SummaryHuman choice behavior often reflects a competition between inflexible computationally efficient control on the one hand and a slower more flexible system of control on the other. This distinction is well captured by model-free and model-based reinforcement learning algorithms. Here, studying human subjects, we show it is possible to shift the balance of control between these systems by disruption of right dorsolateral prefrontal cortex, such that participants manifest a dominance of the less optimal model-free control. In contrast, disruption of left dorsolateral prefrontal cortex impaired model-based performance only in those participants with low working memory capacity.

Value-based choices are influenced both by risk in potential outcomes and by whether outcomes reflect potential gains or losses. These variables are held to be related in a specific fashion, manifest in risk aversion for gains and risk seeking for losses. Instead, we hypothesized that there are independent impacts of risk and loss on choice such that, depending on context, subjects can show either risk aversion for gains and risk seeking for losses or the exact opposite. We demonstrate this independence in a gambling task, by selectively reversing a loss-induced effect (causing more gambling for gains than losses and the reverse) while leaving risk aversion unaffected. Consistent with these dissociable behavioral impacts of risk and loss, fMRI data revealed dissociable neural correlates of these variables, with parietal cortex tracking risk and orbitofrontal cortex and striatum tracking loss. Based on our neural data, we hypothesized that risk and loss influence action selection through approach-avoidance mechanisms, a hypothesis supported in an experiment in which we show valence and risk-dependent reaction time effects in line with this putative mechanism. We suggest that in the choice process risk and loss can independently engage approach-avoidance mechanisms. This can provide a novel explanation for how risk influences action selection and explains both classically described choice behavior as well as behavioral patterns not predicted by existing theory.

Risky choice entails a need to appraise all possible outcomes and integrate this information with individual risk preference. Risk is frequently quantified solely by statistical variance of outcomes, but here we provide evidence that individuals’ choice behaviour is sensitive to both dispersion (variance) and asymmetry (skewness) of outcomes. Using a novel behavioural paradigm in humans, we independently manipulated these ‘summary statistics’ while scanning subjects with fMRI. We show that a behavioural sensitivity to variance and skewness is mirrored in neuroanatomically dissociable representations of these quantities, with parietal cortex showing sensitivity to the former and prefrontal cortex and ventral striatum to the latter. Furthermore, integration of these objective risk metrics with subjective risk preference is expressed in a subject-specific coupling between neural activity and choice behaviour in anterior insula. Our findings show that risk is neither monolithic from a behavioural nor neural perspective and its decomposition is evident both in distinct behavioural preferences and in segregated underlying brain representations.