Stress is a physiological response to internal and external events we call “stressors”. Response to the same daily stressors varies across individuals and seems to be higher for women. A possible explanation for this phenomenon is that women perceive sociality, relationships, and intimacy—important sources of both stress and wellbeing—differently from how men experience them. In this study, we investigate how gender, attachment, and touch avoidance predict stress responses on a sample of 335 Italians (216 females; age = 35.82 ± 14.32). Moreover, we analyze the network of relationships between these variables through multiple linear regression and exploratory network analysis techniques. The results recontextualize the role of gender in determining stress responses in terms of (lack of) confidence and touch avoidance toward family members; attitudes toward relationships seem to be the main determinants of stress responses. These results have implications for reducing stress in both clinical settings and at a social level.

Detecting the emotional state of others from facial expressions is a key ability in emotional competence and several instruments have been developed to assess it. Typical emotion recognition tests are assumed to be unidimensional, use pictures or videos of emotional portrayals as stimuli, and ask the participant which emotion is depicted in each stimulus. However, using actor portrayals adds a layer of difficulty in developing such a test: the portrayals may fail to be convincing and may convey a different emotion than intended. For this reason, evaluating and selecting stimuli is of crucial importance. Existing tests typically base item evaluation on consensus or expert judgment, but these methods could favor items with high agreement over items that better differentiate ability levels and they could not formally test the item pool for unidimensionality. To address these issues, the authors propose a new test, named Facial Expression Recognition Test (FERT), developed using an item response theory two-parameter logistic model. Data from 1,002 online participants were analyzed using both a unidimensional and a bifactor model, and showed that the item pool could be considered unidimensional. The selection was based on the items' discrimination parameters, retaining only the most informative items to investigate the latent ability. The resulting 36-item test was reliable and quick to administer. The authors found both a gender difference in the ability to recognize emotions and a decline of such ability with age. The PsychoPy implementation of the test and the scoring script are available on a Github repository. (PsycINFO Database Record (c) 2018 APA, all rights reserved).