Discussion
The present study on decision-making under risk and ambiguity
investigated the decisions of participants under different levels of
uncertainty. We aimed to contrast risk and ambiguity on the behavioral
and the neural level by experimentally varying these two types of
uncertainty. Subjects were exposed to three different levels of risk:
i.e., low, high, and mixed-risk and two levels of ambiguity: i.e. high
and low ambiguity. Probabilities and outcomes were composed such that
the expected value for all decisions was the same. Further, we defined
the different levels of risk based on a suggestions of Rothschild &
Stiglitz (1970) as the spread of
potential outcomes. On the behavioral level, data confirmed classical
effects of risk and ambiguity (see
Ellsberg, 1961;
Kahneman et al., 1997;
Kahneman & Tversky, 1979;
Knight, 1921). Subjects behaved
significantly more cautiously when they had to decide between one
high-risk option and one low-risk option. In addition, we found an
effect of ambiguity on decision-making. High ambiguity – i.e., during a
condition where any probability information was absent – led to the
highest percentage of cautious decisions.
On the neuronal level, we found increased activity in the
parahippocampal region during mixed-risk trials compared to high- and
low-risk trials in the risk block indicating an effect specific for the
risk difference between options. This region is closely linked to the
amygdala (e.g. Roy et al., 2009;
Stein et al., 2007) and part of the
network that became activated during loss aversion in previous research
(e.g. Canessa et al., 2013) and we are
thus able to replicate earlier findings.
In the ambiguity block we found activation of the supplementary motor
area (SMA), which we showed for mixed-risk trials with low ambiguity in
comparison to high- and low-risk trials with low ambiguity. SMA
activation has been repeatedly related to reinforcement signals that may
be used to guide decision-making and action planning under risk and
uncertainty. Recently, Canessa and colleagues
(2013) found greater activation in SMA
for individuals with greater loss aversion. Our data also corroborated
earlier findings that activity of the insular plays an important role in
the emotional evaluation of ambiguous decision in conditions for which
information about probabilities of options are missing.
Higher ambiguity activated the dorsolateral prefrontal cortex (DLPFC).
Activation of this region was previously found to be related to
ambiguity (Krain et al., 2006). This
activation is likely related to rule-based action selection and likely
indicates the conscious or cognitive evaluation and manipulation of the
relevant information in working memory and the integration of negative
evaluative emotional information from the amygdala under ambiguous
choice conditions. In addition, the inferior parietal cortex (IPC) was
during mixed trials with high ambiguity in comparison to mixed trials
with low ambiguity as well. Accordingly, DLPFC and IPC accordingly seem
to respond specifically to higher ambiguity independent of risk. This
region may be directly linked to the DLPFC activity and was described as
neural index of working memory functions
(e.g. Friedman & Goldman Rakic, 1994)
and mathematical operations (e.g. Arsalidou
& Taylor, 2011). Further, we found significant inferior frontal gyrus
activation under high ambiguity when participants where exposed to mixed
risk blocks as compared to low- and high-risk blocks, which indicates a
role in the interaction of risk and ambiguity. This also replicates
previous research (e.g. Bach et al., 2009;
Hsu et al., 2005).
We could also identify greater activity in the dACC for gain and in the
insula and lateral OFC for loss in the mixed-risk condition as compared
to the average activation during low- and high-risk levels. This ensured
that our activations were independent from the overall level of risk and
the overall level of outcomes or from EV. Activation of dACC was also
observed during action selection under uncertain reward (Hampton &
O’Doherty,(2007) and by Christopoulos et
al. (2009) who characterized dACC
activity as an objective metric of risk. Furthermore, Canessa et al.
(2013) showed increased activation in
clusters of the dACC for the parameter representing the magnitude of
gains and deactivation for the magnitude of losses. Our results
replicate these findings and are particularly well in line with those of
Christopoulos et al. (2009) who observed
stronger activity in dACC and more cautious decisions during conditions
of pure risk. It has further been suggested that dACC activity reflects
conflict (Botvinick et al., 2001) or a
decrease in reward expectation (Holroyd &
Coles, 2002) when the choice condition is risky.
Further, activation of the OFC was found during decisions under risk
(Bach et al., 2009;
Bhatt & Camerer, 2005;
Krain et al., 2006). Bhatt et al.
(2005) associated the OFC activity to the
probability of gains, which was positively correlated with the level of
ambiguity, whereas Krain et al. (2006)
described a bilateral OFC activation for decisions under risk. We found
activations in the OFC in mixed-risk trials when the possible loss was
integrated into the analysis. Our results are in compliance to
Kringelbach & Rolls (2004) suggesting
that activity in the lateral OFC “is related to the evaluation of
punishers which may lead to a change in ongoing behavior”. Therefore,
we suggest that the present orbitofrontal activation relates to the
coding of punishment contingencies and may be related to the emotional
evaluation of losses in the decision alternatives. This is underlined by
additional activation of the insular cortex, which is an important
structure for the integration of emotions, subjective awareness, and
experience, and the representation of internal states of affect and
arousal (e.g. Craig, 2011;
Duerden et al., 2013). In summary, the
effects of losses seem to be primarily of emotional nature reflecting
the representation of the value of losses and at the same time the
effect of losses on subjective emotional awareness in conditions of
mixed risk.
The fMRI analysis of integrated gain and loss parameters that compared
the two ambiguous situations, identified significant activations for
gains. Here, the amygdala was activated. This is in compliance with
studies both on risk and ambiguity (Breiter
et al., 2001 ; Hsu et al., 2005;
Krain et al., 2006;
Kuhnen & Knutson, 2005;
Platt & Huettel, 2008). Since our
experimental design controls for the overall level of risk, outcomes and
EV, our result may suggest that increasingly high wins under ambiguity
may act as a conditioned cue for excessive risk. This may bias
subsequent processing towards a more negative evaluation of the risky
gamble in total. This seems to be in line with the amygdala’s prominent
role in affective conditioning (e.g.
LeDoux, 2003).
Further, we asked the participants why they chose an option.
Correlational analyses indicated that focusing on the amount and the
probability of the gain in pure-risk situations led to incautious
decisions. This provides further evidence that risk-taking becomes
modulated by the expected value of options. Thus, we observed higher
risk-taking under decision-conditions where a gain is more likely (for
related evidence see Fochmann et al.,
2017; Vorhold et al., 2007). In
contrast, when participants realize a greater likelihood of loss in
ambiguous situations, the behavior is more cautious. One limitation of
the present study is certainly sample size in particular, in relation to
the correlational findings. Future research should aim to corroborate
these findings in larger samples.
In summary, for a risky context dorsal anterior cingulate cortex (dACC),
insula, and OFC were activated in mixed trials that led to the highest
percentage of cautious decisions in risk trials. Accordingly, these
regions might reflect risk aversion in decision-making under risk and
support previous research (e.g. Krain et
al., 2006; Platt & Huettel, 2008;
Tobler et al., 2007). Because we
integrated potential losses, our findings do not provide evidence for a
clear differentiation of risk aversion and loss aversion. Recent
evidence suggests that these two kinds of effects have the same neural
basis. For example, Canessa et al. (2013)
found a significant correlation of loss aversion with risk aversion and
Rabin (2000) postulated that loss
aversion explains risk aversion in gambles with wins and losses. In
particular, our experimental design focuses on the evaluation of humans
in decision situations with different amounts of uncertainty that may
all drive cautious decisions The behavioral phenomenon of risk aversion
may be motivationally driven by loss aversion. In line with this
argument, we found activation in regions that were demonstrated being
relevant for loss aversion by Canessa et al.
(2013) and/or linked to these areas,
i.e., activation of the amygdala and the posterior insula; the SMA, the
parahippocampal region. In summary, our results corroborate and extend
the findings of Canessa et al. (2013) and
supports a link between loss aversion and risk aversion and highlights a
neural network for it. For a more extensive analysis and interpretation
of individual differences in our data, our sample was too small.
Concerning ambiguity, we found that activity of the amygdala and
activation of the IFG was present in mixed trials with high ambiguity
when participants made the most cautious decisions. Further, an increase
in ambiguity from low to high was indicated by increased activity in
DLPFC and parietal cortex. Again our findings corroborate previous
results showing activity for dorsolateral prefrontal cortex (DLPFC),
amygdala, inferior frontal cortex, and parietal cortex (e.g.
Bach et al., 2009;
Hsu et al., 2005;
Krain et al., 2006) in the ambiguous
context and not in the risky context for trials inducing cautious
decisions.
Taken together, we replicated that both kinds of uncertainty – risk and
ambiguity – induce more cautious decision-making. As argued by
Rothschild & Stiglitz (1970), the risk
of options can be described by the spread of outcomes and risk avoidance
behavior is primarily driven by the difference of risk between options.
Ambiguity amplifies behavioral risk avoidance in such situations
entailing risk differences between options in a non-additive,
interactive way. Although some of our evidence suggests that risk
avoidance under both ambiguity and risk may be driven by neural systems
related to loss aversion, the systematic differences in patterns of
neural responses between conditions of risk and ambiguity suggest basic
differences between risk and ambiguity
(Ellsberg, 1961;
Knight, 1921) and their underlying neural
sources (e.g. Bach et al., 2009;
Hsu et al., 2005;
Krain et al., 2006). Our own findings
particularly support a special role of working memory related structures
in DLPFC and parietal cortex that showed significantly different
activity between high and low ambiguity conditions. The latter
structures might be involved in integrating information from affective
structures like posterior insula and amygdala in order to compensate for
the missing cognitive numerical information to assess uncertainty and
potential losses.
Acknowledgements: This study was supported by a grant from
VolkswagenStiftung to JH.