From both a behavioral and theoretical perspective, the value and risk of an option along with the agent’s risk aversion are the basic factors implicated in the choice behavior. Our approach in studying choice behavior was to first identify the components of the system (magnitude, risk and risk aversion) and then to piece them together to produce a function relating them to a behavioral outcome (i.e. the choice). To achieve this, we first located the neuronal responses that are more relevant to decision factors. In the final step we tested whether these responses can indeed describe the function of the system (i.e. detect the behavioral choice).
Previous studies have uncovered the neural correlates of independent decision factors as well. Our design disentangled decision parameters from utility encoding, took into account the behaviorally demonstrated risk attitudes of each participant and minimized any learning elements. The present findings suggest that the computational and theoretical deconstruction of the decision procedure into specific parameters meets with distinct BOLD responses, which could contribute crucial inputs for actual choices.
In two different experiments, where important behavioral parameters were differentiated, we found distinct neuronal responses towards different decision factors. The striatum was particularly responsive to changes in magnitude, dorsal anterior cingulate (dACC) was involved in –mainly objective- risk coding, and inferior frontal gyrus (IFG) signaled risk aversion. Importantly, by combining the information from these different brain regions, these BOLD responses were informative enough to allow an ideal observer to detect the overt choice: a risky choice was more probable when striatal and cingulate activity was higher, whereas increased BOLD signals from IFG correlated with increased probability of a safe choice.
High correlations between BOLD responses and personality traits have been recently criticized (Vul et al., 2009
). While the critique is highly controversial and disputable (see Lieberman et al., 2009
), our study nevertheless escapes the criticism as we use two separate sets of data (and experimental designs) to evaluate our hypotheses; in both experiments, the brain regions BOLD response correlated with the behavioral measurement. In addition, the principal measurement (risk aversion) is not approached as a personality trait but rather as a behavioral measurement. Finally, the regions reflecting individual differences in risk processing were identified independently from those coding risk.
Value, objective and subjective risk
Our results show that VSt activation increases with increasing value. Importantly, our analysis suggests that striatal activity encoded value, independent from utility. Due to our design, the term ‘value’ refers to either ‘magnitude’ (first experiment) or ‘expected value’ (second experiment). We need to underline that our experiments do not clarify to which of these two parameters the VSt is responsive to, as such a study would require a design that includes different levels of probabilities. This should be addressed in future studies, though similar issues have been tested by previous reports; our results are in line with their results linking striatal activity to computing value (or processing its components)(Knutson et al., 2005
; Abler et al. 2006
; Yacubian et al., 2006
; Tobler et al., 2007
According to the present results, dACC activity increases when the forthcoming choice has higher risk. The magnitude of this increase does not covary with individual differences in the estimation of risk (risk aversion). Therefore, dACC activity seems to preferentially mirror an objective metric of risk. In addition, we found no differences in dACC activity with respect to the utility of the subsequent choice. Previous studies have implicated dACC activity with the volatility of the reward environment (Behrens et al. 2007
), whereas Critchley et al. (2001)
relate the increased BOLD response of ACC in anticipation of risky outcomes to autonomic arousal. We also control for conflict of choice (Carter et al., 1998
), which is a common function ascribed to ACC, as both alternatives are equally preferred – therefore conflict in every trial of the first experiment is maximal. A possible caveat of our study is that both experiments have a relatively small number of participants; this might significantly lower the power to find correlations. Although that the size of our sample was sufficient to detect correlations in IFG, the fact that we did not find a correlation of dACC BOLD response to risk does not necessarily preclude the possibility that this area might also be sensitive in subject-wise differences in risk assessments.
Yet, it should be emphasized that the brain responses attributed to specific decision parameters are not exclusive but mainly preferential. Our study adopted a formal definition of risk, which is independent of changes in probabilities; this is a crucially different aspect of risk (Rushworth and Behrens 2008
). The control of probability might be a contributing factor for not finding risk signals in brain structures such as insula (Critchley et al., 2001
; Preuschoff et al., 2008) and areas of prefrontal cortex (Rogers et al., 1999
; Elliott et al., 1999
). Yet, a thorough examination of risk-related choice behavior necessitates the detailed, separate identification of the different facets of risk. Given that variance is the first moment of a distribution, it is evident that it is one of the primary aspects of risk.
IFG BOLD responses found in this study reflected risk aversion. The currently observed BOLD response of IFG is located within right dorsolateral prefrontal cortex whose stimulation accordingly modulates risk aversion (Fecteau et al. 2007
; Knoch et al. 2006
). Our results demonstrate that this area does not influence the objective evaluation of risk but rather the subjective perception of the riskiness of the option. Further analysis suggests that this IFG BOLD response functions as a ‘safety’ signal, as it shows higher response to safer options, especially for more risk averse participants.
Combined BOLD signals contributing to decision making
To use an analogy, in perceptual decisions, the choice can be decoded by comparing neuronal activity between areas that are selectively tuned to the basic characteristics of each option (for instance areas sensitive to either faces or houses, Heekeren et al., 2004
). Lee et al. (2007)
suggest that, in order to make a choice, the brain should collect information on different decision parameters and then combine this information in an effective way to produce the choice. In economic choices, specific values are assigned to the individual options; these values are modulated, among others, by the risk of the options. The conjecture that risk has an influence on value constitutes the key characteristic of one of the prominent theories in economic decision making, namely the mean variance approach (Levy and Markowitz 1979
, Preuschoff et al., 2006
; Rangel et al., 2008
). Essentially, the underlying hypothesis is that the overt choice is the output of internal processes combining the neuronal information pertaining to each choice parameter. Our experiment followed this rationale of combined decision parameters.
We indeed found a group of areas that are sensitive to specific decision parameters. Logistic regression analysis of signals from different regions revealed relationships not obvious from single-structure analysis. The relationship between activity and choice can be approximated by a competing activity between striatum and dACC on one hand, correlating with riskier choices, and IFG, on the other hand, holding an inhibitory, risk averse role.
Our analysis brings forward the possibility of evaluating the effect of ‘virtual’ lesions in the implicated areas. Striatal and cingulate lesions would potentially be associated with less risk averse (and more risk neutral) choices. A striatal lesion could reduce the ability to evaluate magnitude, an effect which is also implied by negative motivational changes in patients with globus pallidus lesions (Vijayaraghavan et al., 2008
). Nevertheless, such a lesion could be compensated by functions in other areas, namely ventromedial prefrontal cortex. In addition, our prediction is that lateral prefrontal cortex lesions will lead to riskier choices, which as said before is in accordance with neuromodulatory studies (Fecteau et al. 2007
; Knoch et al. 2006
). A recent study (Gianotti et al., 2009
) also suggested that participants with higher baseline cortical activity in the right prefrontal cortex are more risk averse. In addition, patients with predominantly right-sided prefrontal lesions demonstrate a riskier behavior (Clark et al., 2003
It should be noted that individual brain regions, and especially striatum, independently have high ROC values. The latter suggests that encoding of isolated decision parameters already contains information able to decode the choice. Yet, the incorporation and appropriate combination of information stemming from aptly selected regions improves the overall representation of the choice behavior.
Cognitive functions such as decision making might necessitate the combination of signals from different brain areas instead of contributions from a single structure. Such distributed neuronal contributions to cognitive functions have also been found in other paradigms, such as emotional perceptual decisions (Pessoa and Padmala, 2007) and a probabilistic-reversal learning task (Hampton and O’Doherty, 2007
). Our study demonstrates that neural combinations of information can be beneficial on economic decisions under risk, as well.
It has been suggested (MacDonald et al. 2000
; Fleck et al. 2006
) that dACC engagement indexes conflict and the need for cognitive control (Barch et al. 2001
, whereas DLPFC assumes a more evaluative role, including cognitive control and response selection. Importantly, Rushworth et al. (2004)
suggest that the main function of ACC is to perform a cost-benefit analysis in order to guide action. The present results fit in that framework. Dorsal ACC evaluates the riskiness of the situation (which which may correspond to an evaluation of costs and benefits), indexing the need to engage cognitive control over the competing choice between a risky and a safe alternative. Higher risk requires higher cognitive control in comparison to low risk trials. Therefore, dorsal ACC activity signals whether and to what extent cognitive control is needed according to the riskiness of the situation, whereas IFG / DLPFC activity idiosyncratically guides the choice according to risk attitudes.
In conclusion, our analysis sheds light to the mechanisms employed in decision-making under risk. Behavioral evidence suggests that the output of the choice process heavily depends on the statistical properties of the options. This implies that the brain not only encodes these properties but also combines them to produce the overt choice. An analogous mechanism is suggested by our data. From a more general point of view, the generation and combination of neuronal signals representing lower-level properties of the stimulus might be a general decision making mechanism across different modalities (Heekeren et al., 2008