What is risk taking? In their seminal work, Jessor and
Jessor (1977) de?ned risk taking as “behavior that is socially de?ned as a
problem, a source of concern, or as undesirable by the norms of conventional
society and the institutions of adult authority, and its occurrence usually
elicits some kind of social control response” (Jessor & Jessor 1977, 33.o.).
While the lay and clinical definition of risk-taking
is often used in the sense of engaging in a behavior that could potentially
have a negative outcome. (Defoe et al., 2015).

There are many validated and frequently used risk-
taking tasks that we can choose from when planning an experiment. Despite the
numerous refined risky decision-making tasks, there is still no consensus about
the definition of risk. Decision theory usually
describes risky situations as choices between lotteries characterized by
outputs (gains and/or losses) and their probabilities (Defoe et al., 2015).

The core characteristic of the term risk as used in
the judgment and decision literature is outcome variability: The option with
the widest range of possible outcomes is considered the riskiest option (Defoe et al., 2015).

All these tasks measure the risk one is willing to
take in a situation, but which of them is able to predict real life risk
taking? Do they measure the same thing or are there differences? That is the
question I would like to answer in this paper.

Studies show that cognition is
difficult and costy, partially because of peoples’ limited processing capacity,
including attention (Markiewicz
& Kubi?ska, 2015). Among other things, this is why decision makers first
try to simplify the problem in a so called “editing phase” (Markiewicz & Kubi?ska, 2015).  Because of
attention limitations they do not use all of the available information when
making a decision. Although many researce investigate the order of information,
relatively little has been said about differences in information use in
affective (hot) and cognitive (cold) risk processing. Do people concentrate on
different risk characteristics (losses of, and gains on, stakes, and their
probabilities) in emotional risk taking (e.g., parachute jumping) compared to
cognitive risk taking (e.g., pension scheme decisions)? Some studies have
demonstrated that the impact of probabilities is strongly diminished for
affect-rich outcomes (Markiewicz
& Kubi?ska, 2015). However, these studies used outcomes of different
valences, assuming that medical outcomes are affect-rich and that monetary
outcomes are affect-poor (Markiewicz
& Kubi?ska, 2015).    Most of
the tests listed below are measuring affective (hot) risk taking.


Analogue Risk Taking-Youth version

The BART-Y test is a version of the
BART test for adolencents. The participants (pubescent
adolescents) are instructed to pump a balloon. The explosion point of
the ballon is randomized and varies per every trial. With each pump the
participant can win one point, but each pump also increases the chance of an
explosion, resulting the loss of all the gained points for that balloon. If
participants stop pumping the balloon, they then earn all of the points accumulated
so far for that. Risk-taking
was measured as the average number of pumps on unexploded balloons. The BART-Y task is considered to be a hot risk taking
task, as the reward or loss is presented right after the participant makes the
decision about stopping or continuing the pumping. Although, the strength of
the influence of the emotions can be reduced by delaying the rewards, which
could make this task a little less like a hot task.

BART score is related to selfreported engagement in
real-world risk-taking behaviors including substance use and delinquency/safety
behaviors from middle adolescents to young adults (Dahne et al., 2013). The
task was found to be associated with self-reported risk behaviors, such as
alcohol use, substance use, gambling, delinquency, and risky sexual behavior (Dahne
et al., 2013). BART performance appears to be related to early engagement in
substance use as well as to risk behaviors that relates to substance use and
also the task seem to predict young adolescents’ propensity for future risks
even if they have not yet started to engage in health-compromising activities (Dahne
et al., 2013).


The wheel of fortune

The computerized version of the wheel of fortune (WOF)
task is a 2-choice decision-making task with probabilistic monetary outcomes (Defoe et al., 2015).  On each trial, a wheel (a circle divided into 2 slices of different size
and of 2 different colors) is presented to participants (82 adolescents with no
lifetime history of psychiatric illness). Throughout the task, 4 types of
monetary wheels are presented in random order, differing on probability of the
reward magnitude. Smaller slices are always paired with the higher reward magnitude.  Participants are instructed to select 1 of
the slices, naming the color. If the computer randomly selects the same color
as the participant does, the participant wins the designated amount of money.
However, the participant wins nothing if the computer randomly selects the
other color. Risk-taking was measured with a percent risky selections score,
which was computed using the number of times 10% and 30% probability options
were selected relative to the total number of times that the 10/90 and 30/70
wheels were presented (Defoe et
al., 2015).  Participants got feedback right after they made the
decision and chose a wheel; therefore this task is also considered as a hot
risk taking task.

Greater frequency of low-probability (high-risk)
choices on the win–no win version of the WOF predicted substance-related
problems, including drug involvement and related psychiatric and psychosocial
problems (Dahne et al., 2013). However, lowprobability (low-risk) choice on the
lose –no lose version of the task did not predict substance-related problems
(Dahne et al., 2013). Although individual differences in risky selections on
the WOF were associated with risk-taking behavior and substance related
problems, the prevalence of substance-related problems was low, probably
because this was a selected group at extremely low-risk for psychopathology.


The Iowa Gambling Task

This task is quite similar to the previous one. On
both, participants (substance abusers and non-substance abusers of cocaine and
marijuana) have to choose between high-risk, high gain and low-risk, low gain
options. In this task for each trial, participants have to choose one card at a
time from 1 of 4 decks that differ in payoffs and losses. Selections from the 2
“disadvantageous” decks are followed by a higher reward (on most trials), but
also by higher (unpredictable) losses; thus, the final result is “overall net
loss” (Defoe et al., 2015). The 2 “advantageous” decks are followed by lower
rewards on most trials but also by lower (unpredictable) losses; thus, the final
result is “overall net gain”. Risk-taking wasoperationalized as the mean number
of choices from the deck with the highest outcome variability ( the “risky”
deck).Participants learn about the experienced outcomes and the differences
between the decks through trials and errors, as the feedback about the
decisions they made are immediate, which also make this task a hot risk taking
task. In studies using the IGT, risk-taking is typically operationalized as the
number of choices from the 2 advantageous decks minus the 2 disadvantageous
decks (Defoe et al.,

Results indicated that both cocaine users and
marijuana users performed worse than controls on the total IGT net score (total
score across sessions 1 and 2) (Dahne et al., 2013). Furthermore, all groups
exhibited between- session learning, but the rate of learning differed between
groups such that cocaine users exhibited less learning than marijuana users and
marijuana users exhibited less learning than control (Dahne et al., 2013).


The framing spinner task

In the Framing Spinner Task, participants (153
students) make a choice between 2 spinners with an arrow in the middle: One spinner
is completely red representing a sure option and the other spinner had varying
proportions of blue and red representing a gamble. Risk levels varied as
follows: one-half, two-thirds, and three-fourths chance of winning nothing (gain
frame) and one-half, two-thirds, and three-fourths chance of losing something (loss
frame) (Reyna, Estrada, DeMarinis,
Myers, Stanisz, & Mills, 2011). Reward levels varied between low ($5), medium ($20), and high ($150). In
loss problems, participants began with an endowment, from which subsequent
losses were deducted, whereas in the gain frames participants begin with no
money. The displayed net outcomes were the same for both frames. Risk-taking
was operationalized as the proportion of gamble choices. In this hot risk
taking task, on each trial, after participants selected their choice, they rated
their degree of preference too, which only strenghtened their feelings about
making the decision and made them concentrate more on the emotional part of
their decision making.

In this task reasoning was the most consistent predictor
of real-life risk taking. The Gist factor (The four gist measures included the
Categorical Risk scale, the Gist Principles scale, a Global Risk question, and
a Global Benefits question) was associated with fewer sexual partners, but the
Verbatim/Reverse Framing factor was associated with more sexual partners (Reyna et al., 2011). Intentions
to have sex, sexual behavior, and number of partners decreased when gist-based
reasoning was triggered by retrieval cues in questions about perceived risk,
whereas intentions to have sex and number of partners increased when
verbatim-based reasoning was triggered by different retrieval cues in questions
about perceived risk (Reyna et al.,


The knife switches task (The
daredevil task)

The participants (44 children ageing from 4 years 9
months to 6 years 5 months; mean age: 5 years 6 months) are seated in front of
a panel of 10 small knife switches and are told that 9 of these switches were
“safe” and one was a “disaster” switch. The participant is instructed to pull
one of the switches. If the participant pulls a safe switch, he (or she) is
allowed to put one spoon full of M&M’s candies into a glass bowl. The
participant then has to decide whether to pull another switch in an attempt to
win another spoonful of candy or to stop and keep the candy. If a participant
pulls the disaster switch, he (or she) looses all the accumulated candy. The
game ends when the participant either stops and collects his candy or pulls the
disaster switch and loose all of it. Risk-taking was operationalized as the
number of pulled switched. This task is also a hot risk taking task, as
participants can see the tempting reward (the candy) and got the feedback about
gaining or not gaining some immediately after making the decision.

In the research, participants were devided into two
groups (risk avoiding and risk-taking) according to their performance in the
knife switches task, and then they had to participate in a traffic task (Hoffrage, Weber, Hertwig & Chase, 2003).   In the
task a car heading towards the point the children were supposed to cross the
street was presented and the children had to make a stop (not crossing the
street) or a go (crossing the street) decision. The researchers measured the
maximum time and distance in which the participant is still willing to cross
the street in front of the car.

According to the research, risk takers had a
significantly higher hypothetical accident rate (3.7%; 61 of 1,654) than risk
avoiders (0.6%; 5 of 857) (Hoffrage, Weber,
Hertwig & Chase, 2003).   This difference appears to be small, however,
when viewed in terms of Rosenthal and Rubin’s (1982) binomial effect-size
display, it amounts to a difference of 9 percentage points between the two
groups (Hoffrage, Weber, Hertwig & Chase,
2003).   Risk takers (using the
gambling classification) tolerated shorter leeway times than risk avoiders and also
had a higher hypothetical accident rate, that is, a higher percentage of go
decisions (out of all gaps) that left a leeway time of less than 3 s –which is the
time children needed to run across the street on average (Hoffrage, Weber, Hertwig & Chase, 2003).


Cardsorting Task (Hot version; Cold version)

The CCT begins with a presentation of
32 (gain or loss) cards and a score of 0 points. Participants (497 students) are asked
to turn over cards. A round ends when participants encounter a loss card, or if
the participants stop turning over cards to collect all the gains. Per round,
three variables vary systematically: the magnitude of gain, the magnitude of
loss, and the gain/loss probability (Defoe et al., 2015).

In the Cold version of the CCT,
participants state in advance how many cards they want to turn over, in the hot
version participants turn over cards one-by-one until they decide to stop. In
the Hot version, participants receive feedback immediately after turning over a
card, while in the Cold version they receive feedback at the end of the final

 On average, respondents performing the Hot
task disclosed more cards (M = 27.185; SD =
3.173) than those taking part in the Cold condition (M =
14.000; SD = 5.303) (Markiewicz & Kubi?ska, 2015).  Those
participating in the cold condition also displayed higher information use, regardless
of the measure employed. Cold condition participants paid more attention
(compared to hot condition) to the amount of gain and less to probability
information (Markiewicz & Kubi?ska,
Our results are in line with other CCT studies (Buelow, 2015) saying “It appears that
participants in the CCT-cold condition paid greater attention to all three
information use variables in making decisions than those in the CCT-hot
condition, in which riskier performance was only associated with loss
probability.” (Markiewicz & Kubi?ska, 2015,



In this paper I examined 6 risk taking tasks (one cold
and 4 hot task), helping reasearchers to decide in what context which task
should be used.  Altough relatively little can we tell about cold risk taking tasks, as
there are a lot less task that measures the cold aspect of risk taking, than
ones measuring the hot aspect, it seems like decision makers place different
weights on risky situation parameters (gain amount, loss amount, and
probability of gains), while making risky decisions in cold and hot tasks.

I have found that the above mentioned tasks were
validated in very different ways. Some were correlated with questionaires and
some with risk taking in traffic situations. Some are correlated with
experiences that actually happened in the past or with the subject’s current
lifestyle (BART-Y, Wheel of fortune, IGT) and some are correlated with
imaginary/ hypothetical situations in the present (The knife switches task). As
these aspects are very different, they might do not measure the same kind of
risk taking. Can we expect correlation between cold and hot tasks?  Also it is questionable, if we can announce
that the test measures risk taking, just because it is validated by another
type of risk taking task. It is also a good question, whether it is important
to correlate with other tests, as they most likely will not measure exactly the

To understand these
tests more and find answers to these questions, a metaanalysis should be done,
correlating all the risk taking tasks with each other. The conclusion is, that
there are many aspects of risk taking that can be measured and we cannot claim
that one is better than the other.

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