Notes. RMSE = Root mean squared error. N2 = maximal amplitude for
Nogo N2 signal. P3a = mean amplitude of Nogo P3a signal. P3b = mean
amplitude of Nogo P3b signal. IES = inverse efficiency score. Nested cv
= nested cross validation. p value = proportion of RMSE from permutation
test smaller than RMSE from nested cv.
Discussion
The present study investigated the relationship between self-reported
impulsivity and compulsivity with response inhibition in a large
non-clinical sample. In order to do this, we obtained behavioral (RT,
Nogo accuracy and inverse efficiency score) and electrophysiological
(N2, P3a, P3b) data from a standard Go/Nogo task. Data were analyzed
with robust linear regression as well as regression trees, which allow
non-linear exploration via a machine-learning algorithm. The linear
analyses yielded no significant associations of impulsivity or
compulsivity with task performance or EEG measures except for the effect
of the UPPS subscale premeditation on Nogo accuracy and IES. We did not
observe any non-linear relationship either, as the regression trees did
not appear to outperform the linear regression or permutated models as
measured by RMSE . Possible explanations for this will be
discussed below.
Task and sample characteristics
Firstly, as pointed out before, literature on the relationship between
impulsivity and compulsivity and response inhibition is ambiguous, as
said effect is often not found at either the behavioral or
psychophysiological level. This could be due to the fact that the
Go/Nogo tasks employed are not always entirely comparable. One feature
of the tasks seems to be emotion: Accordingly, adapted Go/Nogo tasks
show an incline in activation for emotional vs. neutral stimuli, which
in turn is modulated by higher trait impulsivity. This could be shown
for Nogo P3 amplitude as well as activation in the middle temporal gyrus
, an area linked to disorders of cognitive control . Behaviorally,
emotional stimuli seem to impair response inhibition , especially when
the demand on executive functions is high . Since emotion and
physiological arousal are intricately linked, arousal is thought to be
related to impulsivity as well . For example, have shown scores on the
BIS-11 to be positively correlated to corticolimbic structures such as
the ventral amygdala, caudate and dorsal anterior cingulate gyrus, while
negatively correlated with control circuits, e.g., the ventral
prefrontal cortex. Thus, trait impulsivity might be associated with an
imbalance of physiological arousal and inhibitory control. This would
likely also reflect on self-report measures, as these items refer to
behavior in the individual’s everyday life, which is not taking place in
an experimental setting, but influenced by states of affect and arousal.
Furthermore, both impulsivity and compulsivity are defined by affective
elements and the respective behavior is heavily influenced by emotion.
However, the Go/Nogo paradigm in the present study was designed to
examine response inhibition as a cognitive process, hence emotion or
arousal processes were not involved. This may have attenuated the
relationship between response inhibition and its neurophysiological
correlates and dispositional impulsivity and compulsivity as indexed by
the BIS-11, UPPS and OCI-R. Similarly, in studies investigating the role
of cognitive load, the negative effect of trait impulsivity on response
inhibition became even more pronounced when cognitive demands were high
or executive functioning itself was worse . Again, research on
compulsivity mainly focuses on individuals with OCD, but there decreases
in stop-signal task performance as well as activity in the supplementary
motor area and inferior parietal lobule have been found with higher
cognitive demand as well . As our Go/Nogo paradigm was intentionally low
on cognitive demand, possible relationships to impulsive or compulsive
behavior in the participant’s everyday life, where they are confronted
with a myriad of different stimuli and simultaneous processes, may have
been dampened.
One could also raise the question how our participants’ questionnaire
scores relate to other studies, as this might affect comparability.
Regarding the literature mentioned above that examined healthy control
groups, our scores for impulsivity and compulsivity (see table 2) are
quite similar to their distributions or lie between their groups of
high- and low-scoring participants . Notably, our impulsivity scores
strongly resemble those of the respective general population samples
used for validation of the BIS-11 and UPPS , while participants in the
original publications of the OCI-R achieved higher scores . In some
cases, our participants achieved slightly higher impulsivity scores than
in other studies using healthy control participants . In sum, our sample
characteristics resemble those of earlier studies.
Yet, our analyses also differ from those with clinical populations,
where results appear more stable. As OCD is defined by obsessive and
compulsive symptomatology, individuals with OCD score generally score
higher on the OCI-R . Further deficits in behavioral response inhibition
have been found , as well as alterations in Nogo ERP signals and higher
BIS-11 scores predicted by symptom severity . Similar changes have been
found for other disorders often associated with impulsivity and/or
compulsivity such as substance use , behavioral addictions , borderline
personality disorder , and schizophrenia . Again, other studies have
found elevated scores on varying impulsivity or compulsivity measures in
these clinical groups compared to our sample . A possible explanation
for the difference is that high manifestations of trait impulsivity and
compulsivity, as often seen in clinical groups, are needed for its
effects to unfold. Present psychopathology may also exacerbate the
effect of trait impulsivity and compulsivity when typically compensating
processes are impaired. This could be due to either to adverse effects
of the mental disorder itself or a common predisposition that
facilitated its onset.
It is thus not surprising that many of the studies that did find
significant differences in behavioral or electrocortical response
inhibition did so via group comparisons based on diagnostic status or
high vs. low scores on impulsivity or compulsivity measures. A clear
effect is probably found more easily in these distinct groups than in
our rather homogenous sample. Furthermore, using patient groups as
proxies for high impulsivity and compulsivity complicates
interpretability, as potential confounding factors increase. For
example, impairments in neurocognitive functions such as attention and
processing speed that would in turn affect performance in a Go/Nogo task
are reported among others for OCD , alcohol use disorder and borderline
personality disorder .
Methodological considerations
When analyzed directly, impulsivity as indexed by self-report measures
and cognitive tasks are associated with everyday behavior but show
little or no correlation with each other . Presumably these measures do
not simply reflect one overarching characteristic of impulsivity, but
instead tap into different aspects of it. Sharma et al. (2014) proposed
self-report scales to depict more long-term, emotionally laden
response-patterns, which would also complement the findings on emotional
response inhibition tasks described earlier. Cognitive tasks, on the
other hand, may represent purer and “microlevel” processes, which
would then also apply to the event-related potentials we analyzed. Thus,
this problem of construct validity could explain the lacking association
between self-report scales and behavioral and psychophysiological
variables, as they measure rather independent dimensions.
Limitations and future directions
It should also be noted that the effect of impulsivity or compulsivity
could affect the time course of the ERP instead of their amplitude.
Later onsets have been found for the N2 with higher impulsivity and for
individuals with OCD , while P3 latency was positively associated with
impulsivity on a trend level . Thus, alterations in inhibitory control
might not be represented in the strength of the conflict or inhibitory
processing, but in the time and resources needed. In an exploratory
analysis we did not find the N2 latency to be explained by impulsivity
or compulsivity. Unfortunately, as the P3a and P3b were computed through
the mean in a specific time window, latencies could not be reported.
Future studies investigating the effect on the time course of response
inhibition would be desirable. Another problem arises from the use of
the gradient-boosting regression trees for the analysis of possible
non-linear effects. This method is rather susceptible to the influence
of outliers , as it explicitly focuses on the data points that could not
be explained in the previous iterations. While this might have a slight
effect on the reliability of our analyses, we chose not to exclude
outliers to ensure a better depiction of the true population in our
sample.
Conclusions
In sum, our non-clinical sample did not show any clear linear or
non-linear relationship between self-reported impulsivity and
compulsivity on the one hand and response inhibition as indexed by
behavioral performance and N2, P3a and P3b signals in a Go/Nogo paradigm
on the other hand. Possibly the effect of said personality traits on
response inhibition does not hold true in a general population sample,
but needs a clinical sample to unfold, or else to be exacerbated by
adaptations to the Go/Nogo task. As research on the effect of
impulsivity and compulsivity on cognitive control is vital, but as of
yet discordant, a discussion on the best ways to uncover possible
associations and interactions is highly warranted. With our
comparatively large sample and the different statistical methods we
applied, we hope this is a first step in elucidating the question at
hand.