Humans are endowed with a natural sense of fairness that permeates social perceptions and interactions. This moral stance is so ubiquitous that we may not notice it as a fundamental component of daily decision making and in the workings of many legal, political, and social systems. Emotion plays a pivotal role in moral experience by assigning human values to events, objects, and actions. Although the brain correlates of basic emotions have been explored, the neural organization of "moral emotions" in the human brain remains poorly understood. Using functional magnetic resonance imaging and a passive visual task, we show that both basic and moral emotions activate the amygdala, thalamus, and upper midbrain. The orbital and medial prefrontal cortex and the superior temporal sulcus are also recruited by viewing scenes evocative of moral emotions. Our results indicate that the orbital and medial sectors of the prefrontal cortex and the superior temporal sulcus region, which are critical regions for social behavior and perception, play a central role in moral appraisals. We suggest that the automatic tagging of ordinary social events with moral values may be an important mechanism for implicit social behaviors in humans.
In the past years, mass univariate statistical analyses of neuroimaging data have been complemented by the use of multivariate pattern analyses, especially based on machine learning models. While these allow an increased sensitivity for the detection of spatially distributed effects compared to univariate techniques, they lack an established and accessible software framework. The goal of this work was to build a toolbox comprising all the necessary functionalities for multivariate analyses of neuroimaging data, based on machine learning models. The “Pattern Recognition for Neuroimaging Toolbox” (PRoNTo) is open-source, cross-platform, MATLAB-based and SPM compatible, therefore being suitable for both cognitive and clinical neuroscience research. In addition, it is designed to facilitate novel contributions from developers, aiming to improve the interaction between the neuroimaging and machine learning communities. Here, we introduce PRoNTo by presenting examples of possible research questions that can be addressed with the machine learning framework implemented in PRoNTo, and cannot be easily investigated with mass univariate statistical analysis.
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