Introduction
Decision-making is influenced by the value of outcomes, the probability
of outcomes, and the ambiguity or risk of outcomes (e.g.
Kahneman et al., 1997;
Kahneman & Tversky, 1979). According to
a classic distinction between ambiguity and risk derived from Knight
(e.g. Chen & Epstein, 2002;
Huettel et al., 2006;
Knight, 1921;
Krain et al., 2006), a decision situation
is defined risky when the actual outcome of the decision between options
is unknown, but the probability distribution for different outcomes is
known. In contrast, a decision is ambiguous when the actual outcome and
the probability distribution of potential outcomes are both unknown
(Ellsberg, 1961;
Knight, 1921). In all these cases,
preferences of decision options are defined according to the chance
distributions of options, i.e., according to their expected value (EV).
For risk, these chances are taken to be objective, whereas for
uncertainty, they are subjective. Furthermore, the size of the risk of
options can be described by the spread of outcomes
(Rothschild & Stiglitz, 1970), and the
size of ambiguity by the lack of information
(Ellsberg, 1961;
Knight, 1921). The size of risk and
ambiguity reduce the utility of an option. Accordingly, the goal of the
present study was to vary experimentally both risk and ambiguity in
order to compare them on the behavioral and on the neural level.
Usually, people are even more averse to ambiguous options than to risky
options supposedly also indicating a reduction in subjective utility.
During the past decades, many different studies investigated behavior of
people under risk and ambiguity. Many different experimental approaches
were used, which differed in paradigms and parameters, e. g. various
kinds of gambles, known and risky options, or risky options only,
gambles with possible gains and losses or only with gains, and special
tasks like the Iowa Gambling Task (Bechara
et al., 1997, 2005;
Li et al., 2010), Blackjack
(Hewig et al., 2010;
Hewig et al., 2009;
Hewig et al., 2007;
Hewig et al., 2008) or the Balloon
Analogue Risk Task (Lejuez et al., 2002;
Mussel et al., 2015;
Rao et al., 2008). Recent studies also
used functional magnetic resonance imaging (fMRI) to identify blood
oxygen level dependent (BOLD) responses to risky and ambiguous
situations in order to reveal its neural underpinnings (e.g.
Bach et al., 2009;
Hsu et al., 2005;
Smith et al., 2002;
Tobler et al., 2007). Some of these
studies found activations of brain areas associated with EV, like the
ventral striatum (Breiter et al., 2001;
Knutson et al., 2001;
Knutson et al., 2005;
Tobler et al., 2007;
Yacubian et al., 2006) or the anterior
cingulate cortex (Brown & Braver, 2005,
2007, 2008;
Kuhnen & Knutson, 2005). Others compared
ambiguous and risky decision-making and showed that ambiguous
decision-making is related to increased neural activity in the
dorsolateral prefrontal cortex (DLPFC), amygdala, posterior inferior
frontal cortex, and posterior parietal cortex (e.g.
Bach et al., 2009;
Hsu et al., 2005), whereas risky
decision-making is related to activity in OFC, ACC, and parietal cortex
(e.g. Krain et al., 2006;
Platt & Huettel, 2008;
Tobler et al., 2007). These differences
may indicate that ambiguity imposes not merely more intense uncertainty
but might even represent a different kind of uncertainty, which is based
on a different neural processing by different neural sources. To
differentiate between risky and ambiguous decisions, the magnitude, the
probability, and the EV of decision options have to be defined as
independent experimental variables and the functional neural structures
have to be described that account for their differential
phenomenological cognitive and behavioral functions.
To master such challenges, Tom, Fox, Trepel, and Poldrack
(2007) investigated risky gambles using a
parametric experimental design to assess BOLD responses related to risk
and loss aversion. The network functional relevant for gains included
regions in dorsal and ventral striatum, ventromedial prefrontal cortex
(VMPFC), ventrolateral prefrontal cortex (VLPFC), ACC, OFC, and other
dopaminergic structures whereas structures relevant for losses included
the striatum, the VMPFC, ventral ACC, and the medial OFC. In relation to
loss aversion, they showed activity in bilateral ventral striatum,
bilateral lateral and superior PFC (pre-supplementary motor area), and
right inferior parietal cortex. The authors termed this pattern a neural
system of loss aversion.
In a subsequent study also using parametric analyses, Canessa et al.
(2013) replicated activations in regions
in the left ventral striatum and in the posterior frontomedial cortex in
response to gains and losses. In addition, they found an interesting
differential pattern of activation between losses and gains of the right
posterior insula and the parietal operculum. These areas showed greater
activation to increasing losses than deactivation to gains. An opposite
pattern was found in the left ventral striatum and the frontomedial
cortex, which showed larger loss-related deactivation than gain-related
activation. Furthermore, they identified a loss-related network
involving the right amygdala, putamen, and portions of the right
posterior insula, indicating that the neural system of loss aversion
involves the amygdala, thalamus, striatum, and posterior insula.
Based on these studies using fMRI we expected risk-related activity in
dorsal and ventral striatum, ventromedial prefrontal cortex (VMPFC),
ventrolateral prefrontal cortex (VLPFC), dACC (mainly BA 32), OFC,
parahippocampus, inferior frontal gyrus IFG (BA 47), SMA (BA 6),
frontomedial cortex, and insular cortex in mixed-risk trials for the
parameter “wins”. In contrast, for the parameter ”losses” we expected
activity in insula, parietal operculum, amygdala, thalamus, striatum (in
particular ventral), VMPFC, ventral ACC, the medial OFC, bilateral
lateral and superior PFC (pre-supplementary motor area), and right
inferior parietal cortex. We further expected ambiguity-related activity
in dorsolateral prefrontal cortex (DLPFC), amygdala, posterior inferior
frontal cortex, and posterior parietal cortex. Since our experimental
design eliminated the confounding influence of average risk levels
between our experimental conditions, the remaining regions are
considered to relate specifically to the risk difference in mixed
gambles.
In the gambling paradigm used here, the probability of outcomes was
known to the subjects to elicit decisions under risk in one part of the
experiment, and in the other part of the experiment the probability of
outcomes was unknown in order to induce decisions under ambiguity. We
completely avoided fully known outcomes, because risk and ambiguity may
appear to be more similar in their presence because they both entail
some uncertainty as compared to known outcomes. Instead, we varied both
risk and ambiguity systematically to contrast and compare them to each
other. The degree of risk was varied experimentally by using three
different combinations of high- and low-risk options in each trial of a
two-choice decision-making task. High-risk gambles comprised two
high-risk options, low-risk gambles comprised two low-risk options and
mixed-risk gambles comprised a high- and a low-risk option. EV was
always the same in each gamble. This enabled us to experimentally
separate influences of the difference in risk from the overall level of
risk or EV and from the overall level or magnitude of gains and losses.
Previous studies used known outcomes versus risky gambles, or known
outcomes versus ambiguous gambles. Thus, for each decision between two
gambles the average risk level of both alternatives (high/low) is
confounded with the risk difference between the two alternatives. For
example, many studies use a known outcome option and a risky option. The
risky option can vary in its variance of outcomes. Thus, the average
risk of both alternatives and the average magnitude of the outcomes are
confounded with the difference in risk between the two alternatives. A
trial with a known option and a low-risk option has lower overall risk,
lower difference in risk between the options, and lower outcome
magnitudes. Whereas a trial with a known option and a high-risk option
has higher overall risk, higher difference in risk, and higher outcome
magnitudes. Accordingly, a difference in brain activation in the
comparison of these two kinds of trials may be due to any of these three
differences between trials. For this reason, we combined 2 kinds of
options: high-risk options (larger variance in outcomes) and low-risk
options (smaller variance in outcomes). Thus, we created a high-risk
condition with two high-risk alternatives, a low-risk condition with two
low-risk alternatives and a mixed risk condition with a low and a
high-risk option. If we now compare brain activity between the mixed
condition and the average of the two other conditions, we may
disentangle risk level and risk difference.
We expected that minimizing the risk is primarily triggered by the
difference of outcome variance between the two options, which should
lead to the strongest risk aversive effects in mixed gambles. We further
varied the degree of ambiguity experimentally in another block of trials
by using one condition without any information about the probability
(high ambiguity) as compared to another condition where we provided a
range of probabilities for each outcome (low ambiguity). In a narrow
sense of decision-making under risk, where all information is available,
the latter trials with high ambiguity are not decision-making under
risk. However, according to the definition of the degree of risk with
the spread or variance of outcomes these trials can still be recognized
in terms of higher or lower risk, since the magnitude of the difference
between the potential wins and losses (variance of outcomes) is a
function of risk level even in the absence of probability information.
This allows a systematic comparison of decision-making under risk with
decision-making under ambiguity across experimental blocks keeping all
other aspects of the task comparable.
In the present study, we aimed to clarify and extend previous findings
by using mixed-risk trials with and without ambiguity and compared them
to high- and low-risk trials. Furthermore, we aimed to compare risk and
ambiguity directly with each other. Thus, the target condition is as
similar as possible to the control conditions. We focused on the moment
of decision-making in our analyses. Following previous research
(Canessa et al., 2013;
Tom et al., 2007), we also used a
parametric fMRI design to separate effects that are due to potential
gains from those of potential losses. Importantly, previous research
focused on the idea of neural loss aversion, which is related to the
different degree of brain activity in response to gains and losses, and
supposedly drives cautious decision-making under risk. In addition,
Vorhold and colleagues (2007) showed that
subjective ratings of risk are moderating decision-making. We therefore
also assessed ratings of riskiness and reports about reasons of
individual decision-making.