Summary
The identification of human violence determinants has sparked multiple questions from different academic fields. Innovative methodological assessments of the weight and interaction of multiple determinants are still required. Here, we examine multiple features potentially associated with confessed acts of violence in ex-members of illegal armed groups in Colombia (N = 26,349) through deep learning and feature-derived machine learning. We assessed 162 social-contextual and individual mental health potential predictors of historical data regarding consequentialist, appetitive, retaliative, and reactive domains of violence. Deep learning yields high accuracy using the full set of determinants. Progressive feature elimination revealed that contextual factors were more important than individual factors. Combined social network adversities, membership identification, and normalization of violence were among the more accurate social-contextual factors. To a lesser extent the best individual factors were personality traits (borderline, paranoid, and antisocial) and psychiatric symptoms. The results provide a population-based computational classification regarding historical assessments of violence in vulnerable populations.
Achieving justice could be considered a complex social decision-making scenario. Despite the relevance of social decisions for legal contexts, these processes have still not been explored for individuals who work as criminal judges dispensing justice. To bridge the gap, we used a complex social decision-making task (Ultimatum game) and tracked a heart rate variability measurement: the square root of the mean squared differences of successive NN intervals (RMSSD) at their baseline (as an implicit measurement that tracks emotion regulation behavior) for criminal judges (n = 24) and a control group (n = 27). Our results revealed that, compared to controls, judges were slower and rejected a bigger proportion of unfair offers. Moreover, the rate of rejections and the reaction times were predicted by higher RMSSD scores for the judges. This study provides evidence about the impact of legal background and expertise in complex social decision-making. Our results contribute to understanding how expertise can shape criminal judges’ social behaviors and pave the way for promising new research into the cognitive and physiological factors associated with social decision-making.
System (IAPS) is a battery of images used to induce discrete emotional reactions. In this study an IAPS subsample of 200 images was analysed to elicit discrete negative emotions and propose a new categorization of them, according to which discrete negative emotions (disgust, fear, sadness, or anger) they induce, in contrast to a dimensional model of emotion including emotional valence, intensity, and dominance, usually used in the literature. Through a sample by convenience, 447 participants of 3 universities in Bogotá, Colombia, were recruited and shown 60 IAPS images and asked them to what extent they felt fear, sadness, disgust, anger, happiness, or satisfaction when looking at each image. By using the overlap of 95% confidence intervals of the mean of 6 emotions ratings for every image, results revealed that 51.5% of images induced simple emotions (19.5% fear, 16.5% sadness, 13.0% disgust and 2.5% anger), 43% of images induced complex emotions, including more than one negative emotion, 1.5% emotions mixed one negative and one positive emotion, and 4% were undetermined emotions.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.