmental Disorder Not Otherwise
Specified (PDDNOS). After the diagnosis, ADHD and ASD symptoms were
additionally assessed by standardised questionnaires (see below).
Written informed consent was obtained from all parents and all 12-year-old children
assented to the study. The study was approved by the Medical Ethical Committee of the
University Medical Center Groningen.
Of the 35 children with ADHD, 31 children were Mph responders, who all took this
drug during the main part of the year preceding the experiment. These Mph responders
were randomly assigned to an Mph-treated or medication-free condition. Those
assigned to the medication-free condition were asked to discontinue Mph-intake for at
least 17 hours before they entered the experiment. This period was considered long
enough due to an expected clearance within 4 to 5 times the half life of Mph, which is
about 3,5 hours. The remaining four of the 35 children with ADHD did not yet use
medication and were, therefore, directly assigned to the medication-free group. All
children in the ASD group were medication-free at the time of the experiment.
Table 1 shows a summary of the group characteristics and the corresponding post hoc
comparisons. Intelligence was measured by assessing the Wechsler Intelligence Scale
for Children-III (WISC-III) on another day than the experiment and all children had a
full-scale Intelligence Quotient at or above 80. The four groups neither differed in age
nor in intelligence (see Table 1). The ratio of boys and girls was approximately 5:1,
which did not differ significantly between groups. As measured by a self-report list for
handedness (Van Strien, 2003) the majority of the children was right handed or had a
tendency to right handedness. The ratio of left: ambidexter: right did not differ
significantly between groups.
For measuring ADHD symptoms in the clinical groups, the ADHD section of the
Diagnostic Interview Schedule for Children-IV was administered to the parents (DISC-
IV: Shaffer, Fisher, Lucas, Dulcan, & Schwab-Stone, 2000). The Dutch translation of
this structured interview was used (Ferdinand & Van der Ende, 1998). Moreover, the
Conners’ Teacher Rating Scale- Revised (CTRS-R) was administered to the teachers of
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the clinical children (Conners, 1990; Conners, 1999). All children with ADHD scored
either in the clinical range of the DISC-IV or in the borderline range of the CTRS-R.
Except for five children, all children with ADHD scored within the clinical range of at
least one of the ADHD subscales of the DISC-IV (attentional problems or hyperactive-
impulsive problems). As 31 of the 35 children with ADHD were well-responding to
Mph, medication-intake during the period that was questioned by the interview very
likely caused lower scores than would have been obtained at the time of the diagnosis.
This may explain why five children scored below threshold on both subscales of the
DISC-IV ADHD section. These children were all Mph-responders, but still scored
minimally four out of nine symptoms of at least one of the DISC-IV subscales. Most
important, however, children in both ADHD groups showed significantly more
attentional problems and hyperactive-impulsive behaviour than the children in the ASD
group on the DISC-IV (see Table 1).
For assessing autistic-type behaviour in the clinical groups, parents were administered
the Dutch translation of the Social Communication Questionnaire (SCQ: Rutter, Bailey,
& Lord, 2003), which is a recently developed screening tool for ASD based on the
Autism Diagnostic Interview-Revised (Lord, Rutter, & Le Couteur, 1994). To date, two
validation studies have revealed that the SCQ is a valid measure for discriminating ASD
from non-ASD cases with a cut-off of ≥ 15 (Berument, Rutter, Lord, Pickles, & Bailey,
1999; Chandler et al., 2007). All children included in the ASD group scored at or above
this cut-off. Additional information on the children’s social functioning was derived
from the CSBQ. The total scores of both questionnaires confirmed that the children with
ASD showed significantly more autistic-like symptoms than the children with ADHD
(see Table 1).
TASK
FEEDBACK CONDITIONS AND STIMULUS MATERIAL
All children were tested in the morning or the afternoon by means of a probabilistic
learning paradigm originating from Holroyd and Coles (2002), which had been adopted
in a curtailed form from Crone and colleagues (2004). In this learning task four
coloured pictures (A, B, C and D) belonging to the categories ‘animals’, ‘fruits’,
‘music’ and ‘sports’ (Microsoft Clipart ®) were randomly presented to the children. For
each of the four pictures, the children had to find out which of two keys to press by
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attending the performance feedback after their response. The children were, however,
ignorant of the two feedback conditions that were assigned to the stimuli. The first two
stimuli (A and B) were followed by informative feedback. Pressing the left key to
picture A, resulted in positive feedback whereas pressing the right key resulted in
negative feedback. For picture B this coupling was opposite: pressing the left key to
picture B, resulted in negative feedback while pressing the right resulted in positive
feedback. The second two stimuli (C and D) were followed by uninformative feedback.
The feedback valence for picture C was always positive and the valence for picture D
was always negative; the feedback outcome, therefore, was independent of the child’s
response. The children randomly received nine learning blocks, each consisting of 96
stimulus presentations (trials). Each block initiated a new learning process, because
each block contained four new pictures for which the correct stimulus-response
combination had to be learned. In Table 2 the distribution of the feedback conditions
within one task block is given. Note that by randomly presenting the pictures, the
feedback conditions were randomly distributed within one block too. The number of
trials for each feedback valence within the informative feedback condition was variable,
because it depended on the error rate of the child. In the uninformative condition, the
number of trials for both positive and negative feedback was 24. The total number of
experimental trials was 864 (9*96).
Each trial started with the presentation of one out of the four stimuli, which stayed on
the screen for the total duration of the individual deadline time (thus not terminated by
the response, see section 2.2.2). The feedback stimulus appeared 1000 ms after stimulus
offset and stayed on the screen for 1500 ms. The trial was closed by a variable Intertrial
Interval (ITI), which lasted for 500, 750 or 1000 ms. See for a schematic overview of
the trial structure Figure 1.
Informational value Picture Valence # Trials
Informative (48 trials) A Positive = left key 24 - error rate
Negative = right key error rate
B Positive = right key 24 - error rate
Negative = left key error rate
Uninformative (48 trials) C Positive = left and right key 24
D Negative = left and right key 24
Task block consisting of 96 trials
TABLE 2. Distribution of feedback conditions within one task block. Four pictures that were repeatedly
presented in every task block were either coupled to informative feedback (A and B) or to
uninformative feedback (C and D). Originally published in (Groen et al., 2007).
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TASK INSTRUCTIONS AND PROCEDURE
In every block, the children were instructed to win as many points as they could. In
order to elicit enough error trials for computing error-related potentials in the
informative feedback condition, the children were, however, forced to respond quickly
by instructing them also to respond within a response deadline. To take into account
individual differences in response speed an individual deadline was computed for every
child. This individual deadline time (mean reaction time + 10%) had been determined
after practicing before the start of the experimental blocks in a special deadline
determination block. When they responded too late a black square appeared on the
screen, indicating a loss of two points. Positive feedback (green square) and negative
feedback (red square) indicated the win or loss of one point respectively. The children
started with 52 points at the start of each task block, which could maximally add up to
100 points at the end of a block.
The children were seated on a comfortable chair in front of a computer screen in a room
that was separated from a control room by a one-way screen. After a standardised
instruction the children performed a short practice block consisting of 24 trials, which
was followed by the deadline block consisting of 96 trials. After application of the
electrodes the children performed the nine experimental blocks (each lasting between 6
and 7 minutes). After five experimental blocks there was a break of 20 minutes. At the
FIGURE 1. Time course of a single trial. Within one task block each trial started with the presentation of
one out of four stimuli. The feedback stimulus appeared 1000 ms after stimulus off-set and stayed on the screen for 1500 ms. The next trial started after a variable Inter Trial Interval (ITI) of 500, 750 or
1000 ms. Originally published in (Groen, Wijers, Mulder, Minderaa, & Althaus, 2007).
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end of the experiment the children received a present (a toy), independent of their
scores.
COMPUTATION OF PERFORMANCE MEASURES
The probabilistic learning task was built and presented by means of the program E-
Prime (version 1.1; Psychological Software Tools). Key type (left or right), reaction
time (RT) and accuracy of the response were recorded for every trial. To investigate the
process of learning each block was cut into four consecutive sections (quartiles), which
were then averaged across the nine blocks. Three performance measures were computed
for all quartiles: RTs, individual SDs of RTs and percentage of correct responses.
ELECTROENCEPHALOGRAM RECORDINGS AND COMPUTATION OF ERPS
The EEG was recorded using a lycra stretch cap (Electro-Cap Center BV) with 21
electrodes, placed according to the 10-20 system (O1, Oz, O2, P3, P5, P7, Pz, P4, P6,
P8, C3, Cz, C4, F3, Fz, F4, F7, F8, FP1, FPz en FP2). Vertical and horizontal eye
movements were recorded with electrodes respectively above and next to the left eye.
For all channels Ag-AgCl electrodes were used and impedances were kept below 10
kΩ. Using the REFA-40 system (TMS International B.V.), all channels were amplified
with filters set at a time constant of 1 second and a cut-off frequency of 130 Hz (low
pass). The data from all channels were recorded with a sampling rate of 500 Hz using
Portilab (version 1.10, TMS International B.V.). Using BrainVision (version 1.05, Brain
Products), the signals were off-line filtered with a 0.25 Hz high pass and 30 Hz low pass
filter, and referenced to the left ear electrode.
To investigate the ERN and Pe, EEG segments were cut around the children’s responses
ranging from 500 ms before to 800 ms after response onset, with the first 200 ms
serving as a baseline. This was done for both response types, i.e. correct and incorrect
responses. Segments for investigating prefeedback and feedback-induced ERPs were
separately cut around the feedback stimulus, in order to keep the number of rejected
segments due to artefacts as low as possible. For the prefeedback SPN the segments
ranged from 1000 ms before to 200 ms after feedback onset, with the first 200 ms of the
segment serving as a baseline. For the feedback ERN and feedback P3, segments ranged
from -200 ms to 1000 ms after feedback onset, with the first 200 ms serving as a
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baseline. All segments were scanned for artefacts. Segments with high or low activity
(exceeding 200 µV) and/or spikes and/or drift due to large eye-movements, head or
body movements, or equipment failure were removed before the analyses. Segments
with eye movements and blinks were kept and corrected, adopting the standard Gratton
& Coles procedure (Gratton et al., 1983). For every child the segments were then
averaged separately for all electrode positions and all feedback conditions. To
investigate the process of learning each of the nine learning blocks was cut into two task
sections (halves), which were then averaged across the nine blocks, i.e. for all first
halves and second halves separately.
DATA ANALYSES
Performance measures were analysed by means of a repeated measures ANOVA (SPSS,
version 14.0) with task section (quartile 1 to 4) as the within subject variable and group
(TD, ASD, ADHD, ADHD Mph) as the between subjects variable. This was done for
the mean percentage of correct responses, mean RT and individual SDs of RTs in the
informative condition. Repeated contrasts for the factor quartile were computed to
investigate changes from quartile to quartile.
For statistical analyses of the ERPs, mean amplitude values were computed for
successive time intervals of every ERP average. This method allows for more precisely
detecting latencies of effects than investigating one broad interval. For the relatively
short lasting components, i.e. ERN and P2a, 20 ms intervals were computed, while for
relatively long lasting components, i.e. Pe, prefeedback SPN and later feedback-induced
components, 50 ms intervals were computed. For the response-locked ERPs, 20 ms
mean amplitude values were computed in the time period of -300 to 100 ms for
investigating the ERN, resulting in 20 intervals, and 50 ms mean amplitude values in
the time period of 100 to 800 ms for investigating the Pe, resulting in 14 time intervals.
For the feedback-induced ERPs, 20 ms mean amplitude values were computed in the
time period of 120 to 240 ms for investigating the P2a, resulting in 6 intervals, and 50
ms mean amplitude values in the time period of 200 to 1000 ms for investigating later
feedback-induced components, resulting in 16 intervals. The electrode positions of
interest were Fz, Cz and Pz, as the ERN and feedback ERN have been described to have
a midline frontocentral topography (Falkenstein et al., 1991; Gehring et al., 1993) and
the Pe a more widespread centroparietal topography (Falkenstein et al., 1991; Davies et
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al., 2001). On all successive intervals repeated measures ANOVAs were conducted by
applying a 3*2*2 design, with as within subject variables electrode position (Pz vs. Cz
vs. Fz), response type (correct vs. incorrect) in case of response-locked segments or
valence (positive vs. negative) in case of feedback-locked segments, and section (first
vs. second section). The factor group (TD, ASD, ADHD, ADHD Mph) was used as the
between subjects variable.
For the prefeedback ERPs, 50 ms mean amplitude values were computed in the time
period of -800 to 0 ms, resulting in 16 intervals. The electrodes of interest for the
prefeedback SPN were the left and right frontal, central and parietal electrode sites,
because feedback manipulations have been shown to modulate this slow wave on these
electrode positions (Chwilla & Brunia, 1991; Kotani et al., 2001). Repeated measures
ANOVAs were conducted by applying a 3*2*2*2 design on each interval, with the
within subject variables electrode position (F3/4 vs. C3/4 vs. P3/4), hemisphere (left vs.
right), valence (positive vs. negative), and section (first vs. second section). Again the
factor group (TD, ASD, ADHD, ADHD Mph) was used as the between subjects
variable.
Main effects of group and interactions with group were specified for those intervals that
were significant (p < .05) or showed a trend to significance (p < .10) with minimally
medium effect sizes (η2 ≥ .06). Group differences were inspected by means of five post
hoc pairwise group comparisons: TD vs. ASD, TD vs. ADHD Mph, TD vs. ADHD,
ADHD vs. ADHD Mph, ADHD vs. ASD.
Because analyses were performed for multiple successive intervals there was an
increasing risk of capitalisation on chance. Therefore, effects were only considered
meaningful if three or more consecutive intervals were significant (p < .05) or showed a
trend to significance (p < .10) in combination with a minimally medium effect size (η2 ≥
.06). The chance of finding three consecutive effects with each showing a significance
level of at least p = .10 in a series of 20 intervals (e.g. in case of the ERN) is reduced to
18 * 0.10 * 0.10 * 0.10 = 0.018, which is below the significance criterion of p = .05. In
case of the P2a, which is a rather short-lasting component investigated in only 6
intervals, two consecutive effects with p < .10 were considered to suffice, for the chance
of finding two consecutive effects, each with p = .10, is 5* 0.10 * 0.10 = 0.05. From
periods with ranges of (nearly) significant successive intervals, the minimum and
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maximum F-values (Fmin and Fmax) are reported with the smallest corresponding levels
of significance. For all of the above-mentioned analyses, Greenhouse-Geisser adjusted
p-values and the epsilon correction factor are reported for within subject factors with
more than two levels, with the unadjusted degrees of freedom and F-values. Moreover,
the partial eta squared effect sizes (η2) are reported (Stevens, 2002).
RESULTS
In the following section, only the informative feedback condition will be described,
because this condition provides most information on performance monitoring processes.
A previous report on the present sample of TD children indicated that in the
uninformative condition less performance monitoring activity is present than in the
informative condition, suggesting that the task manipulations were effective (Groen et
al., 2007).
PERFORMANCE MEASURES
ACCURACY
First of all, the groups neither differed in the duration of their individual deadlines
(mean 785 ms, SD 93 ms) nor in their percentage of late responses (mean 6%, SD
5.5%). Trials with late responses were excluded from further analyses. As can be seen
in Figure 2, the overall accuracy on the probabilistic learning task in the informative
condition was higher for the TD group in comparison to all clinical groups, despite
similar deadlines of their response times. This is expressed by an effect of group
(F(3,68) = 3.1, p < .05, η2 = .12) and significant contrasts of all clinical groups with the
TD group (TD vs. ADHD: p < .01; TD vs. ADHD Mph: p < .05; TD vs. ASD: p < .05).
All groups increased in accuracy as the learning task progressed but the learning rate,
i.e. the steepness of the learning curves, did not differ between groups. This is expressed
by a main effect of quartile (F(3,204) = 202.2, p < .001, η2 = .75) and the absence of an
interaction of quartile with group. In Figure 2 this can be observed as an increase in
accuracy across quartiles.
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FIGURE 2. Performance measures for four successive task sections. From top to bottom, the
percentage of accurate responses, mean reaction time (RT) and individual standard deviations (SDs) of RTs in the informative condition are depicted.
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REACTION TIMES
Within the informative condition the groups did not differ in their mean RT for correct
trials and none of the groups showed a learning effect for these RTs as the learning task
progressed (see Figure 2). All groups, however, showed a decrease in RT variability as
the task progressed, which is expressed by a main effect of quartile for the individual
SDs of RTs (F(3,204) = 68.2, p < .001, η2 = .50) and absence of an interaction with
group. In Figure 2, this can be seen as a decrease in the magnitude of the individual SD
of RTs across quartiles. Overall, however, the medication-free ADHD group was more
variable in their correct RTs than the TD group (see Figure 2). This is expressed by a
main effect of group for the individual SDs of RTs (F(3,68) = 3.3, p < .05, η2 = .13),
(nearly) significant contrasts of all groups with the medication-free ADHD group
(ADHD vs. TD: p < .01; ADHD vs. ADHD Mph: p < .05; ADHD vs. ASD: p < .10),
and absence of significant contrasts among the other groups.
In the informative condition the children were faster on incorrect trials than on correct
trials (463 ms vs. 496 ms), except for the medication-free ADHD group (481 ms vs. 487
ms). This is expressed by a significant effect of response type (F(1,68) = 89.5, p < .001,
η2 = .57) and an interaction of group by response type (F(3,68) = 6.9, p < .001, η2 =
.23), with significant contrasts indicating that the difference between incorrect and
correct RTs was smaller in the medication-free ADHD group compared to the other
groups (ADHD vs. TD: p < .001; ADHD vs. ADHD Mph: p < .01; ADHD vs. ASD: p <
.01). The other groups did not differ.
ERPS
NUMBER OF TRIALS IN THE ERP-ANALYSES
When measuring EEG in children, it is more difficult than in adults to obtain ERPs that
are free from artefacts resulting from head movements and eye movements (De Boer,
Scott, & Nelson, 2005). This holds to an even greater extent for children suffering from
ADHD. In some children not enough artefact-free error trials could be obtained in the
second task half, due to low error rates in combination with high artefact frequencies.
This explains the deviant degrees of freedom in some comparisons.
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RESPONSE-LOCKED POTENTIALS
ERN (-300 MS TO 100 MS)
Within the ERN period an overall effect of response type was present at Fz from –120
to 80 ms (Fmin(1,66) = 10.3, p < .01, η2 = .14; Fmax(1,66) = 70.7, p < .001, η2 = .52) and
at Cz from –180 to 80 ms (Fmin(1,66) = 6.9, p < .05, η2 = .10; Fmax(1,66) = 113.7, p <
.001, η2 = .63). The amplitude of the ERN differed between groups at Fz only. This is
expressed by interactions of response type by group from –40 to 80 ms at Fz with
medium to large effect size (Fmin(3,66) = 2.5, p < .10, η2 = .10; Fmax(3,66) = 3.8, p < .05,
η2 = .15) and absence of such interactions at Cz. Post hoc pairwise group comparisons
are summarised in Table 3. As can be seen in Figure 3, both the Mph-treated and
medication-free ADHD group showed smaller ERN amplitudes than the TD group. The
ASD group did not differ from the TD group in ERN amplitude, but could be
differentiated from the medication-free ADHD group with medium to large effect size.
In Figure 4a the mean ERN amplitudes, separated for task section, are given for each
group.
ERN AND LEARNING (-300 MS TO 100 MS)
The ERN amplitude at Fz differed between the first and second section, which is
reflected by an interactions between response type and section from –40 to 100 ms
(Fmin(1,66) = 4.6, p < .05, η2 = .07; Fmax(1,66) = 19.7, p < .001, η2 = .23). However, this
learning effect differed between groups, which is reflected by interactions of response
type by section by group from –40 to 100 ms with medium to large effect sizes
(Fmin(3,66) = 2.5, p < .10, η2 = .10; Fmax(3,66) = 4.1, p < .01, η2 = .16). Post hoc
pairwise group comparisons are summarised in Table 3, showing that both the Mph-
treated ADHD group and ASD group differ from the TD group in their learning effect
on the ERN. As can be seen in Figure 3, the ERN amplitude is larger in the second than
the first section for the TD group and it appears to be smaller in the clinical groups.
Analyses on the individual group level revealed that both the ASD and medication-free
ADHD group show an increase in ERN amplitude with learning, but that these effects
lasted shorter than in the TD group, while the Mph-treated ADHD group did not show a
significant learning effect. There were interactions of response type by section in the
ASD group from 0 to 80 ms (Fmin(1,19) = 4.4, p < .10, η2 = .20; Fmax(1,19) = 10.8, p <
.01, η2 = .38), in the medication-free ADHD group from 20 to 80 ms (Fmin(1,16) = 4.4, p
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85
< .10, η2 = .23; Fmax(1,16) = 6.7, p < .05, η2 = .31), in the TD group from -100 to 60 ms
(Fmin (1, 17) = 6.5, p < .05, η2 = .28; Fmax (1, 17) = 14.4, p < .001, η2 = .46) and such
interactions were absent in the Mph-treated ADHD group. In Figure 4a the mean ERN
amplitudes, separated for task section, are given for each group.
TABLE 3. Post hoc pairwise group comparisons among the experimental groups for the response-locked
ERP components.
interval (ms) df F p η2 interval (ms) df F p η2
TD vs. ADHD min 1,32 3.0 .09 .09 min ns
max 1,32 4.4 < .05 .12 max ns
TD vs. ADHD Mph min 1,33 3.6 < .05 .10 min 1,33 4.1 .05 .11
max 1,33 7.1 < .05 .18 max 1,33 7.1 < .05 .18
TD vs. ASD min ns min 1,35 3.2 .08 .08
max ns max 1,35 6.1 < .05 .18
ADHD vs. ADHD Mph min ns min ns
max ns max ns
ADHD vs. ASD -160 - 60 min 1,33 2.6 .10 .07 min ns
max 1,33 5.5 < .05 .14 max ns
interval (ms) df F p η2 interval (ms) df F p η2
TD vs. ADHD min 1,32 5.9 < .05 .16 min 1,32 4.7 < .05 .13
max 1,32 8.4 < .01 .21 max 1,32 5.8 < .05 .15
TD vs. ADHD Mph min ns min ns
max ns max ns
TD vs. ASD min ns min ns
max ns max ns
ADHD vs. ADHD Mph min ns min 1,31 3.6 .07 .11
max ns max 1,31 9.3 < .01 .23
ADHD vs. ASD min ns min 1,33 3.3 .08 .09
max ns max 1,33 6.3 < .05 .16
Response type*section*group
Fz: ERN learning effectFz: ERN amplitudeResponse type*group
-100 - 20
-60 - 60
150 - 400
-100 - 20
-40 - 40
Response type*group
Pz: Pe learning effectResponse type*section*group
Pz: Pe amplitude
100 - 400
150 - 250
150 - 250
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FIGURE 3. Response-locked ERPs. ERP waveforms time-locked to the response (0 ms) are depicted
at Fz, Cz and Pz for the informative condition. For both the first and second section of the task separate waveforms are shown for correct and incorrect responses.
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FIGURE 4. Bar charts of mean ERP amplitudes. Mean amplitudes and standard deviations for the
response- and feedback-locked ERPs, separated for the first and second task section, are depicted. A and B depict mean amplitude differences of incorrect minus correct responses of the ERN and Pe
respectively. C depicts mean absolute amplitudes of the P2a. D depicts mean amplitude differences of
negative minus positive feedback of the late positivity. E and F depict mean absolute amplitudes of the
prefeedback SPN for positive and negative feedback respectively.
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Pe (100 ms to 800 ms)
Within the Pe period a main effect of response type was present at Pz ranging from 100
to 650 ms (Fmin(1,66) = 14.0, p < .001, η2 = .18; Fmax(1,66) = 370.5, p < .001, η2 = .85).
Although the overall interactions of response type and group showed only a trend to
significance, effect sizes in the interval of 150 to 400 ms were medium (Fmin(3,66) =
1.8, p < .10, η2 = .08; Fmax(3,66) = 2.6, p < .10, η2 = .11). Post hoc pairwise group
comparisons for the Pe amplitude at Pz, as summarised in Table 3, confirmed the
impression from Figure 3 that the medication-free ADHD group showed a decreased Pe
amplitude compared to the TD group. The Mph-treated ADHD group and ASD group
did not differ significantly from the medication-free ADHD group (p-values > .05), but
these group differences approached significance showing medium effect sizes. In Figure
4b the mean Pe amplitudes, separated for task section, are given for each group.
PE AND LEARNING (100 MS TO 800 MS)
The effect of response type differed between the first and second section, which is
reflected by significant interactions of response type and section at Pz ranging from 100
to 550 ms (Fmin(1,66) = 8.7, p < .05, η2 = .12; Fmax(1,66) = 60.9, p < .001, η2 = .48). As
can be seen in Figure 3, the Pe is larger in the second section than in the first section but
this learning effect is found substantially smaller and of later appearance for the
medication-free ADHD group than for the other groups. This finding is expressed by
overall significant response type by section by group interactions ranging from 150 to
250 ms (Fmin(3,66) = 3.2, p < .05, η2 = .12; Fmax(3,66) = 3.9, p < .001, η2 = .13). Post
hoc pairwise group comparisons for the section by response type interaction, as
summarised in Table 3, revealed that the medication-free ADHD group differed
significantly from the TD group. The medication-free ADHD group, moreover, differed
(nearly) significantly from the ASD and Mph-treated ADHD group with medium to
large effect sizes. In Figure 4b the mean Pe amplitudes, separated for task section, are
given for each group.
ERN/ PE AND SYMPTOM PRESENTATION
For investigating possible associations between the ERN and Pe and the behavioural
problems in the clinical groups, correlations were computed between the subscales of
the SCQ as well as the DISC-IV ADHD section and difference values of these ERP-
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components to correct an incorrect responses. This was done separately for autistic
symptoms (SCQ) in the ASD group and ADHD symptoms (DISC-IV) in the ADHD
group. Among the clinical groups, moreover, correlations were computed with the
internalising and externalising scales of the CBCL.
The only nearly significant correlation found, was a positive correlation between the
CBCL internalising scale in the ERN amplitude at Fz (r(52) = .26, p = .06). This means
that with increasing internalising problems, the ERN amplitude increased also.
Inspection of the scatterplot indicated that neither outliers nor eventual subgroups of the
internalising scale could explain this correlation.
FEEDBACK-INDUCED POTENTIALS
P2A (120 MS TO 240 MS)
As can be seen in Figure 5, a positive peak can be observed at Fz and Cz around 185 ms
after feedback onset. This frontal positive component has been described as the P2a or
Frontal Selection Positivity (Potts, Martin, Burton, & Montague, 2006b; Potts, 2004a).
The P2a was larger for negative feedback than for positive feedback, which is reflected
by effects of feedback valence from 160 to 240 ms at Fz with small to large effect sizes
(Fmin(1,59) = 3.0, p < .10, η2 = .05; Fmax(1,59) = 8.6, p < .01, η2 = .13) and large effect
sizes at Cz (Fmin(1,59) = 13.0, p < .001, η2 = .18; Fmax(1,59) = 24.4, p < .001, η2 = .30).
No group differences could be observed for this effect.
TABLE 4. Post hoc pairwise group comparisons among the experimental groups for the feedback-
induced ERP components. 'Min' and 'max' refer to the interval with the minimum F-value and
maximum F-value respectively within the entire (nearly) significant period.
interval (ms) df F p η2 interval (ms) df F p η2
TD vs. ADHD 140 - 200 min 1,29 3.6 .07 .11 min ns
max 1,29 4.5 < .05 .14 max ns
TD vs. ADHD Mph min ns min 1,29 3.4 .07 .11
max ns max 1,29 7.3 < .05 .20
TD vs. ASD min ns min 1,33 3.6 .07 .10
max ns max 1,33 4.4 < .05 .12
ADHD vs. ADHD Mph 140 - 220 min 1,26 5.1 < .05 .16 min ns
max 1,26 12.0 < .01 .32 max ns
ADHD vs. ASD 160 - 200 min 1,30 4.2 < .05 .12 min ns
max 1,30 5.2 < .05 .15 max ns
450 - 950
500 - 700
Fz: P2a learning effectsection*group
Pz: Late positivity amplitudevalence*group
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FIGURE 5. Feedback-induced ERPs. Feedback-induced ERP waveforms time-locked to feedback
onset (0 ms) are depicted at Fz, Cz and Pz for the informative condition. For both the first and second section of the task separate waveforms are shown for positive and negative feedback.
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P2A AND LEARNING (120 MS TO 240 MS)
The P2a amplitude decreased from the first section of the task to the second section to
an equal extent for positive and negative feedback. This is reflected by effects of section
at Fz from 160 to 200 ms with small to medium effect sizes (Fmin(1,59) = 3.1, p < .10, η2
= .05; Fmax(1,59) = 5.0, p < .05, η2 = .08) and at Cz from 160 to 240 ms with medium to
large effect sizes (Fmin(1,59) = 4.4, p < .05, η2 = .07; Fmax(1,59) = 17.5, p < .001, η2 =
.23). Only at Fz did the groups differ in this effect from 160 to 200 ms, which is
reflected by overall interactions of section by group at Fz with medium effect sizes
(Fmin(1,59) = 2.3, p < .10, η2 = .10; Fmax(1,59) = 2.8, p < .05, η2 = .13). Post hoc
pairwise group comparisons, as summarised in Table 4, showed that the medication-free
ADHD group differed from the TD, ASD and Mph-treated ADHD group in their effect
of section. As can be seen in Figure 5, the latter groups all showed a decrease in P2a
amplitude from the first to the second section, while the medication-free group did not.
At Cz no group differences were present. In Figure 4c the mean P2a amplitudes,
separated for task section, are given for each group.
FEEDBACK P3 AND LATE POSITIVITY (200 MS TO 1000 MS)
For all groups, the P2a was followed by a positive component, which showed a
centroparietal maximum and which was larger for negative than for positive feedback;
the feedback P3. This is reflected by significant effects of feedback valence from 200 to
400 ms at Cz and from 200 to 500 ms at Pz (Cz: Fmin(1,59) = 16.1, p < .001, η2 = .21;
Fmax(1,59) = 29.8, p < .001, η2 = .34; Pz: Fmin(1,59) = 4.8, p < .05, η2 = .08; Fmax(1,59) =
34.5, p < .001, η2 = .37). No group differences emerged in the early interval of the
feedback P3, but after 450 ms the groups differed in their effect of valence for a late
positivity (see Figure 5). Although significant valence by group interactions at Pz were
only short-lasting from 600 to 700 ms (Fmin(3,59) = 2.5, p < .10, η2 = .11; Fmax(3,59) =
3.0, p < .05, η2 = .13) and from 850 to 1000 ms (Fmin(3,59) = 3.4, p < .05, η2 = .15;
Fmax(3,59) = 4.3, p < .01, η2 = .18), effect sizes of this interaction ranged from medium
to large for all intervals between 450 to 1000 ms. Post hoc group comparisons, as
summarised in Table 4, showed that the valence effects at Pz in for this late positivity
was (nearly) significantly larger for the TD group compared to the Mph-treated ADHD
group and ASD group. This was, however, not significant for the comparison of the
medication-free children with ADHD and TD children, but effects for this comparison
approached significance with small to medium effect size from 450 ms to 750 ms. In
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Figure 4d the mean amplitude differences of the late positivity, separated for task
section, are given for each group.
FEEDBACK P3, LATE POSITIVITY AND LEARNING (200 MS TO 1000 MS)
Overall, the feedback P3 amplitude decreased from the first to the second section at Cz
and Pz independently of feedback valence, which can be seen in Figure 5. At Cz this
effect was confined to the feedback P3 interval from 200 to 350 ms (Cz: Fmin(1,59) =
4.6, p < .05, η2 = .07; Fmax(1,59) = 8.3, p < .01, η2 = .12), but at Pz this effect lasted to
750 ms after feedback onset (from 250 ms to 750 ms; Fmin(1,59) = 6.6, p < .05, η2 = .10;
Fmax(1,59) = 20.7, p < .001, η2 = .26). There were no significant group differences for
these learning effects.
PREFEEDBACK POTENTIALS
PREFEEDBACK SPN (-800 MS TO 0 MS)
In the interval between stimulus offset and feedback onset a negative slow wave
developed in preparation of negative feedback for all groups (see Figure 6). The
prefeedback potential to positive feedback, however, was less negative than the
potential to negative feedback for the clinical groups and, over centroparietal electrode
positions, it was even positive for the TD group. Overall (for the electrode positions F3/
F4, C3/ C4, P3/ P4), the prefeedback potentials were more negative over the right
hemisphere than over the left, which is expressed by an effect of hemisphere from -400
to 0 ms (Fmin(1,64) = 10.5, p < .05, η2 = .14; Fmax(1,64) = 55.2, p < .001, η2 = .46). The
effect of hemisphere was strongest over centrofrontal electrode positions, which is
expressed by an overall interaction of electrode by hemisphere from -750 to 0 ms
(Fmin(2,128) = 6.1, p < .01, η2 = .09; Fmax(2,128) = 19.3, p < .001, η2 = .23) and
significant long-lasting effects of hemisphere with medium to large effect sizes at F3/F4
and C3/C4 (F3/F4 -500 to 0 ms: Fmin(1,64) = 4.6, p < .05, η2 = .07; Fmax(1,64) = 60.6, p
< .001, η2 = .49; C3/C4 -500 to 0 ms: Fmin(1,64) = 4.2, p < .05, η2 = .06; Fmax(1,64) =
60.1, p < .001, η2 = .48). Yet, there were no significant interactions of hemisphere by
group nor of hemisphere by valence and, therefore, this factor will not be taken into
account in the further analyses.
Analyses at F3/ F4, C3/ C4, P3/ P4 revealed effects of feedback valence with a
maximum at centroparietal electrode positions. This is reflected by significant
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interactions of electrode position by valence for the entire prefeedback period
(Fmin(2,128) = 4.6, p < .05, η2 = .07; Fmax(2,128) = 9.9, p < .001, η2 = .13) with the
effects being largest for parietal electrodes (Fmin(1,64) = 18.2, p < .001, η2 = .22;
Fmax(1,64) = 57.4, p < .001, η2 = .47) and smaller for frontal electrode positions
(Fmin(1,64) = 5.9, p < .05, η2 = .08; Fmax(1,64) = 39.3, p < .001, η2 = .38). As can be seen
in Figure 6, the clinical groups showed smaller differences between positive and
negative feedback than the TD group. These group differences were maximal at P3/P4
and, therefore, further analyses are confined to this electrode pair. There was a
significant valence by group interaction from –650 to 0 ms at P3/P4 (Fmin(3,64) = 3.1, p
< .05, η2 = .13; Fmax(3,64) = 6.5, p < .01, η2 = .23) and no interaction with hemisphere.
Further analyses were conducted for positive and negative feedback separately, because
Figure 6 suggested differential group effects for positive and negative feedback. The
clinical groups showed similar prefeedback amplitudes to negative feedback as the TD
group (see Table 5), whereas the prefeedback potential to positive feedback was more
positive for the TD group compared to all clinical groups (see Table 5). In Figures 4e
and 4f the mean prefeedback SPN amplitudes to positive and negative feedback,
separated for task section, are given for each group.
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FIGURE 6. Prefeedback ERPs. Prefeedback ERP waveforms time-locked to feedback onset (0 ms) are
depicted at P3 and P4 for the informative condition. For both the first and second section of the task
separate waveforms are shown for positive and negative feedback.
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PREFEEDBACK SPN AND LEARNING (-800 MS TO 0 MS)
The groups differed in learning effects on their prefeedback potentials to positive and
negative feedback. This is reflected by a significant valence by section by group
interaction from -700 to -200 ms at P3/P4 (Fmin(3,64) = 2.5, p < .10, η2 = .11; Fmax(3,64)
= 5.1, p < .01, η2 = .19). As can be seen in Figure 6, in the TD and Mph-treated ADHD
group, the prefeedback potential to positive feedback grew more positive as the task
progressed. The TD and Mph-treated ADHD groups differed (nearly) significantly from
the medication-free ADHD group and ASD group for this learning effect, as reflected
by post hoc pairwise group comparisons (see Table 5).
As can be seen in Figure 6, the prefeedback potential to negative feedback of the TD
group grew more negative as the task progressed, but this effect disappeared around 500
ms before feedback onset. The clinical groups did not show such early learning effect to
negative feedback, as is reflected by post hoc pairwise comparisons with the TD group
(see Table 5). Both the Mph-treated ADHD group and the ASD group showed a later
TABLE 5. Post hoc pairwise group comparisons among the experimental groups for the prefeedback
potentials. 'Min' and 'max' refer to the interval with the minimum F-value and maximum F-value respectively within the entire (nearly) significant period.
interval (ms) df F p η2 interval (ms) df F p η2
TD vs. ADHD -500 - 0 min 1,34 4.7 < .05 .12 -750 - 0 min 1,34 2.2 ns .06
max 1,34 9.9 < .01 .23 max 1,34 11.5 < .01 .25
TD vs. ADHD Mph -500 - 0 min 1,33 4.0 .06 .11 min ns
max 1,33 5.8 < .05 .15 max ns
TD vs. ASD -600 - 0 min 1,35 2.6 ns .07 -600 - 0 min 1,35 3.3 .08 .08
max 1,35 4.2 < .05 .11 max 1,35 6.7 < .05 .16
ADHD vs. ADHD Mph min ns -600 - 0 min 1,33 1.8 ns .05
max ns max 1,33 4.5 < .05 .12
ADHD vs. ASD min ns min ns
max ns max ns
interval (ms) df F p η2 interval (ms) df F p η2
TD vs. ADHD min ns min ns
max ns max ns
TD vs. ADHD Mph min ns -750 - -350 min 1,31 2.1 ns .06
max ns max 1,31 7.0 < .05 .19
TD vs. ASD min ns -700 - -250 min 1,35 2.5 ns .07
max ns max 1,35 9.8 < .01 .22
ADHD vs. ADHD Mph min ns -550 - -350 min 1,29 2.0 ns .06
max ns max 1,29 5.2 < .05 .15
ADHD vs. ASD min ns -550 - -250 min 1,33 1.9 ns .06
max ns max 1,33 5.6 < .05 .15
Group Section*group
Group Section*group
Negative feedback: amplitude Negative feedback: learning effect
P3/P4: Prefeedback potentialsPositive feedback: amplitude Positive feedback: learning effect
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learning effect; their prefeedback potential to negative feedback grew less negative with
task progression from about 500 ms before feedback onset. From about -250 ms this
learning effect for negative feedback, however, did no longer differ significantly from
the TD group. The medication-free ADHD group did not show a learning effect for the
prefeedback potential to negative feedback, as is expressed by (nearly) significant post
hoc pairwise comparisons with the Mph-treated ADHD group and the ASD group. In
Figures 4e and 4f the mean prefeedback SPN amplitudes to positive and negative
feedback, separated for task section, are given for each group.
DISCUSSION
RESPONSE MONITORING
Recent psychophysiological and performance studies have suggested performance
monitoring deficiencies in the developmental disorders ADHD and ASD. Although both
children with ADHD and children with ASD performed worse on the probabilistic
learning task than TD children, the ERP data in the present study revealed a response
monitoring deficit in children with ADHD only. This was reflected by decreased ERN
and Pe amplitudes in medication-free children with ADHD compared to age and
intelligence matched TD children and children with ASD. Apart from this, it must be
mentioned that the ERN in the present study showed a peak latency around response
onset, which is much earlier than the usually observed peak latency between 40 and 100
ms in adults (Gehring et al., 1990; Falkenstein et al., 1991). The early peak latency may
be explained by a time delay between electromyografic activity onset in the finger, to
which the ERN may be closely time-locked (Gehring et al., 1990), and the actual
registrated mechanical response. This time delay may be as long as 80 to 131 ms (Burle,
Possamai, Vidal, Bonnet, & Hasbroucq, 2002). However, the effects of response type
emerged as early as 180 ms before the response, which has also been observed in
previous developmental studies (although not explicitly mentioned in the text: Davies et
al., 2004; Santesso et al., 2006). Such early error-related differences may, therefore, be
specific for children and may deserve some more attention in future studies.
Notwithstanding the early occurrence of the ERN, the amplitudes of both the ERN and
Pe suggest that children with ADHD have a deficit in both early error detection and
later error awareness. The finding of an attenuated ERN adds to the rather inconsistent
literature on the size of the ERN amplitude in ADHD. One explanation for these
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inconsistent findings may be the heterogeneity of the investigated ADHD groups in
general, with some patients having more attentional problems, others having more
hyperactive-impulsive problems and still others showing comorbid problems like
disruptive behaviour or internalising problems. As all these symptoms may be related to
distinct neurobiological sources (Sagvolden, Johansen, Aase, & Russell, 2005b), the
outcomes of ERP research may be vulnerable to the composition of the samples,
especially when samples are small. For future studies it is recommended to include
larger samples of children with ADHD, allowing to control for differences in symptom
presentation and comorbid conditions. A decreased response-related ERN in children
with ADHD like in the present study would, however, be in line with the bulk of
neuroimaging studies suggesting that frontostriatal dopamine pathways are
hypofunctional in ADHD (Castellanos & Tannock, 2002b; Durston, 2003b; Bush,
Valera, & Seidman, 2005a; Dickstein, Bannon, Castellanos, & Milham, 2006a). A
disturbance of frontostriatal processes, and a concomitant error processing deficit, may
explain (part of the) self-regulatory problems that children with ADHD experience in
everyday life, such as inconsistent, inaccurate and poorly regulated behaviour as well as
deficits in self-regulated learning.
In contrast to the ERN, the finding of a smaller error-related Pe amplitude with
increased learning in children with ADHD, adds to a more consistent literature and thus
strengthens the suggestion of reduced error awareness in ADHD. Reduced error
awareness may hamper children with ADHD in learning from their mistakes and, on the
longer term, to develop adaptive behaviour. As the Pe amplitude has been found to be
related to post-error slowing in healthy adults (Nieuwenhuis et al., 2001; Hajcak et al.,
2003b), the attenuated Pe amplitude is in line with findings of reduced post error
slowing in children with ADHD (Sergeant & Van der Meere, 1988b; Schachar et al.,
2004a; Wiersema et al., 2005). Equivalent to the P3 (Nieuwenhuis et al., 2005), the Pe
has been suggested to reflect phasic noradrenaline responses from the LC-NE system in
response to errors (Leuthold & Sommer, 1999b; Davies et al., 2001; Overbeek et al.,
2005; O'Connell et al., 2007). In healthy brains, such quick arousal responses from the
LC-NE system increase the state of alertness and sensory information processing
(Berridge & Waterhouse, 2003). Decreased activity of this system, as reflected by an
attenuated Pe amplitude, suggests that children with ADHD do not benefit as much
from their errors as TD children do. An attenuated Pe in ADHD, moreover, agrees with
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the catecholamine hypothesis that next to the dopaminergic system, the NE system is
involved in the pathology of ADHD (Pliszka, 2005).
Interestingly, children with ADHD that took their normal dose of Mph at the time of the
experiment showed a normalised Pe amplitude, which is in agreement with the recent
placebo-controlled study by Jonkman and colleagues (2007). Especially the learning
effect on the Pe was larger for the Mph-treated ADHD group compared to the
medication-free ADHD group, while at the same time the Mph-treated ADHD group
could not be differentiated from the TD. Because of its hypothesised noradrenergic
origin, the normalised Pe in Mph-treated children with ADHD may be explained by the
stimulating effect of Mph on the noradrenaline system. Again agreeing with the study of
Jonkman and colleaugues (2007), Mph did not modulate the ERN amplitude in children
with ADHD. This is contradictory to evidence from adult studies showing that
stimulants like Mph boost the response-locked ERN amplitude (De Bruijn et al., 2004;
De Bruijn et al., 2005). Concludingly, the present data, as well as the data by Jonkman
and colleagues, suggest that Mph improves conscious error processing in ADHD, but
not early error detection. It may be hypothesised that the effect of Mph in children with
ADHD, regarding performance monitoring in particular, is mediated through its
noradrenergic component rather than through its dopaminergic one.
In contrast to the ADHD group, the children with ASD showed no response monitoring
deficits, as neither differences in overall ERN nor in Pe amplitude were found in
comparison to TD children. This finding is in line with the only electrophysiological
study on performance monitoring in ASD by Henderson and colleagues (2006), who
also report an intact ERN in a similar ASD group. In contrast to the Henderson study,
however, the present study found no associations between response monitoring
components and autistic-type symptoms within the ASD group. Although not specific to
ASD, we did find that clinical children scoring high on internalising problems (i.e.
withdrawn behaviour, somatic problems, anxious/ depressive behaviour) show larger
ERN amplitudes. This is in line with several adult studies showing that people
characterized by high negative affect show increased ERN amplitudes (Hajcak,
McDonald, & Simons, 2004). Apart from this relationship, spared internal monitoring in
ASD contrasts with several performance studies suggesting self-monitoring deficits in
ASD (Russell & Jarrold, 1998; Mundy, 2003; Bogte, Flamma, Van der Meere, & Van
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Engeland, 2007). It must be remarked, however, that the present conclusions may not
extend to patients suffering from the full-blown syndrome of autism or Asperger,
because the present study only included children with a sub-threshold form of autism.
FEEDBACK MONITORING
In none of the experimental groups did the feedback stimuli elicit a typical feedback
ERN (for a detailed discussion on the possible causes of this remarkable finding we
refer to an earlier report; Groen et al., 2007). Instead, a frontocentral P2a component
was observed, which has only recently been described to occur in response to feedback
stimuli (Van Meel et al., 2005b; Potts et al., 2006b). In the present study the P2a
amplitude was increased in response to negative feedback compared to positive
feedback and may be interpreted as a general attentional reaction to motivationally
salient stimuli, as this component has repeatedly been found to increase when the task
relevance of stimuli increases (Falkenstein, Hoormann, Hohnsbein, & Kleinsorge,
2003b; Potts, 2004a). Van Meel and colleagues (2005b) also described a generally
increased P2a in response to negative feedback, that, in contrast to our study, was found
to be smaller in children with ADHD compared to TD children. The authors suggested
that the early discrimination or categorisation of motivationally relevant stimuli may be
disturbed in ADHD. The present study could not replicate this finding and hence
confirm such early disturbance of feedback processing in children with ADHD. The
other way around, the medication-free children with ADHD did not show a decrease in
P2a amplitude to negative feedback when learning the task. This suggests that for these
children the negative feedback kept its relevance during the whole task, whereas it
decreased in relevance with task progression for the other groups, i.e. the TD group,
ASD group and the Mph-treated ADHD group.
Moreover, the present results suggest deficits in late external feedback processing in
both children with ADHD and children with ASD. The TD children showed an
increased late positivity (from 450 ms after feedback onset) to negative opposed to
positive feedback, which was attenuated in the ASD and Mph-treated ADHD group.
The comparison of the medication-free ADHD group and TD group did not reveal
significant differences for this positivity, but in the investigated ERP period some group
interactions approached significance and showed medium effect sizes. The direction of
these nonsignificant effects is in agreement with the study by Van Meel and colleagues
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(2005), who also reported an attenuated late positivity to negative feedback in children
with ADHD. We hypothesise that the observed late positivity is similar to the Late
Positive Potential (LPP). The LPP is elicited by highly arousing pleasant and unpleasant
pictures and is thought to reflect increased attention to affective-motivational stimuli
(Schupp et al., 2000a; Cuthbert, Schupp, Bradley, Birbaumer, & Lang, 2000a; Hajcak et
al., 2006) and may, therefore, be the affective counterpart of the traditional P3. The LPP
has been hypothesised to index perceptual processing in the visual cortex, that is
facilitated or amplified by amygdala-activity (Bradley et al., 2003b; Hajcak et al.,
2006). Decreased LPP amplitudes in children with ADHD and ASD may reflect
diminished processing of negative feedback stimuli as a result of lower affective
responsiveness to these stimuli. The clinical children in the present study may not
benefit from the affective value of negative feedback like the TD children do, i.e. they
may suffer from decreased ‘motivated attention’ (Vuilleumier, 2005).
Different from the LPP, the ‘traditional’ P3 amplitude to the feedback stimuli (which in
the present study ranges from 200 to 450 ms after feedback onset) did not discriminate
the children with ADHD and ASD from the TD children. All groups showed an
enlarged feedback P3 to negative feedback compared to positive feedback, which may
be the reflection of updating task-rules from long-term memory in response to error
feedback (Donchin & Coles, 1988). Our finding of an intact feedback-related P3 in
medication-free children with ADHD as well as a decreased response-related Pe appears
contradictory to recent studies proposing that both components have a similar
neurobiological source (see for an overview: Overbeek et al., 2005). Further research
should investigate the functional and neurobiological relationship of the response-
locked Pe and feedback-locked P3.
FEEDBACK ANTICIPATION
To complete our search for performance monitoring deficits in ADHD and children
with ASD we also investigated anticipatory processes before feedback onset by
investigating prefeedback potentials. Different from our previous report (Groen et al.,
2007), analyses of the prefeedback SPN in the present study were extended to the entire
prefeedback interval, because group differences appeared earlier than in the originally
chosen interval just before feedback onset. Overall, the prefeedback SPN amplitude to
negative feedback did not differ between groups, suggesting that the clinical groups
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have no fundamental problems in anticipating negative feedback. This finding in
medication-free children with ADHD contrasts with the study by Van Meel and
colleagues (in preparation), who found diminished prefeedback SPN amplitudes to
negative feedback in their sample. In contrast to the TD children, the medication-free
children with ADHD in the present study did not show a decrease in prefeedback SPN
amplitude with task progression, suggesting that they did not learn to predict the
negative feedback and that the negative feedback kept its relevance during the whole
task. This finding is in accordance with the diminished learning effects on both the
response-locked Pe and the feedback-locked P2a in this group. It may be speculated that
the diminished Pe reflects why the negative feedback remains relevant to them:
diminished conscious error processing at the time of the response makes it harder to
predict the feedback outcome. This reasoning is also compatible with the findings in the
Mph-treated children with ADHD; together with the ‘normalised’ Pe amplitude, they
also showed ’normalised’ learning effects on the P2a and prefeedback SPN. The ability
of the Mph-treated children with ADHD to predict negative feedback and adjust
anticipation may be related to the ‘normalising’ effect of Mph on the Pe amplitude.
Regarding the prefeedback potential to positive feedback, all clinical groups showed
less positive, and even negative, prefeedback SPN amplitudes compared to the TD
children. As a more negative amplitude of this potential has been related to increased
anticipation of upcoming feedback stimuli (Böcker et al., 2001; Bastiaansen et al.,
2002), a negative prefeedback SPN in the clinical groups, opposed to the positive
potential in the TD group, suggests that upcoming positive feedback is more relevant to
the clinical than to the TD children. One explanation may be that anticipation to positive
feedback is less necessary for the TD children, because they are more confident about
pressing the correct key. This is in correspondence with their higher level of accuracy
on the task in comparison to the clinical groups. When considering the effects of task
progression, the fact that only the TD and Mph-treated ADHD group showed a more
positive prefeedback potential suggests that for these groups the upcoming positive
feedback became less relevant as the task had been learned. The medication-free ADHD
group and the ASD group did not show this learning effect, suggesting that for these
groups positive feedback kept its relevance during the whole task.
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In conclusion, the findings on the prefeedback SPN suggest that both children with
ADHD and children with ASD do anticipate upcoming positive and negative feedback.
In case of positive feedback, the medication-free ADHD group and ASD group may
even attach more value to the upcoming feedback than the TD children, particularly as
learning progresses throughout the task. The absence of learning effects on the
prefeedback SPN to both positive and negative feedback in the medication-free ADHD
group fits with the decreased learning effects on the response-locked and feedback-
induced ERPs. We are rather reserved to draw conclusions about the underlying
neurobiological origins of the prefeedback SPN in the present study, because especially
in the TD group, the appearance of this slow wave deviates from what has been
described in adult literature, i.e. the timing of the effects and its polarity.
CONCLUSIONS
Both the Mph-treated and medication-free ADHD group as well as the ASD group
achieved a lower accuracy level than the TD group on the probabilistic learning task.
The ERPs, however, revealed that the three groups could be differentiated on a set of
component processes of error and feedback processing. In contrast to the TD children
and children with ASD, the medication-free children with ADHD are suggested having
a deficit in shifting from feedback monitoring to response monitoring while learning by
performance feedback. This is reflected by decreased response monitoring components
(ERN and Pe) and diminished learning effects on the feedback-related components
(prefeedback SPN, P2a). Increased effects of learning on the ERPs in the Mph-treated
ADHD group compared to the medication-free ADHD group provide some evidence for
a modulating effect of Mph on response monitoring (Pe), feedback anticipation
(prefeedback SPN) and feedback processing (P2a) in children with ADHD. The ASD
group showed no deficits in response monitoring (ERN and Pe) and no deviating
learning effects on negative feedback anticipation (prefeedback SPN) and early
feedback processing (P2a). However, the ASD group as well as the Mph-treated ADHD
group showed aberrant late feedback processing (LPP), suggesting diminished affective
processing of external error information, i.e. ‘motivated attention’ in both disorders.
Although the ERP figures and analyses also suggested such deficit in medication-free
ADHD children, these effects did not reach statistical significance. Overall, the present
study shows that error and feedback-related ERPs are a useful tool for (1) dissociating
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ADHD from ASD and (2) elucidating medication effects in ADHD on specific aspects
of EFs.
ACKNOWLEDGEMENTS
This work was supported by grants from the Protestants Christelijke Kinderuitzending
(PCK). The authors thank the following people for their help in data collection: Diana
de Boer, Johannes Boerma, Harma Moorlag and Klaas van der Lingen.
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CHAPTER 4
EVOKED HEART RATE ANALYSES OF ERROR AND FEEDBACK
SENSITIVITY IN ADHD AND AUTISTIC SPECTRUM DISORDER
YVONNE GROEN
LAMBERTUS J.M. MULDER
ALBERTUS A. WIJERS
RUUD B. MINDERAA
MONIKA ALTHAUS
A revised version of the study described in this chapter has been published in Biological
Psychology, entitled:
Methylphenidate improves diminished error and feedback sensitivity in ADHD: An
Evoked Heart Rate analysis, 82, 45-53, 2009.
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ABSTRACT
Attention Deficit Hyperactivity Disorder (ADHD) and Autistic Spectrum Disorder
(ASD) are two major developmental disorders that have both been related to a
decreased sensitivity to errors and feedback. Children with ADHD on and off
Methylphenidate (Mph), children with ASD and typically developing (TD) children
performed a selective attention task with three feedback conditions: reward, punishment
and no feedback. Evoked Heart Rate (EHR) responses were computed for correct and
error trials. All groups performed more efficient with performance feedback than
without. EHR analyses, however, showed that enhanced EHR decelerations on error
trials seen in TD children were absent in the medication-free ADHD group for all
feedback conditions. The Mph-treated ADHD group showed ‘normalised’ EHR
decelerations on error trials in the punishment and no feedback condition. The ASD
group neither differed significantly from the TD group nor from the medication-free
ADHD group in the EHR responses, but effects showed medium effect sizes.
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INTRODUCTION
OUTLINE
Two major developmental disorders, Attention Deficit Hyperactivity Disorder (ADHD)
and Autistic Spectrum Disorder (ASD), have been associated with executive
functioning deficits (Barkley, 1997; Pennington & Ozonoff, 1996; Russell, 1997). One
important executive function for regulating goal-directed behaviour is the continuous
monitoring of performance (Stuss, 1992) and, successively, to make use of external cues
for appropriately adjusting performance, such as performance feedback, reward and
punishment. Both ADHD and ASD have been associated with a diminished capacity of
monitoring their behaviour and feedback from their environment. In the present study,
children with these disorders, who were matched for age and intelligence, are directly
compared on their capacity to monitor different types of feedback by investigating their
performance as well as their autonomic responsiveness to feedback. Autonomic
measures may provide insight into feedback sensitivity of these children, that cannot be
obtained by performance measures alone.
ADHD, ASD AND FEEDBACK SENSITIVITY
Children with ADHD are characterised by symptoms of inattentiveness, impulsivity and
hyperactivity (American Psychiatric Association, 2000). Several explanatory theories of
ADHD have proposed that the core symptoms of this disorder are the result of a
motivational deficit, which is expressed by an aberrant sensitivity to reinforcing stimuli.
The nature of this abnormal sensitivity is, however, unclear. For instance, ADHD has
been associated with (1) a preference for small immediate reward over large delayed
reward (Rapport et al., 1986; Sonuga-Barke, Taylor, Sembi, & Smith, 1992; Sagvolden
et al., 2005a), (2) an increased sensitivity to punishing feedback (Carlson et al., 2000;
Carlson & Tamm, 2000) and (3) a generally elevated threshold for the reinforcing
effects of both rewarding and punishing feedback (Haenlein & Caul, 1987; Slusarek,
Velling, Bunk, & Eggers, 2001). A recent review on the impact of reinforcement
contingencies on ADHD by Luman and colleagues (2005) concluded that children with
ADHD have problems in keeping optimal performance when they have to rely solely on
their intrinsic motivation (Douglas & Parry, 1994; Sergeant, 2000). Across studies,
appropriate reinforcement contingencies were found to have a positive effect on task
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performance and self-reported motivation in children with ADHD and, regarding task
performance, there was some evidence that this positive effect was even more
prominent in these children compared to typically developing (TD) children (Luman et
al., 2005).
Somewhat contradictory to the findings on the behavioural level, electrocortical (Groen
et al., 2008; Van Meel et al., 2005b) and cardiovascular studies (Crone, Jennings, &
Van der Molen, 2003b; Luman et al., 2008; Iaboni, Douglas, & Ditto, 1997; Luman et
al., 2007) suggest that children with ADHD process feedback to a lesser extent than TD
children. More specifically, two EEG Event-Related Potential (ERP) studies have found
that children with ADHD show a decreased late positive amplitude to error feedback
than TD children (Groen et al., 2008; Van Meel et al., 2005b), which is hypothesised to
reflect diminished affective processing of these stimuli (Groen et al., 2008). Three
studies investigating evoked heart rate (EHR) responses to feedback stimuli, found that
the heart rate of children with ADHD is less responsive to feedback stimuli compared to
TD children (Crone et al., 2003b; Luman et al., 2008; Luman et al., 2007). Crone and
colleagues (2003b), moreover, found that the EHR of children with ADHD
discriminates less between rewarding and punishing feedback stimuli compared to TD
children. Lastly, Iaboni and colleagues (1997) investigated heart rate and skin
conductance responsiveness in children with ADHD across task blocks of reward and
non reward (extinction). In comparison to TD children, the children with ADHD
showed faster heart rate habituation when rewarded and failed to show a skin
conductance response when non reward was introduced. Overall, these findings suggest
that ADHD is associated with a decreased psychophysiological responsiveness to
performance feedback.
Reduced physiological responsiveness to feedback fits into an influential theoretical
account of ADHD introduced by Quay (Quay, 1988a; Quay, 1988b; Quay, 1997). Quay
explained ADHD behaviour in terms of Gray’s (1985; 1987) psychobiological theory,
suggesting that three separate but interactive brain systems motivate behaviour. Two of
these are particularly relevant for ADHD: the Behavioural Inhibition System (BIS) and
the Behavioural Activation System (BAS). The BIS is an aversive motivational system
responsible for the inhibition of ongoing behaviour in situations that involve aversive
cues such as punishment and reward extinction. The BAS on the other hand is an
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appetitive motivational system and is responsible for activating behaviour associated
with reward or active avoidance of punishment. The neural basis of the BIS primarily
involves noradrenergic and serotonergic cerebral pathways, whereas dopaminergic
pathways are at the basis of the BAS (Gray, 1985; 1987). Quay argues that children
with ADHD have an underactive BIS, which may make them less sensitive to
punishment and non reward, resulting in more approach behaviour to these type of
events. As error feedback can be regarded as an aversive cue, reduced electrocortical
(Groen et al., 2008; Van Meel et al., 2005b) and EHR responses to error feedback
(Crone et al., 2003b) support the hypothesis of an underactive BIS in ADHD. The
reported faster habituation to reward by Iaboni and colleagues (1997), however,
suggests that children with ADHD also suffer from an underactive BAS.
Children suffering from Autistic Spectrum Disorders (ASD) are hampered in their
social and communicative abilities and show stereotype interests and behaviours
(American Psychiatric Association, 2000). With regard to ASD no motivational theories
like in ADHD have been proposed, but some studies have questioned the feedback
sensitivity of children with ASD (Dawson et al., 2002; Garretson et al., 1990; Ingersoll
et al., 2003). Studies using performance measures agree on the finding that children
with ASD are less sensitive to the rewarding value of social stimuli. For example,
smiling or words of appreciation optimise task performance in TD children, but not in
children with an ASD (Garretson et al., 1990). Other studies have suggested a
diminished sensitivity to non-social reinforcement and feedback in ASD as well
(Althaus et al., 1996; Dawson et al., 2001). A recent ERP study on feedback processing
of our own group (Groen et al., 2008) revealed that children with ASD process
performance feedback to a lesser extent, similarly to the sample of children with
ADHD. In continuation of that study, the autonomic responsiveness of both children
with ADHD and ASD to feedback will be investigated in the present study.
EVOKED HEART RATE RESPONSES TO ERRORS AND FEEDBACK
It is well-known that heart rate is responsive to the processing of emotional information.
Since the early seventies heart rate is also known to show beat-to-beat changes in
reaction to cognitive information processing (Lacey & Lacey, 1974). Since then it has
been firmly established that heart rate decelerates briefly, i.e. the time between
successive heartbeats (Inter Beat Intervals: IBIs) increases, when subjects (1) prepare a
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response or anticipate an imperative stimulus, (2) must inhibit or select (competing)
responses and (3) process relevant feedback (see for a review: Jennings & Van der
Molen, 2002). Jennings and Van der Molen (2002) propose that the transient heart rate
decelerations (also referred to as ‘postponed heart rate accelerations’), generally
accompany the inhibition of cognitive information processes. This central inhibition
allows for an increase in cognitive control and enhanced task focus. Error feedback is
the pre-eminent signal that ongoing behaviour is no longer appropriate and that
increased cognitive control is needed: ongoing processing must be inhibited and
alternative strategies must be selected and executed. In agreement with the central
inhibition theory of Jennings and Van der Molen (2002) it has consistently been found
that heart rate briefly decelerates both when subjects commit error responses (Crone et
al., 2006; Hajcak et al., 2003b) and when they are confronted with error feedback
(Crone et al., 2003c; Somsen et al., 2000; Van der Veen et al., 2004). As heart rate
decelerations to errors and feedback have been proposed to go along with the central
inhibition of information processing, they may be the reflection of increased BIS
activity to aversive events in terms of the model by Gray (1985; 1987).
Previous developmental studies have shown that EHR measures are reliable indices of
feedback processing in children (Crone et al., 2004; Crone et al., 2006; Groen et al.,
2007). Just like in adults (Crone et al., 2003c; Van der Veen et al., 2004), children’s
heart rate decelerates to a larger extent after error feedback than after positive feedback
(Crone et al., 2004; Crone et al., 2006; Groen et al., 2007). Numerous studies have
investigated the antecedent conditions of these error and feedback-related decelerations.
Firstly, a series of studies has demonstrated that heart rate deceleration is enhanced in
informative feedback conditions compared to uninformative feedback conditions (Crone
et al., 2003c; Crone et al., 2004; Crone, Bunge, de Klerk, & Van der Molen, 2005;
Groen et al., 2007). Secondly, heart rate deceleration to error feedback is sensitive to the
degree in which feedback is utilised to adjust performance. For instance, a trial-to-trial
analysis by Van der Veen (2004) revealed that heart rate deceleration is enhanced on
error trials that are appropriately adjusted on the next trial compared to error trials that
are not adjusted. Moreover, in a study by Somsen and colleagues (2000) good
performers showed larger heart rate decelerations to error feedback than bad performers.
Thirdly, heart rate deceleration is dependent on the violation of expectations; when the
feedback outcome is unexpected the deceleration is larger, independent of whether its
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outcome is positive or negative (Crone et al., 2003c; Crone et al., 2004; Crone et al.,
2005; Somsen et al., 2000).
THE PRESENT STUDY
In the present study a selective attention task with hierarchical stimuli was adopted. For
investigating the impact of different reinforcement approaches children with ADHD on
and off Methylphenidate, children with ASD and TD children, three feedback
conditions were used: a no feedback condition and a reward and punishment condition.
The children earned money in all three conditions, but in the no feedback condition they
did not receive any form of feedback on their performance. This manipulation allows
for investigating whether the children have difficulties in their intrinsic motivation when
not provided with feedback on their performance. In the reward condition emphasis was
put on gains (money was earned for correct responses), while in the punishment
condition emphasis was put on losses (money was lost for error responses). These
manipulations allow for investigating differential effects of positive and negative
reinforcement strategies. For investigating both the behavioural and autonomic impact
of the different feedback conditions, task performance (accuracy and reaction time
measures) as well as beat-to-beat EHR responses to feedback stimuli were measured.
Because children with ADHD have been proposed to have a deficit in intrinsic
motivation, they are expected to performance worse in especially the no feedback
condition. It is, moreover, expected that both children with ADHD and children with
ASD are autonomically less responsive to feedback stimuli than TD children, which
may be expressed by generally attenuated EHR responses to performance feedback.
Because children with ADHD have been proposed to suffer from an underactive BIS,
they are expected to show less pronounced EHR decelerations to error feedback
(indicating the loss of money) in particular when compared to TD children and children
with ASD. This hypothetical deficit may be normalised in Mph-treated children with
ADHD, because Mph may increase BIS activity through its stimulating effect on the
noradrenergic system in the brain (Pliszka, 2005).
Moreover, although no performance feedback is provided in the no feedback
condition, error trials may still elicit EHR deceleration, because heart rate is also known
to decelerate in response to self-monitored errors (Crone et al., 2006; Groen et al., 2007;
Hajcak et al., 2003b). The EHR of TD children may thus be expected to discriminate
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between correct and error trials in the no feedback condition. The children with ASD
are expected to show a pattern similar to the TD children, because previous ERP studies
could not indicate deficits in the self-monitoring of errors in these children (Groen et al.,
2008; Henderson et al., 2006). In contrary, the EHR of children with ADHD may
discriminate to a lesser extent between correct and error trials in this condition, as ERP
(Groen et al., 2008; Jonkman et al., 2007; Van Meel et al., 2007) and post error slowing
(Schachar et al., 2004b; Sergeant & Van der Meere, 1988a; Wiersema et al., 2005)
studies do suggest deficits in the self-monitoring of errors. Mph-treated children with
ADHD may not show this hypothesised deficit, as ERP (Groen et al., 2008; Jonkman et
al., 2007) and post error slowing (De Sonneville et al., 1994b; Krusch et al., 1996a)
studies have indicated that Mph improves the self-monitoring of errors.
METHODS
SUBJECTS
This study included 68 children who were assigned to four experimental groups: a
control group with TD children (n = 18), a medication-free ADHD group (n = 16), a
Methylphenidate (Mph)-treated ADHD group (n = 16) and an ASD group (n = 18).
Written informed consent was obtained from all parents and all 12-year-old children
assented to the study. The study was approved by the Medical Ethical Committee of the
University Medical Center Groningen.
The TD children were recruited from primary schools in the city of Groningen and by
advertisement in the newsletter of the University Medical Centre in Groningen
(UMCG). For assessing the presence of a wide range of childhood psychopathology, the
Child Behavioural Checklist was filled out by the parents of all children (CBCL:
Achenbach & Rescorla, 2001). None of the TD children scored within the clinical range
of the total problem scale of the CBCL (see Table 1).
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TABLE 1 Group characteristics.
Measures χ2
Handedness
(left/ambidexter/right)ns _
Gender (male/female) ns _
Mph intake in past year
(on/off)<.001 TD,ASD***<ADHD<ADHD Mph*
Measures Mean SD M SD M SD M SD pAge (years) 11,4 0,9 11,4 0,9 11,4 0,8 11,7 0,8 ns _
Total IQ 103 9,5 103 10,0 98 11,3 100 13,4 ns _
Verbal IQ 107 10,4 103 12,4 100 13,2 102 10,1 ns _
Performal IQ 97 12,8 103 11 96 12,7 98 16,9 ns _
Social Communication Questionnaire (SCQ)Total _ 20,2 4,1 7,1 4,4 4,9 1,7 <.001 ADHD Mph, ADHD< ASD***
Social interaction _ 8,4 2,8 2,8 2,2 0,9 1,1 <.001ADHD < ADHD Mph*;
ADHD, ADHD Mph < ASD***
Communication _ 6,6 1,9 2,7 1,8 2,6 1,2 <.001 ADHD Mph, ADHD< ASD***
Repetitive and Stereotype
Behaviour_ 4,2 1,5 1,1 1,5 1,1 0,9 <.001 ADHD Mph, ADHD< ASD***
Diagnostic Interview Schedule for Children (DISC) ADHD sectionAttentional Problems _ 7,3 5,0 12,6 5,1 12,9 3,5 <.001 ASD**< ADHD Mph, ADHD
Hyperactive Impulsive
Behaviour_ 3,1 3,6 13,3 3,0 12,9 5,2 <.001 ASD***< ADHD Mph, ADHD
Child Behavioural Checklist (CBCL)
Total Problems 14,8 11,5 52,5 24,0 47,8 26,3 59,8 21,3 <.001TD***< ADHD Mph, ADHD,
ASD
Ratio: Clinical/ Not clinical 0/18 9/9 6/10 10/6
Internalizing Problems 4,3 4,4 15,3 8,7 8,7 8,0 11,4 8,5 <.01 TD*< ADHD, ASD
Ratio: Clinical/ Not clinical 1/18 11/7 2/14 7/9
Externalizing Problems 3,5 3,5 10,2 10,4 13,3 7,4 17,6 7,2 <.001 TD*< ADHD Mph, ADHD
Ratio: Clinical/ Not clinical 0/18 4/14 5/11 8/8
Conners Teacher Rating Scale- Revised (CTRS-R)Oppositional _ 49,2 6,0 59,3 10,0 58,9 13,9 <.01 ASD*< ADHD Mph, ADHD
Inattentive/Cognitive Problems_ 53,3 11,0 55,0 8,1 57,3 13,6 ns
Hyperactivity-Impulsivity _ 52,8 6,3 66,3 9,4 64,2 14,4 <.01 ASD**< ADHD Mph, ADHD
Anxious/Shy _ 67,6 13,4 62,8 13,5 64,8 11,4 ns
Perfectionism _ 54,9 11,8 56,1 12,1 53,3 9,1 ns
Social Problems _ 69,2 15,0 58,3 9,0 59,2 15,4 <.05 ADHD Mph^< ASD
ADHD index _ 55,6 10,9 63,8 7,7 63,7 14,9 .07
= p < .10; * = p < .05; ** = p < .01; *** = p < .001
0/18
14/4 15/1 14/2
1/17 15/1 12/4
12/6
Ratio Ratio Ratio
0/4/14 0/3/15 0/1/15 0/2/14
Ratio
Bonferroni corrected
post hoc analysesn = 18 n = 18 n = 16 n = 16TD ASD ADHD Mph ADHD
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ADHD and ASD had been diagnosed by independent experienced child psychiatrists of
our Department of Child- and Adolescent Psychiatry, according to the diagnostic
criteria of the DSM-IV-TR (American Psychiatric Association, 2000). Regarding
ADHD, only children with the combined type were included, which required
pervasiveness (at home and at school) of both inattentive symptoms and hyperactive-
impulsive symptoms observed during at least six months. Some symptoms causing
impairment were present before age 7 years. The diagnosis ASD required serious and
pervasive disabilities in the development of social and communicative skills, and
presence of stereotype interests and behaviour. These symptoms, however, did not meet
the criteria for a full-blown Autistic or Asperger Disorder because of late age onset,
atypical symptomatology, or subthreshold symptomatology, or all of these and were
consequently diagnosed as having Pervasive Developmental Disorder Not Otherwise
Specified (PDDNOS). After the diagnosis, ADHD and ASD symptoms were
additionally assessed by standardised questionnaires.
Of the 32 children with ADHD, 28 children were Mph responders, who all had taken
this drug during the main part of the year preceding the experiment (except for one boy
who had started the treatment two months before). The four children with ADHD that
did not yet use medication for their ADHD-symptoms were directly assigned to the
medication-free condition. Then, the Mph responders were randomly assigned to the
Mph-treated (n = 16) or medication-free condition (n = 16). Those assigned to the
medication-free condition were asked to discontinue Mph-intake for at least 17 hours
before they entered the experiment. These children did not show fewer ADHD
symptoms (see description below). All children in the ASD group were free from
medication at the time of the experiment.
Table 1 shows a summary of the group characteristics. Intelligence was measured by
means of the Wechsler Intelligence Scale for Children-III (WISC-III) and all children
had a full-scale intelligence at or above an Intelligence Quotient (IQ) of 80. The four
groups neither differed in age nor in IQ. The ratio of boys and girls was approximately
4:1, which did not differ significantly between groups. As measured by a self-report list
for handedness (Van Strien, 2003) none of the children was left-handed.
For measuring ADHD symptoms in the clinical groups, the ADHD section of the
Diagnostic Interview Schedule for Children-IV was administered to the parents (DISC-
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IV: Shaffer et al., 2000; Dutch translation: Ferdinand & Van der Ende, 1998).
Moreover, the Conners’ Teacher Rating Scale-Revised (CTRS-R) was administrated to
the teachers of the clinical children (Conners, 1990; Conners, 1999). All children with
ADHD scored in the clinical range of the DISC-IV ADHD section or at least in
borderline range of the CTRS-R. As 28 of the 32 children with ADHD were well-
responding to Mph, medication-intake in the period that was questioned by the
interview is likely to have caused an underreport of ADHD symptoms. As can be seen
in Table 1, children in both the Mph-treated and medication-free ADHD group,
however, showed significantly more attention deficit and hyperactive-impulsive
symptoms than the children in the ASD group on the DISC-IV. Moreover, the Mph-
treated and medication-free ADHD group could not be differentiated from each other on
the DISC-IV scores.
For assessing autistic-type behaviour in the clinical groups, parents were administered
the Dutch translation of the Social Communication Questionnaire (SCQ: Rutter et al.,
2003), which is a screening tool for ASD based on the Autism Diagnostic Interview-
Revised (Lord et al., 1994). To date, two validation studies have revealed that the SCQ
is a valid measure for discriminating ASD from non-ASD cases with a cut-off of ≥ 15
(Berument et al., 1999; Chandler et al., 2007). All children with ASD scored at or above
this cut-off. The total scores of this questionnaire confirmed that the children with ASD
showed far and significantly more autistic-like symptoms than the children with ADHD
(see Table 1).
GLOBAL/ LOCAL TASK
In the global/ local task the children were asked to sort hierarchical stimuli according to
shape and while doing so to earn as much money as possible. The hierarchical stimuli
consisted of one large geometric figure (circle, square or triangle), which was built up
from smaller geometric figures (circles, squares or triangles). The figures were
constructed so that the large figure and the small figures were equally salient (ES)
(Yovel, Yovel, & Levy, 2001). The sizes were 3.1° for the global and 0.45° for the local
figures with a viewing distance of 55 cm. Within one block the stimuli consisted of two
possible geometric figures (circles and squares, squares and triangles, or circles and
triangles). Each geometric figure was assigned to one of two keys, e.g. the right key
should be pressed for a circle and the left for a square. During global blocks, the
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children were asked to attend only to the large figures and during the local blocks the
children were asked to attend only to the small figures. The stimulus sets of the global
and local blocks were identical. The hierarchical figures could be congruent (50 % of
the trials), i.e. the required response is equal for both levels, or incongruent (50 % of the
trials), i.e. the required response for the attended level is opposite to the one required for
the unattended level. Congruent figures for example consisted of a large circle
composed of smaller circles, while an incongruent circle for example consisted of a
large circle composed of smaller squares. The children performed six global and six
local blocks, each consisting of 80 trials and four ‘warming-up’ trials at the start. See
Figure1 for the structure and timing of the trial.
The stimulus presentation in the task was machine-paced. To take into account
individual differences in response speed, individual deadline times were adopted per
subject, which were computed separately for global congruent and incongruent trials as
well as for local congruent and incongruent trials. These individual deadline times
(mean reaction time in one condition + 10%) were determined in one local and one
global deadline determination block that preceded the experimental blocks, but followed
two short practice blocks. All children were emphasised to earn as much money as
possible, but were at the same time forced to react quickly as late reactions resulted in a
penalty of 0.02 €.
The 12 blocks were divided across three feedback conditions: no feedback, reward and
punishment. This resulted in four blocks per feedback condition (320 trials), with each
feedback condition containing two global and two local blocks (160 trials each). In the
FIGURE 1 Trial structure.
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no feedback condition the children received no information about their performance;
each response was followed by a question mark. After finishing a no feedback block the
children received 0.70 € independent of their performance. In the reward condition the
children started with 0.00 € and only correct responses resulted in a gain of 0.01 €. Gain
and no gain were indicated by ‘+ 1 c’ (in green) and ‘+ 0 c’ (in red) respectively. In the
punishment condition the children started with 0.80 € and only incorrect responses
resulted in a loss of 0.01 €. Loss and no loss were indicated by ‘- 1 c’ (in red) and ‘- 0 c’
(in green) respectively. After every block the children received their earned money.
ELECTROCARDIOGRAM AND COMPUTATION OF EHR RESPONSES
The electrocardiogram (ECG) was recorded using two Ag-AgCl electrodes that were
placed at the right side of the thorax between the collarbone and the sternum and at the
left side between the two lower ribs. The ECG was recorded with a sampling rate of 500
Hz. R-peaks were detected online using Portilab (version 1.10, Twente Medical
Systems International). To include only validly recorded interbeat intervals (IBIs), the
IBIs were corrected for artefacts using Carspan (version 1.20). In this program for
analysing cardiovascular data, a procedure was adopted in which intervals that deviated
more than four SDs from a running mean of 60 seconds were set as possible artefacts.
Using a linear interpolation algorithm, corrections were made in case a set of additional
criteria was met (for a more detailed description, see Mulder, 1992). Finally, all data
were visually inspected in order to check for adequate corrections.
In order to inspect EHR responses to feedback, six sequential IBIs surrounding the
feedback stimuli were extracted from the R-peak series. IBI0 was the interval in which
the feedback was presented. This IBI was followed by two successive intervals: IBI1
and IBI2. The other three intervals were those preceding the feedback stimulus: IBI-3,
IBI-2 and IBI-1. IBI-3 was chosen as a natural baseline, because the task manipulations
appeared not to significantly influence IBI lengths at IBI-3 (p > .10).
DATA ANALYSES
Performance measures were analysed by means of a 3*2*4 mixed ANOVA design
(SPSS version 15.0) with the within subject variables feedback (no feedback, reward
and punishment) and level (global, local) and the between subjects variable group (TD,
ADHD, ADHD Mph, ASD). This was done for the mean percentage of correct
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responses and mean RT. For investigating post error slowing, the mean RT for correct
trials after error responses and the mean RT for correct trials after correct responses was
computed. Post error slowing was analysed by means of a 3*2*4 mixed ANOVA design
with the within subject variables feedback (no feedback, reward and punishment) and
response type (post error RT and post correct RT) and the between subjects variable
group (TD, ADHD, ADHD Mph, ASD). For all performance measures simple contrasts
were computed for the factor feedback, with the no feedback condition as the reference.
The EHR analyses were confined to IBI-1, IBI0 and IBI1, because previous research
has indicated that the most robust effects of error responses and error feedback on EHR
responses occur around feedback onset (Groen et al., 2007; Luman et al., 2007). A
3*3*2*4 mixed ANOVA design was applied to the IBIs, with the within subject
variables sequence (IBI-1, IBI0 and IBI1), feedback (no feedback, reward and
punishment) and response type (correct, error). Simple contrasts were computed for the
factor feedback, with the no feedback condition as the reference. Repeated contrasts
were computed for the factor sequence to investigate changes between successive IBIs.
The factor group (TD, ASD, ADHD, ADHD mph) was used as the between subjects
variable. Significant group (interaction) effects (p < .05) as well as effects with a trend
to significance (p < .10) with effect sizes ranging from medium (.06 ≤ η2 < .14) to large
(η2 ≥ .14) (Stevens, 2002), were further specified by making post hoc pairwise group
comparisons for the following five pairs: TD vs. ADHD, TD vs. ADHD Mph, TD vs.
ASD, ADHD vs. ADHD Mph and ADHD vs. ASD. For all analyses the partial eta
squared effect sizes are reported (Stevens, 2002). To account for possible violations of
the sphericity assumption for within subject factors with more than two levels,
Greenhouse-Geisser adjusted p-values and the epsilon correction factor are reported
together with the unadjusted degrees of freedom and F-values.
RESULTS
PERFORMANCE MEASURES
DEADLINES AND LATE REACTIONS
The groups did not differ significantly in duration of the mean individual deadline
(Mean 754 ms, SD 148 ms) nor in their mean percentage of late responses (Mean 10%,
SD 4.7%). For all groups the mean individual deadline was shorter in the global than in
the local condition (Mean 731 ms, SD 151 ms vs. Mean 778 ms, SD 157 ms), which is
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reflected by a main effect of level (F(1,64) = 22.3, p < .001, η2 = .26). For all groups the
mean percentage of late responses was larger in the global condition than in the local
(Mean 11%, SD 5.2% vs. Mean 9%, SD 5.2%), which is reflected by a main effect of
level (F(1,64) = 15.7, p < .001, η2 = .20). There were no interactions with group for
these measures. All groups had a higher percentage of late reactions in the no feedback
condition than in the reward and punishment conditions, which is expressed by a main
effect of feedback (F(2,128) = 16.5, p < .001, η2 = .21, ε = .80) and absence of any
interaction with group. Trials with late reactions were excluded from further analyses.
ACCURACY
The TD group was more accurate on the task than all clinical groups (85% vs. 77 %
respectively). This is expressed by a main effect of group (F(3,64) = 3.0, p < .05, η2 =
.12) and significant contrasts of all clinical groups with the TD group (TD vs. ADHD: p
< .05; TD vs. ADHD Mph: p < .05; TD vs. ASD: p < .05). For the TD and Mph-treated
ADHD group accuracy did not differ between the global and local condition, but both
the medication-free ADHD and ASD group were less accurate in the local than the
global condition. This is expressed by a trend to significance for the interaction of level
by group with medium effect size (F(3,64) = 2.6, p < .10, η2 = .11) and (nearly)
significant contrasts of the TD group with the medication-free ADHD group (F(1,32) =
2.9, p < .10, η2 = .08) as well as the ASD group (F(1,34) = 11.7, p < .01, η2 = .26).
Regarding the feedback conditions, all groups performed at a lower accuracy level in
the no feedback condition compared to the conditions with feedback (see Figure 2).
This is reflected by a main effect of feedback (F(2,128) = 25.1, p < .001, η2 = .28),
absence of an interaction with group (p > .10) and significant contrasts for the factor
feedback showing that only the no feedback condition differed significantly from the
other feedback conditions (no feedback vs. reward: p < .001; no feedback vs.
punishment: p < .001; reward vs. punishment: p > .10). These feedback effects did not
differ between the global and local condition.
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FIGURE 2 Performance measures. The diagrams present the percentage of correct responses
(accuracy), mean RT and the amount of post error slowing, separated for the three feedback conditions. Error bars represent Standard Errors.
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RT
The groups did not differ in mean RT (485 ms, SD 99 ms) and none of the groups
showed an effect of attended level (global or local) in their RTs. This is reflected by the
absence of a main effect of group (p > .10), level (p > .10) or interaction of these factors
(p > .10). As can be seen in the middle part of Figure 2, all groups responded faster in
the no feedback condition than in the conditions with feedback. This is reflected by a
main effect of feedback (F(2,128) = 8.9, p < .01, η2 = .12, ε = .83), significant feedback
effects for the contrasts of no feedback vs. reward (p < .01) and no feedback vs.
punishment (p < .01) and absence of any interaction between feedback and group. These
feedback effects, moreover, did not differ between the global and local condition.
POST ERROR SLOWING
For all groups RTs after error trials were longer than RTs after correct trials (561 ms,
SD 142 ms vs. 492 ms, SD 87), which is reflected by a main effect of response type
(F(1,64) = 63.6, p < .001, η2 = .50) and absence of an interaction with group (p > .10).
The lower part of Figure 2 depicts the difference between RTs after error trials and RTs
after correct trials, i.e. the amount of post error slowing, separated for the groups and
feedback conditions. Analyses indicated that for all groups the amount of post error
slowing is reduced in the no feedback condition. This is reflected by an effect of
response type by feedback (F(2,128) = 7.4, p < .01, η2 = .10, ε = .98), absence of an
interaction with group (p > .10) and contrasts for the factor feedback showing that only
the no feedback condition differed significantly from the two feedback conditions (no
feedback vs. reward: p < .01; no feedback vs. punishment: p < .01; reward vs.
punishment: p > .10). Although Figure 2 suggests that this pattern differs for the TD
group, i.e. reduced post error slowing in the punishment condition, this could not be
verified statistically. Figure 2 also suggests that the ASD group shows an overall pattern
of reduced post error slowing. The overall group by response type interaction, however,
did not reach significance but showed medium effect size (F(3,64) = 2.1, p = .11, η2 =
.09). Post hoc pairwise group comparisons of the ASD group with the TD and
medication-free ADHD group showed (nearly) significant response type by group
interactions with medium and large effect sizes (ASD vs. TD: F(1,34) = 6.06, p < .05, η2
= .15; ASD vs. ADHD: F(1,32) = 4.12, p = .05, η2 = .11).
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EHR
As can be seen in Figure 3, the general EHR pattern to the feedback stimuli of all
groups is characterised by an initial deceleration from IBI-3 to IBI-2 and an acceleratory
recovery for all groups. Figure 3 suggests that only the TD and Mph-treated ADHD
group show clearly enhanced EHR decelerations on error trials in comparison to correct
trials. No main effects of group or feedback type (p-values > .10) were found in the
analysis of the three IBIs around feedback onset (IBI-1, IBI0 and IBI1).
However, an overall significant response type by group interaction was found (F(3,64)
= 4.3, p < .01, η2 = .17). Post hoc pairwaise group comparisons, as summarised in Table
2, revealed significant response type by group interactions for the comparison of the
medication-free ADHD group with both the TD and the Mph-treated ADHD group.
These effects were independent of the feedback condition, as for these group
comparisons significant three-way interactions of feedback by response type and group
were absent. As can be seen in Figure 3, both the TD and Mph-treated group show
enhanced EHR decelerations on error trials compared to correct trials, while these were
absent in the medication-free ADHD group. Analyses per group confirmed that the
medication-free ADHD group showed no effect of response type at all (p > .10), while
all the other groups did (TD: F(1,17) = 9.9, p < .01, η2 = .37; ADHD Mph: F(1,15) =
23.4, p < .001, η2 = .61; ASD: F(1,17) = 3.8, p < .10, η2 = .18).
For the comparison of the Mph-treated ADHD group with the TD group a response type
by group interaction was present that did appear to be dependent on the feedback
condition (see Table 2). This was reflected by an overall, nearly significant, three-way
interaction of feedback by response type by group with medium effect size (F(6,128) =
2.1, p = .06, η2 = .09). Figure 3 suggests a difference in EHR deceleration on error trials
between these two groups for the reward condition only. Simple contrasts for the
feedback conditions indeed revealed a significant feedback by response type by group
interaction for the contrast reward vs. no feedback (F(1,32) = 5.4, p < .05, η2 = .14) and
absence of this interaction for the contrast punishment vs. no feedback.
Although Figure 3 also suggests smaller EHR decelerations on error trials for the ASD
group, especially in the no feedback condition, this could not be statistically supported
by post hoc pairwise group comparisons. No significant interactions of response type by
group or by feedback emerged in the post hoc pairwise group comparisons of the ASD
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group with the medication-free ADHD or TD group. However, for these comparisons
the response type by group interactions approached significance and showed medium
effect sizes (see Table 2).
Apart from these response type effects, a significant effect of sequence (F(2,128) = 3.7,
p < .05, η2 = .06) confirmed the deceleration-acceleration pattern of IBI’s around the
feedback stimuli. Repeated contrasts for the factor sequence indicated that across
groups IBI’s changed from IBI0 to IBI1 (F(1,64) = 10.9, p < .01, η2 = .15), but not from
IBI-1 to IBI0 (p > .10). A nearly significant three-way interaction of sequence by
response type by group was present (F(6,128) = 2.1, p = .07, η2 = .09). Post hoc
pairwise group comparisons, however, indicated that only the Mph-treated ADHD
group deviated from the TD group in their effect of response type from IBI0 to IBI1
(TD vs. ADHD Mph: contrast IBI0 vs. IBI1: F(1,32) = 4.0, p = .05, η2 = .11). Figure 3
suggests that Mph-treated children with ADHD show a somewhat steeper EHR
acceleration from IBI0 to IBI1 on error trials than the TD children.
CORRELATIONS OF EHR RESPONSES WITH PERFORMANCE MEASURES
EHR deceleration to error trials across feedback conditions (computed as the IBI
difference between error and positive feedback across the three tested IBIs) was
positively correlated with the accuracy level: r(68) = .23, p = .06 at IBI-1, r(68) = .24, p
TABLE 2 Post hoc group comparisons for the feedback-related EHR responses.
df F p η2
Overall 3,64 4.3 <.01 .17
TD vs. ADHD 1,32 7.6 <.05 .19
TD vs. ADHD Mph
TD vs. ASD 1,34 3.0 .09 .08
ADHD vs. ADHD Mph 1,30 12.7 <.01 .30
ADHD vs. ASD 1,32 2.4 .13 .09
df F p η2
Overall 6,128 2.1 .06 .09
TD vs. ADHD
TD vs. ADHD Mph 2,64 3.9 <.05 .11
TD vs. ASD
ADHD vs. ADHD Mph 2,60 1.9 .16 .06
ADHD vs. ASD
Response type*group
Feedback*response type*group
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< .05 at IBI0 and r(68) = .30, p < .05 at IBI1. This indicates that children showing larger
EHR decelerations on error trials also attain higher accuracy levels. When computed for
the clinical groups solely the correlation at IBI1 remained present (r(50) = .26, p = .07),
suggesting that this association is not just the result of the difference in accuracy level
between the TD group and the clinical groups. Across all groups, no significant
correlations were found between EHR deceleration on error trials and post error slowing
or RT, but both post error slowing and RT correlated significantly with accuracy level
(post error slowing: r(68) = .63, p <.001); RT (r(68) = .55, p < .001). This indicates that
children showing more post error slowing and longer reaction times attain higher
accuracy levels. These correlations could neither be explained by the difference in
accuracy level between the TD group and the clinical groups, because they remained
present when computed for the clinical groups solely.
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FIGURE 3 Interbeat Intervals (IBI) time-locked to feedback presentation (IBI0). Separate values are given for correct and error trials for the three feedback conditions.
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DISCUSSION
The primary goal of the present study was to investigate feedback sensitivity in children
with ADHD, on and off Mph, and children with ASD. Regarding task performance, all
clinical children were about 8% less accurate on the global/ local task than the TD
children, but performed both the global and local condition far above chance level (for a
short discussion on the global/ local manipulation see footnote1). All groups performed
more efficient when provided with performance feedback, i.e. in the reward and
punishment condition compared to the condition without feedback; they responded
slower and more accurate, showed less late responses and more post error slowing. The
present study thus suggests that at the behavioural level all children, irrespective of
psychopathology and medication, benefit from the receipt of performance feedback
compared to a condition in which performance must be self-monitored. The medication-
free children with ADHD did not perform worse in the condition without feedback and
may thus be suggested to be able to keep up their performance by their own motivation
in a similar way as the other groups. This finding provides no evidence for a deficit in
intrinsic motivation in children with ADHD as was previously suggested (Luman et al.,
2005; Douglas & Parry, 1994; Sergeant, 2000). Different from these findings on the
behavioural level, the EHR analyses provided some evidence for differential error and
feedback sensitivity in the children with ADHD on and off Mph and children with ASD.
1 The global/ local manipulation had been chosen for exploring hemispheric processing of hierarchical
stimuli in ADHD and ASD, in combination with the different feedback conditions. For this EHR study, however,
the global and local conditions were merged because the number of error trials was too low to split the analyses
for these conditions. Although the phenomenon is still debated, local/ detailed information is thought to be
processed in the left hemisphere of the brain, while global/ holistic information is thought to be processed in the
right hemisphere (Fink et al., 1997; Heinze, Hinrichs, Scholz, Burchert, & Mangun, 1998; Navon, 1977).
Subjects with ASD are thought to preferably process detailed/ local information and to have problems with more
holistic/ global processing (see for a review: Happe & Frith, 2006). The performance data of the present task,
however, suggest the opposite: the children with ASD were less accurate in the local than in the global task, and,
interestingly, this also applied to the medication-free children with ADHD. ERP analyses of the present task are
planned to investigate (hemispheric) processing of global and local stimuli in these psychopathological groups.
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As expected TD children showed enhanced EHR decelerations on error trials compared
to correct trials, both when they had to self-monitor their errors in the no feedback
condition and when they were provided with rewarding or punishing feedback. The
Mph-treated children with ADHD showed a similar pattern, except in the reward
condition, where they showed a smaller EHR deceleration on error trials. In contrast,
the medication-free children with ADHD showed no EHR decelerations on error trials
in any of the feedback conditions. Lastly, the ASD group could neither be differentiated
from the ADHD group nor from the TD group in their EHR responses, but group effects
for the EHR decelerations on error trials approached significance with both groups.
Furthermore, positive correlations of the EHR decelerations on error trials and task
accuracy indicate that children with larger decelerations are more accurate on the task.
This finding is in agreement with the theory that error-related EHR deceleration is
sensitive to the degree in which feedback is utilised to adjust performance (Van der
Veen et al., 2004; Somsen et al., 2000).
As hypothesised, medication-free children with ADHD thus appeared physiologically
less responsive to error commission and error feedback than TD children, as their EHR
did not discriminate between error and correct trials both when they were refrained from
performance feedback and when they were provided with rewarding and punishing
feedback. Decreased autonomic responsiveness to error feedback is in line with a
previous report by Crone and colleagues (2003b) who showed that the EHR of
medication-free children with ADHD discriminates to a lesser extent between positive
(reward or escape of punishment) and negative (punishment or losing reward) feedback
in comparison to TD children. Together, these findings suggest that children with
ADHD are less sensitive to different types of aversive events, such as error commission
and error feedback, punishment and loss of reward, and support the hypothesis of an
underactive BIS in children with ADHD (Quay, 1988a; Quay, 1988b). As EHR
decelerations in general have been suggested to reflect the central inhibition of ongoing
processes, permitting the assessment of error sources and enhanced task focus (Jennings
& Van der Molen, 2002), medication-free children with ADHD may benefit less than
TD children from error commission and feedback for the adjustment of their
performance. The present findings agree with the daily life experience that children with
ADHD take longer than TD children to stop showing undesired behaviour when
provided with punishment or when their behaviour is ignored.
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The present results, moreover, indicate that autonomic responsiveness to feedback is
increased in Mph-treated children with ADHD compared to medication-free children
with ADHD, as they do show clear EHR deceleration on error trials. Mph-treated
children with ADHD could not be discriminated from TD children in the no feedback
and punishment condition, suggesting that Mph ‘normalises’ error sensitivity in children
with ADHD in those conditions. However, in the reward condition, where emphasis was
on gain, EHR responsiveness to error feedback did not fully ‘normalise’. The
‘normalised’ physiological response of Mph-treated children with ADHD to error
commission and punishment is of great clinical relevance, because it suggests that the
first-choice treatment of ADHD improves both self-monitoring of errors and sensitivity
to punishment. This improvement may increase the susceptibility to behavioural
therapies in children with ADHD, as these therapies typically involve (parental)
feedback on their performance and/or contingency management (e.g. token economy
system). This is in line with findings of behavioural therapy combined with stimulant
treatment being superior over behavioural therapy alone in reducing ADHD symptoms
(The MTA Cooperative Group, 1999).
Improved autonomic responsiveness to error commission and punishment in Mph-
treated children with ADHD also suggests that Mph stimulates the underactive BIS
system in ADHD. Mph-induced BIS activation can be explained by the enhancing effect
of Mph on the neurotransmission of noradrenaline, as one of the major components of
the BIS system are the noradrenergic pathways in the brain (Gray, 1985; Gray, 1987).
Mph is known to influence these pathways by increasing extracellular levels of
noradrenaline (Pliszka, 2005; Seeman & Madras, 1998). Interestingly, two recent ERP
studies have suggested that Mph modulates an electrocortical error processing
component, the error Positivity (Pe), in ADHD (Groen et al., 2008, this study included
nearly identical experimental groups as described in the present study ; Jonkman et al.,
2007). This component is thought to reflect phasic responses from the Locus Coereleus
(LC) noradrenaline system. Together, these findings raise the hypothesis that the (LC)
noradrenaline system is hypoactive in children with ADHD when they are faced with
aversive stimuli, such as errors and negative feedback. In healthy brains quick arousal
responses from the noradrenaline system increase the state of alertness and sensory
intake (Berridge & Waterhouse, 2003) and as a consequence facilitate the inhibition of
ongoing processes. This may be a neurobiological explanation of why children with
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ADHD benefit to a lesser extent from error commission and feedback and the
‘normalising’ effect of Mph in ADHD.
It is interesting that the Mph-treated ADHD group shows ‘normalised’ EHR responses
to error commission in the no feedback condition. In this condition no performance
feedback was given to the children and, therefore, EHR decelerations on error trials
must have been elicited by the self-monitoring of error responses. Mph-treated children
with ADHD thus do not differ from TD children in the self-monitoring of their errors.
EHR deceleration to self-monitored errors has been proposed to be functionally related
to and to share the neuronal source of the Error-Related Negativity (ERN) (Crone et al.,
2003c; Crone et al., 2004; Groen et al., 2007; Jennings & Van der Molen, 2002; Somsen
et al., 2000), which is an ERP component reflecting the earliest processing of error
responses (Falkenstein et al., 1991; Gehring et al., 1993). Error-related EHR
deceleration may, therefore, be the autonomic equivalent of the ERN. The increased
EHR responses to self-monitored errors found in the Mph-treated children opposed to
medication-free children with ADHD would thus predict increases in ERN amplitude in
subjects treated with Mph. Indeed stimulants like Mph have been found to enhance
ERN amplitudes in healthy adults (De Bruijn et al., 2004; De Bruijn et al., 2005).
However, recent studies in Mph-treated children with ADHD, including one of our own
group describing nearly identical ADHD samples, have shown that Mph selectively
normalises the Pe, but not the ERN (Groen et al., 2008; Jonkman et al., 2007). Possibly,
error-related EHR decelerations reflect somewhat different aspects of error processing
than the ERN (Van der Veen et al., 2004; Van der Veen, Mies, Van der Molen, &
Evers, 2008). For future studies it may be interesting to further elucidate the central
source(s) of error-related EHR decelerations by exploring the relationship between
error-related EHR decelerations and electrocortical components of error processing in
larger samples of subjects.
Finally, the children with ASD could neither be differentiated from the medication-free
children with ADHD nor from TD children in their autonomic responsiveness to error
commission and feedback. However, the effects of the comparisons with both groups
approached significance with medium effect sizes, suggesting that larger sample sizes
would have resulted in significant effects. Although this finding does not allow for firm
conclusions, it suggests that children with an ASD may be physiologically less sensitive
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to error commission and error feedback. Decreased physiological sensitivity to feedback
would be in line with our previous report, showing that a nearly identical sample of
children with ASD showed diminished late positive electrocortical responses to error
feedback, suggesting diminished affective processing of feedback stimuli (Groen et al.,
2008). Decreased physiological sensitivity to self-monitored errors, however, would
contradict our previous results, as the children with ASD in that study did not differ
from TD children in ERN amplitude to self-monitored errors (Groen et al., 2008; see for
similar results: Henderson et al., 2006). This again questions whether the error-related
EHR deceleration is the autonomic equivalent of the ERN.
The non significant group effects regarding the ASD group may be due to several
factors. Firstly, the present ASD sample included children with a subthreshold form of
autism (Pervasive Developmental Disorder Not Otherwise Specified; American
Psychiatric Association, 2000), a subgroup on the least disabled side of the autistic
spectrum. Possibly, subjects on the more disabled side of the autistic spectrum are more
compromised in their error and feedback sensitivity. This reasoning is supported by a
study of Henderson and colleagues (2006), reporting a positive correlation between a
measure of parent-reported impairment in social interactions and the ERN amplitude in
children with ASD. This suggests that subjects with more severe problems in social
interactions have also more impaired error monitoring. For future studies it is, therefore,
recommended to include children classified within the wider spectrum of Autistic
Disorder for testing this hypothesis. Secondly, although none of the children with ASD
met the criteria for ADHD of the combined type, the children in the ASD sample may
have shown some ADHD symptoms, especially inattention symptoms. This is quite
likely, for in general it is found that ADHD symptoms are common in children with
ASD (Jensen et al., 1997; Frazier et al., 2006). We performed additional analyses to test
for this, but no significant differences were found between children in the ASD group
with high and low ADHD ratings. For future studies on error and feedback processing
in ASD, it is recommended using larger samples and including subjects with subtypes
from the entire autistic spectrum, allowing for the comparison of more and less disabled
subjects with ASD. Moreover, ADHD symptoms should definitely be taken into
account when investigating performance monitoring in samples with ASD.
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ACKNOWLEDGEMENTS
This work was supported by grants from the Protestants Christelijke Kinderuitzending
(PCK). The authors thank the following people for their help in data collection: Diana
de Boer, Johannes Boerma, Harma Moorlag, Klaas van der Lingen and Brenda
Waggeveld.
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CHAPTER 5
DIFFERENTIAL EFFECTS OF 5-HTTLPR AND DRD2/ANKK1
POLYMORPHISMS ON ELECTROCORTICAL MEASURES OF
ERROR AND FEEDBACK PROCESSING IN CHILDREN
MONIKA ALTHAUS
YVONNE GROEN
ALBERTUS A. WIJERS
LAMBERTUS J.M. MULDER
RUUD B. MINDERAA
IDO P. KEMA
JANNEKE D.A. DIJCK
CATHARINA A. HARTMAN
PIETER J. HOEKSTRA
The study described in this chapter has been published in Clinical Neurophysiology,
120, 93-107, 2009.
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ABSTRACT
Objective: Applying a probabilistic learning task we examined the influence of
functional polymorphisms of the serotonin transporter gene (5-HTTLPR) and the D2
dopamine receptor gene (DRD2/ANKK1) on error and feedback processing by
measuring electrocortical Event-Related Potentials (ERPs) in 10-to12 year old children.
Methods: Three pairwise group comparisons were conducted on four distinguishable
ERP components, two of which were response-related, the other two feedback-related.
Results: Our ERP data revealed that children carrying the short (S) variant of the 5-
HTTLPR gene process their errors more intensively while exhibiting less habituation to
negative feedback with task progression compared to children who are homozygous for
the 5-HTTLPR long (L) variant. Children possessing the Taq1 A variant of the DRD2
gene showed greater sensitivity to negative feedback and, as opposed to Taq1 A non-
carriers, a diminishing sensitivity to positive feedback with task progression. Regarding
error processing, children possessing both the S variant of the 5-HTTLPR and the Taq1
A allele of the DRD2 gene showed a picture quite similar to that of the 5-HTTLPR S
carriers and regarding feedback processing quite similar to that of the DRD2 Taq1 A
carriers. Conclusions: Our findings support the hypotheses that the 5-HTTLPR S allele
may predispose to (performance) anxiety, while DRD2 Taq1 A allele may predispose to
the reward deficiency syndrome. Significance: The results may further enhance our
understanding of known associations between these polymorphisms and
psychopathology.
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INTRODUCTION
Psychiatric disorders have a well-established genetic background (Sanders et al., 2004).
Yet, only relatively few specific genetic polymorphisms have been identified as being
associated with mental disorders. Moreover, associations of these polymorphisms with
the disorders are typically weak. This may be explained by the large heterogeneity and
complexity of clinical phenotypes that are based on rather global diagnostic criteria
(Faraone et al., 2005).
Recent advances in the field of imaging genetics suggest that the effects of genes on
brain morphology and function are larger than those on disease phenotypes. Hariri and
Weinberger (2003) created the concept of “imaging genomics”, in which the phenotype
has been proposed to be the physiological response of the brain during specific
information processing (Brown & Hariri, 2006). Given the much stronger link between
genomic variation and brain activity, samples to be investigated may be much smaller
than those that have been used in patient-control comparisons based on clinical
diagnoses (Fallgatter et al., 2004).
The present study employed the measurement of brain function by means of
electroencephalogram (EEG) Event-Related Potentials (ERPs) obtained during error
and feedback processing in relation to common polymorphisms of two genes, the
serotonin transporter (5-HTTLPR) gene and the D2 dopamine receptor (DRD2) gene.
These two genes may differentially contribute to learning from feedback on errors as
they have been suggested to mediate different personality traits that, however, have both
been found to predispose for alcohol dependence (Wu et al., 2008). Based on a
neurobiological learning model (Gray, 1985; Gray, 1987), Cloninger (1987b; 1987a)
suggested that alcohol dependency might develop from either an overactive behavioral
inhibition system (BIS) which is associated with harm avoidance and proposed to be
mediated by serotonergic processes or an overactive behavior activation system (BAS),
which is associated with novelty seeking and should be mediated by the dopaminergic
system. .
Given that both error and feedback processing form an important part of learning during
childhood and ERPs related to error and feedback processing have indeed been found to
occur not only in adults but also in children (e.g. Davies et al., 2001; Van Meel et al.,
2005b; Groen et al., 2007), our study was conducted on a group of primary school-aged
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children. These children had participated in an experiment that was conducted to
investigate whether different psychopathological conditions differentially affect error
and feedback processing (Groen et al., 2008).
The 5-HTTLPR gene plays an important role in serotonergic neurotransmission by
facilitating serotonin (5HT) reuptake from the synaptic cleft (Heils et al., 1995). It is
known to have two alleles, which differ in the number of variable repeat sequences in
the promoter region: a low activity short (S) variant and a long (L) variant. Compared to
carriers of the L variant, individuals carrying the S variant have repeatedly been
suggested to be prone to anxiety-related personality traits (Brown & Hariri, 2006; Jacob
et al., 2004; Sen et al., 2004), to show augmented neural processing of aversive stimuli
(Canli et al., 2005) and greater sensitivity to stimuli associated with punishment (Finger
et al., 2007) as well as to be liable to alcohol dependence (e.g. Feinn, Nellissery, &
Kranzler, 2005; Lin et al., 2007). Although recently a tri-allelic variation has been
identified suggesting functionally different polymorphisms within the long variant (e.g.
Hu et al., 2006), we followed the vast amount of literature by grouping according to the
assumption that the S variant functionally dominates upon the L variant (Hariri et al.,
2005; Otte, McCaffery, Ali, & Whooley, 2007) and therefore compared a group of
homozygous carriers of the L variant (LL) with carriers of at least one S allele (SL and
SS).
The DRD2 gene has multiple allelic forms, one of which, the Taq1 A1 polymorphism
has been related to a reduced D2 dopamine receptor binding affinity (Noble, 2003) and
lower dopamine receptor density in the striatum (Jonsson et al., 1999). Its presence has
been suggested to play a central role in the neuromodulation of appetitive behaviors,
and to be associated with smoking and alcoholism (Bowirrat & Oscar-Berman, 2005;
Munafo, Brown, & Hariri, 2008; Preuss, Zill, Koller, Bondy, & Soyka, 2007), gambling
(Comings et al., 1996), and sensitivity to stress (Bau, Almeida, & Hutz, 2000; Pani,
Porcella, & Gessa, 2000). The DRD2 Taq1 A1 allele has therefore been related to what
is conceptualized as the Reward Deficiency syndrome, pointing to an inefficiency in the
acquired reward system (Bowirrat & Oscar-Berman, 2005). Different from natural
rewards that include the satisfaction of only physiological drives, acquired rewards are
defined as positive reinforcers, i.e. events that increase the probability of a subsequent
response. Note that although the Taq1 A1 variant has recently been described to alter an
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amino acid in a protein kinase gene (ankyrin repeat and kinase domain containing 1;
ANKK1) identified in a less than 10 kilobase downstream region of the DRD2 locus
(Neville, Johnstone, & Walton, 2004) implying the possibility that changes in ANKK1
activity may explain the described associations between the DRD2 variant and
neuropsychiatric disorders, we decided to keep referring to the variant as the DRD2
Taq1 A1 polymorphism, because this agrees with the nomenclature used in the majority
of published studies to date.
Augmented neural processing of aversive stimuli, greater sensitivity to stimuli
associated with punishment, and a less efficient processing of positive reinforcement
can be studied by measuring electrocortical correlates of error and feedback processing.
This type of processing is generally referred to as performance monitoring (Stuss et al.,
1995; Ullsperger, 2006). Performance monitoring is described as a process of adapting
behavior by making use of negative and positive feedback from the environment or
comparing the action at hand to an internal representation of the intended action. These
abilities are conceptualized as external and internal performance monitoring,
respectively (Müller et al., 2005). Since the early nineties they have been thoroughly
studied by means of EEG ERPs (e.g. Falkenstein et al., 1991; Gehring et al., 1990;
Miltner et al., 1997). A paradigm allowing for measuring both aspects of internal and
external performance monitoring originates from the probabilistic learning task
developed by Holroyd and Coles (2002). In this task, subjects are required to learn
particular stimulus-response combinations by making use of performance feedback that
is contingent to their responses. It allows for investigating the transition from external to
internal monitoring as learning by feedback proceeds throughout the course of the task.
To this end ERPs that are time-locked to the response as well as time-locked to the
feedback stimuli are examined.
Our study was conducted on a group of primary school-aged children who had
participated in an experiment that was aimed at investigating whether different
psychopathological conditions differentially affect error and feedback processing
(Groen et al., 2008). From the majority of these children DNA samples were obtained.
The task applied was a modified version of the probabilistic learning paradigm adapted
for completion by children (Crone et al., 2004; Groen et al., 2007). This task has been
shown to evoke several distinguishable ERP components that were highly sensitive to
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the task manipulations (Groen et al., 2007) as well as to differences between children
with different types of psychopathology (Van Meel et al., 2005b; Groen et al., 2008). In
the present study we investigated four of these components, two of them related to the
children’s response and two related to the feedback upon their responses.
Response-locked components were an early error-related negativity (Falkenstein et al.,
1991; Gehring et al., 1990; Gehring et al., 1993; Groen et al., 2007; Van Meel et al.,
2007) with a fronto-central scalp distribution and an onset at or shortly before the
commission of an incorrect response until about 100 ms thereafter, as well as a later
occurring error-related positivity (Pe) peaking approximately 200-400 ms post response
with a maximum at parietal electrode sites (Davies et al., 2001; Falkenstein et al., 1991;
Groen et al., 2007). While the ERN has been associated with a rather unconscious
process of error detection, the Pe has been suggested to reflect conscious error
processing that facilitates adaptive behavior (Davies et al., 2001; Leuthold & Sommer,
1999a; O'Connell et al., 2007; Overbeek et al., 2005). Both components have been
found to be increased in response to the commission of errors.
Concerning the feedback-locked ERPs, a feedback-related ERN as described in
previous studies (Gehring & Willoughby, 2002; Holroyd & Coles, 2002; Müller et al.,
2005; Van Meel et al., 2005b) appeared not to be sensitive to the manipulations of the
task paradigm applied in the present study (see Groen et al., 2007). Yet, two other
components were shown to vary with the task conditions. These were a feedback-related
P3, maximal at centro-parietal sites in the range of 200 to 450 ms after feedback onset
and another, later (up from 450 ms after feedback onset) occurring and longer lasting
centro-parietal positivity, which is referred to as the Late Positive Potential. Both were
found to be larger in response to negative feedback as compared to positive feedback.
While the feedback-P3 has been suggested to reflect the updating of task rules from
long term memory in response to error feedback (Donchin & Coles, 1988; Groen et al.,
2007), the LPP, has been thought to reflect increased attention to affective-motivational
stimuli because it has repeatedly been found in response to highly arousing pleasant and
unpleasant pictures (Cuthbert et al., 2000b; Hajcak et al., 2006; Schupp et al., 2000b).
In a group of healthy children, the response-locked error potentials could be shown to
increase when learning proceeded while the feedback-locked potentials appeared to
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decrease with task progression, thus reflecting an increase in internal monitoring that is
accompanied by decreasing dependency on external feedback (Groen et al., 2007).
The present study investigated whether the above-described error- and feedback-related
ERP components are differentially affected by the serotonergic 5-HTTLPR and
dopaminergic DRD2 polymorphisms as well as whether the combined occurrence of the
short 5-HTTLPR variant with the DRD2 Taq1 A1 allele might amplify possible effects.
Given that enhanced neural processing of aversive stimuli and greater sensitivity to
stimuli associated with punishment has been reported for carriers of the 5-HTTLPR S-
allele we expected the group of children with one or both S-alleles to show greater
sensitivity to internally monitored errors (ERN and Pe) as well as to externally
monitored negative feedback compared to the children with the L alleles. More
specifically, this would imply the occurrence of a greater early frontal negativity and a
greater somewhat later observable parietal positivity related to incorrect responses as
well as a greater parietal response to negative feedback in S allele carriers as compared
to homozygous L allele carriers . As the Taq1 A1 allele of the DRD2 gene has
predominantly been related to deficient reward processing we expected the children
carrying this allele to be different from the non-carriers specifically in their feedback-
related ERP responses, in particular their LPP related to positive feedback. Group
differences emerging from these pairwise group comparisons might become even more
evident when comparing the children carrying both the S-variant of the 5-HTTLPR and
the Taq1 A1 variant of the DRD2 gene to the children possessing neither of these
variants.
METHODS
SUBJECTS
The sample consisted of 65 normally intelligent children (51 boys and 14 girls; mean
age=11.41, SD=0.91, range=10-12 years), either with a Pervasive Developmental
Disorder (PDD; N=18), or Attention Deficit Hyperactivity Disorder (ADHD, N=27;), or
being healthy controls (N=20) who had all participated in an experiment investigating
performance monitoring, and of whom DNA samples had been taken for genotyping.
Clinical diagnoses, as established by independent child psychiatrists, were based on
DSM-IV-TR criteria (American Psychiatric Association, 2000) and several standardized
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PDD and ADHD behavior questionnaires (for more details we refer to Groen et al.,
2007).
In the original study by Groen and colleagues (2008) there were 35 children with
ADHD, thirty-one of whom were methylphenidate (MPH) responders, all taking this
drug during the main part of the year preceding the experiment. These MPH responders
were 9 randomly assigned to an MPH-treated or medication-free condition. Given an
MPH half-life of about two hours, those assigned to the medication-free condition were
asked to discontinue MPH-intake for at least 17 hours before they entered the
experiment. The remaining four of the 35 children with ADHD did not yet use
medication for their ADHD-symptoms and were directly assigned to the medication-
free group.
For the present study, DNA was obtained from 27 children with ADHD; 13 were taking
MPH, while 14 had been medication-free for at least 17 hours at the time of the
experiment, two of them not having used MPH before. All children in the PDD group
were medication-free at the time of the experiment; children taking any other
psychotropic drug were excluded from the study. How the heterogeneity of the sample
has been dealt with when grouping according to the polymorphisms is described below.
Aim and study procedures were fully explained to the patients and their parents before
written consent was obtained from the parents as well as the12-year-olds. The study had
been approved by the medical ethics committee of the University Medical Center
Groningen.
GENOTYPING
Buccal smears were collected using cervical brushes. For the sake of obtaining reliable
DNA three samples were taken from each of the participating children, one in the
morning, one in the noon and one in the evening. The samples were stored in buffer
containing proteinase K and sodioum dodecylsulfate. DNA was isolated using salt
extraction followed by iso-propanol precipitation. Based on validation experiments in
the laboratory, we expect an error rate below 1% for the 5-HTTLPR genotyping
because errors were minimized by cross-checks during the crucial steps by the
technicians and the use of automated systems for samples and PCR buffers. For the
DRD2, all samples were duplicated with a verification rate of 100%.
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DRD-2 GENOTYPING
The DRD-2 Taq 1 polymorphism was determined using real-time polymerase chain
reaction (PCR). We used primers DRD2-GAF (5’-GCAACACAGCCATCCTCAAAG-
3’ and DRD2-GAR (5’-GTGCAGCTCACTCCATCCT-3’) for DNA amplification and
probes DRD2-GAV2 (VIC-CTGCCTCGACCAGC) and DRD2-GAM (FAM-
CTGCCTTGACCAGC) to detect the Taq I A2 allele (G) and Taq I A1 (A) allele
respectively (Assay by design, Applied Biosystems, Nieuwerkerk a/d IJssel, The
Netherlands). PCR was performed using Taqman Universal master mix (Applied
Biosystems) and 5‰ bovine serum albumin. After initial denaturation (95 °C, 10
minutes) amplification took place using 40 cycles of denaturation (92 °C, 15 seconds)
and annealing/extension (60 °C, 60 seconds). PCR and detection were carried out using
an Applied Biosystems 7500 Real-Time PCR system. Primer and probe sequences were
based on the NCBI sequence AF050737.
GENOTYPING 5-HTTLPR
5-HTTLPR genotypes were determined using the HTTp2a and HTTp2B primer set to
amplify 406 (S) and 450 (L) bp fragments using PCR (Cook et al., 1997). The LA, LG
and S alleles13 were determined by incubation of the PCR product with the restriction
enzyme Msp I (New England Biolabs, Westburg, Leusden, The Netherlands) for at least
3 hours at 37° C. Msp I cuts the GGCC sequence, resulting in fragments of 329, 62, and
59 (LA), 174, 155, 62 and 59 bp (LG), and 285, 62 and 59 bp (S) respectively. The
resulting restriction fragments were separated using a 2% agarose gel and visualized
using GelStar (SYBR-green; Cambrex Bio Science, Rockland, ME).
GROUPING BY VARIANTS OF THE 5-HTTLPR SEROTONIN TRANSPORTER GENE
Fifteen children were 5-HTTPLR S homozygotes (SS), 20 L homozygotes (LL), and 30
heterozygotes (SL). Assuming functional dominance of the S allele (Brown & Hariri,
2006), two groups were formed consisting of 45 S carriers (SS/SL) and the 20 LL
carriers, respectively. Since these groups were not equal with respect to the presence of
the DRD2 Taq1 A1 allele, gender, clinical, and medication status, we matched 20 of the
45 S carriers to those with only L variants on these variables. This matching was
considered necessary as effects of gender, clinical diagnosis, and the use of MPH on the
investigated ERPs have previously been reported (Davies et al., 2001; Van Meel et al.,
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2005b; Jonkman et al., 2007; Groen et al., 2008). It resulted in two groups of 20
children perfectly matched on the DRD2 gene variants, clinical status, and medication,
with, however, 3 more girls (n=6) in the LL group. The distribution of the children
across the matching variables before and after matching is presented in Table 1. The
two 5-HTTLPR groups did not differ in age [M1 = M2 = 11.4] or intelligence [M1 (SD)
= 103.8 (13.2); M2 (SD) = 102.4 (10.6)].
GROUPING BY THE DRD2 GENE VARIANTS
Twenty-three children possessed at least one Taq1 A1 allele (3 of them had both
copies), the remaining 42 children were non-carriers (GG). As here again neither the
presence of the S and L variants of the 5-HTTPLR gene nor the children’s clinical status
and gender were equally distributed across the groups we matched the groups by these
three variables taking the distributions of the Taq1 A1 group as point of departure.
Dropping two girls from the Taq1 A1 group resulted in two groups of 21 children who
were perfectly matched on the 5-HTTPLR variants, gender, and clinical status, and
medication (Table 2). The groups did not differ in age [M1 = M2 = 11.5] or intelligence
[M1 (SD) = 102.1 (13); M2 (SD) = 101.1 (11.1)].
GROUPING BY COMBINED VARIANTS OF THE 5-HTTPLR AND DRD2 GENE
Another two groups were formed for the comparison of those children carrying both one
or two 5-HTTPLR S alleles and the DRD2 Taq1 A1 allele (SA: n = 17) to those
children possessing neither of them (LL/GG: n= 14). Due to small sample sizes, these
groups could not be matched according to gender and clinical or medication status.
However, the groups turned out rather similar on these variables (see Table 3) as well as
on age [M1 (SD)=11.5 (0.9); M2 (SD)= 11.3 (0.9)] and intelligence [M1 (SD) = 100.4
(10.9); M2 (SD) = 101.6 (11)].
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TABLE 3: Distribution of the two groups carrying either both the DRD2 Taq1 A1 and 5-HTTLPR S allele or none of these, respectively.
boys girls boys girls
Controls 4 3 7 3 1 4
ADHD+ 2 0 2 2 0 2
ADHD¯ 3 0 3 1 2 3
PDD 5 0 5 5 0 5
14 3 17 11 3 14
5-HTTLPR / DRD2
SA LL/GG
TABLE 2. Distribution of the children in the two DRD2 groups across gender, clinical status, and
medication (ADHD+: taking MPH; ADHD-: being MPH-free during the experiment). Numbers
between brackets present the original numbers from which the matched groups were drawn.
boys girls boys girls boys girls boys girls
Controls 2 2 3 1 8 2 2 3 1 8 (10)
ADHD+ 3 0 1 0 4 3 0 1 0 4 (9)
ADHD¯ 3 0 0 0 3 3 0 0 0 3 (11)
PDD 4 1 1 0 6 4 1 1 0 6 (12)
12 3 5 1 21 12 (23) 3 (5) 5 (11) 1 (3) 21 (42)
DRD2
Taq1A GG
SS/SL LL SS/SL LL
TABLE 1. Distribution of the children in the two 5-HTTLPR groups across gender, clinical status, and
medication (ADHD+: taking MPH; ADHD-: being MPH-free during the experiment). Numbers between brackets present the original numbers from which the matched groups were drawn.
boys girls boys girls boys girls boys girls
Controls 3 1 1 2 7 3 1 1 2 7 (13)
ADHD+ 2 0 2 0 4 2 0 2 0 4 (9)
ADHD¯ 1 2 0 0 3 3 0 0 0 3 (11)
PDD 5 0 0 1 6 5 0 1 0 6 (12)
11 3 3 3 20 13 (23) 1 (5) 4 (14) 2 (3) 20 (45)
5-HTTLPR
LL SS/SL
GG Taq1A GG Taq1A
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TASK AND EXPERIMENTAL PROCEDURE
The children performed a probabilistic learning task in which they had to learn
stimulus-response (S-R) combinations by making use of performance feedback. As the
task has been thoroughly described in a previous report of our group (Groen et al.,
2007), only the essentials of the task are described here. The whole experiment
consisted of nine different task blocks. Within each block, which consisted of 96
stimulus presentations (trials), four colored pictures belonging to the categories animals,
fruits, music, and sports, were randomly presented on a PC screen. For each of the four
pictures, the children had to discover which of two keys to press by attending to
feedback stimuli. In the beginning of each block, they were ignorant of the two
feedback conditions that were assigned to the stimuli. Two of the four pictures (A and
B) were always followed by informative feedback. Pressing the left key to picture A
always resulted in positive feedback (indicated by a green square appearing at the PC
screen), while pressing the right key resulted in negative feedback (indicated by a red
square). For picture B this coupling was opposite: pressing the right key resulted in
positive feedback and pressing the left key was followed by negative feedback. The
other two pictures (C and D) were followed by uninformative feedback. The feedback
valence for picture C was always positive and that for picture D always negative, i.e.,
the feedback stimuli were unrelated to the children’s response. The uninformative
feedback condition had been included to control for the validity of the feedback
manipulations. An example of a single trial is presented in Figure 1.
FIGURE 1. Time course of a single trial. Within one task block each trial started with the
presentation of one out of four stimuli. The feedback stimulus appeared 1000 ms after stimulus
off-set and stayed on the screen for 1500 ms. The next trial started after a variable Inter Trial
Interval (ITI) of 500, 750, or 1000 ms. Originally published in Groen et al., 2007.
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Each of the nine blocks, which were randomly presented, initiated a new learning
process with four new pictures. Within the informative condition the number of trials
for each feedback valence was variable as it depended on the individual error rate of the
child. In the uninformative condition the number of trials for both positive and negative
feedback was 24. Instructions were to win as many points as possible. Positive feedback
and negative feedback indicated the win or loss of one point, respectively. Feedback
indicating loss of two points appeared on the screen when the child responded too late,
i.e. after a previously determined individual deadline. This individual response deadline
(mean reaction time + 10%) was introduced to elicit enough error trials for computing
error-related potentials and to take into account individual differences in response
speed. It was determined in a deadline determination block before the start of the actual
experiment, in which a black square appeared on the screen when children responded
too late. The children started with 52 points in the beginning of each block, which could
add up to a maximum of 100 points. Standardized instructions and a practice block of
24 trials preceded the deadline determination block containing 96 trials. When prepared
for physiological recording the children performed the 9 task blocks with a 20 minutes
break after the fifth block. At the end of the experiment all children received a present
independent of the number of points they won.
PERFORMANCE MEASURES
The task was built and presented by means of the program E-Prime (version 1.1;
Psychological Software Tools). Key type (left or right), reaction time (RT), and
response accuracy were recorded for every trial. To investigate the process of learning
in the informative feedback condition, each block was cut into four consecutive sections
(quartiles), which were then averaged across the nine blocks. Three performance
measures were computed for all quartiles: percentage of correct responses, RTs and
individual standard deviations of the RTs (SDRT).
EEG RECORDINGS AND COMPUTATION OF ERPS
The EEG was recorded using a lycra stretch cap (Electro-Cap Center BV) with 21
electrodes, placed according to the 10-20 system (O1, Oz, O2, P3, P5, P7, Pz, P4, P6,
P8, C3, Cz, C4, F3, Fz, F4, F7, F8, FP1, FPz, and FP2). Vertical and horizontal eye
movements were recorded with electrodes above and next to the left eye, respectively.
For all channels Ag-AgCl electrodes were used and impedances were kept below 10
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kΩ, which we considered low enough given the extremely high input impedance (109
kΩ) of our amplifier (Feree et al., 2001). All channels were amplified with filters set at a
time constant of 1 s and a low pass cut-off frequency of 130 Hz (REFA-40 system TMS
International B.V.). The gain of the pre-amplifier was 20, but the rest of the system was
a digital amplifier after 22-bits sampling. Details can be found at
http://www.tmsi.com/?id=7. The signals were recorded with a sampling rate of 500 Hz
(Portilab, version 1.10, TMS International B.V.), off-line filtered with a 0.25 Hz high
pass and 30 Hz low pass filter, and referenced to the left ear electrode (BrainVision;
version 1.05, Brain Products).
ERPs were computed for the informative feedback conditions only, as for this
condition, effects of response type (correct vs. incorrect), feedback valence (positive vs.
negative) and learning were shown to be most pronounced (Groen et al., 2007). We
moreover confined our analyses to those electrode positions that had previously
revealed the greatest effects of these task manipulations, i.e., Fz and Pz for the
response-locked ERN and Pe, respectively, and Pz for the feedback-locked P3 and LPP.
To investigate the error-related ERN and Pe, EEG segments were cut around the
children’s responses ranging from 500 ms before to 800 ms after response onset, with
the first 200 ms serving as a baseline. This was done for both response types, i.e. correct
and incorrect responses. Segments for investigating the feedback-induced P3 and LPP
were cut around the feedback stimulus, in order to keep the number of rejected
segments due to artifacts as low as possible. These segments ranged from -200 ms to
1000 ms after feedback onset, with the first 200 ms serving as a baseline. All segments
were scanned for artifacts. Segments with very high or low activity (exceeding ±200
µV) and/or spikes and/or drift due to large eye-movements, head or body movements, or
equipment failure were removed before the analyses. Segments with eye blinks were
kept and corrected, adopting the Gratton & Coles procedure (Gratton et al., 1983). For
every child the segments were then averaged separately for the different electrode
positions and each of the response or feedback conditions. Moreover, to study the
process of learning, segments were separately averaged for the first halves and second
halves of the task blocks.
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STATISTICAL ANALYSES
Performance measures were analyzed by means of a repeated measures analysis of
variance (ANOVA) with the within subject variable “task section” (quartile 1 to 4) and
the between subjects variable “group”. Dependent measures were mean percentage of
correct responses, mean RT, and SDRT. There were separate runs for the group
comparisons regarding(1) 5-HTTPR S carriers vs. non S carriers; (2) DRD2 Taq1 A
carriers vs. non-Taq1 A; and (3) 5-HTTPR/DRD2 combination: SA vs. LG.
As we could not exactly know what latency is likely to contain group or task
manipulation effects, statistical analyses of the ERP components were conducted on
mean amplitude values that were computed for successive intervals. For the short-
lasting ERN, intervals of 20 ms were chosen, whereas for the longer lasting
components, i.e. the Pe, the feedback P3, and LPP, intervals of 50 ms were chosen. On
all successive intervals repeated measures ANOVAs were conducted by applying a 2*2
design, with as within subject variables (1) “response type” (correct vs. incorrect) in
case of response-locked segments or “valence” (positive vs. negative) in case of
feedback-locked segments and (2) task “half” (first vs. second half of the task, each
containing the mean values of the nine blocks). Again, in three separate runs the factor
“group” with the levels described above was entered as a between subjects variable.
Analyses on mean amplitudes of multiple successive intervals may, however increase
the experiment-wise Type I error. As there were 10 intervals for the ERN (running from
100 ms before until 100 ms after the response), 10 for the Pe (running from 100 ms to
600 ms post response), 5 for the feedback-P3, and 9 for the LPP (running from 200 ms
to 450 ms and 450 ms to 900 ms post feedback, respectively) effects of a single interval
were considered meaningful only when both statistically significant (p ≤ .05) and with a
high effect size (η2 ≥ .14 (Stevens, 2002). Effects with medium effect size (η2 ≥ .06),
even when only marginally significant (.05 ≤ p ≤ .1), had to occur in three or more
successive ERN, Pe, or LPP intervals and at least two successive feedback-P3 intervals
in order to be considered meaningful, since the chance of finding three consecutive
effects at a p = .1 rejection level within a series of, for example, 10 successive intervals
is reduced to 8 x 0.1 x 0.1 x 0.1 = 0.009, while finding two consecutive effects at a p =
.1 rejection level in a series of 5 intervals is reduced to 4 x 0.1 x 0.1 = 0.04. For
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consecutive intervals the minimum and maximum F-values (Fmin and Fmax, respectively)
with the corresponding levels of significance and effect sizes (η2 ) are reported.
RESULTS
PERFORMANCE MEASURES
For all three group comparisons, significant quartile effects were found on mean
percentage of correct responses (p < .001, η2 > .70) and response time variability
(SDRT; p < .001, η2 > .45), which respectively increased and decreased with task
progression. Mean percentages of correct responses varied across the groups from
61.1% to 64.0% in the first quartile and from 77.2% to 85.8% in the last quartile of the
task. Mean RTs varied across the groups from 476.81 ms to 495.95 ms in the first
quartile and from 476.35 to 489.28 ms in the fourth quartile.There were no significant
group effects or group by quartile interactions for any of the performance measures.
EVENT-RELATED POTENTIALS
Task manipulation effects (tested on the whole group of 65 children) as well as group
effects are summarized in Tables 4 and 5. Figures 2, 3, and 4 show the ERPs for the 3
pair-wise group comparisons conducted on the response-locked ERN and Pe (Figures 2
and 3) and feedback-locked P3 and LPP (Figure 4). Group interaction effects are
depicted in Figures 5a through 5i. While the tables present (minimum and maximum) F-
ratios (of consecutive intervals) with corresponding p-values and effect sizes the figures
show mean amplitudes of the (successive) intervals that contained group effects with at
least medium effect sizes. Note that all interactions shown in the figures are significant
at p ≤ .05.
Before group comparisons on ERP amplitudes were carried out we checked whether the
corresponding groups differed in the number of trials included in the (condition-
dependent) ERP averages. Across groups the mean number of trials varied from 25 to
35 in the incorrect response condition and from 122 to 144 in the correct response
condition of the second task half. For none of the 8 conditions (i.e. 4 response-locked
and 4 feedback-locked conditions) differed the compared groups significantly from each
other in their number of trials included.
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RESPONSE-LOCKED: ERN ON FZ
The existence of an ERN on error trials is reflected by main effects of “response type”,
while significant two-way interactions “response type by task half” reflect the expected
greater ERN in the second half of the task (see Table 4 and Figure 2).
Comparison of the 5-HTTLPR-based groups revealed main effects of “group” for ten
successive intervals and three-way interactions “response type by task half by group”
for the three successive intervals running from -60 to 0 ms (Table 4). Figure 2 (a and b)
shows that 5-HTTLPR S allele carriers exhibit a greater ERN in especially the second
task half. Post hoc comparison of the two groups on the incorrect responses of the
second task half revealed significant group differences [t(38) = 2.89; p = .006]. Mean
amplitude values of these intervals are depicted in Figure 5a demonstrating this 3-way
interaction.
Comparison of the DRD2-based groups showed that there were neither main effects of
group nor significant interactions of group with any of the task variables for any of the
investigated intervals (Figures 2c and 2d).
Comparison of the 5-HTTLPR/DRD2 combination groups resulted in a significant
three-way interaction “response type by task half by group” with a high effect size for
the interval running from -20 to 0 ms (Table 4). Figure 2 (e and f) shows that children
carrying both the 5-HTTLPR S allele and the DRD2 Taq1 A1 variant exhibit a greater
ERN in especially the second task half [t(29) = 2.8; p = .01]. Mean values are presented
in Figure 5b.
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ERN at Fz (-100 to 100 ms) Pe at Pz (100 to 600 ms)
Task manipulations Fmin(1, 64); p; η2 Fmax(1,64); p; η
2 Fmin(1,64); p; η
2 Fmax(1,64); p; η
2
-100 ms to 60 ms 100 ms to 550 ms response type 14.9; < .001; .19 59.2; <.001; .49 13.5; < .001; .18 306; < .001; .83
-80 ms to 40 ms 100 ms to 450 ms response type by task half 4.2; .04; .07 14.9; <.001; .19 5.4; .02; .08 57.1; < .001 ; .48
Group comparisons 1) 5-HTTLPR: SS/SL vs. LL Fmin(1, 38); p; η
2 Fmax(1,38); p; η
2 Fmin(1, 38); p; η2 Fmax(1,38); p; η
2 - 80 ms to 80 ms group
3.7; .06; .09 5.3; .03; .12
n.s.
200 ms to 400 ms group by response type n.s. 4.1; .05; .10 6.1; .02; .14
- 60 ms to 0 group by response type by task half 3.7; .06; .09 6.6; .01; .15
n.s.
2) DRD2: Taq 1 A vs. GG Fmin(1, 40); p; η2 Fmax(1,40); p; η
2 Fmin(1, 40); p; η2 Fmax(1,40); p; η
2 group n.s. n.s.
group by response type n.s. n.s.
group by response type by task half
n.s. n.s.
3) 5-HTTLPR / DRD2:
SS/SL + Taq1 A vs. LL + GG
Fmin(1, 29); p; η2
Fmax(1,29); p; η2
Fmin(1, 29); p; η2 Fmax(1,29); p; η
2
group n.s. n.s.
group by response type n.s. n.s. - 20 ms to 0 group by response type by
task half 5.3; .03; .15
n.s.
TABLE 4. ANOVA results of the response-locked ERN and Pe measured at Fz and Pz, respectively. F-
ratios and corresponding significance levels as well as effect sizes for interaction effects are presented
below the (successive) intervals for which they were found. Note that minimal F-ratios (Fmin ) with a p ≥
.5 < .1 form part of a series of successive effects with at least medium effect size (η2 ≥ .06).
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P3 at Pz (200 – 450 ms) LPP at Pz (450 to 900 ms)
Task manipulations Fmin(1, 64); p; η2
Fmax(1,64); p; η2 Fmin(1,64); p; η
2 Fmax(1,64); p; η
2
200 ms to 450 ms 450 ms to 800 ms feedback valence 12.9; .001; .17 29.8; < .001; .31 4.1; .05; .06 9.5; .003; .13
500 ms to 700 ms valence by task half n.s. 4.0; .05; .06 6.5; .01; .09
Group comparisons 1) 5-HTTLPR: SS/SL vs. LL Fmin(1, 38); p; η
2 Fmax(1,38); p; η
2 Fmin(1, 38); p; η2 Fmax(1,38); p; η
2 group n.s. n.s.
group by valence n.s. n.s. 350 ms to 450 ms group by valence type by
task half 2.82; .1; .07; 4.8; .03; .11
n.s.
2) DRD2: Taq 1 A vs. GG Fmin(1, 40); p; η2
Fmax(1,40); p; η2 Fmin(1, 40); p; η
2 Fmax(1,40); p; η2
group n.s. n.s. 350 ms to 450 ms 450 ms to 850 ms group by valence
3.66; .06; .08 4.06; .05; .09 2.89; .1; .07; 9.3; .004; .19
450 ms to 550 ms group by valence by task half
n.s. 4.4; .04; .10 5.8; .02; .13
3) 5-HTTLPR / DRD2:
SS/SL + Taq1 A vs LL + GG
Fmin(1, 27*); p;η2
Fmax(1,27); p; η2 Fmin(1, 27); p; η
2 Fmax(1,27); p; η2
group n.s. n.s. 300 ms to 450 ms 450 ms to 650 ms group by valence
2.9; .1; .10; 4.8; .04; .15 3.08; .1; .10 3.66; .07; .12
400-450 ms 450 ms to 550 ms group by valence by task half 5.4; .03; .17 5.4; .03; .19 6.7; .02; .20
TABLE 5. ANOVA results of the feedback-locked P3 and LPP both measured at Pz. F-ratios and
corresponding significance levels as well as effect sizes for interaction effects are presented below the (successive) intervals for which they were found. Note that minimal F-ratios (Fmin ) with a p ≥ .5 < .1
form part of a series of successive effects with at least medium effect size (η2 ≥ .06).
* Note: degrees of freedom are smaller than for the ERN / Pe comparisons, because two children (one
in each group) had too few trials left for reliable ERP averaging due to too many artifacts in the EEG
signals.
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FIGURE 2. ERN time-locked to the response (0 ms) at Fz. ERPs are depicted for both the first and
second half of the task and correct and incorrect responses; a) 5-HTTLPR S carriers; b) homozygous
5-HTTLPR L carriers; c) DRD2 Taq1 A1 carriers; d) DRD2 Taq1 A1 non-carriers; e) carriers of both
the 5-HTTLPR S variant and the Taq1 A1 allele of the DRD2 gene; f) carriers of neither the 5-
HTTLPR S variant nor the Taq1 A1 allele of the DRD2 gene.
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FIGURE 3. Pe time-locked to the response (0 ms) at Pz. ERPs are depicted for both the first and second
half of the task and correct and incorrect responses; a) 5-HTTLPR S carriers; b) homozygous 5-
HTTLPR L carriers; c) DRD2 Taq1 A1 carriers; d) DRD2 Taq1 A1 non-carriers; e) carriers of both the 5-HTTLPR S variant and the Taq1 A1 allele of the DRD2 gene; f) carriers of neither the 5-HTTLPR S
variant nor the Taq1 A1 allele of the DRD2 gene.
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RESPONSE-LOCKED POTENTIALS: PE ON PZ
The ANOVA on the task variables revealed significant effects of “response type” as
well as significant interactions “response type by task half” (see Table 4). Amplitudes
were greater for incorrect than for correct responses with this effect being significantly
greater for the second task half (see Figure 3). This corroborates the presence of a
response-dependent Pe.
The only significant group effect was found for the 5-HTTLPR polymorphism. Here we
found significant “response type by group” interactions for the four successive intervals
running from 200 to 400 ms after feedback occurrence (Table 4). Figure 3 (a and b)
shows that the amplitude difference between correct and incorrect responses is greater
for the 5-HTTLPR S allele carriers than for the L allele carriers. This two-way
interaction is depicted in Figure 5c presenting the mean amplitudes of the four intervals.
FEEDBACK-LOCKED POTENTIALS: P3 AND LPP ON PZ
Test of the task manipulations resulted in significant main effects of feedback valence
for the five successive P3 and for seven successive LPP intervals. As expected, these
effects reflected larger amplitudes for negative feedback stimuli. In general, these
feedback effects were smaller during the second task half as is reflected by significant
two-way interactions “valence by task half”, for the four LPP intervals running from
500 ms to 700 ms after feedback (Table 5 and Figure 4).
Comparison of the 5-HTTLPR-based groups showed that these groups differed
significantly with respect to task half dependent differences in only their P3 amplitude.
This is reflected by three-way interactions “valence by task half by group” for the two
P3 intervals running from 350 ms to 450 ms after feedback (Table 5). Figure 4 (a and b)
shows that, within these P3 intervals, only 5-HTTLPR L carriers demonstrate a
decreased response to negative feedback during the second task half. Figure 5d
illustrates this 3-way interaction on the mean amplitudes of the two intervals. Post hoc
comparison on the difference between negative feedback responses during the first and
second task half showed nearly significant group differences [t(38) = 1.86; p = .07] with
medium effect size (η2 = .08).
Comparison of the DRD2-based groups resulted in “valence by group” interactions for
ten successive intervals comprising both the P3 (350 ms to 450 ms) and LPP (450 ms to
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850 ms). Moreover, significant three-way interactions “valence by task half by group”
were found for the two intervals within the LPP period running from 450 to 550 ms
(Table 5). Figure 4 (c and d) shows that Taq1 A1 allele carriers demonstrate greater
amplitudes in response to negative compared to positive feedback during both task
halves while no such prominent difference is seen for the Taq1 A1 non-carriers. In this
latter group no feedback valence effect was present for the first task half while for the
second task half it was even reversed, with greater amplitudes for positive than for
negative feedback. This interaction is reflected by Figure 5e. Figure 4 (c and d)
moreover shows that in contrast to noncarriers, the Taq1 A1 carriers demonstrate a
decreased response to positive feedback during the second half of the task. When testing
group differences on the children’s responses to only positive feedback, we indeed
found (nearly) significant “task half by group” interactions with medium effect sizes for
the six successive intervals running from 250 ms until 550 ms after the feedback
stimulus [Fmin(1,40) = 3.04; p = .1; η2 = .07; Fmax(1,40) = 4.27; p = .04; η2 = .10]. Mean
amplitude values of these intervals are depicted in Figure 5f reflecting this two-way
interaction, which was statistically significant [F(1,40) = 4.25; p = .046; η2 = .10].
Comparison of the 5-HTTLPR/DRD2 combination groups revealed (nearly) significant
“valence by group” interactions (with medium and high effect size) for three successive
P3 and four successive LPP intervals as well as significant 3-way interactions “valence
by task half by group” (with high effect sizes) for two successive LPP intervals (see
Table 5). In Figures 4 (e and f) and 5 (g and h) we see that the group possessing both the
5-HTTLPR S variant and the DRD2 Taq1 A1 variant exhibits greater feedback valence
differences for the P3 and LPP intervals than does the other group. The 3-way
interactions for the LPP moreover reflect that, different from noncarriers, the children
with both the 5-HTTLPR S allele and DRD2 Taq1 A1 variant respond with a decreased
potential to positive feedback during the second task half (Figure 5h). Analysis on the
mean amplitudes of the same six intervals as computed for the DRD2 groups revealed
again a significant interaction between group and task half for only the ERP responses
to positive feedback [F(1,29) = 4.63; p = .04; η2 = .14]. This interaction is depicted in
Figure 5i.
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FIGURE 4. P3 and LPP time-locked to the feedback stimulus (0 ms) at Pz. ERPs are depicted for both
the first and second half of the task and positive and negative feedback; a) 5-HTTLPR S carriers; b) homozygous 5-HTTLPR L carriers; c) DRD2 Taq1 A1 carriers; d) DRD2 Taq1 A1 non-carriers; e)
carriers of both the 5-HTTLPR S variant and the Taq1 A1 allele of the DRD2 gene; f) carriers of
neither the 5-HTTLPR S variant nor the Taq1 A1 allele of the DRD2 gene.
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FIGURE 5. Mean amplitudes (with standard errors) of the intervals that turned out to contain
significant group by task variable effects. Figures 5a through 5c reflect the group interactions found for the response-locked ERN and Pe. Figures 5d through 5i reflect the interactions found for the
feedback-locked P3 and LPP.
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SUMMARY OF THE MAIN FINDINGS
Comparison of the two 5-HTTLPR groups revealed significant differences in the ERN,
Pe, and feedback-related P3, but there were no differences in the feedback-related LPP.
Regarding the response-locked ERN we found a significantly greater response to errors
during especially the second task half in the group with the S variant (Figure 2, a and b;
Figure 5a). For the later occurring response-locked Pe it was again the group with the S
variant showing the greater response to errors (Figure 3, a and b; Figure 5c). Moreover,
only the L carriers showed a significantly decreased P3 response to negative feedback
during the second task half reflecting a decreased dependency on negative feedback
developing with task progression (Figure 4, a and b; Figure 5d ).
Comparison of the two DRD2 groups revealed significant differences in only their
feedback-related P3 and LPP. Taq1 A1 allele carriers exhibited a greater sensitivity to
negative feedback in general (Figure 4c and d; Figure 5e), and – different from the non-
carrier group – a decreased sensitivity to positive feedback during the second task half
in particular (Figure 4, c and d; Figure 5f).
Finally, the two 5-HTTLPR/DRD2 combination groups differed significantly from each
other in their response-related ERN (Figure 2, e and f; Figure 5b) as well as their
feedback-related P3 and LPP (Figure 4e and 4f). LPP differences referred again to the
DRD2 Taq A1 / 5-HTTLPR S group showing a greater sensitivity to negative feedback
during both task halves (Figure 5h) and a decreased sensitivity to positive feedback
during the second task half (Figure 5i). Comparison of the effect sizes suggests that the
task manipulation dependent group effects on the feedback P3 and LPP, as reflected by
the significant 3-way interactions, are larger for the combination group than for the 5-
HTTLPR-matched DRD2 group comparison
DISCUSSION
The present study demonstrates that the serotonin transporter gene 5-HTTLPR and the
dopamine D2 receptor gene DRD2 differentially affect distinct aspects of error and
feedback processing. Whereas children with the short variant of the 5-HTTLPR gene
appeared to show greater sensitivity to error processing, children possessing the DRD2
Taq1 A1 allele differed in their sensitivity to both negative and positive feedback, as
compared to children who did not possess the respective gene variants. As the groups
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did not differ in their task performances, the group differences that were seen in the
ERPs could not be explained by differences in the number of committed errors
(Ullsperger, 2006).
Both the ERN and Pe were found to be significantly more enhanced in the 5-HTTLPR S
group than in the LL group, the ERN especially during the second task session. These
findings are in line with those by Fallgatter and colleagues (Fallgatter et al., 2004) who
also reported on both an enhanced ERN and Pe amplitude in a (smaller) sample of adult
S allele carriers compared to a sample of age- and gender-matched homozygous L allele
carriers. The ERN has been considered to reflect anterior cingulate cortex (ACC)
activity in response to a negative reinforcement signal from the dopaminergic
mesencephalon (Holroyd & Coles, 2002), and indeed there are several studies that
indicated the ACC as the main source of the ERN (Taylor et al., 2007). The
serotonergic part in the generation of the ERN might therefore in the first place be
explained by the role of the ACC, which has previously been described as a structure
that is rich in 5-HT receptors (Haznedar et al., 1997), while moreover ACC metabolic
activity has been reported to be normalized in depressive patients through using the
serotonin reuptake inhibitor sertraline (Mann et al., 1996).
Variations in the ERN, however, might also be explained by the involvement of cortico-
limbic circuits, in which both the ACC and the amygdala play an important role. Similar
to the ACC, the amygdala is innervated by serotonergic neurons, and 5-HT receptors are
present throughout its sub-nuclei (Azmitia & Gannon, 1986; Smith, Daunais, Nader, &
Porrino, 1999). A series of independently conducted neuro-imaging studies (functional
magnetic resonance imaging [fMRI] and positron emission tomography) on both phobic
patients and healthy adults revealed that subjects carrying the 5-HTTLPR S allele
exhibited significantly increased amygdala activity when processing aversive stimuli or
engaging in anxiety provoking activity such as public speaking (Bertolino et al., 2005;
Brown & Hariri, 2006; Heinz et al., 2005). Here it is important to note that Brown and
Hariri (2006) were able to show that the S-allele-driven enlarged amygdala
responsiveness appeared to be equally pronounced in both sexes and in carriers of one
or two S-alleles. Another extensive (f)MRI study conducted by Pezawas and colleagues
(2005) revealed that 5-HTTLPR S-allele carriers showed significantly reduced grey
matter volume of both the perigenual ACC and the amygdala, with moreover less
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structural covariation and a weaker functional connectivity between the amygdala and
the ACC, suggesting amygdala hyper-responsivity to be due to weaker inhibitory
control by the ACC.
Given the above-mentioned findings of greater amygdala responsiveness to
(social)performance-anxiety evoking situations and aversive, especially fear- and anger-
expressing stimuli as well as the repeatedly reported increased sensitivity to stress
(Caspi et al., 2003; Covault et al., 2007) exhibited by carriers of the 5-HTTLPR S allele,
we suggest that an increased ERN as a measure of the individual’s sensitivity to error
commission may reflect a predisposition to serotonergically driven (social)
performance-anxiety. This suggestion may be further supported by the nearly significant
positive correlation (r(52) = .26, p = .06) between the magnitude of the ERN and
children’s scores on a scale reflecting internalizing (i.e. anxiety- and depression-related)
behavior as measured by the Child Behavior Checklist (Achenbach & Rescorla, 2001),
which has previously been found for the group of patients included in the present study
(accepted for publication in this journal: Groen et al., 2008).
Concerning the second response-related somewhat later occurring error positivity, the
Pe, we also found an effect of only the 5-HTTLPR polymorphisms, the S allele carriers
showing significantly larger amplitudes in response to errors. The Pe has been proposed
to be a P3-like response (O'Connell et al., 2007; Overbeek et al., 2005) reflecting phasic
changes in locus ceruleus norepinephrine (LC-NE) activity (Nieuwenhuis et al., 2005).
Moreover, as, in contrast to the ERN, the Pe was shown to be related to the post-error
slowing of response times, it has been suggested to reflect error awareness and
subsequent adaptive behavior (Overbeek et al., 2005). Yet, how could LC activity be
affected by the serotonin transporter gene? As the association between 5-HTTLPR
polymorphisms and amygdala activation has been rather well-established (see for a
review Munafo et al., 2008), here again the amygdala may play a mediating role. Phasic
changes in LC activity have been repeatedly observed to be triggered by signals that the
LC receives from especially the central nucleus (CeN) of the amygdala, which has been
proposed to not only participate in emotional learning but also in attentional i.e.
conscious processing (Bouret, Duvel, Onat, & Sara, 2003; LeDoux, 2007). These CeN-
related LC responses are associated with the predictive value or meaning of a stimulus
rather than with its physical properties (see for a review Bouret et al., 2003). The
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somewhat longer pathway along the LC may therefore explain not only the later
occurrence of the Pe as compared to that of the ERN but also its proposed functional
meaning i.e., reflecting error awareness. Possessing the short variant of the 5-HTTLPR
gene may hence play an important role in both unconscious error detection and the
conscious processing of errors needed for behavior adjustment.
Turning to the feedback-related ERPs, and, first of all, to the feedback P3, we found that
the 5-HTTLPR S allele carriers showed no decrease in (negative) feedback dependency,
a decrease that has previously been found to accompany increased internal monitoring
as is expressed by an increasing ERN with task progression. As, however, these
children did show an increase in internal monitoring, we propose that the absence of a
decreased feedback P3 with task progression reflects a remaining state of alertness to
negative feedback stimuli. We speculate that this may be due to an enhanced sensitivity
to negative information or criticism. In combination with greater error sensitivity and
error awareness this would agree with the notion that carriers of the 5-HTTLPR S allele
have a predisposition to developing (social) performance anxiety.
DRD2 polymorphism dependent variations were especially found for the later occurring
and longer lasting LPP complex. Our findings suggest that carrying the Taq1 A1 allele
is associated with a generally greater sensitivity to negative feedback, yet at the same
time diminishing sensitivity to positive feedback with task progression. Although our
findings do not agree with recently published results from a fMRI study (Klein et al.,
2007) where a weaker response to negative feedback in DRD2 Taq1 A carriers was
found, they do agree with another finding from that study reporting on a reduced
reward-related increase in nucleus accumbens (NAc) activity in Taq1 A1 allele carriers,
as well as with results from previous studies suggesting the DRD2 Taq1 A1
polymorphism to be involved in the Reward Deficiency syndrome (Balleine, Delgado,
& Hikosaka, 2007; Bowirrat & Oscar-Berman, 2005). The Reward Deficiency
syndrome has been referred to as a reduced sensitivity to reward associated with
abnormalities in dopaminergically driven cortico-striatal brain regions including the
ventral striatum and the NAc. Striatal D2 signaling has been shown to regulate
motivational processes in mice (Drew et al., 2007), and the NAc in particular has been
proposed to be the dopaminergic structure that is most reliably linked to reward-related
processes and alcohol dependence (Bowirrat & Oscar-Berman, 2005). Our findings on
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the LPP may be in support of the hypothesis that gradually getting insensitive to a
regularly offered positive reinforcement, as found here for only the DRD2 Taq1 A1
allele carrying children, may lead to seeking other types of reward in order to keep the
neuronal release of dopamine at a level that counteracts the rise of negative feelings
(Bowirrat & Oscar-Berman, 2005).
Returning to the common association found for both genes with alcohol dependence
(e.g. Feinn et al., 2005; Bowirrat & Oscar-Berman, 2005; Preuss et al., 2007) there
hence may be indeed different neurophysiological systems and mechanisms leading to
the same behavior: it may arise from the need to reduce anxiety-related feelings (i.e. an
overactive BIS) mediated by the S allele of the 5-HTTLPR gene or have a reward and
sensation-seeking origin (related to an overactive BAS) that is mediated by the Taq1 A1
allele of the DRD2 gene. The latter may explain the reported liability to other types of
drug addiction and gambling as well. Our findings of differential 5-HTTLPR and DRD2
effects on ERPs that are related to the distinct aspects of error and feedback processing
as outlined above are quite supportive of this hypothesis.
Still, the mechanisms underlying reward processing are probably more complex than
resulting from dopamine release alone. Bowirrat and Oscar-Berman (2005) describe a
“reward cascade” involving the release of serotonin that finally leads to a fine tuning of
dopamine release by stimulating enkephalin which in turn inhibits the release of γ-
aminobutyric acid. The authors therefore pointed to the combined effects of various
genes for different neurotransmitters resulting in a final inefficiency of the reward
system.
Our findings on the feedback-related ERP differences between the two groups formed
on the basis of both the 5-HTTLPR and the DRD2 gene suggest the involvement of both
serotonin and dopamine in feedback processing as, different from the response-related
ERPs, group by task variable interactioneffects appeared to be greater in the comparison
of the 5-HTTLPR and DRD2 combination groups than in the comparison of the groups
that were formed on the basis of the DRD2 gene alone. The ERP plots presented in
Figures 2 and 4 are suggestive of such combined effects. Yet, as direct comparison of
these groups with any of the 5-HTTLPR or DRD2 groups is complicated by overlap in
participants a straightforward conclusion about additive or interactive effects of the two
polymorphisms cannot be drawn.
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Other study limitations need to be acknowledged. First, although many differences
reached statistical significance, our sample size was relatively small, not allowing for
the study of the recently suggested tri-allelic 5-HTTLPR genotypes (Hu et al., 2006) or
to form groups large enough in order to statistically test interactive effects between the
5-HTTLPR and DRD2 variants. A second weakness may have been the use of a
somewhat heterogeneous study sample consisting of children with ADHD or PDD
along with healthy controls. To control for this heterogeneity, however, we succeeded in
optimal matching of groups with regard to both genotype and clinical status. With
respect to gender, however, there were three more girls in the 5-HTTLPR LL group
than in the S group. As previously an interaction effect of age and gender on the ERN
has been found, with girls showing a smaller amplitude at the age of 10 but amplitudes
similar to boys at the age of 11 and 12 (Davies et al., 2004), we also tested for
interaction effects of gender by response type on amplitudes in the ERN (Fz) intervals.
We did so on the whole group of 65 children (51 boys and 14 girls) participating in this
study and indeed found a significant interactions (p < .05) for the intervals running from
-100 ms before to 20 ms after the response. These, however, showed greater negativities
to incorrect responses for the girls. The smaller ERN found for the 5-HTTLPR LL
group is therefore unlikely to be caused by the three more girls who, moreover, had a
mean age of 11.8 (SD = 0.68) years, which was slightly higher than that of the three
girls in the 5-HTTLPR S group (M = 11.1; SD = .41).
One finally might question whether the same results were obtained in a sample of
adults, as the structures involved in the generation of the ERP components investigated
may not yet have been fully matured. Next to the fact that genetic profiles are invariant
there are two arguments for assuming comparability. (1) Our findings on the 5-
HTTLPR polymorphisms agree with those on the ERN and Pe of an adult study
conducted by Fallgatter and colleagues (2004), and (2) all components investigated
have previously been found in adult studies and shown to be sensitive to the same type
of task manipulations in healthy children (Groen et al., 2007) of the same ages as
investigated in the present study.
In conclusion, the present study points to differential effects of common polymorphisms
of the 5-HTTLPR and DRD2 genes on reinforcement-related learning, with 5-HTTLPR
S carriers having increased sensitivity to error processing, and DRD2 Taq1 A1 carriers
CHAPTER 5
164
exhibiting greater sensitivity to negative feedback and task progression dependent
decreasing sensitivity to positive feedback. Our findings are in line with what has
repeatedly been suggested in the literature i.e. the 5-HTTLPR S allele contributing to a
predisposition for anxiety-related behavior and the DRD2 Taq1 A1 allele to a
predisposition for the reward deficiency syndrome.
CHAPTER 6
GENERAL CONCLUSIONS AND DISCUSSION
GENERAL CONCLUSIONS AND DISCUSSION
166
SUMMARY OF THE KEY FINDINGS
CHAPTER 2 describes the psychophysiology of error and feedback processing in
preadolescent TD children (10 to 12 years old) while they perform a feedback-based
learning task. In this paradigm, called the probabilistic learning task, the children are
asked to discover which button to press for which picture. They are unaware that some
pictures are coupled with informative feedback (the feedback is related to their
response), whereas others are coupled with uninformative feedback (no matter what
response, the feedback is either always correct, or always incorrect because it is related
to the stimulus). As expected the children make more and more correct responses as the
learning task progresses when provided with informative feedback; there is a learning
curve in accuracy. When provided with uninformative feedback the children keep
changing their type of response, suggesting that they are actively ‘testing’ which button
to press for which picture. The children are ‘testing’ more when provided with always
negative feedback than with always positive feedback, but for both feedback valences
this testing behaviour decreases with task progression.
Both the ERP and EHR measures show that the children learn to discriminate
informative from uninformative feedback during task progression. Within the
informative condition the ERPs show that with task progression feedback-related ERPs
decrease in amplitude, i.e. the P2a, P3/LPP and prefeedback SPN, while response-
related ERPs to errors increase in amplitude, i.e. ERN and Pe. This implies that the
children make a transition from feedback-related monitoring to response-related
monitoring when they are learning by performance feedback. The EHR pattern parallels
this transition, by a shift in timing from a more feedback-related HR deceleration to a
more response-related HR deceleration. Significant positive correlations were found
between the ERN amplitude and EHR deceleration on error trials, whereas no
correlations were found between the other ERP components and EHR deceleration on
error trials. The similarity in the functional characteristics of the ERN and the EHR
deceleration on error trials on the one hand, and the positive correlations between the
two on the other, suggest that they reflect activity of the same error monitoring system.
CHAPTER 3 describes the electrocortical processing of errors and feedback in Mph-
treated and medication-free children with ADHD and children with ASD. Using the
same probabilistic learning paradigm, these clinical groups were analysed in
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comparison to the TD group described in CHAPTER 2. All experimental groups showed
an equally steep learning curve for accuracy with task progression, but the overall
achieved accuracy level was lower for the clinical groups than for the TD group.
Regarding the performance measures the clinical groups could only be discriminated on
reaction time variability; the medication-free ADHD children responded more variably
than the ASD children and the Mph-treated ADHD group.
The clinical groups could, moreover, be discriminated on a set of ERP components
evoked by error and feedback processing. The response-related ERPs suggest that, in
contrast to TD children and children with ASD, the medication-free children with
ADHD have a deficit in both early error detection, as reflected by an attenuated ERN,
and subsequent conscious error processing, as reflected by an attenuated Pe. The
feedback-related ERPs in addition, suggested that in comparison to these children the
medication-free children with ADHD showed diminished learning effects on feedback
anticipation and early feedback processing, as reflected by the prefeedback SPN and
P2a respectively. This suggests that while children with ASD and TD children are
becoming less dependent on the feedback while learning, the medication-free children
with ADHD are staying dependent on the upcoming feedback stimuli throughout the
learning task. However, in comparison to the TD children both children with ADHD
(although this only concerned a trend to significance) and children with ASD appeared
to be compromised in late feedback processing, as reflected by an attenuated LPP.
Apart from evidence for a dissociation of ADHD and ASD on aspects of error and
feedback processing, CHAPTER 3 provides some evidence for a stimulating effect of
Mph on the electrocortical processing of errors and feedback. Compared to the
medication-free ADHD group, the Mph-treated group showed ‘normalised’ conscious
error processing, as reflected by a learning effect on the Pe, as well as ‘normalised’
learning effects on negative feedback anticipation and early feedback processing, as
reflected by increased learning effects on the prefeedback SPN and P2a, respectively.
We speculate that the ‘normalised’ conscious error processing in Mph-treated children
with ADHD facilitates predicting feedback outcome, explaining the ‘normalised’
feedback-related learning effects. Finally, no stimulating effect of Mph was found on
the compromised later feedback processing in children with ADHD.
GENERAL CONCLUSIONS AND DISCUSSION
168
CHAPTER 4 describes the autonomic responsiveness to errors and feedback in Mph-
treated and medication-free children with ADHD and children with ASD. These clinical
groups are compared to TD children, by making use of a selective attention paradigm
with three feedback conditions: reward, punishment and no feedback. All experimental
groups performed more efficient when provided with performance feedback, i.e. in the
reward and punishment condition compared to the condition without feedback they
responded slower and more accurately, showed less late responses and more post error
slowing. The three clinical groups were, however, less accurate on the task than the TD
group. The EHR analyses showed that the error-related HR decelerations elicited in the
TD group in all feedback conditions, were absent in the medication-free ADHD group.
Interestingly, the ASD group neither differed significantly from the TD group nor from
the medication-free ADHD group in their error-related HR decelerations, but non-
significant group effects showed medium effect sizes with both groups. The results of
this study suggest that the medication-free children with ADHD are autonomically less
responsive to errors as well as to negative feedback. However, regarding the children
with ASD the findings do not allow for strong conclusions about their autonomic
responsiveness to errors and feedback.
CHAPER 4, moreover provides evidence for a stimulating effect of Mph on the
autonomic responsiveness to errors and feedback in children with ADHD. Mph-treated
children with ADHD showed ‘normalised’ EHR decelerations on error trials in the
punishment and no feedback condition. In the reward condition, where emphasis was on
gain, EHR decelerations to negative feedback did not fully ‘normalise’. Mph, may thus
only stimulate the autonomic responsiveness to self-detected errors and punishment and
to a lesser extent to the absence of reward.
Finally, CHAPTER 5 describes the electrocortical processing of errors and feedback
during feedback-based learning in a large part of the sample described in CHAPTER 3.
However, instead of grouping the children by developmental disorder, they were
regrouped by the presence of common polymorphisms of two genes. These were (1) the
low activity short (S) variant and the long (L) variant of the serotonin transporter (5-
HTTLPR) gene and (2) the presence or absence of the Taq1 A1 polymorphism of the
D2 dopamine receptor (DRD2) gene. Although recently the Taq1 A1 polymorphism has
been related to the activity of another gene (ANKK1), we decided to keep referring to
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the DRD2 Taq1 A1 polymorphism, because this agrees best with the nomenclature used
in the majority of published studies to date. The performance measures indicated that
the compared experimental groups showed an equally steep learning curve for accuracy
with task progression and that these groups did not differ in their overall achieved
accuracy level or in mean RT.
The electrocortical data, however, provide evidence that the two genes involved in
serotonergic and dopaminergic neurotransmission, are differentially affecting error and
feedback processing. Children possessing the low activity S variant of the 5-HTTLPR
gene appear to process both errors and negative feedback more intensively compared to
children possessing the L variant, as was reflected by an enhanced ERN/Pe as well as a
decreased learning effect on the feedback-related P3. The effects were interpreted as a
greater sensitivity to errors and a remaining state of alertness to negative feedback
during feedback-based learning. In contrast, children possessing the Taq1 A1
polymorphism of the DRD2 gene processed negative feedback, but not errors, more
intensively than non-carriers, while showing a decreasing responsiveness to positive
feedback during task progression. This was reflected by an enhanced feedback P3/LPP
to negative feedback and a decreased P3/LPP to positive feedback with task
progression.
These findings suggest that carriers of the S variant of the 5-HTTLPR gene process
aversive events more intensively opposed to carriers of the L variant, while carriers of
the Taq1 A1 polymorphism of the DRD2 gene process aversive feedback more
intensively while showing habituation to correct, or appetitive, feedback opposed to non
carriers. The results of this chapter are, moreover, suggestive of combined effects in
children possessing both the 5-HTTLPR S variant and the DRD2 Taq1 A1 variant.
These children equalled the carriers of the S variant of the 5-HTTLPR gene regarding
error processing, as reflected by an increased learning effect on the ERN. They,
however, equalled the DRD2 Taq1 A1 variant regarding feedback processing, as
reflected by an enhanced feedback P3/LPP to negative feedback and a decreased
P3/LPP to positive feedback with task progression.
GENERAL CONCLUSIONS AND DISCUSSION
170
CAN ADHD AND ASD BE DISCRIMINATED ON THE
PSYCHOPHYSIOLOGY OF ERROR AND FEEDBACK PROCESSING?
First of all, this thesis shows that psychophysiological measures are a useful tool for
investigating differences between different neurodevelopmental disorders on aspects of
EFs. These measures give insight into the underlying component processes of these
functions that cannot be easily detected by performance measures alone. Using
cognitive tasks with different feedback manipulations, children with ADHD and
children with ASD could not be discriminated by their task performance. One exception
was the discrimination of medication-free children with ADHD and children with ASD
on individual response variability (CHAPTER 3). This finding fits with previous findings
in ADHD, as large individual variability in RTs seems to be the most universal finding
in ADHD research thus far (Kuntsi, Oosterlaan, & Stevenson, 2001; Van Meel,
Oosterlaan, Heslenfeld, & Sergeant, 2005a). The dissociation with ASD, moreover,
suggests that large individual variability in RTs may be specific for ADHD.
The electrophysiological measures in this thesis provide evidence for a dissociation of
ADHD and ASD in monitoring error responses. CHAPTER 3 shows that while
medication-free children with ADHD show decreased error-related components (during
the progression of feedback-based learning), children with ASD show no such deficit.
The ERN is hypothesised to reflect phasic dACC activity from the mesencephalic
dopamine system (Holroyd & Coles, 2002). The finding of a decreased ERN amplitude
in ADHD could, therefore, be in line with the bulk of neuroimaging studies, suggesting
that frontostriatal dopamine pathways are hypofunctional in ADHD (Bush, Valera, &
Seidman, 2005b; Castellanos & Tannock, 2002a; Dickstein, Bannon, Castellanos, &
Milham, 2006b; Durston, 2003a). The decreased Pe, moreover, suggests that children
with ADHD show attenuated phasic noradrenaline responses from the LC-NE system in
response to errors, as the Pe may reflect activity of this system (see the GENERAL
INTRODUCTION for an argumentation, Davies et al., 2001; Leuthold & Sommer, 1999a;
O'Connell et al., 2007; Overbeek et al., 2005). Attenuated error processing components
in ADHD may thus be in agreement with the catecholamine hypothesis that both
dopamine and noradrenaline are involved in the psychopathology of ADHD (Arnsten,
2006; Oades et al., 2005). However, as serotonergic neurotransmission has recently also
been found to be involved in monitoring error responses (Fallgatter et al., 2004, this
thesis), serotonin may additionally play a role in the psychopathology of ADHD.
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Reduced error processing in children with ADHD may hamper them in learning from
their mistakes and, on the longer term, to develop behaviour that is well-adjusted to the
environment. No evidence for such error monitoring impairment was present for the
ASD group, suggesting that the found error processing deficit is specific for ADHD.
It must be addressed that decreased error processing is inconsistently found in children
with ADHD. One explanation for this inconsistency may be the heterogeneity of the
disorder in general. The variation in ADHD symptoms is large, both between and within
individuals, both in severity and number. As specific patterns of symptoms may be
related to distinct neurobiological sources (Sagvolden et al., 2005a), the outcomes of
ERP research that usually investigates small samples of subjects, are vulnerable to the
composition of the samples. CHAPTER 5 of this thesis shows that there are between
subject variations in the style of error and feedback processing that are affected by
individual differences in genetic profile. Such genetically determined individual
differences, which appear to be independent of the type of developmental disorder, may
contribute to the heterogeneity of ADHD symptoms in general and the inconsistency of
findings regarding error and feedback processing in general. Next to the heterogeneity
of the ADHD samples, other factors, like task nature and difficulty and/or
methodological issues, like group differences in error rates, may influence findings
regarding error processing in ADHD (see for a discussion: Jonkman et al., 2007).
Moreover, the motivational context of the task appears of great importance when
investigating error and feedback processing in ADHD (e.g. task instructions and reward
contingencies; Holroyd, Baker, Kerns, & Muller, 2008).
Although we did not observe a feedback ERN in any of the experimental groups, other
feedback-related ERP components showed that the TD children and children with ASD
became less dependent on the feedback during learning. This was reflected by decreased
feedback anticipation (prefeedback SPN) and early feedback processing (P2a). The
medication-free children with ADHD, however, stayed dependent on the upcoming
feedback, as expressed by absent learning effects on these components. Using other
paradigms, other studies showed enhanced feedback processing in ADHD, as reflected
by enhanced feedback ERN (Van Meel et al., 2005b; Holroyd et al., 2008). We
speculate that the remaining dependency on feedback is the consequence of a deficit in
response monitoring, because disturbed conscious error processing at the time of the
GENERAL CONCLUSIONS AND DISCUSSION
172
response makes it hard to predict the feedback outcome. This interpretation may fit with
the ‘error-likelihood theory’ by Brown and Braver (2005), stating that the ACC learns
to predict the likelihood of the occurrence of an error or negative reinforcement given a
specific task condition (e.g. a specific stimulus-response combination). This function
serves as an early warning system that recruits cognitive control. In terms of this model,
the combination of diminished error processing and remaining dependency on negative
feedback during feedback-based learning in medication-free children with ADHD, may
be interpreted as a failure to prevent undesired consequences.
CHAPTER 3 also provides some evidence for less intensive late processing of negative
feedback in both medication-free children with ADHD and children with ASD. Both
groups showed smaller late positive potentials to negative feedback in comparison to
the TD children. In this chapter it is speculated that both children with ADHD and
children with ASD process the affective value of negative feedback less intensively.
They seem to suffer from decreased motivated attention, i.e. they may benefit to a lesser
extent from increased attentional processing when stimuli carry emotional or
motivational value (Vuilleumier, 2005).
In CHAPTER 4, it was tested whether children with ADHD and children with ASD could
be discriminated in their autonomic responsiveness to errors and feedback. The
medication-free children with ADHD failed to show EHR decelerations on error trials
in all feedback conditions. These findings imply that these children lack autonomic
responses when they are faced with aversive events, which is in line with previous
studies showing that heart rate of children with ADHD is less responsive to feedback in
general (Crone et al., 2003a; Luman et al., 2007; Luman et al., 2008) and to punishing
feedback in particular (Crone et al., 2003a). As EHR decelerations have been proposed
to reflect the inhibition of ongoing processes in the brain (Jennings & Van der Molen,
2002), medication-free children with ADHD may benefit less than TD children from
errors and feedback for adjusting their performance. These few, but quite consistent
findings support the hypothesis that medication-free children with ADHD suffer from
an underactive Behavioural Inhibition System (BIS), which is an aversive motivational
system responsible for the inhibition of ongoing behaviour in situations that involve
aversive stimuli such as punishment and reward extinction (Quay, 1988a; Quay, 1988b).
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173
Also, the children with ASD appeared to show smaller EHR decelerations on error
trials, but they could not be significantly discriminated from the medication-free
children with ADHD nor from the TD children. The lack of significant group effects of
the ASD group with both the ADHD and TD group restrains us to make firm
conclusions about the autonomic responsiveness to errors and feedback in children with
ASD (see CHAPTER 4 for a discussion).
As already mentioned in the GENERAL INTRODUCTION, both the dACC and rACC, along
with striatal and limbic structures and interconnected prefrontal areas, are involved in
error and feedback processing (Taylor et al., 2007). The findings of both diminished
electrocortical error processing (decreased ERN) and diminished autonomic error and
feedback processing (decreased error-related EHR deceleration) in medication-free
ADHD children may point to a decreased ACC function in ADHD. The ACC serves as
a ‘bridge’ between lower level brain structures involved in motivational (basal ganglia,
striatum) and affective or emotional processing (limbic system) and the prefrontal
cortex that is involved in a broad range of EFs (Bush et al., 2000). Decreased ACC
function in children with ADHD, as reflected by diminished error processing may,
therefore, imply that these children have difficulties in integrating ‘hot’ and ‘cool’
information for regulating their behaviour. The findings in the present thesis regarding
ADHD, therefore, support recent theoretical models of ADHD that stress the
involvement of both motivational and cognitive deficits (Sagvolden et al., 2005a;
Sonuga-Barke, 2002; Nigg & Casey, 2005; Sergeant, 2000). Moreover, more and more
work in neuroscience suggests that motivational and cognitive deficits in ADHD are
functionally and neurobiologically related (Nigg & Casey, 2005; Nigg, 2001),
suggesting that children with ADHD suffer from a ‘motivational cognitive deficit’ in
regulating their behaviour.
When interpreting the dissociations between ADHD and ASD regarding error and
feedback processing in this thesis, we have to bear in mind that the tested ASD children
in the present study were diagnosed as having PDDNOS (American Psychiatric
Association, 2000). This category of the Autistic Spectrum presents a subthreshold form
of autism, for which no positive criteria are formulated. Therefore, the results of this
thesis may not be generalised to children with the full-blown Autistic or Asperger
Disorder. Perhaps, children on the more disabled side of the Autistic Spectrum actually
GENERAL CONCLUSIONS AND DISCUSSION
174
are compromised in error or feedback processing. This reasoning is supported by a
study of Henderson and colleagues (2006), that reports a positive correlation between a
measure of impairment in social interactions and the ERN amplitude in children with
PDDNOS. Although we were not able to replicate this finding, this correlation suggests
that subjects with more severe problems in social interactions have also impaired error
monitoring. Moreover, the Henderson study found that more verbally capable patients
showed larger ERN amplitudes. Other neuroimaging studies report that ACC activity is
negatively associated with symptom presentation in autism (Haznedar et al., 2000;
Ohnishi et al., 2000). For future studies it is recommended to include children classified
within the wider spectrum of Autistic Disorder for testing this hypothesis. Given the
deficits in error and feedback processing in medication-free children with ADHD,
ADHD symptoms should definitely be controlled for when investigating error and
feedback processing in ASD or any other psychopathological condition.
DOES METHYLPHENIDATE STIMULATE ERROR AND FEEDBACK
PROCESSING IN ADHD?
Both CHAPTER 3 and CHAPTER 4 provide some evidence for a stimulating effect of Mph
on both the electrocortical and autonomic sensitivity to errors and feedback. CHAPTER 3
reports a selective effect of Mph-intake on the error-related ERP component reflecting
conscious error processing, i.e. the Pe, which was increased with feedback-based
learning in Mph-treated children with ADHD opposed to medication-free children with
ADHD. This finding is in line with a small placebo-controlled study by Jonkman and
colleagues (2007). CHAPTER 4 reports a stimulating effect of Mph-intake in children
with ADHD on their autonomic responsiveness to aversive stimuli such as errors and
punishment, but not to the absence of reward.
Both the electrocortical and autonomic findings are speculated to result from the
stimulating effect of Mph on the LC-NE system. It was recently suggested that
stimulants like Mph may decrease long-term baseline NE activity in the LC while
increasing phasic NE release (Pliszka, 2005). Several authors have suggested that the
conscious processing of motivationally relevant and salient stimuli, such as errors, are
linked to phasic responses of the LC-NE system (Davies et al., 2001; Leuthold &
Sommer, 1999a; O'Connell et al., 2007; Overbeek et al., 2005; Jonkman et al., 2007;
Nieuwenhuis et al., 2005). The greater error-related Pe amplitude in Mph-treated
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175
children with ADHD compared to medication-free children with ADHD may thus be
the reflection of a ‘normalised’ phasic responsiveness of the LC-NE system to
motivationally relevant events. The stimulating effect of Mph on the EHR decelerations
to errors and punishment may also be the result of this ‘normalised’ phasic
responsiveness of the LC-NE system. Error- and feedback-related EHR decelerations
may namely be considered the reflection of activity of the BIS system, which is (next to
serotonin) associated with activity in the noradrenergic pathways (Gray, 1985; Gray,
1987). This thesis, therefore, raises the hypothesis that the effect of Mph in children
with ADHD, regarding performance monitoring or cognitive control in particular, is
mediated through its noradrenergic component rather than trough its dopaminergic one.
One major limitation in the interpretation of the Mph-effects in this thesis is, however,
that these effects were not investigated in a placebo-controlled within subject design,
allowing for repeated measures of the same subjects in both a medicated and
medication-free condition. The group differences found may, therefore, not (only) be
the result of the medication manipulation, but also of differences in the characteristics
of the compared groups. This was, however, accounted for by matching the groups on
age and intelligence. Moreover, both children in the Mph-treated group and in the
medication-free group did not differ in severity of their ADHD symptoms (as measured
by the DISC-IV) and both groups contained an equal ratio of children with clinically
relevant externalising problems (as measured by the CBCL). Still, the present results
need verification by using a double-blind placebo-controlled cross-over design, for
making firm conclusions about the effects of Mph on aspects of error and feedback
processing. Despite this study limitation, these findings are promising and raise new
hypotheses for further investigation.
(HOW) DO ELECTROCORTICAL AND AUTONOMIC CORRELATES
OF ERROR AND FEEDBACK PROCESSING RELATE?
This thesis argues that error-related EHR deceleration and the ERN are the reflection of
one and the same error monitoring system. CHAPTER 2 provides evidence that in TD
children both measures show similar functional characteristics in a probabilistic
learning paradigm. Both are sensitive to the informative value of the feedback stimuli
and both reflect a shift from feedback monitoring to response monitoring as learning
proceeds. Moreover, significant positive correlations between error-related EHR
GENERAL CONCLUSIONS AND DISCUSSION
176
deceleration and the ERN amplitude, and absence of such correlations with the other
ERP components, provide direct evidence that the two measures are related indeed.
These findings support the hypothesis that the error-related EHR deceleration is the
autonomic equivalent of the ERN (Somsen et al., 2000; Crone et al., 2003c).
Further support for this hypothesis comes from studies suggesting a shared neural
substrate of the two measures. On the one hand the dACC and the rACC are
convincingly involved in error processing and have been identified as potential neuronal
sources of error-related ERPs (Taylor et al., 2007). On the other hand the ACC, and the
dACC in particular, forms part of a system that is involved in the generation of
autonomic arousal during volitional effortful cognitive processing (Critchley et al.,
2003; Critchley, 2005). Together, these findings raise the hypothesis that the ACC
provides for autonomic warnings signals (i.e. the EHR decelerative response) when
errors are detected (cf. Jennings & Van der Molen, 2002). Such warning signals may
serve to couple cognitive information processing with the appropriate emotional state
and somato-visceral support.
Damasio (1994) argues that cognitive information processing is influenced by
physiological changes in the body that are related to emotion. These physiological
changes, which he called somatic markers, benefit everyday life complex decision-
making. In the light of this somatic marker hypothesis, the error-related EHR
decelerations may be regarded as somatic markers of erring. A similar proposal has
been made by Hajcak and colleagues (2003b) for increased Skin Conductance Response
(SCR) activity during error processing. They reported a positive correlation between the
Pe amplitude and SCR activity on error trials and suggested the Pe triggers subsequent
autonomic nervous system activity. In contrast to our findings, however, they did not
observe a significant correlation between error-related EHR deceleration and the ERN
amplitude, although the direction of this latter non significant correlation was the same
(r = .378, ns; see Table 1, p. 899; Hajcak et al., 2003b). Future studies should confirm
the suggested differential associations between specific error-related ERP components
and sympathetic (SCR) and parasympathetic (EHR responses) measures of error
processing of the autonomic nervous system by investigating larger subject samples.
Together, the findings to date do suggest that the full range of performance monitoring
processes rely on the interplay of electrocortical and peripheral changes in body state.
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In line with the somatic marker hypothesis (Damasio, 1994), this thesis hypothesises
that the observed EHR decelerations in response to aversive events, such as errors and
negative feedback, eventually benefit cognitive information processing. Previous
research has indicated that patients lacking somatic markers (in this case SCR) due to
brain lesions, have severe impairments in personal and social decision-making although
their intellectual capabilities are spared (Bechara, Damasio, & Damasio, 2000; Bechara
& Van der Linden, 2005). In CHAPTER 2, a mechanism is described by which heart rate
changes are fed back to the brain and how they may influence subsequent information
processing. In this mechanism, the nucleus tractus solitarius (NTS) plays a key role.
This nucleus has strong projections to the main source nucleus of noradrenaline in the
brain, the locus coereleus (LC) (Berntson et al., 2003; Berridge & Waterhouse, 2003).
Via the NTS-LC feedback-loop of autonomic changes, error- and feedback-related EHR
decelerations may have a functional impact on the quality of information processing and
learning.
The finding in CHAPTER 4 of absent error-related EHR decelerations in medication-free
children with ADHD implies that these children lack this type of somatic marker of
erring. In addition to a primary deficit in the processing of errors in the brain, children
with ADHD may be refrained from the benefiting effects of the somatic markers of
erring on cognitive information processing and learning. Possibly, children with ASD
may also suffer to some extent from weaker somatic markers. Weaker somatic markers
or the lack of them may impair children with ADHD and ASD in personal decision-
making in their every day life.
The findings of CHAPTER 4 indirectly question the hypothesis that error-related EHR
deceleration is the autonomic equivalent of the ERN. This chapter provides evidence
that Mph-intake in children with ADHD ‘restores’ the absent EHR deceleration to error
commission and punishment. This finding is explained in the light of the
psychobiological BIS-BAS theory (Gray, 1985; Gray, 1987), by hypothesising that Mph
stimulates the underactive BIS system in children with ADHD, and thereby their
decreased phasic responsiveness of the LC-NE system. This reasoning is in agreement
with the found stimulating effect of Mph on the Pe in children with ADHD, as this
component is presumed to have a noradrenergic origin (see CHAPTER 3 and Jonkman et
al., 2007). However, as the ERN has been hypothesised to have a dopaminergic origin
GENERAL CONCLUSIONS AND DISCUSSION
178
(Holroyd & Coles, 2002), this reasoning is inconsistent with the hypothesis that error-
related EHR deceleration is the autonomic equivalent of the ERN. This contradiction
calls for further testing the several hypotheses regarding the biological basis of error and
feedback processing. Firstly, (1) is the error-related EHR deceleration really the
equivalent of the ERN or does it reflect different aspects of error processing, such as
more affective properties of the event (as proposed by Van der Veen et al., 2004; Van
der Veen et al., 2008). Secondly, (2) does Mph selectively stimulate noradrenergic
aspects of error and feedback processing (see De Bruijn et al., 2004; De Bruijn et al.,
2005)? And third, (3) is the ERN truly the reflection of phasic dopamine responses as
proposed by Holroyd and Coles (2002) or are other neurotransmitters involved?
DO SPECIFIC GENETIC FACTORS INFLUENCE THE
PSYCHOPHYSIOLOGY OF ERROR AND FEEDBACK PROCESSING?
GENETICALLY BASED STYLES OF ERROR AND FEEDBACK PROCESSING
CHAPTER 5 provides evidence that common polymorphisms of the 5-HTTLPR en DRD2
genes, involved in the neurotransmission of serotonin and dopamine respectively,
differentially affect the electrophysiology of error and feedback processing. On the one
hand, carriers of the low activity short variant of the 5-HTTLPR gene showed increased
electrophysiological responsiveness to errors and negative feedback opposed to carriers
of the long variant. On the other hand, carriers of the Taq1A1 variant of the DRD2 gene
showed increased electrophysiological responsiveness to negative feedback (but not to
errors) and decreasing responsiveness to positive feedback with task progression. These
different styles of error and feedback processing appeared to exist independently of the
children’s psychopathological condition to which the children originally belonged, i.e.
ADHD, ASD or TD, because the groups compared were matched for the children’s
psychopathological condition. Moreover, Tables 1, 2 and 3 of Chapter 5 illustrate that
the original distribution of the investigated polymorphisms (before the matching
procedure) was quite similar across the children’s psychopathological conditions. In
other words, this chapter demonstrates that there are naturally determined variations in
the style of error and feedback processing, which appear to be independent of
psychopathological phenotypes. It is argued that these different styles are related to
specific personality types and a predisposition for developing specific
psychopathological disorders.
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179
When proposing his general biosocial theory of personality, Cloninger (1987b) argued
that personality variants can be defined in basic terms of sensitivity to novelty,
punishment and reward. He distinguished three genetically independent dimensions of
personality that depend on different neurotransmitter systems in the brain: the novelty
seeking, harm avoidance and reward dependence dimension. Variations in and
interactions between these biogenetic systems within individuals may lead to a wide
range of personality types, which may be adaptive or maladaptive. The novelty seeking
dimension would reflect activity of the dopamine system and may be associated with
BAS activity in the model of Gray (Gray, 1985; Gray, 1987). The harm avoidance
dimension would reflect activity from the serotonin system and may be associated with
BIS activity in Gray’s model. Finally, the reward dependence system would reflect
noradrenaline activity. Cloninger argues that the genetic predisposition to these three
dimensions are independently set in each individual, but stresses that the underlying
brain systems are interconnected.
In the light of Cloninger’s biosocial personality theory and Gray’s psychobiological
BIS/BAS theory of personality, it is interesting that the polymorphisms investigated in
CHAPTER 5 have previously been related to different personality types on the one hand
and different styles of error processing on the other.
The S variant of the 5-HTTLPR gene has been related to neuroticism (e.g. Lesch et al.,
1996; Jang et al., 2001) and greater amydala responsivity (Hariri et al., 2005).
Individuals scoring high on neuroticism have a tendency to experience negative
emotional states, such as anxiety and depression, are less capable to handle stressful
situations and are more likely to interprete ordinary situations as threatening. In the
model of Cloninger (1987b) these individuals may score high on the harm avoidance
(BIS) dimension. The enhanced sensitivity to errors and negative feedback of the 5-
HTTLPR S carriers, found in CHAPTER 5 and by Fallgatter and colleagues (2005), may
be due to a serotonin driven enhanced sensitivity to negative information or criticism.
Carriers of this allele may have a predisposition to developing (social) performance
anxiety.
The Taq1 A1 variant of the DRD2 gene, on the other hand, has been associated with
symptoms of an antisocial personality disorder and increased novelty seeking (Noble,
2003; Noble et al., 1998). Moreover, this polymorphism has been associated with the
GENERAL CONCLUSIONS AND DISCUSSION
180
Reward Deficiency Syndrome characterised by a reduced sensitivity to reward
associated with dopaminergic cortico-striatal brain regions (Bowirrat & Oscar-Berman,
2005; Balleine et al., 2007). This is in line with found associations between the Taq1A1
allele and different types of addictive behaviour (Bowirrat & Oscar-Berman, 2005;
Comings et al., 1996; Preuss et al., 2007). The decreasing sensitivity to confirming,
positive feedback with task progression in the Taq1 A1 carriers, found in CHAPTER 5,
may be due to a gradually diminishing sensitivity to regularly administered positive
reinforcement. Quick extinction of the physiological response to positive feedback has,
therefore, been suggested to reflect a predisposition for seeking other types of reward,
such as alcohol or other substances.
Yet, both investigated polymorphisms have been found to predispose for alcohol
dependence (Wu et al., 2008; Feinn et al., 2005; Bowirrat & Oscar-Berman, 2005;
Preuss et al., 2007). So, as already suggested by Cloninger (1987a), different personality
types, characterised by different neurophysiological systems and mechanisms, may lead
to the same observable behaviour of alcohol dependence. Alcoholism may arise from
the need to reduce negative emotional states, such as anxiety or depressive feelings
mediated by the S allele of the 5-HTTLPR gene, but it may also have a reward and
novelty seeking origin mediated by the Taq1 A1 allele of the DRD2 gene. This is an
example of how the same phenotype may have a genetically heterogenous origin.
IDENTIFYING ENDOPHENOTYPES OF PSYCHOPATHOLOGICAL DISORDERS
To date, it has proven to be difficult to consistently pinpoint specific polymorphisms
that are associated with psychopathological disorders; the associations found have
generally been very weak. This is possibly due to the large heterogeneity and
complexity of psychiatric phenotypes (Faraone et al., 2005). The results of CHAPTER 5
provide evidence for a genetic basis of the electrophysiological correlates of error and
feedback processing. These correlates may be considered endophenotypes that are more
closely linked to the neurobiological substrate of a disorder and, therefore, to the genes
that code for the proteins finally making up the substrate. Measuring these
endophenotypes enhances the chance of finding associations of genes and a particular
disorder. The style of error and feedback processing may represent one of the
underlying characteristics of different psychopathological disorders.
CHAPTER 6
181
The ERN in particular seems to meet several important criteria for being considered an
endophenotype of several psychopathological disorders. Some of these criteria are: (1)
high heritability, (2) established neuroanatomical and neurochemical substrates, (3)
association with psychiatric disorders, and (4) correlations with conceptually relevant
measures of temperament and personality (Gottesman & Gould, 2003). Regarding the
heritability criterium, more and more evidence becomes available that the ERN
amplitude is heritable (Albrecht et al., 2008; Anokhin et al., 2008) and related to
specific gene variants (Fallgatter et al., 2004; Frank, D'Lauro, & Curran, 2007; Kramer
et al., 2007). Regarding the involved neurobiological substrate, the ACC has been
repeatedly and consistently indicated as the main neuronal source of the ERN (Taylor et
al., 2007). Next to these criteria, the style of performance monitoring has been
associated with several psychiatric disorders and related personality traits. Literature
suggests that relatively large ERN amplitudes are related to internalising
psychopathology and personality traits, while relatively small ERN amplitudes are
related to externalising psychopathology and personality traits.
Regarding internalising, enhanced ERN amplitudes are for example found in
individuals experiencing negative affect/distress (Luu, Collins, & Tucker, 2000; Hajcak
et al., 2004; Hajcak & Simons, 2002; Boksem, Tops, Wester, Meijman, & Lorist, 2006)
and in individuals suffering from Obsessive Compulsive Disorder (OCD) or depression
(OCD: Gehring, Himle, & Nisenson, 2000; Johannes et al., 2001; Hajcak, McDonald, &
Simons, 2003a; Depression: Tucker, Luu, Frishkoff, Quiring, & Poulsen, 2003).
Regarding externalising, attenuated ERN amplitudes have been suggested as a potential
endophenotype of externalising psychopathology and personality traits, which include
childhood conduct problems, adult antisocial behaviour and substance-use disorders
(Hall, Bernat, & Patrick, 2007). Recently, however, a large electrophysiological study
on error processing by Albrecht and colleagues (2008) proposes that an attenuated ERN
may also be a potential endophenotype of ADHD. Both boys with ADHD (n = 68) and
their unaffected siblings (n = 18) showed decreased ERN amplitudes compared to
unrelated TD boys (n = 22). Indeed more and more studies, including CHAPTER 3 of this
thesis, provide evidence for an attenuated ERN amplitude to error responses in ADHD.
Given the association of externalising and an attenuated ERN amplitude, it is strongly
recommended to control for externalising problem behaviour for further research on the
GENERAL CONCLUSIONS AND DISCUSSION
182
ERN as an endophenotype of ADHD, such as Oppositional Defiant Disorder and
Conduct Disorder, but also for externalising personality traits.
Next to the ERN, the feedback-related P3/LPP may also serve as a potential
endophenotype of psychopathological disorders. CHAPTER 5 shows that individuals
associated with a decreased dopamine transmission in the striatal system, linked to the
Taq1A1 variant of the DRD2/ANKK1 gene, show enhanced sensitivity to negative
feedback, but at the same time quick habituation to positive/confirming feedback as
reflected by decreased P3/LPP responses to positive feedback during feedback-based
learning. This latter quick habituation of the P3/LPP to positive/confirming feedback
may be a potential endophenotype of externalising psychopathology. Gradually getting
insensitive to regularly offered reinforcement, may lead to seeking other types of
reward in order to keep the neuronal release of dopamine at a level that counteracts the
rise of negative feelings (Bowirrat & Oscar-Berman, 2005). For identifying the
feedback-related P3/LPP as an endophenotype more work is, however, needed for
further establishing the neurobiological substrate of this ERP complex. The present
study suggests involvement of the dopaminergic system, but other studies have
suggested involvement of other neurotransmitter systems as well (Nieuwenhuis et al.,
2005; Hajcak et al., 2006). It may, however, turn out to be difficult to elucidate the
underlying neuronal source(s), because the P3/LPP occurs relatively late in information
processing and, consequently, may reflect the interplay of several neurobiological
systems.
In short, more and more evidence becomes available that the style of error and feedback
processing is related to (a predisposition to developing) a range of psychopathological
conditions, but also to a range of personality traits. In connection with this, the style of
error and feedback processing seems to be related with intra-individual differences in
the underlying biogenetic systems (Cloninger, 1987b). For future research on the
psychophysiology of error and feedback processing in general and in developmental
disorder in specific, it is recommended to control for the presence of (comorbid)
psychopathology and personality traits. With regard to controlling for personality traits,
personality questionnaires should be included, like for example the BIS/BAS Scales
(Carver & White, 1994), which are based on Gray’s (Gray, 1985; Gray, 1987)
biopsychological theory of personality. Moreover, for understanding the biogenetic
CHAPTER 6
183
variations in the style of error and feedback processing, it seems a good strategy to form
subgroups according to the presence of alleles of candidate genes for
psychopathological conditions. These suggested research strategies all call for large
subject samples that allow for the formation of subgroups and the computation of
correlations.
CLINICAL IMPLICATIONS
Using neuropsychological tasks and performance measures, both ADHD and ASD have
previously been related to EF deficits (Barkley, 1997; Pennington & Ozonoff, 1996;
Russell, 1997). Using psychophysiological measures, this thesis provides some
evidence that children with ADHD and children with ASD can be discriminated from
each other on component processes of EFs. The electrophysiological measures in
particular showed that children with ADHD have marked impairments in feedback-
based learning and monitoring their error responses, while the children with ASD are
relatively spared regarding these functions. Other psychiatric disorders, like OCD,
Schizophrenia and Depression, have also been related to altered performance
monitoring (Ullsperger, 2006). In future, psychophysiological measures, and
electrophysiological measures in particular, may become additional diagnostic tools for
performance monitoring examination in single patients (cf. Ullsperger, 2006). These
tools may then serve to better characterize the cognitive abilities of patients and/or to
quantify functional recovery of therapeutic effects. For now, these measures are valid
tools for scientific research that provide insight into the neurobiological processes in
psychiatric and/or developmental disorders, which help refining theoretical models.
The deficits of error and feedback processing found in children with ADHD may
explain some of the everyday problems that children with ADHD and their fellow
humans deal with. Parents and teachers of children with ADHD often report that the
child does not follow the daily rules and practice, while they are repeated over and over
again. The rules just do not seem to sink in. The found deficits in feedback-based
learning may explain why this is so difficult in children with ADHD. The brain system
that is responsible for continuously monitoring whether behaviour is successful or not,
works less efficient in children with ADHD. Inadequate behaviour triggers a weaker
warning signal in these children compared to TD children. At that moment they may not
fully realise that they are doing wrong, which consequently leads to wrong or
GENERAL CONCLUSIONS AND DISCUSSION
184
maladjusted behaviour. They may rely more on feedback from their environment than
TD children.
In line with this reasoning, during Behavioural Parent Training at our outpatient clinic
ACCARE in Groningen, parents are teached that their child diagnosed with a
developmental disorder takes more time than TD children to learn adequate behaviour
and to unlearn inadequate behaviour. Behavioural therapies typically involve (parental)
feedback on the child’s performance and/or contingency management (e.g. token
economy system). Based on the psychophysiological findings in this thesis, it may be
predicted that children with ASD are better responders to behavioural therapy than
children with ADHD, when equivocal predictable feedback is used. Children with ASD
may, however, profit less from social feedback, like words of appreciation or a smile.
Moreover, it is predicted that Mph-treated children with ADHD are better responders to
behavioural therapy than medication-free children with ADHD. To date some studies
have indeed found small but significant additional effects of behavioural therapy next to
Mph-treatment in reducing ADHD symptoms as well as social and behavioural
problems (Pelham et al., 1993; Safren et al., 2005; Klein, Abikoff, Hechtman, & Weiss,
2004; Wells et al., 2000; Swanson et al., 2001).
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CHAPTER 7
NEDERLANDSE SAMENVATTING
NEDERLANDSE SAMENVATTING
212
HOOFDSTUK 1
Dit proefschrift beschrijft de psychofysiologie van de verwerking van fouten en
feedback bij kinderen in de leeftijd van 10 tot en met 12 jaar met een
Aandachtstekortstoornis met hyperactiviteit (ADHD) en kinderen met een Autisme
spectrum stoornis (ASS). Bij ADHD is er sprake van beperkende aandachtsproblemen,
hyperactiviteit en impulsiviteit, terwijl bij ASS sprake is van beperkende problemen in
de sociale interactie en communicatie en de aanwezigheid van stereotiepe
gedragspatronen en interesses. Bij psychofysiologisch onderzoek worden lichamelijke
reacties gemeten om mentale informatieverwerking te beschrijven en beter te begrijpen.
Het verwerken van fouten en feedback is een belangrijk onderdeel van de zogenaamde
‘regelfuncties’, die in de vakliteratuur ‘executieve functies’ worden genoemd. Deze
regelfuncties zijn vooral nodig voor het verwerken van nieuwe en complexe informatie
en zijn essentieel voor het tot stand komen van doelgericht en aangepast gedrag. Het
steeds controleren van het eigen gedrag en de reacties uit de omgeving is van belang om
vast te stellen of het huidige gedrag nog steeds gepast en succesvol is, of dat het juist
aangepast moet worden. Voorgaand onderzoek heeft aangetoond dat
psychofysiologische metingen aan de hersenen (electrocorticale metingen) en het hart
(autonome metingen) deze functies goed in kaart kunnen brengen. Veel onderzoek naar
de executieve functies gebruikt alleen prestatiematen, waaronder bijvoorbeeld
reactietijden en accuratesse. Het voordeel van psychofysiologisch onderzoek boven
onderzoek met alleen prestatiematen is, dat het enerzijds inzicht biedt in de
deelprocessen die ten grondslag liggen aan deze functies en anderzijds in de
neurobiologische aard er van.
Binnen dit kader onderzoekt dit proefschrift één hoofdvraagstelling met daarbij drie
deelvraagstellingen, waarvan de theoretische achtergrond uiteengezet is in HOOFDSTUK
1 van dit proefschrift. De hoofdvraagstelling, beschreven in de HOOFDSTUKKEN 3 EN 4,
is of kinderen met de ontwikkelingsstoornissen ADHD en ASS moeilijkheden hebben
bij de verwerking van fouten en feedback en of ze van elkaar onderscheiden kunnen
worden in specifieke aspecten van deze vaardigheden. Hoewel ADHD en ASS in het
handboek voor psychische stoornissen (DSM-IV-TR: American Psychiatric
Association, 2000) omschreven staan als twee duidelijk van elkaar verschillende
classificaties, blijkt het in de klinische praktijk vaak lastig om ze te onderscheiden. Veel
kinderen met ADHD laten ASS symptomen zien en andersom. Daarnaast worden beide
NEDERLANDSE SAMENVATTING
213
stoornissen in verband gebracht met tekortkomingen in de executieve functies,
waaronder tekortkomingen in de verwerking van fouten en feedback.
De eerste deelvraagstelling, beschreven in HOOFDSTUK 2, is óf en hoe electrocorticale
en autonome maten van de verwerking van fouten en feedback aan elkaar gerelateerd
zijn. Deze vraagstelling werd onderzocht door beide soorten psychofysiologische
metingen uit te voeren terwijl gezonde controle kinderen een feedbackgestuurde
leertaak uitvoerden. In dit proefschrift bestonden de electrocorticale maten uit
‘hersenpotentialen’ (Event Related Potentials: ERP’s) die uit het ElectroEncefalogram
(EEG) werden berekend, terwijl de autonome maten bestonden uit patronen van
kortdurende hartslagveranderingen (Evoked Heart Rate: EHR) die uit het
ElectroCardiogram (ECG) werden berekend. De relatie tussen deze electrocorticale en
autonome maten is een onderbelicht onderwerp in de vakliteratuur. Bovendien is er nog
maar weinig kennis over deze maten in relatie tot de verwerking van fouten en feedback
bij kinderen, hoewel recentelijk steeds meer ontwikkelingsstudies verschijnen.
De tweede deelvraagstelling, eveneens beschreven in de HOOFDSTUKKEN 3 EN 4, is of
Methylfenidaat de verwerking van fouten en feedback stimuleert bij kinderen met
ADHD. De grootste pijler in de behandeling van ADHD is het voorschrijven van
laaggedoseerde stimulantia, waaronder Methylfenidaat. Deze vorm van behandeling
vermindert duidelijk en snel de kernsymptomen van ADHD, maar tot op heden is er nog
weinig bekend over de invloed die deze behandeling heeft op de verwerking van fouten
en feedback.
De derde en laatste deelvraagstelling, beschreven in HOOFDSTUK 5, is of enkele
genetische factoren de verwerking van fouten en feedback beïnvloeden. In dit hoofdstuk
werden twee genen onderzocht waarvan in de literatuur aanwijzingen zijn dat ze
gerelateerd zijn aan psychiatrische aandoeningen. Binnen de psychiatrie richt steeds
meer onderzoek zich op de speurtocht naar zogenaamde ‘endofenotypes’.
Endofenotypes zijn biologische eigenschappen, die gekoppeld zijn aan een genetisch
risico voor een bepaalde aandoening. Door endofenotypes te identificeren kan
uiteindelijk de genetische achtergrond van stoornissen beter bepaald worden.
Psychofysiologische maten van de verwerking van fouten en feedback zijn mogelijke
kandidaten voor endofenotypes. Bij onderzoek naar de genetische achtergrond van deze
maten worden de grenzen van psychopathologische classificaties (tijdelijk) losgelaten.
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Hierdoor draagt deze manier van onderzoeken bij aan het inzicht in natuurlijke
verschillen tussen personen in de verwerking van fouten en feedback, die onafhankelijk
zijn van een stoornis.
HOOFDSTUK 2
HOOFDSTUK 2 beschrijft de psychofysiologie van de verwerking van fouten en feedback
bij zich normaal ontwikkelende preadolescente kinderen (10 tot 12 jaar) terwijl zij een
feedbackgestuurde leertaak uitvoeren. In dit paradigma, de probabilistische leertaak
genoemd, wordt de kinderen gevraagd uit te zoeken op welke van twee knoppen ze
moeten drukken bij welk plaatje. Zonder dat ze het weten, zijn bepaalde plaatjes
gekoppeld aan informatieve feedback (de feedback is gekoppeld aan de reactie), terwijl
andere plaatjes gekoppeld zijn aan niet-informatieve feedback (welke reactie ze ook
geven, het is óf altijd correct óf altijd fout). In dit experiment komt naar voren dat zich
normaal ontwikkelende kinderen gedurende de leertaak steeds meer correcte reacties
geven wanneer ze informatieve feedback krijgen; er is sprake van een leercurve in
accuratesse. Wanneer ze in de niet-informatieve conditie altijd negatieve feedback
krijgen veranderen ze vaker van knop dan wanneer ze altijd positieve feedback krijgen.
Kinderen zijn dus, vooral als ze negatieve feedback krijgen, actief op zoek naar de juiste
knop voor het plaatje. Dit actief zoeken naar de juiste knop vermindert naarmate de taak
vordert; ook hierin zien we een leercurve.
Uit de psychofysiologische maten komt naar voren dat de kinderen, naarmate de taak
vordert, leren onderscheid te maken tussen informatieve en niet-informatieve feedback.
In de niet-informatieve conditie laten zij in vergelijking tot de informatieve conditie
afgezwakte ERP amplitudes en EHR patronen zien in reactie op fouten en negatieve
feedback. Binnen de informatieve conditie laten zowel de electrocorticale als autonome
maten bovendien een gelijkwaardig leereffect zien. De ERP amplitudes gerelateerd aan
de feedback zwakken af naarmate er geleerd wordt, terwijl de ERP amplitudes
gerelateerd aan een foute druk op de knop, m.a.w. aan foute responsen, juist toenemen.
In het EHR patroon zien we een vergelijkbare verschuiving in timing van de EHR
vertraging die optreedt bij fouten; naarmate de kinderen leren verschuift de EHR
vertraging van feedback gerelateerd naar meer respons gerelateerd. Deze resultaten
geven aan dat zich normaal ontwikkelende kinderen naarmate ze leren minder
afhankelijk worden van feedback over hun gedrag en dat ze hun eigen gedrag gaan
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controleren. Er vindt als het ware een verschuiving plaats van extern controleren naar
intern controleren van gedrag.
Correlationele analyses, waarbij verbanden tussen verschillende maten kunnen worden
gezocht, wezen uit dat de autonome en elektrofysiologische maten aan elkaar
gerelateerd zijn. De aan fouten gerelateerde EHR vertraging bleek significant te
correleren met één van de ERP componenten: de respons gerelateerde Error-Related
Negativity (ERN). Van de ERN wordt gedacht dat deze de allereerste onbewuste
detectie van een fout weerspiegelt. De bron van deze component ligt in een dieper
gelegen deel van de voorste hersenen: de Anterieure Cingulate Gyrus (ACC). Gezien de
functionele overeenkomsten en het correlationele verband tussen de respons
gerelateerde ERN en de aan fouten gerelateerde EHR vertraging, zouden deze twee
maten de reflectie kunnen zijn van hetzelfde foutendetectie systeem. Deze hypothese
wordt gesteund door het feit dat de ACC ook deel uitmaakt van een systeem in het brein
dat betrokken is bij het genereren van aanpassingen van de autonome toestand tijdens de
informatieverwerking.
Volgens de ‘somatische bestempelinghypothese’ van Damasio (1994) gaan beslissingen
die we in het dagelijks leven nemen samen met veranderingen in onze lichamelijke
toestand. Deze lichamelijke veranderingen worden op hun beurt weer teruggekoppeld
naar het brein. De kern van de somatische bestempelinghypothese is dat door deze
terugkoppeling ons ‘gevoel’ invloed heeft op deze beslissingen en de verdere kwaliteit
van de informatieverwerking. De aan fouten gerelateerde EHR vertraging is mogelijk
een somatische stempel van het maken van fouten. In dit hoofdstuk wordt een mogelijk
mechanisme beschreven waarlangs de EHR vertraging teruggekoppeld wordt naar het
brein. Volgens dit terugkoppelingsmechanisme kunnen de EHR vertragingen bijdragen
aan de alertheid op binnenkomende prikkels en aan het leren van je fouten.
HOOFDSTUK 3
HOOFDSTUK 3 beschrijft de elektrofysiologische verwerking van fouten en feedback bij
kinderen met ADHD en kinderen met ASS tijdens dezelfde feedbackgestuurde leertaak
als beschreven in HOOFDSTUK 2. Deze in leeftijd en intelligentie vergelijkbare kinderen
werden met elkáár vergeleken, maar ook met de zich normaal ontwikkelende kinderen
beschreven in HOOFDSTUK 2. Daarnaast was de ADHD groep opgesplitst in een groep
die Methylfenidaat gebruikte en een groep die geen medicatie gebruikte ten tijde van het
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onderzoek. Alle groepen lieten in de informatieve conditie van de leertaak een even
steile leercurve in accuratesse zien, maar de kinderen met een ontwikkelingsstoornis
waren gemiddeld minder accuraat dan de zich normaal ontwikkelende kinderen. De
kinderen verschilden niet in gemiddelde reactietijd, maar de medicatievrije kinderen
met ADHD waren variabeler in hun reactietijden. Zij lieten meer wisselingen zien in
korte en lange reactietijden dan de andere kinderen.
De ERP’s wezen echter uit dat kinderen met ADHD gedurende de feedback gestuurde
leertaak in geringere mate leren hun eigen fouten te controleren dan de zich normaal
ontwikkelende kinderen en kinderen met ASS. Dit bleek uit kleinere amplitudes van de
aan foute responsen gerelateerde ERP’s: de ERN en de ‘error Positivity’ (Pe). Over de
Pe wordt gedacht dat deze de latere bewuste verwerking van een fout reflecteert.
Daarnaast wezen de feedback gerelateerde ERP’s uit dat kinderen met ADHD
gedurende de leertaak in sterkere mate afhankelijk blijven van de feedback dan de zich
normaal ontwikkelende kinderen en kinderen met ASS. Zij lieten in mindere mate een
afname zien op een component die feedback anticipatie reflecteert en een component
die vroege aandachtprocessen voor de feedback reflecteert. Deze resultaten suggereren
dat kinderen met ADHD in mindere mate leren om hun gedrag intern te controleren en
dat zij meer gericht blijven op feedback van buitenaf. Ze kunnen als het ware niet de
gevolgen van hun eigen gedrag voorspellen.
In de vergelijking van de kinderen met ADHD met en zonder Methylfenidaat kwam
naar voren dat gemediceerde kinderen met ADHD voor een deel beter leren om hun
eigen fouten te controleren. Niet de ERN, maar wel de Pe, bleek bij de gemediceerde
kinderen met ADHD groter in amplitude naarmate de leertaak vorderde. Dit geeft aan
dat Methylfenidaat bij kinderen met ADHD een stimulerend effect heeft op de
bewustwording van fouten, maar niet op de vroege detectie er van. Daarnaast werden de
gemediceerde kinderen met ADHD net als de zich normaal ontwikkelende kinderen,
gedurende de leertaak minder afhankelijk van feedback. Methylfenidaat lijkt bij
kinderen met ADHD dus een positief effect te hebben op het intern controleren van
gedrag en het voorspellen van de gevolgen van hun gedrag. Hierbij moet echter wel
worden opgemerkt dat een herhaling van het onderzoek gewenst is, waarbij het effect
van Methylfenidaat binnen dezelfde proefpersonen wordt onderzocht. In dit onderzoek
vergeleken we verschillende groepen kinderen waardoor de gevonden verschillen
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mogelijk aan andere factoren te wijten zijn dan aan de medicatie (hoewel ze niet
verschilden in leeftijd, intelligentie en het aantal ADHD symptomen).
De kinderen met ASS bleken in de elektrofysiologische verwerking van fouten en
feedback erg te lijken op zich normaal ontwikkelende kinderen. De ERP’s van deze
groep reflecteerden eenzelfde overgang van extern controleren van gedrag naar het
intern controleren er van. Echter, onafhankelijk van het leren suggereerden de feedback
gerelateerde ERP’s dat deze kinderen, evenals de kinderen met ADHD, negatieve
feedback minder intensief verwerken dan de zich normaal ontwikkelende kinderen. De
zich normaal ontwikkelende kinderen lieten in reactie op negatieve feedback een forse
late positieve ERP amplitude (Late Positive Potential) zien, die in verband wordt
gebracht met de emotionele verwerking van de feedback. Deze resultaten suggereren dat
negatieve feedback bij zowel kinderen met ASS als kinderen met ADHD een geringere
emotionele reactie oproept en dat zij hier dus minder waarde aan hechten dan zich
normaal ontwikkelende kinderen.
HOOFDSTUK 4
HOOFDSTUK 4 beschrijft de autonome reacties van kinderen met ADHD en ASS op
verschillende soorten feedback tijdens een simpele selectieve aandachtstaak, waarbij ze
geometrische figuren (cirkels, vierkanten en driehoeken) moesten sorteren. In de
beloningsconditie kregen de kinderen 1 cent bij een goede respons en 0 cent voor een
foute, waardoor de nadruk lag op beloning. In de strafconditie verloren de kinderen 1
cent voor een foute respons en kregen ze 0 cent voor een goede, waardoor de nadruk lag
op straf. In de geen feedback conditie werd elke respons gevolgd door een vraagteken,
waardoor de kinderen dus geen betekenisvolle feedback kregen over hun responsen.
Ook in dit experiment werden kinderen met ADHD en ASS met elkáár vergeleken en
met een zich normaal ontwikkelende groep kinderen. Bovendien werden wederom
kinderen met ADHD met en zonder Methylfenidaat met elkaar vergeleken. De
beschreven groepen in dit experiment overlappen grotendeels met die beschreven in het
experiment van HOOFDSTUK 3. De groep met zich normaal ontwikkelende kinderen was
identiek.
Alle groepen presteerden efficiënter met dan zonder feedback, hoewel de klinische
groepen over het geheel genomen minder accuraat waren dan de zich normaal
ontwikkelende kinderen. In zowel de belonings- als strafconditie reageerden de
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kinderen in vergelijking tot de ‘geen feedback’ conditie trager, maar accurater, lieten ze
minder te late reacties zien en vertraagden ze hun reactiesnelheid meer als ze een fout
hadden gemaakt. Dit laatste effect, waarbij de reactiesnelheid vertraagt na het opmerken
van een fout wordt ‘post error slowing’ genoemd en zou een bewuste strategische
aanpassing van het gedrag na een fout reflecteren. Deze resultaten suggereren dat alle
kinderen, onafhankelijk van hun ontwikkelingsstoornis of medicatie, baat hebben bij het
krijgen van feedback over hun gedrag.
In tegenstelling tot de prestatiematen bleken de autonome maten voor feedback
gevoeligheid de groepen wel van elkaar te kunnen onderscheiden. Terwijl de zich
normaal ontwikkelende kinderen in alle feedback condities duidelijke vertragingen in de
EHR patronen lieten zien in reactie op fouten, waren deze afwezig bij de medicatievrije
kinderen met ADHD. Deze bevinding suggereert dat kinderen met ADHD de
somatische stempels missen bij het maken van fouten. Bij zich normaal ontwikkelende
kinderen wordt gedacht dat deze somatische stempels een belangrijke bijdrage leveren
aan de alertheid op binnenkomende prikkels en aan het leren van je fouten.
Medicatievrije kinderen met ADHD kunnen dus niet of minder profiteren van deze
positieve bijdrage.
Hoewel de plaatjes van de EHR patronen voor de kinderen met ASS ook geringere
harstslag vertragingen in reactie op fouten suggereerden, verschilden deze kinderen niet
significant van de zich normaal ontwikkelende kinderen en de kinderen met ADHD. Dit
betekent dat vanuit wetenschappelijk oogpunt geen harde conclusies mogen worden
getrokken over de autonome gevoeligheid voor fouten bij deze kinderen. Op basis van
de effectgroottes is de verwachting echter wel dat de verschillen significant zullen zijn
wanneer grotere groepen kinderen worden onderzocht. Een mogelijke oorzaak van de
niet significante groepseffecten is de relatief geringe ernst van de ASS problematiek in
de onderzoeksgroep. Voorgaande onderzoeken suggereren namelijk dat personen met
ernstiger vormen van ASS, ook ernstiger tekortkomingen hebben in het verwerken van
fouten en feedback. Voor vervolgonderzoek wordt aangeraden een breder spectrum aan
ASS problematiek te onderzoeken. Echter, de ogenschijnlijk geringere
hartslagvertraging bij kinderen met ASS zou ook te maken kunnen hebben met de
aanwezigheid van ADHD symptomen in de onderzoeksgroep. Dit proefschrift laat zien
dat kinderen met ADHD duidelijke beperkingen hebben in de verwerking van fouten en
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feedback. Bij verder onderzoek naar gevoeligheid voor fouten en feedback bij ASS
moet dus rekening worden gehouden met de aanwezigheid van bijkomende ADHD
symptomen.
In de vergelijking van de kinderen met ADHD met en zonder Methylfenidaat kwam
naar voren dat de hartslagvertraging in reactie op fouten bij gemediceerde kinderen met
ADHD ‘normaliseerde’ in de geen feedback en strafconditie. Gemediceerde kinderen
met ADHD lijken dus gevoeliger dan medicatievrije kinderen met ADHD voor straf en
het zelf controleren van hun fouten (in de ‘geen feedback’ conditie). Deze bevinding
suggereert dat Methylfenidaat voor een deel de somatische stempels van het maken van
fouten herstelt bij kinderen met ADHD. Gemediceerde kinderen met ADHD profiteren
dus mogelijk meer van de bijdrage die deze autonome veranderingen leveren aan de
verdere informatieverwerking. Net als in HOOFDSTUK 3, moet ook bij deze resultaten
worden opgemerkt dat een herhaling van het onderzoek gewenst is, waarbij de effecten
van Methylfenidaat binnen dezelfde personen worden onderzocht.
HOOFDSTUK 5
HOOFDSTUK 5 onderzocht de invloed van twee genen op de elektrofysiologische
verwerking van fouten en feedback tijdens dezelfde feedbackgestuurde leertaak als
beschreven in de HOOFDSTUKKEN 2 EN 3. Bij de gehanteerde onderzoeksstrategie
werden de in HOOFDSTUK 3 beschreven zich normaal ontwikkelende kinderen en
kinderen met ontwikkelingsstoornissen opnieuw gegroepeerd naar de verschillende
varianten van deze twee genen. De nieuwe groepen werden volledig vergelijkbaar
gemaakt ten aanzien van de aanwezigheid van het type ontwikkelingsstoornis. De
onderzochte genen waren het 5-HTTLPR gen en het DRD2(/ANKK1) gen, waarvan
bekend is dat ze de werking van verschillende neurotransmitters beïnvloeden.
Neurotransmitters zijn lichaamseigen stoffen die van belang zijn voor de
signaaloverdracht tussen zenuwcellen. Algemeen voorkomende onschuldige variaties in
deze genen, polymorfismen, zorgen voor natuurlijke verschillen tussen personen in deze
signaaloverdracht. Dragers van de korte variant van het 5-HTTLPR gen worden in
vergelijking tot dragers van de lange variant in verband gebracht met een lage activiteit
van de neurotransmitter serotonine. Dragers van de Taq1A1 variant van het DRD2 gen
worden in vergelijking tot de niet-dragers in verband gebracht met een geringere
gevoeligheid voor de neurotransmitter dopamine.
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De aanwezigheid van de bovengenoemde gen polymorfismen bleek niet van invloed op
de prestatiematen. De vergeleken groepen verschilden niet in gemiddelde accuratesse,
reactiesnelheid, variabiliteit van reageren of in de leercurves van deze maten. De
varianten van het 5-HTTLPR en DRD2 gen bleken echter wel op verschillende wijze de
elektrofysiologische verwerking van fouten en feedback te beïnvloeden. Dragers van de
korte variant van het 5-HTTLPR gen lieten een grotere ERN/Pe zien in reactie op foute
responsen en een afgezwakt leereffect op de latere positieve amplitude in reactie op
negatieve feedback. Dragers van dit polymorfisme lijken dus verhoogd gevoelig voor
fouten en negatieve feedback. De polymorfismen van het DRD2 gen bleken niet te
verschillen in hun invloed op de gevoeligheid voor het maken van fouten. Echter,
dragers van de DRD2 Taq1A1 variant lieten in vergelijking tot niet-dragers een vergrote
latere positieve amplitude op negatieve feedback zien, maar juist een afzwakkende
amplitude in reactie op positieve feedback naarmate de taak vorderde. Dit laatste duidt
op een relatief snellere gewenning aan positieve, bevestigende feedback. Bij kinderen
die zowel de korte variant van het 5-HTTLPR gen en de Taq1A1 variant van het DRD2
gen bezitten, leken de effecten op te tellen. Deze kinderen leken èn verhoogd gevoelig
voor foute responsen èn verhoogd gevoelig voor negatieve feedback, maar tegelijkertijd
verminderd gevoelig voor positieve feedback naarmate de taak vordert.
Deze resultaten geven aan dat er tussen kinderen natuurlijke, genetisch bepaalde,
variaties zijn in de stijl van het verwerken van fouten en feedback, die onafhankelijk
lijken van het aan- of afwezig zijn van een bepaalde ontwikkelingsstoornis. Dit is een
mogelijke verklaring voor de wisselende bevindingen in de literatuur op het gebied van
de elektrofysiologische verwerking van fouten en feedback bij ADHD (hoewel de
bevindingen in dit proefschrift wel consistent wijzen op een verminderde gevoeligheid
voor fouten en feedback bij kinderen met ADHD). Bij kleine onderzoeksgroepen, die
bij psychofysiologisch onderzoek vaak worden gehanteerd, kunnen de uitkomsten
worden beïnvloed door de genetische samenstelling van de groep. Hierbij moet echter
worden opgemerkt dat ook andere factoren, zoals de aard van de taak en de manier van
analyseren, een oorzaak kunnen zijn van de wisselende bevindingen.
Deze bevindingen van genetisch bepaalde variaties in stijl van de verwerking van fouten
en feedback, dragen bij aan de speurtocht naar endofenotypes. Elektrofysiologische
maten van de verwerking van fouten en feedback, en in het bijzonder de aan fouten
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gerelateerde ERN, zijn mogelijke endofenotypes van verschillende psychiatrische
stoornissen. Een belangrijk criterium voor de identificatie van endofenotypes is
namelijk erfelijkheid. Wat betreft de ERN zijn er nu meerdere studies die aantonen dat
de amplitude wordt beïnvloed door erfelijke factoren. De amplitude van de ERN wordt
daarnaast in verband gebracht met verschillende psychiatrische stoornissen en
gerelateerde persoonlijkheidskenmerken. Een vergrote ERN is mogelijk een
endofenotype van internaliserende psychopathologie, zoals angst en depressie. De
bevindingen uit HOOFDSTUK 5 duiden op betrokkenheid van het 5-HTTLPR gen en het
serotonerge systeem bij dit endofenotype. Een snelle gewenning aan positieve,
bevestigende feedback, zoals weerspiegeld door een snel afnemende late positieve
potentiaal in reactie op positieve feedback, is mogelijk een endofenotype van
externaliserende psychopathologie, zoals de (oppositioneel-opstandige) gedragsstoornis
en de antisociale persoonlijkheidsstoornis. De bevindingen uit HOOFDSTUK 5 duiden op
de betrokkenheid van het DRD2/ANKK1 gen en het dopaminerge systeem bij dit
endofenotype. Recentelijk zijn er ook steeds meer aanwijzingen dat een zwakke ERN
een mogelijk endofenotype van ADHD is. Bij verder onderzoek naar dit endofenotype
is het echter belangrijk om te controleren voor externaliserende gedragskenmerken,
omdat een zwakke ERN ook in verband wordt gebracht met externaliserende
psychopathologie. Het bepalen van de genetische achtergrond van verschillende
endofenoypes draagt uiteindelijk bij aan de kennis over de genen die betrokken zijn bij
(een kwetsbaarheid voor het ontwikkelingen van) verschillende psychiatrische
stoornissen.
HOOFDSTUK 6
In dit laatste hoofdstuk worden de hoofdbevindingen van dit proefschrift samengevat en
per vraagstelling bediscussieerd.
Dit proefschrift biedt aanknopingspunten voor een bevestigend antwoord op de
hoofdvraagstelling of kinderen met ADHD en kinderen met ASS onderscheiden kunnen
worden in de psychofysiologie van de verwerking van fouten en feedback. Vooral
kinderen met ADHD hebben tekortkomingen in de verwerking van fouten en feedback,
terwijl kinderen met ASS op dit onderdeel van de executieve functies veel meer lijken
op zich normaal ontwikkelende kinderen. Opvallend was dat de kinderen met
ontwikkelingsstoornissen op basis van hun prestaties niet konden worden onderscheiden
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(met uitzondering van één maat: de variabiliteit van reactietijden), maar wel op basis
van de psychofysiologische maten. Dit betekent echter niet dat we in de klinische
praktijk kinderen met ADHD en ASS van elkaar kunnen onderscheiden op basis van
dergelijke psychofysiologische maten. Op dit moment zijn deze maten een belangrijk
wetenschappelijk hulpmiddel om inzicht te krijgen in neurobiologische processen die
ten grondslag liggen aan ontwikkelingsstoornissen of psychiatrische stoornissen en om
theoretische modellen te verfijnen. In de toekomst zouden dit soort maten wel gebruikt
kunnen worden om, in combinatie met andere (neuropsychologische) tests, de
cognitieve vaardigheden van een patiënt te beschrijven.
De in dit proefschrift beschreven electrocorticale en autonome maten van de verwerking
van fouten en feedback bleken op verschillende manieren met elkaar samenhangen. De
kortdurende hartslagveranderingen die samengaan met het verwerken van fouten en
negatieve feedback bleken functionele overeenkomsten te laten zien met de
electrocorticale maten. Daarnaast werd een correlationeel verband gevonden tussen deze
hartslagveranderingen en één van de electrocorticale maten: de ERN. In dit proefschrift
wordt een mechanisme beschreven waardoor deze hartslagveranderingen kunnen
bijdragen aan de alertheid op binnenkomende prikkels en aan het leren van je fouten. De
kinderen met ADHD bleken de aan fouten gerelateerde EHR vertraging te missen,
waardoor ze mogelijk in mindere mate profiteren van deze positieve bijdrage aan de
verdere informatieverwerking. Voor de kinderen met ASS leek dit in geringere mate
ook te gelden, maar er kunnen geen harde conclusies getrokken worden omdat deze
laatste resultaten niet significant waren.
Methylfenidaat bleek bij kinderen met ADHD een stimulerend effect te hebben op
deelaspecten van de verwerking van fouten en feedback. De elektrofysiologische maten
gaven aan dat het middel een positieve uitwerking heeft op feedbackgestuurd leren en
het intern controleren van gedrag. Daarnaast bleek dat Methylfenidaat een stimulerend
effect had op de hartslagveranderingen in reactie op fouten en straf. De effecten van
Methylfenidaat op deelaspecten van de verwerking van fouten en feedback werpen
nieuwe hypotheses op over de werking van het middel op verschillende
neurotransmitter systemen in het brein die een rol spelen bij executieve functies
enerzijds en de neurobiologische aard van ADHD anderzijds.
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Tot slot bleek de aanwezigheid van de algemeen voorkomende varianten van twee
genen (5-HTTLPR en DRD2/ANKK1) verschillend van invloed te zijn op de
elektrofysiologische verwerking van fouten en feedback. Deze resultaten geven aan dat
er tussen personen genetisch bepaalde verschillen bestaan in de stijl van het verwerken
van fouten en feedback, die onafhankelijk lijken van het aan- of afwezig zijn van een
bepaalde ontwikkelingsstoornis. Zowel deze stijlen van verwerking als de varianten van
de onderzochte genen worden in verband gebracht met verschillende
persoonlijkheidskenmerken, maar ook met verschillende psychiatrische stoornissen.
Elektrofysiologische maten van de verwerking van fouten en feedback zijn mogelijke
endofenotypes, die uiteindelijk van belang zijn voor het bepalen van de genetische
achtergrond van verschillende psychiatrische stoornissen.
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DANKWOORD
DANKWOORD
226
Het doen van onderzoek en het schrijven van een proefschrift gaat niet zonder pieken en
dalen en allerminst alleen. Veel mensen ben ik veel dank verschuldigd door de steun of
hulp die zij hebben geboden tijdens het onderzoeksproject.
Allereerst wil ik mijn promotor Ruud Minderaa bedanken voor de mogelijkheid die hij
mij heeft geboden om te kunnen promoveren en zijn vertrouwen in mij. Ik waardeer de
motiverende gesprekken, vooral in de laatste fase van het project.
Het idee voor dit proefschrift vloeit voort uit het onderzoek van mijn co-promotor
Monika Althaus, die zich al jaren inspant voor een psychofysiologische onderzoekslijn
bij het UCKJP te Groningen. Ik vind het een eer om met dit proefschrift bij te dragen
aan deze onderzoekslijn. Bijzonder aan deze lijn is onder andere de nauwe
samenwerking met Ben Mulder en Berry Wijers van de afdeling Experimentele en
Arbeidspsychologie van de faculteit Gedrags- en Maatschappijwetenschappen. Door de
diversiteit aan kennis van mijn drie co-promoteren heb ik veel geleerd over
uiteenlopende onderzoeksgebieden, maar gedrieën hebben ze mij vooral geleerd kritisch
te lezen, te accepteren dat ‘de waarheid’ niet bestaat in de wetenschap en om niet alle
beperkingen van mijn stukken uitgebreid te beschrijven… Monika, Ben & Berry, ik
vind het ontzettend fijn dat bij jullie de deur altijd openstaat voor een luisterend oor of
deskundig advies.
De kinderen die in dit proefschrift staan beschreven en hun ouders ben ik erg dankbaar
voor hun medewerking aan dit onderzoek. Zij hebben vrijwillig heel wat uren en
energie opgeofferd voor de wetenschap. Zonder hun medewerking zou dit onderzoek
niet mogelijk zijn geweest.
Voor technische ondersteuning tijdens de ontwerpfase en dataverzameling van het
onderzoek kon ik altijd rekenen op de hulp van de Instrumentatiedienst Psychologie.
Namen als Joop Clots, Jaap Ruiter, Jaap Bos, Mark Span, Peter Albronda en Pieter van
Zandbergen mogen daarom ook zeker niet missen in dit dankwoord. Als ik het heb over
Electro-caps, Ag-AgCl elektrodes, stompe naalden, REFA systemen, TMS
International, PortiLab, E-prime, Hermes, Heart en R weten deze mensen precies wat ik
bedoel.
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Tijdens de dataverzameling heb ik bij een groot aantal kinderen intelligentietests
afgenomen. Bij deze bedank ik Agnes Brunnekreef voor de training en Riet Til voor de
supervisie die ik hiervoor kreeg. Ook bedank ik de dames van het toenmalige
Psychologisch Onderzoeksteam voor het afnemen van intelligentietesten bij een aantal
van de kinderen beschreven in dit proefschrift.
In totaal hebben een vijftal stagiaires mij geholpen met de dataverzameling van het
onderzoek. Brenda Waggeveld, Klaas van der Lingen, Johannes Boerma, Diana de Boer
en Marrit Tigchelaar, maar ook Harma Moorlag als onderzoeksassistent; ik ben jullie
ontzettend dankbaar voor de hulp bij het werven van kinderen, voor menig uur dat we
samen hebben doorgebracht in de ‘donkere (test)kamer’, voor dataschoning, voor
literatuuronderzoek en alle gezellige of diepgaande gesprekken die we voerden
(uitgelokt door die donkere kamer).
In de eerste zin van dit dankwoord noemde ik al de ‘pieken en dalen’ die je tegenkomt
tijdens een promotietraject. Er waren zoveel pieken: de goedkeuring van het project
door de METc (januari 2005), het starten van de dataverzameling (februari 2005) en …
het eindigen ervan (juli 2006), mijn eerste publicatie (juli 2007), de tweede (augustus
2008), de derde (mei 2009) en de dag dat dit proefschrift (inhoudelijk gezien) af was
(30 november 2008 :-). Maar zonder dalen zijn er geen pieken. Terugkijkend waren het
eerste (2003/2004) en het laatste jaar (2008) voor mij ‘daljaren’; het eerste door de
ogenschijnlijke onoverzichtelijkheid van het project plus de lange reistijden van wonen
en werken en het laatste door de veel te drukke combinatie van een nieuwe baan, het
afschrijven van een proefschrift en een (groeiend) gezin.
En zo terugkijkend besef je dat je niet zonder de mensen om je heen kunt, zoals familie,
vriendinnen en buren. Maar vooral niet zonder mijn rots in de branding: Bauke. Bauke,
je hebt me té goed leren kennen tijdens de ‘dalen’ en me zo vaak gesteund en …
getolereerd. Maar we hebben zoveel prachtige dingen meegemaakt en ik hoop dat het er
alleen maar meer worden!
Ook moet gezegd worden dat ik zó blij ben met al mijn collega’s van de research en de
poli. Maar in het bijzonder waardeer ik de collega (ex)promovendi, die precies weten
tegen welk leed je aanloopt en hoe je dat kunt verzachten, met wie de dagelijkse
beslommeringen flair krijgen en met wie je de pieken kunt vieren. Ik noem een paar
DANKWOORD
228
collega’s in het bijzonder, maar ieder ander is natuurlijk in mijn gedachten: Judith,
Netty, Neeltje, Mark-Peter, Esther, Julie, Liza, Sanne, Karin, Agnes, Laura (x2),
Barbara, Monica, Andrea, Pieter, Annelies, Harma, Anne en Hans.
En tot slot Judith en Mariëlle, mijn paranimfen. Fantastisch dat jullie met mij toeleven
naar de dag van mijn promotie en deze dag met mij willen delen!
CURRICULUM VITAE
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Yvonne Groen werd geboren op 27 december 1979 te Ruinerwold. In 1998 behaalde zij
haar VWO-diploma aan de Openbare Scholengemeenschap ‘De Groene Driehoek’ te
Hoogeveen. In datzelfde jaar ging zij Psychologie studeren aan de Rijksuniversteit
Groningen met als hoofdrichting Functieleer en als nevenrichting Neuro-
/Biopsychologie. Tijdens haar studie verrichte zij een wetenschappelijke stage bij het
Universitair Centrum voor Kinder- en Jeugdpsychiatrie (UCKJP) te Groningen. Onder
begeleiding van mw. Dr. M. Althaus en dr. A.A. Wijers rondde zij deze stage af met een
afstudeerscriptie getiteld ‘Gezichtsherkenning bij kinderen’. In augustus 2003 studeerde
zij af.
Aansluitend begon zij in september 2003 bij de vakgroep Psychiatrie van de Faculteit
der Medische Wetenschappen (inmiddels Universitair Medisch Centrum Groningen)
aan het promotietraject dat leidde tot dit proefschrift onder begeleiding van prof. Dr.
R.B. Minderaa, mw. Dr. M. Althaus, Dr. L.J.M. Mulder en Dr. A.A. Wijers. Vanaf
februari 2008 tot heden is Yvonne werkzaam als basispsycholoog op de polikiniek van
Accare te Groningen. Na de promotie zal zij naast deze baan in deeltijd starten als post-
doc onderzoeker bij Accare te Groningen.
Yvonne is geregistreerd partner met Bauke Brouwer en samen hebben ze een dochter
(Meike, 2 jaar) en een zoon (Marten, pasgeboren).
