Social high performers under stress behave more prosocially and detect happy emotions better in a male sample.

Psychosocial stress is increasing in society, impacting our lives in all social domains. However, the conditions under which stress facilitates ("tend-and-befriend") or hinders ("fight-or-flight") social approach remain elusive. We tested whether previous heterogeneous findings might be resolved by accounting for individual differences in social performance under stress. For that purpose, we introduce the novel Trier Social Stress Test (TSST) social performance index that was aggregated across ratings from two independent observers. Moreover, we apply an innovative setup enabling electroencephalographic (EEG) data to be measured inside an electrically-shielded cabin during stress, namely the TSST-EEG. Relying on a sample of 59 healthy male participants, we collected behavioral (i.e., sharing resources with others) and cognitive (i.e., detecting facial emotional expressions) approach patterns while participants experienced either acute psychosocial stress (n = 31) or no stress (control condition; n = 28) and while EEG was being recorded. During stress exposure, high-performing participants behaved more prosocially, and differentiated better between happy and neutral emotions on both behavioral and neurophysiological levels (revealed by intensity differences in a N170-like response). Overall, our findings demonstrate the added value of both the novel TSST social performance index and the novel TSST-EEG setup. By showing that high social performance during the TSST is associated with behavioral, cognitive, and neurophysiological approach patterns, our study offers valuable insights into adaptive or maladaptive psychobiological mechanisms in coping with psychosocial stress. Future stress research should address the role of social performance differences during stress in social interaction to better understand the behavioral consequences of psychosocial stress in humans.

A Student's Guide to Randomization Statistics for Multichannel Event-Related Potentials Using Ragu.

In this paper, we present a multivariate approach to analyze multi-channel event-related potential (ERP) data using randomization statistics. The MATLAB-based open source toolbox Randomization Graphical User interface (Ragu) provides, among other methods, a test for topographic consistency, a topographic analysis of variance, t-mapping and microstate analyses. Up to two within-subject factors and one between-subject factor, each with an open number of levels, can be defined and analyzed in Ragu. Ragu analyses include all sensor signals and no a-priori models have to be applied during the analyses. Additionally, periods of significant effects can be controlled for multiple testing using global overall statistics over time. Here, we introduce the different alternatives to apply Ragu, based on a step by step analysis of an example study. This example study examined the neural activity in response to semantic unexpected sentence endings in exchange students at the beginning of their stay and after staying in a foreign-language country for 5 months.