People are particularly sensitive to injustice. Accordingly, deeper knowledge regarding the processes that underlie the perception of injustice, and the subsequent decisions to either punish transgressors or compensate victims, is of important social value. By combining a novel decision-making paradigm with functional neuroimaging, we identified specific brain networks that are involved with both the perception of, and response to, social injustice, with reward-related regions preferentially involved in punishment compared with compensation. Developing a computational model of punishment allowed for disentangling the neural mechanisms and psychological motives underlying decisions of whether to punish and, subsequently, of how severely to punish. Results show that the neural mechanisms underlying punishment differ depending on whether one is directly affected by the injustice, or whether one is a third-party observer of a violation occurring to another. Specifically, the anterior insula was involved in decisions to punish following harm, whereas, in third-party scenarios, we found amygdala activity associated with punishment severity. Additionally, we used a pharmacological intervention using oxytocin, and found that oxytocin influenced participants' fairness expectations, and in particular enhanced the frequency of low punishments. Together, these results not only provide more insight into the fundamental brain mechanisms underlying punishment and compensation, but also illustrate the importance of taking an explorative, multimethod approach when unraveling the complex components of everyday decision-making. The perception of injustice is a fundamental precursor to many disagreements, from small struggles at the dinner table to wasteful conflict between cultures and countries. Despite its clear importance, relatively little is known about how the brain processes these violations. Taking an interdisciplinary approach, we combine methods from neuroscience, psychology, and economics to explore the neurobiological mechanisms involved in both the perception of injustice as well as the punishment and compensation decisions that follow. Using a novel behavioral paradigm, we identified specific brain networks, developed a computational model of punishment, and found that administrating the neuropeptide oxytocin increases the administration of low punishments of norm violations in particular. Results provide valuable insights into the fundamental neurobiological mechanisms underlying social injustice.

How do we decide to keep interacting (e.g., stay) with a social partner or to switch (e.g., leave) to another? This paper investigated the neural mechanisms of stay/leave decision-making. We hypothesized that these decisions fit within a framework of value-based decision-making, and explored four potential mechanisms underlying a hypothesized bias to stay. Twenty-six participants underwent functional Magnetic Resonance Imaging (fMRI) while completing social and nonsocial versions of a stay/leave decision-making task. On each trial, participants chose between four alternative options, after which they received a monetary reward. Crucially, in the social condition, reward magnitude was ostensibly determined by the generosity of social partners, whereas in the nonsocial condition, reward amounts were ostensibly determined in a preprogrammed manner. Results demonstrated that participants were more likely to stay with options of relatively high expected value, with these values updated through Reinforcement Learning mechanisms and represented neurally within ventromedial prefrontal cortex. Moreover, we demonstrated that greater brain activity in ventromedial prefrontal cortex, caudate nucleus, and septo-hypothalamic regions for social versus nonsocial decisions to stay may underlie a bias towards staying with social partners in particular. These findings complement existing social psychological theories by investigating the neural mechanisms of actual stay/leave decisions. ARTICLE HISTORY

In this paper, we highlight several historical developments in the neuroscience of ethics as well as recent advances that forecast the experimental research to come. We argue, in particular, that our understanding of the moral brain will benefit from the further use of a formal, mathematical approach to the construction and testing of alternative theories, such as that found in the field of neuroeconomics. The use of economic modeling to understand the psychological processes underlying distributional preferences and charitable giving is reviewed to illustrate this potential. We also consider some obstacles to such an approach, notably the challenge of capturing substantive moral values within a mathematical model.

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Abstract-We deal with estimation of multiple dipoles from combined MEG and EEG time-series. We use a sequential Monte Carlo algorithm to characterize the posterior distribution of the number of dipoles and their locations. By considering three test cases, we show that using the combined data the method can localize sources that are not easily (or not at all) visible with either of the two individual data alone. In addition, the posterior distribution from combined data exhibits a lower variance, i.e. lower uncertainty, than the posterior from single device.