Despite moral prohibitions on hurting other humans, some social contexts allow for harmful actions such as killing of others. One example is warfare, where killing enemy soldiers is seen as morally justified. Yet, the neural underpinnings distinguishing between justified and unjustified killing are largely unknown. To improve understanding of the neural processes involved in justified and unjustified killing, participants had to imagine being the perpetrator whilst watching 'first-person perspective' animated videos where they shot enemy soldiers ('justified violence') and innocent civilians ('unjustified violence'). When participants imagined themselves shooting civilians compared with soldiers, greater activation was found in the lateral orbitofrontal cortex (OFC). Regression analysis revealed that the more guilt participants felt about shooting civilians, the greater the response in the lateral OFC. Effective connectivity analyses further revealed an increased coupling between lateral OFC and the temporoparietal junction (TPJ) when shooting civilians. The results show that the neural mechanisms typically implicated with harming others, such as the OFC, become less active when the violence against a particular group is seen as justified. This study therefore provides unique insight into how normal individuals can become aggressors in specific situations.

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After the 2016 double dissolution election, the 45th Australian Parliament was formed. At the time of its swearing in, the Senate of the 45th Australian Parliament consisted of nine political parties, the largest number in the history of the Australian Parliament. Due to the breadth of the political spectrum that the Senate represented, the situation presented an interesting opportunity for the study of political interactions in the Australian context. Using publicly available Senate voting data in 2016, we quantitatively analyzed two aspects of the Senate. Firstly, we analyzed the degree to which each of the non-government parties of the Senate are pro-or anti-government. Secondly, we analyzed the degree to which the votes of each of the non-government Senate parties are in concordance or discordance with one another. We utilized the fully-visible Boltzmann machine (FVBM) model in order to conduct these analyses. The FVBM is an artificial neural network that can be viewed as a multivariate generalization of the Bernoulli distribution. Via a maximum pseudolikelihood estimation approach, we conducted parameter estimation and constructed hypotheses test that revealed the interaction structures within the Australian Senate. The conclusions that we drew are well-supported by external sources of information.

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