In our everyday life, we often have to make decisions with risky consequences, such as choosing a restaurant for dinner or choosing a form of retirement saving. To date, however, little is known about how the brain processes risk. Recent conceptualizations of risky decision making highlight that it is generally associated with emotions but do not specify how emotions are implicated in risk processing. Moreover, little is known about risk processing in non-choice situations and how potential losses influence risk processing. Here we used quantitative meta-analyses of functional magnetic resonance imaging experiments on risk processing in the brain to investigate (1) how risk processing is influenced by emotions, (2) how it differs between choice and non-choice situations, and (3) how it changes when losses are possible. By showing that, over a range of experiments and paradigms, risk is consistently represented in the anterior insula, a brain region known to process aversive emotions such as anxiety, disappointment, or regret, we provide evidence that risk processing is influenced by emotions. Furthermore, our results show risk-related activity in the dorsolateral prefrontal cortex and the parietal cortex in choice situations but not in situations in which no choice is involved or a choice has already been made. The anterior insula was predominantly active in the presence of potential losses, indicating that potential losses modulate risk processing.

The ability to rapidly and flexibly adapt decisions to available rewards is crucial for survival in dynamic environments. Rewardbased decisions are guided by reward expectations that are updated based on prediction errors, and processing of these errors involves dopaminergic neuromodulation in the striatum. To test the hypothesis that the COMT gene Val 158 Met polymorphism leads to interindividual differences in reward-based learning, we used the neuromodulatory role of dopamine in signaling prediction errors. We show a behavioral advantage for the phylogenetically ancestral Val/Val genotype in an instrumental reversal learning task that requires rapid and flexible adaptation of decisions to changing reward contingencies in a dynamic environment. Implementing a reinforcement learning model with a dynamic learning rate to estimate prediction error and learning rate for each trial, we discovered that a higher and more flexible learning rate underlies the advantage of the Val/Val genotype. Model-based fMRI analysis revealed that greater and more differentiated striatal fMRI responses to prediction errors reflect this advantage on the neurobiological level. Learning rate-dependent changes in effective connectivity between the striatum and prefrontal cortex were greater in the Val/Val than Met/Met genotype, suggesting that the advantage results from a downstream effect of the prefrontal cortex that is presumably mediated by differences in dopamine metabolism. These results show a critical role of dopamine in processing the weight a particular prediction error has on the expectation updating for the next decision, thereby providing important insights into neurobiological mechanisms underlying the ability to rapidly and flexibly adapt decisions to changing reward contingencies.COMT ͉ dopamine ͉ functional MRI ͉ learning rate ͉ reinforcement learning H uman learning spans a wide range of functions from ancient evolutionary accomplishments [e.g., fast and intuitive learning from rewards (1, 2)], to complex executive processes that require high capacities of attention and working memory (3, 4). These functions are supported by interacting brain regions that comprise phylogenetically ancient structures (e.g., the striatum) as well as more recently evolved neocortical structures [e.g., the prefrontal cortex (PFC)]. Still, different learning processes rely on the same neuromodulators. Dopamine (DA) modulates synaptic efficacy in both the striatum and PFC (5) and is thus involved in different kinds of learning. Accordingly, interindividual differences in dopaminergic neuromodulatory mechanisms contribute to performance differences in both simple instrumental learning tasks and complex cognitive tasks (6-10).One interindividual difference in dopaminergic neuromodulation that has been linked to PFC function is the uniquely human Val 158 Met polymorphism in the enzyme catechol-O-methyltransferase (COMT), which regulates extrasynaptical DA degradation (8-10). COMT activity is lower in individuals homozygous for the mutated allele...

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