1. Risk-taking Propensity and Cognitive Inhibition: a Study on
Adolescents
Valentina Merlini∗
Department of Psychology of Development and Socialization
University of Padua, Veneto, Italy
merlinivalentina@hotmail.com
Abstract
Individual differences in cognitive abilities have been linked to risky choices. The current study investigates the relationship
between cognitive inhibition and risk-taking propensity: previous studies show that high cognitive inhibition results in minor
risk-taking behavior’s displays. We administrated the Balloon Analogue Risk Task (Lejuez, et al., 2002), a behavioral measure
of risk-taking propensity, to a sample of thirty-seven high school students (average age = 16.34). Subjects then completed a
simple three-item test – Cognitive Reflection Test (Frederick, 2005) – to asses cognitive inhibition’s abilities. Data do not show
any statistically significant correlations. To explain these findings, we propose that the Balloon Analogue Risk Task does not
asses risk-taking propensity, as commonly intended, but a sub-dimensional construct of impulsivity theorized by Dickman
(1990): functional impulsivity.
1 Introduction
Recently a behavioral measure of risk-taking propensity
called the Balloon Analogue Risk Task (BART) was designed
(Lejuez, Read, Kahler, Richards, Ramsey, Stuart, Strong, et
al., 2002) in an attempt to avoid some of the limitations of
self-report measures such as demand effects, inaccurate in-
trospections, and unreliability in predicting emerging risk-
taking behaviors. In this computer-based task, participants are
shown a series of animated balloons one at a time. Participants
can inflate the balloon and accumulate, for each pump, some
money/points into a temporary reserve. They are instructed
that by pumping the balloon it could possibly explode and
consequentially the temporary reserve’s content would be lost.
However, anytime during the task participants can choose to
stop pumping and transferring the money/points to a perma-
nent reserve. When the balloon explodes or the money/points
are transferred, the participant’s exposure to the balloon will
end, and a new balloon will appear until all the balloons (i.e.,
trials) have been completed. Individuals’ level of risk-taking
is indexed by the average number of pumps they deliver per
balloon.
BART scores have been compared both to self-report mea-
sures of risk related constructs, such as impulsivity – assessed
by the Eysenck Impulsivity Subscale (Eysenck & Eysenck,
1978) – and to reports of real-world risky behaviors. Perfor-
mances on the BART are consistently associated with self-
reports of risk behaviors (Aklin, Lejue, Zvolensky, Kahler,
& Gwadz, 2005; Lejuez, Aklin, Zvolensky, et al., 2003;
Lejuez et al., 2002; Hopko, Lejuez, Daughters, Aklin, Os-
borne,Simmons, & Strong, 2006). Positive correlations be-
tween BART scores and Eysenck Impulsivity scores were
found in studies conducted on adolescents and young adults
(e.g., Lejuez et al., 2002), while failed to appear when select-
∗This research was completed as part of my final work to obtain the bachelor’s degree in "Social and Work Psychology" at the University of Padua. I would
like to acknowledge my supervisors Franca Agnoli and Sarah Furlan for extensive comments on earlier versions of the manuscript. I am especially indebted to
Sarah Furlan for assistance in analyzing data.
1

2. ing younger samples (Lejuez, Aklin, Daughters, Zvolensky,
Kahler, & Gwadz, 2007). Such results may indicate that the
BART and self-report measures of personality capture different
dimensions of the risk-taking construct.
According to Mata (Mata, Josef, Samanez-Larkin, & Her-
twig, 2011), the BART is an experiential task in which par-
ticipants modify their choices while learning from experience
and feedbacks. In the BART the probability that a balloon
will explode was arranged by constructing an array of N num-
bers, of which "1" is designated to set up the explosion. On
each pump, a number is selected without replacement from
the array. According to this algorithm, if the array contains
the integers 1–128 (as for the original BART) the average
break point would be 64 pumps. Therefore the optimal strat-
egy which allows to maximize the final winnings would be to
pump 63 times and collect on the 64th moment of choice.
In all previous studies (e.g., Lejuez, et al., 2002; Lejuez,
et al., 2003; Lejuez, et al., 2007), participants showed overall
conservative behaviors, as the average number of pumps fell
below the number marking optimal performance. Benjamin
and Robbins (2007) raise the issue of whether "riskier" partici-
pants should instead be categorized as the "more optimizing"
ones. There is, however, a general tendency to increase the av-
erage number of pumps per balloon during the task. According
to this view, the BART is an experiential task in which being
more risky is the result of positive learning and making risky
choices is the adoption of a maximizing decision strategy.
We hypothesized that the inconsistent relationship between
BART scores and impulsivity self-report scales is the result
of BART’s relationship with only one sub-dimension of im-
pulsivity construct: the functional impulsivity, theorized by
Dickman (1990), which is the tendency to engage in rapid,
error-prone information processing (i.e., to act with relatively
little forethought) when such a strategy is rendered optimal
by the individual’s other personality traits. We believe that
functional impulsivity is part of the intuitive reasoning style
that the Fuzzy-trace theory (Brainerd & Reyna, 1990; Reyna
& Brainerd, 1990I) considers the apex of our cognitive devel-
opment.
Being a dual-process model, the Fuzzy-trace theory ac-
knowledges that adult reasoning includes different modes of
processing: one that is analytical, quantitative, and operates on
precise memory representations (named verbatim) and another
that is intuitive, qualitative, and operates on gist representa-
tions (Reyna, Lloyd, & Brainerd, 2003). FTT includes some
major assumptions about cognitive development: it predicts
that reasoning develops from computational (verbatim) to in-
tuitive (gist-based) thinking as a system-wide adaptation to
the limits of information processing, a means of avoiding sys-
tematic errors caused by poor verbatim memory (Brainerd &
Reyna, 1990). This view of intuitive thinking as the apex of
cognitive development is supported by studies of children’s
learning and of adults’ acquisition of expertise, which show a
progression from detail-oriented and computational processes
to fuzzy and intuitive processing (Davidson, 1991; Davidson,
Suppes & Siegel, 1957; Jacobs & Potenza, 1991).
Coherently with the FTT, we consider functional impulsiv-
ity as the result of an intuitive reasoning style adopted to escape
information processing’s limits. As intuition is the apex of
our cognitive development, the lack of relationships between
BART scores and self-report measures of impulsiveness in
younger samples can be explained by their yet not sufficiently
developed cognitive abilities in order to activate the functional
sub-dimension of impulsivity.
To investigate whether the BART is truly a measure of
risk-taking propensity or, due to its structure, could be bet-
ter suited as measure of intuitive thinking, we developed a
research project based upon decision making’s literature. De-
cision making studies have found a correlation between "cog-
nitive inhibition" (the individual ability to cognitive inhibit
automatic wrong representations to substitute them with cor-
rect ones) and risk-taking propensity (Frederick, 2005; Cokely
& Kelly, 2009): high cognitive inhibition capacity results into
less risk-taking behaviors.
In order to verify if the BART is a measure of risk propen-
sity (intended as the tendency to make choices with uncertain
probability of success that often lead to negative consequences
for the individual), we administrated the BART and the Cog-
nitive Reflection Test – a simple three-item test, developed
by Frederick (2005) to asses cognitive inhibition’s individual
ability – to a sample of thirty-seven adolescents (average age
= 16.34), because of their natural disposition to make risky
choices: data shows that impulsive and risky behaviors are
more manifest during adolescence (e.g., Casey, Getz, & Gal-
van, 2008). If the BART is behavioral measure of risk-taking
propensity, according to decision-making literature, high cog-
nitive inhibition assessed by the CRT should results into lower
scores on BART’s indexes of risk-taking propensity.
2

3. 2 Method
2.1 Participants
A sample of thirty-seven high school students (25 males e
12 females) – belonging to a experimental mathematical plan
of studies (P.N.I., Piano Nazionale Informatico) – between
sixteen and seventeen years old (M = 16.34, DS = 0.49) took
part in the experiment.
2.2 Materials and Procedure
2.2.1 Measure of Risk Propensity: Ballon Analouge Risk
Task (BART)
Participants underwent the BART by groups of 9-10 per-
sons, under the supervision of a researcher. Our BART was
adapted for italian users from the original version (Lejuez, et
al., 2002) and was presented as a game, the purpose of which
was to earn as many points as possible in order to win one
of the three final prizes. On a computer screen a small simu-
lated balloon accompanied by a balloon pump, a reset button
labeled "Collect" were displayed. The screen also permanently
presented a points-earned display, a second display listing the
number of the current balloon (X out of 18), a third display
listing the number of total pumps during the task and finally
a display showing the points potentially earned so far from
the current balloon. During the task participants could choose
between pumping the balloon (and accumulate 1 point for each
pump in to a temporary reserve) or to transfer the points to a
permanent reserve by clicking over the button "Collect". Each
pump implied an, unknown to the participant, probability of
exploding: when it did, it was accompanied by a "Pop" sound
and all the points in the temporary reserve were lost. Every
time the balloon exploded or the points were collected, the
participant’s exposure to that balloon ended, and a new bal-
loon appeared until a total of 18 balloons (i.e., trials) had been
completed.
Coherently with previous studies (Lejuez et al, 2002;
Lejuez et al, 2003; Lejuez et al, 2007) we considered the
average number of pumps per trail during the task as index
of risk-taking propensity. As participants knew that risk of
explosion increased at each pump, pumping more is the result
of taking more risks. We also took in consideration latencies of
choice, before pumping the balloon or transferring the points,
believing they could be an asset to explain participant decision
strategies.
Figure 1. Diagram of the Balloon Analogue Risk Task (return Lejuez, 2002)
2.2.2 Measure of Cognitive Inhibition: Cognitive Reflec-
tion Test (CRT)
The CRT is a simple three-item test designed by Frederick
(2005) as a measure of one specific cognitive ability: Cognitive
Inhibition (the ability to suppress representations previously
activated). It is composed by three problems which solution
is easily understood once explained, yet reaching the correct
answer requires the suppression of an intuitive "incorrect" an-
swer that springs "impulsively" to mind. To administrate the
CRT the following three items (taken from the original version)
were translated into italian:
1. A bat and a ball cost $1.10 in total. The bat costs $1.00
more than the ball. How much does the ball cost? ...cents
2. If it takes 5 machines 5 minutes to make 5 widgets, how
3

4. long would it take 100 machines to make 100 widgets?
...minutes
3. In a lake, there is a patch of lily pads. Every day, the
patch doubles in size. If it takes 48 days for the patch
to cover the entire lake, how long would it take for the
patch to cover half of the lake? ...days
The CRT scores are reported on a 0-3 scale, according to
the number of correct answers.
3 Results
Table 1 shows the descriptive statistics of BART’s variables
considered in this study as indexes of risk-taking propensity:
the final score obtained by participants, the average number
of pumps overall and for each set of six trails (in which we
divided the task for better analysis) and the average value of
latency of choice overall and for each set. We intended to ver-
ify if the BART is truly an experiential task, in which choices
are influenced by feedbacks, by looking for changes in the
selected variables as expression of the adoption by participants
of a different behavioral pattern during the task.
The average number of pumps increases during the task, as
we can see by the statistically significant difference between
the three set of trails (F (2, 72) = 4.32, p < .05). Such variance
is especially significant when comparing the first and the last
set of trails (F (1, 72) = 3.02, p < .05). We deduct that, during
the task, there is a change in participants decision strategy.
Overall the tendency to pump more while the task progress
reflects a general increase in risk propensity.
The analysis of latency supports this results. Table 1 shows
that the average latency decreases during the task. Between the
three sets three is a statistically significant difference (F (2, 72)
= 16.77, p <01). In particular, during the first set, participants
exhibit a higher latencies of choice when compared to the other
sets (F (1, 72) = 4.32, p < .05). Decreased latencies could be
the result of a familiarization process with the task.
Analyzing the variance of number of pumps and latency’s
values between each set suggests that participants overall tend
to adopt a behavioral strategy more risk-prone, increasing the
number of pumps and decreasing the time of choice.
Table 1. Final Score, Average Number of Pumps Overall and Number of Pumps for Each Set of Six BAlloons
Total I Set II Set III Set
dependent variable M DS M DS M DS M DS
N. of Pumps* 14.06 6.36 12.87 6.74 13.86 6.55 15.46 5.79
Latency 767.42 354.74 984.51 433.72 688.47 308.68 629.29 321.82
*Index of risk-taking propensity.
3.1 Correlation between BART scores and Cog-
nitive Inhibition (CRT)
The main objective of this thesis was to understand if the
BART is a measure of risk propensity intended as the tendency
to engage in choice with uncertain outcomes and possible nega-
tive consequences. In order to verify it we looked for a relation
between BART scores and CRT scores, according to decision
making literature’s proposition that high cognitive inhibition
corresponds in less risky behaviors displays.
Table 2 contains descriptive statistics of CRT final scores
of our sample of high school students compared to Frederick’s
samples of university students (2005). As displayed in the ta-
ble, participants to our research (belonging to an experimental
plan of studies, P.N.IM, involving high quantities of math and
physics hours) obtained a high average CRT score ( M = 1.97).
Their scores are second only to the university students scores
analyzed by Frederick (2005) who belonged to M.I.T., one of
the most notorious universities across the world.
4

5. Table 2. Cognitive Reflection Test: Results of Our Sample and of Some Samples of University Students from Frederick’s Studies (2005)
Percentage with 0, 1, 2, 3 correct answer
"High" "Low"
Locations at which data were collected Mean CRT score 0 1 2 3 N
Our high school sample (P.N.I.) 1.97 5.6% 30.6% 25.0% 38.9% 36
Massachusetts Institute of Technology 2.18 7% 16% 30% 48% 61
Princeton University 1.63 18% 27% 28% 26% 121
Harvard University 1.43 20% 37% 24% 20% 51
Note: of the original sample of thirty-seven, one participant has been excluded from analysis as he didn’t complete the test.
Table 3 is the correlation’s matrix between selected
BART’s variables (final score, average number of pumps over-
all and for each set) and CRT total score. As we can see from
Table 3, in contrast with our main hypothesis, there are no
correlations between CRT scores and selected BART’s vari-
ables. Specifically, the correlation between CRT scores and
the Average Number of Pumps is not statistically significant (r
(34) = .24 p>.05).
Table 3. Pearson’s Correlation between BART’s Final Score, Average Number of Pumps Overall and for Each Set, and Total CRT Score
1 2 3 4 5 6
BART
1. Final Score – .883* .640* .868* .839* .228
2. Average Number of Pumps – .849* .867* .858* .238
3. Average N. Pumps I Set – .689* .566* .201
4. Average N. Pumps II Set – .678* .114
5. Average N. Pumps III Set – .222
CRT
6. Total Score –
* Correlation is significant at the 0.01 level (two-tailed test).
4 Conclusions
Data analysis show statistically significant differences be-
tween the first set and the last set for the variables "Average
number of pumps" and "Latencies". During the task, time
of choice decreases and the number of pumps (index of risk-
taking propensity) increases: these data suggests that, while
during the first set participants are still familiarizing with the
task, as trails proceed they adopt a more risk-prone behavioral
pattern with less forethoughts.
However, riskiness on the BART (indexed by the final
score, average number of pumps overall and per set, and late-
cies) was not statistically correlated with scores on the CRT.
Specifically, there is no statistically significant correlation be-
tween the average number of overall pumps and CRT scores.
Thus, cognitive inhibition assessed by the CRT does not ac-
count for any significant variance in riskiness on the BART:
we can infer that individuals ability of cognitive inhibition is
not related to changes in participants behavioral patterns.
Therefore, when risk-taking propensity is assessed by the
BART the relationship found by decision-making’s studies
(that high cognitive inhibition capacity results in minor dis-
plays of risky behaviors) is not reflected. These data supports
our view of the BART as a measure of a construct different
from risk-taking propensity as intended by decision-making
literature.
We propose that the BART assess individuals level of func-
tional impulsivity instead of risk-taking propensity. Changes
in latencies and number of pumps across the sets support this
view: while time of choice decreases, the increment of number
of pumps is directed toward the optimal strategy: participants
5

6. think less and act more coherently with the maximizing strat-
egy. We believe these changes varies according to participants
functional impulsivity’s level.
The data presented in the current study suggest that new
researches should be carried out to deepen our knowledge of
BART experimental properties and its relation with the impul-
sivity construct (and specifically the functional sub-dimension
of it). However, several limitations to our study should be
considered.
Firstly, our sample of high school students achieved excep-
tional performances in the CRT, seconds only to M.I.T. scores.
We ought to question ourself if, in the light of these extraor-
dinary results, the absence of relationship between BART’s
variables and CRT scores is not due to our sample’s mathe-
matical abilities. One possibility is that the sample’s plan of
study (consisting in many hours of mathematics and physics)
could result in easiness with math-related problems and such
capacities could make the CRT not determinant as measure of
cognitive inhibition.
Secondly, data show high standard deviations when look-
ing at averages number of pumps and latencies. We believe
wider samples, more trails and lower explosion’s probability
could result in more reliables data and help finding interesting
results. Specifically, latency’s analysis seems to be an over-
looked and yet powerful variable in explicating participant’s
behavioral patterns.
In conclusion ,we believe that this study should lead to new
researches investigating BART properties and its relationship
with impulsivity construct. Specifically, performance on the
task when modifying the magnitude of reward/loss (Bornoval-
ova, Gratz, Daughters, Nick, Delaney-Brumsey, Lynch, Kos-
son, & Lejuez, 2008) and explosion’s probability (Vigil-Colet,
2007) varies in function of individual levels of impulsivity.
Therefore, we invite to future studies that while assessing the
functional dimension of impulsivity vary the structure itself of
the BART.
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