PSYCHOLOGICAL SCIENCE
Research Report
IMPULSIVITY AND INHIBITORY CONTROL
Gordon D. Logan, ^ Russell J. Schachar,^ and Rosemary Tannock^
University of Illinois and '^Hospital for Sick Children
Abstract—We report an experiment testing the hypothesis that im-
puistve behavior reflects a deficit in the ability to inhibit prepotent
responses. Specifically, we examined whether impulsive people re-
spond more slowly to signals to inhibit ("stop signals) than non-
impulsive people. In this experiment, 136 undergraduate students
completed an impulsiviry questionnaire and then participated in a
stop-signal experiment, in which they performed a choice reaction
time (go) task and were asked to inhibit their responses to the go task
when they heard a stop signal. The delay between the go signal and
the stop signal was determined by a tracking procedure designed to
allow subjects to inhibit on 50% of the stop-signal trials. Reaction
time to the go signal did not van' with impulsivity, but estimated
stop-signal reaction time was longer in more impulsive subjects, con-
sistent with the hypothesis and consistent with results from popula-
tions with pathological problems with impulse control.
Impulsivity is an important construct in several domains. In per-
sonality theory, it is part of the construct of extraversion (Eysenck &
Eysenck. 1969; Revelle, Humphreys, Simon, & Gilliland, 1980). In
childhood psychopathology, it is part of the construct of attention
deficit hyperactivity disorder (ADHD) and conduct disorder (Quay,
1988),
And in adult psychopathology, it is an important characteristic
of psychopathic and sociopathic personalities (Gorenstein & New-
man, 1980: Patterson & Newman, 1993), We have found it useful to
operationalize impulsivity in terms of the ability to inhibit prepotent
courses of action: People who are impulsive have trouble inhibiting
action, whereas people who are not impulsive find it easier to do so.
We found .support for this hypothesis in studies of children with
ADHD, They had more trouble inhibiting prepotent actions than con-
trol children with other psychiatric diagnoses and control children
with no apparent psychopathology (for a review, see Schachar, Tan-
nock, & Logan, 1993), Moreover, the inhibitory difficulties of chil-
dren with ADHD were ameliorated by administration of stimulant
medication (methylphenidate), which also improves behavioral symp-
toms of ADHD (including impulsivity: Tannock, Schachar, Carr,
Chajcyzk, & Logan, 1989; Tannock, Schachar, & Logan, 1995), The
purpose of the present article is to examine the relation between
impulsivity and inhibitory control in young adults with no psychiatric
diagnoses.
THE STOP-SIGNAL PARADIGM
Our measure of inhibitory' control comes from the stop-signal
paradigm (Lappin & Eriksen, 1966; Logan & Cowan, 1984; Logan,
Cowan, & Davis, 1984; Oilman, 1973; Osman, Komblum, & Meyer,
1986,
1990; Vince, 1948), The paradigm involves two concurrent
tasks,
a go task and a stop
task.
The go task is a choice reaction time
Address correspondence to Gordon D. Logan, Department of Psychology,
University of Illinois, 6()3 Easi Daniel St,, Champaign, IL 51820; e-mail:
glogan@s.psych,uiuc.edu.
task that requires subjects to discriminate an X from an O, The stop
task, which occurs on 25"* of go-task trials, involves presentation of
a tone (a stop signal) that tells subjects to inhibit their response to the
go task on that trial. Whether or not subjects are able to inhibit
depends on a race between the stop task and the go task: If they finish
the stop task before the go task, they inhibit their response to the go
task. However, if they fini.sh the go task before the stop task, they fail
to inhibit their response to the go task, responding much as they would
if no stop signal had been presented. Thus, inhibitory control depends
on the latency of the response to the go signal (go reaction time) and
the latency of the response to the stop signal (stop-signal reaction
time).
The race model has been developed formally and shown to be
able to account quantitatively for all of the data in stop-signal experi-
ments (Logan & Cowan, 1984: O.sman et al., 1986: for a review, see
Logan, 1994),
We treat the stop-signal paradigm as a model of inhibitory control
of an impulse. We interpret the go signal as the impetus for the
impulse, and we interpret the response to the go signal as the prepotent
response. The stop signal is a control signal that makes the prepotent
response inappropriate. In the real world, the stop signal may come
from an external source, like a teacher or a stoplight, or from an
internal source, like the person's reevaluation of the situation. In the
laboratory, it comes from an external source (the computer) that is
under the control of the experimenter. From this perspective, inhibit-
ing when given a stop signal is evidence of good impulse control, and
failing to inhibit when given a stop signal is evidence of poor impulse
control.
According to the race model, poor inhibitory control could result
from responding too quickly to the go signal or responding too slowly
to the stop signal. Fast responses to the go signal would be executed
before the person could respond to the stop signal, and slow responses
to the stop signal would allow normally speeded responses to the go
signal to escape inhibition. Our studies of ADHD children suggest that
slow stop-signal reaction time is responsible for poor impulse control.
Children with ADHD inhibit less often than control children even
though their go-signal reaction times are longer than control chil-
dren's.
Children with ADHD exhibit disproportionately longer .stop-
signal reaction times and therefore inhibit less often (Schachar &
Logan, 1990; Schachar, Tannock, Marriott, & Logan, 1995), Thus,
one might expect that long stop-signal reaction times are responsible
for inhibitory control deficits in other populations with impulse con-
trol problems (i,e,, young impulsive adults).
MEASURING STOP-SIGNAL REACTION TIME
Unlike go-signal reaction time, stop-signal reaction time cannot be
measured directly. Subjects either itihibit or fail to inhibit when a stop
signal is presented. If they fail to inhibit, stop-signa! reaction time
must have been slower than the observable latency of the go-signal
response, but it is not clear how much slower it was If they success in
inhibiting, stop-signal reaction time must have been faster than go-
signal reaction time, but neither the stop process nor the go process
60
Copyright © 1997 American Psychological Society VOL. 8, NO. 1, JANUARY 1997
PSYCHOLOGICAL SCIENCE
Gordon D. Logan. Russell J. Schachar. and Rosemary Tannock
•rovides an observable re.sponse with a measurable latency. Some-
hing beyond direct observation is required.
The race tnodel of the stop-signal paradigm provides at least three
different ways to estimate stop-signal reaction time (see Logan, 1994).
The methods are relatively complex and require a deep understanding
of the race model (at the level of reaction time distributions). The
purpose of this article, in part, is to introduce a fourth method for
estimating stop-signal reaction time, also derived from the race model,
that is easier to compute and much easier to understand than the other
methods.
In stop-signal experiments, re.searchers vary the delay between the
stop signal and the go signal (stop-signal delay) in order to handicap
the race in favor of one process or the other. Most often, stop-signal
delays are selected at random from a fixed set that is held constant
throughout the experiment (e.g., Logan & Cowan, 1984), but many
researchers let them vary dynamically, contingent on the subject's
behavior (e.g., Osman et al., 1986, 1990; Schachar & Logan, 1990;
Schachar et al., 1995). The new method for estimating stop-signal
reaction time uses a tracking procedure in which stop-signa! delay
changes after ever)' stop-signal trial, increasing by 50 ms if subjects
inhibit and decreasing by 50 ms if they respond. This tracking pro-
cedure, introduced by Osman et al. (1986, 1990), converges on a
stop-signal delay at which signals inhibit 50% of the time. That delay
is important because it represents the amount of handicapping neces-
sary to "tie" the race. At that delay, the stop process and the go
process finish at the same time, on average, and the one that happens
to win on a particular trial depends on random variation. Thus, that
delay is the average point in time at which the stop process finishes,
and that information can be used to estimate stop-signal reaction time.
The estimation of stop-signal reaction time is illustrated in Figure.
1.
The race depends on three quantities—go reaction time, stop-signal
reaction time, and stop-signal delay—and the experimenter knows
two of them. Moreover, because subjects inhibit 50% of the time at
the critical delay, stop-signal reaction time plus stop-signal delay must
equal mean go reaction time. Stop-signa) reaction time can be calcu-
GO-SIGNAL REACTION TIME
STOP-SIGNAL DELAY STOP-SIGNAL REACnON TIME
TIME •
Fig. 1. How to estimate stop-signal reaction time. The top line rep-
resents mean reaction time to the go signal. The bottom line represents
stop-signal delay plus stop-signal reaction time. Stop-signal delay is
adjusted so that subjects inhibit 50% of the time, which means the race
•s tied. Therefore, the two hnes end at the same point in time. Stop-
signal reaction time, which is not directly observable, can be esti-
mated by subtracting stop-signal delay, which is observable, from
nean go-signal reaction time, which is also observable.
lated simply by subtracting stop-signal delay from mean go reaction
time.
EXPERIMENT
We used the new method of estimating stop-signal reaction time to
investigate the relation between impulsivity and inhibitory' control in
young adult subjects. We assessed impulsivity with the impulsivity
subscale from the extraversion scale of the Eysenck Personality In-
ventory. This subscale gives an impulsivity score between 1 (low) and
9 (high). We expected subject.s with higher impulsivity scores to have
longer stop-signal reaction times.
Method
Subject,'!
The subjects were 136 students, 80 male and 56 female, who were
in an introductory psychology laboratory course and volunteered their
data for analysis.
Apparatus and stimuli
The stimuli were presented on IBM-compatible computers con-
nected to a network. The stimuli for the go task were the letters X and
O presented in the center of the screen for 1,000 ms. The X or O was
preceded by a 500-ms fixation point, also presented in the center of
the screen, and followed by a blank screen that was exposed for 1,000
ms.
The stop signal was a lOO-ms, 1000-Hz tone played through the
internal speaker of the computer al the comfortable listening level.
Stop-signal delay was set at 250 ms initially and then adjusted dy-
namically depending on the subject's behavior. The delay increased
by 50 ms if the subject inhibited successfully (making it harder to
inhibit on the next stop-signal trial) and decreased by 50 ms if the
.subject failed to inhibit (making it ea.sier to inhibit on the next stop-
signal trial).
The experimental task involved 512 trials administered in four
128-trial blocks. There were an equal number of A's and Os in each
block. Stop signals were presented on 25% of the trials in each block
(i.e.,
on 32 trials), half of the time with an X and half of the time with
an O. The order in which trials were presented was randomized .sepa-
rately for each subject. Once started, the program ran continuously,
presenting one trial every 2,5 s. It paused every 128 trials to allow
subjects to rest.
The extraversion scale consisted of 22 true-false questions from
the Eysenck Personality Inventory {Eysenck & Eysenck, 1969), 9 of
which made up the impulsivity subscale and 13 of which made up a
sociability subscale. The items were presented in a list format, with
impulsivity and sociability items roughly alternating. The items were
presented in the same order for each subject. The sociability items
were treated as fillers and not analyzed further.
Procedure
Subjects were tested in groups of 10 to 15. The extraversion scale
was administered to all subjects in a group at once. They were told it
was a personality test and they were to decide whether each item
described them, writing "true" beside the item if it did and "false"
if it did not. After the test was completed, subjects scores their own
tests under the experimenter's direction. Each subject produced two
L. 8, NO. 1, JANUARY 1997 61
PSYCHOLOGICAL SCIENCE
lmpulsivity and Inhibitory Control
scores, an impulsivity score between 1 and 9 and a sociability score
between 1 and 13.
After the personality test, the experimenter gave the instructions
for the experimental task. The instructions described the go task first,
telling subjects they would see a fixation point followed by a letter,
and their task was to respond to the letter as quickly as possible
without making errors, pressing the "/" key if the letter was an X and
the "z" key if it was an O. Then the stop task was described. Subjects
were told that occasionally they would hear a tone that told them not
to respond on that trial. They were told to inhibit their response if they
could but not to worry if thy were not able to inhibit. They were told
that the stop signal would occur at different times, so sometimes they
would be abie to stop and sometimes they would not. They were told
not to iet the stop task interfere with the go task, and not to wait for
the stop signal. After the instructions, subjects went into separate
rooms to perform the experimental task.
Results
Averaged over all subjects, mean reaction time on the go task
when no stop signals were presented was 557 ms and mean accuracy
was 94%. The mean probability of responding on a stop-signal trial
was .506, indicating that the tracking algorithm succeeded in converg-
ing on a delay that allowed subjects to inhibit half of the time. The
mean delay was 336 ms, and the mean estimated stop-signal reaction
time was 221 ms, which is typical of values observed in other studies
with young adults (see the following section and Logan & Cowan,
1984).
Impulsivity scores ranged from i to 9, averaging 4.39.
Measures of performance on the go and stop tasks are presented
as a function of impulsivity score in Table 1. Mean go-signal reac-
tion times and stop-signal reaction times are plotted as a function of
impulsivity in Figure 2. Except for the extreme impulsivity scores,
for which there were only a few subjects, go-signal reaction time
appeared to decrease as impulsivity increased. Within-subject vari-
ability and accuracy remained relatively constant. The probability of
responding given a signal did not change much across impulsivity
levels,
but stop-signal reaction time increased with impulsivity, con-
sistent with our hypothesis. In order to keep the probability of re-
1/1
s
Q:
o
o
Q
^—
in
650-1
-
-
-
550-
450-
-
350-
250-
150-
9,'
9
0 1
15
^.
•
,5
2
17
17
/^
3
V- -
27
/''^
4
28
28
•—.«—
5
IMPULSIVITY
26 ,-
26/
. /
6
SCORE
T1
11
7 8
2
2
/
/
/
9
Fig. 2. Mean reaction time (RT) to the go signal (dotted line) and
mean stop-signal reaction time (SSRT; solid line) as a function of
impulsivity score. The number of subjects contributing to each point
is written above each point.
sponding constant in the face of increasing stop-signal reaction times,
the tracking algorithm set the mean delay lower for subjects with
higher impulsivity scores (see Fig. 1).
We tested the significance of these trends in two ways. First, we
computed Pearson product-moment correlations between impulsivity
scores and the various performance measures. The correlation matrix
is presented in Table 2. The only variable that correlated significantly
with impulsivity was stop-signal reaction time. Mean go reaction time
and mean stop-signal delay did not correlate significantly with im-
pulsivity.
Table 1. Measures of performance
impulsivity score
Measure
Go task
Mean RT
SD of RT
P{C)
Stop-signal delay
Mean
SD
Stop-signal RT
1
(n = 9) (n
468
101
.96
248
63
.507
220
on the go
2
= 15)
625
145
.93
445
80
.476
180
Note. Standard deviations are within-subjects (i.e..
responses: /"(RIS) =
and stop tasks
3
(n = 17)
594
125
.92
395
72
.490
199
deviations from
probability of responding given a stop signal.
and estimates of stop-signal reaction
1
4
(1 = 27)
550
128
.94
324
70
.515
226
jnpulsivity score
5
(n = 28)
552
128
.97
337
68
.506
215
each subject's mean). RT =
6
(n = 26)
543
133
.96
321
69
.496
222
reaction time
time as
7
a function of
8
(n = 11) (fl = 1)
570
146
.86
281
68
.559
289
P(C} =
410
99
.92
145
61
.555
265
proportion of correct
9
(« = 2)
507
151
.95
146
69
.527
361
62
VOL. 8, NO. 1, JANUARY 1997
PSYCHOLOGICAL SCIENCE
Gordon
D,
Logan, Russell
J.
Schachar,
and
Rosemary Tannock
Table
2.
Correlations
Go task
MeanRT
SD of RT
P(C)
Stop.-signal delay
Mean
SD
P(R\S)
Stop-signal RT
Mean
SD (between)
between
impulsivity
and the
performance
Impulsivity Mean RT
-.036
.073
-.023
-.131
-.060
.137
.315
4,39
1.79
.799
.003
,951
.846
-
44''
-.160
557
•
203
Go task
SD of RT
-.098
.715
.753
-.317
.017
130
62
Note. N = 136, Correlations greater than ,169 are significant
a.1
p < .05
(two-tailed); RT = reaction time;
measures
P{C)
-.055
-.201
.027
.184
.94
,12
(two-tailed).
P(C) = proportion of correct responses; /'(RIS) =
tile standard deviation of the scores between the subjects
Stop-signal delay
Mean
.849
-.573
-.457
336
226
and correlations
SD
-.579
-.277
70
23
greater than .221
probability of responding given
(i.e.,
deviations from the means across subjects).
F(RIS) Stop-signal RT
.558
,506 221
.0S5 71
are significant at p < .01
a stop signal; SD (between) is
Second,
we
split
the
subjects into
two
groups, half with impulsivity
scores from
1
through
4 and
half with impulsivity scores from
5
through
9,
Measures
of
performance
on the go
task
and
stop task
for
the
two
groups
are
presented
in
Table
3, The
only significant differ-
ence between
the
groups
was
stop-signal reaction time, which
was
faster
in the
low-impulsive subjects than
in the
high-impulsive
suh-
jects.
Mean go-signal reaction time
was
faster
in
high-impulsive
sub-
jects than
in
low-impulsive subjects,
but the
difference
was not sig-
nificant.
VALIDITY
OF THE NEW
METHOD
We tested
the
validity
of
the
new
method
by
computing stop-signal
reaction times with
a
conventional method
and
correlating those
val-
ues with values from
the new
method.
The
conventional method
(de-
scribed
in
detail
hy
Logan
&
Cowan,
1984, pp,
302-303,
and
Logan,
1994,
pp.
215-217) involves estimating
the
point
on the
go-task
re-
action time distribution
at
which
the
stop process finished,
and sub-
tracting
ou!
stop-signal delay
to
calculate stop-signal reaction time
(assuming that stop-signal reaction time
is a
constant).
In
practice, this
method involves
(a)
rank ordering
go
reaction times from trials with
no stop signals;
(b)
finding
the mth go
reaction time, where
m is the
product
of the
probability
of
responding given
a
stop signal
at
some
delay
and the
number
(n) of
responses
in the
go-signal reaction time
distribution (i.e.,
m =
P(respondlsignal)*n);
and (c)
subtracting
the
given stop-signal delay from
the mth
reaction time.
We performed this procedure
for
each subject
for
every delay that
provided
a
"reasonable" amount
of
data, defining "reasonable"
in
.several different ways.
We
obtained
the
highest correlation between
new-method
and
conventional-method stop-signal reaction times
{r =
Table
3.
Measures of performance
scores above
and
below
the
median
Measure
Go task
Mean RT
SD of RT
P(C)
Stop-signal delay
Mean
SD
PCRIS)
Stop-signal RT
Below median
Mean
566
127
.94
358
72
.499
208
Note. Mean impulsivity score was 2.91
median. r(134) >
(two-tailed). RT =
responding given s
on
the
score
SD
209
60
.15
234
24
,073
67
go
and
stop
Above
Mean
548
133
.95
313
68
.512
235
for subjects below the
1.980 is significant at p < .05
reaction time; P(C)
I stop signal.
tasks
for
subjects with impulsivity
median score
SD
198
64
.10
216
22
.095
72
median and 5.87
(two-tailed); f(134) * 2.617 is
Inferential statistics
/(134)
.517
-.529
-.463
1,166
.822
-.902
-2,266
MSE
34,98
10.61
2.12
38.65
3.92
,015
11.91
for subjects above the
significant at p <
= proportion of correct responses; P(RIS) = probability
,01
of
OL. 8, NO. 1, JANUARY 1997
63
PSYCHOLOGICAL SCIENCE
Tmptilsivity atid Itihibitory Control
,763) by analyzing delays that included 10 or more stop-sigtial trials
and for which the probability of responding given the stop signal was
between .1 and .9. With those criteria, 129 out of 136 subjects were
included in the analysis. The mean convetitional .stop-signal reaction
time was 199 ms (compared with 222 using the new method), and the
correlation between conventional stop-signal reaction titne and im-
pulsivity was ,387, /(127) = 2.H, p < ,05, two-tailed.
We obtained the highest correlation between conventional stop-
signal reaction time and impuisivity (r = ,252,;(131) = 2.98,p<.01,
two-tailed) by analyzing delays that included five or more stop-signals
trials and for which the probability of responding given the stop signal
was between .1 and .9. With these criteria. 133 out of 136 subjects
contributed data. The mean stop-signal reaction time was 207 ms
(compared with 217 ms using the new method), and the correlation
between conventional and new stop-signal reaction time was .615.
We were surpri.sed by the low correlations between the methods
for calculating stop-signal reaction time, so we assessed the reliability
of each method by calculating .stop-signal reaction time separately for
odd and even trials for each subject and correlating the values. For the
new method, all 136 subjects contributed data and r = .945, For the
conventional method, applied to delays that included five or more
stop-signal trials with the probability of responding given a signal
between ,1 and .9, only 122 subjects contributed data and r = .395.
This finding suggests that the low correlations between methods were
due to unreliability in the conventional estimates. We suspect the
unreliability of the conventional estimates may be due to some aspect
of the tracking procedure, which was not designed to provide optimal
estimates of conventional stop-signal reaction time. We are planning
to test subjects with the new method and a version of the conventional
method that is optimized for calculating conventional stop-signal re-
action time to provide a more appropriate comparison between meth-
ods.
DISCUSSION
The results showed a significant relation between impulsivity and
inhibitory control: High-impulsive subjects had longer stop-signal re-
action times. This result corroborates our hypothesis that problems
with impulse control stem from difficulties in inhibitory processing
rather than extra facility in executing prepotent responses. That is,
impulsive people appear to have difficulty inhibiting prepotent re-
sponses not because their prepotent responses are exceptionally fast,
but rather because their inhibitory responses, which countermand and
counteract the prepotent responses, are exceptionally slow.
Two factors mitigate these conclusions. First, go-task reaction
times were faster for more impulsive subjects, though the difference
was not significant. Perhaps a more sensitive experiment with a larger
sample of extreme impulsivity scores would reveal a significant dif-
ference. Second, we used only one scale to define impulsivity, so it is
not clear how well our results would generalize to other scales and
other measures. Further research will be required to assess the gen-
erality of our conclusions.
Nevertheless, the results demonstrate the validity and feasibility of
the new measure of stop-signal reaction time. It is easier to compute
and to understand than previous methods, yet it produces similar
values. The tracking procedure is easy to implement, and reliable
estimates of the delay at which subjects inhibit half of the time can be
gathered in a relatively short period of time. Thus, the modified stop-
signal paradigm is a promising tool for future investigations of in-
hibitory control, within and beyond studies of impulsivity.
The experiment demonstrates the utility of the information pro-
cessing approach to personality and psychopathology. This approach
allows us to understand the characteristics of normal and abnormal
personalities in terms of underlying cognitive processes. More gen-
erally, it allows us to see theoretical and empirical commonalities
between areas of investigation that were hitherto separate (also see
Gorenstein & Newman, 1980; Patterson & Newman, 1993).
Acknowledgments—This research was supported in part by Grants BNS
91-09856 and SBR 94-10406 to Gordon Logan from the National Science
Foundation. We are grateful to Brian Compton for help in managing the
computer network, Julie Delheimer for help in analyzing the data, and
David Adams. Rachel Andrews, Brian Cotnpton, Chris Currie, Heidi
Homer. Emilie Lin, and Stan Taylor for testing the subjects. We would like
to thank Bill Revelle for providing us with the impulsivity scale.
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