Why are some people capable of sympathizing with and/or committing acts of political violence, such as attacks aimed at innocent targets? Attempts to construct terrorist profiles based on individual and situational factors, such as clinical, psychological, ethnic, and socio-demographic variables, have largely failed. Although individual and situational factors must be at work, it is clear that they alone cannot explain how certain individuals are radicalized. In this paper, we propose that a comprehensive understanding of radicalization and of how it may (...) lead to political violence requires the integration of information across multiple levels of analysis and interdisciplinary perspectives from evolutionary theory, social, personality and cognitive psychology, political science and neuroscience. Characterization of the structural-functional relationships between neural mechanisms and the cognitive and affective psychological processes that underpin group dynamics, interpersonal processes, values and narratives, as well as micro-sociological processes may reveal latent drivers of radicalization and explain why some people turn to extreme political violence. These drivers may not be observable within a single individual level of scientific enquiry. The integrative, multilevel approach that characterizes social neuroscience has the potential to provide theoretical and empirical clarity regarding the antecedents of radicalization and support for extreme violence. (shrink)

In fMRI research, the goal of correcting for multiple comparisons is to identify areas of activity that reflect true effects, and thus would be expected to replicate in future studies. Finding an appropriate balance between trying to minimize false positives (Type I error) while not being too stringent and omitting true effects (Type II error) can be challenging. Furthermore, the advantages and disadvantages of these types of errors may differ for different areas of study. In many areas of social neuroscience (...) that involve complex processes and considerable individual differences, such as the study of moral judgment, effects are typically smaller and statistical power weaker, leading to the suggestion that less stringent corrections that allow for more sensitivity may be beneficial, but also result in more false positives. Using moral judgment fMRI data, we evaluated four commonly used methods for multiple comparison correction implemented in SPM12 by examining which method produced the most precise overlap with results from a meta-analysis of relevant studies and with results from nonparametric permutation analyses. We found that voxel-wise thresholding with family-wise error correction based on Random Field Theory provides a more precise overlap (i.e., without omitting too few regions or encompassing too many additional regions) than either clusterwise thresholding, Bonferroni correction, or false discovery rate correction methods. (shrink)