Trouble crossing the bridge: altered interhemispheric communication of emotional images in anxiety

Worry is thought to involve a strategy of cognitive avoidance, in which internal verbalization acts to suppress threatening emotional imagery. This study tested the hypothesis that worry-prone individuals would exhibit patterns of between-hemisphere communication that reflect cognitive avoidance. Specifically, the hypothesis predicted slower transfer of threatening images from the left to the right hemisphere among worriers. Event-related potential (ERP) measures of interhemispheric transfer time supported this prediction. Left-to-right hemisphere transfer times for angry faces were relatively slower for individuals scoring high in self-reported worry compared with those scoring low, whereas transfer of happy and neutral faces did not differ between groups. These results suggest that altered interhemispheric communication may constitute one mechanism of cognitive avoidance in worry.     

Error detection and posterror behavior in depressed undergraduates

This study examined the influence of depression on error-monitoring and behavioral compensation after errors, two important aspects of cognitive control. Undergraduates differing in self-reported depression levels completed a modified Stroop task while error-related scalp potentials were recorded. Behaviorally, participants with higher depression scores were disproportionately slower and less accurate after errors in a task condition that included negative emotional words. Physiological results indicated that the amplitudes of the error-related negativity (ERN) and error positivity (Pe), two indices of error detection, were not correlated with depression score. ERN amplitudes predicted behavioral slowdown after errors, but only among more depressed participants in the negative-word condition. Together, the results imply that depression is associated not with an error detection deficit, but rather with alterations in subsequent performance changes, once errors have been identified.     

Anxiety and error monitoring: Increased error sensitivity or altered expectations?

This study tested the prediction that the error-related negativity (ERN), a physiological measure of error monitoring, would be enhanced in anxious individuals, particularly in conditions with threatening cues. Participants made gender judgments about faces whose expressions were either happy, angry, or neutral. Replicating prior studies, midline scalp negativities were greater following errors than following correct responses. In addition, state anxiety interacted with facial expression to predict ERN amplitudes. Counter to predictions, participants high in state anxiety displayed smaller ERNs for angry-face blocks and larger ERNs for happy-face blocks, compared to less anxious participants. These results are inconsistent with the simple notion that anxiety enhances error sensitivity globally. Rather, we interpret the findings within an expectancy violation framework, in which anxious participants have altered expectations for success and failure in the context of happy and angry facial cues, with greater ERN amplitudes when expectations are violated.     

Work-family conflict: experiences and health implications among immigrant Latinos

Work-family conflict research has focused almost exclusively on professional, White adults. The goal of this article was to expand the understanding of culture and industry in shaping experiences and consequences of work-family conflict. Using in-depth interview data (n = 26) and structured survey data (n = 200) from immigrant Latinos employed in the poultry processing industry, the authors evaluated predictions drawn from emerging models emphasizing the influence of cultural characteristics such as collectivism and gender ideology on work-family conflict. Results indicated that immigrant Latinos in poultry processing experienced infrequent work-to-family conflict; both the level and the antecedents of work-to-family conflict differed by gender, with physical demands contributing to greater conflict for women but not men. In addition, there was little evidence that work-family conflict was associated with health in this population. These results demonstrate how traditional models of work-family conflict need to be modified to reflect the needs and circumstances of diverse workers in the new global economy.     

The Individual or the Group: A Multilevel Analysis of Cyberbullying in School Classes

In this study, we focus on the relevance of social influence to explain cyberbullying experiences among German high school students. Social influence is discussed in the context of computer‐mediated communication. To obtain individual and sociostructural data, we conducted a survey study among German high school students (N = 4,282). Using multilevel modeling, we found that the attributes of the school class only contributed to the risk of being involved in cyberbullying to a small extent. Still, procyberbullying norms in class did enhance the risk of perpetration and victimization for students, even more so than their individual beliefs. Previous experiences with bullying and intensive, unrestricted use of the Internet were the strongest individual predictors of cyberbullying involvement.     

Analysis of Multiple Partially Ordered Responses to Belief Items with Don’t Know Option

Understanding beliefs, values, and preferences of patients is a tenet of contemporary health sciences. This application was motivated by the analysis of multiple partially ordered set (poset) responses from an inventory on layman beliefs about diabetes. The partially ordered set arises because of two features in the data—first, the response options contain a Don’t Know (DK) option, and second, there were two consecutive occasions of measurement. As predicted by the common sense model of illness, beliefs about diabetes were not necessarily stable across the two measurement occasions. Instead of analyzing the two occasions separately, we studied the joint responses across the occasions as a poset response. Few analytic methods exist for data structures other than ordered or nominal categories. Poset responses are routinely collapsed and then analyzed as either rank ordered or nominal data, leading to the loss of nuanced information that might be present within poset categories. In this paper we developed a general class of item response models for analyzing the poset data collected from the Common Sense Model of Diabetes Inventory. The inferential object of interest is the latent trait that indicates congruence of belief with the biomedical model. To apply an item response model to the poset diabetes inventory, we proved that a simple coding algorithm circumvents the requirement of writing new codes such that standard IRT software could be directly used for the purpose of item estimation and individual scoring. Simulation experiments were used to examine parameter recovery for the proposed poset model.     

Error-monitoring ability predicts daily stress regulation

ABSTRACT— This study examined whether individual differences in error-related self-regulation predict emotion regulation in daily life, as suggested by a common-systems view of cognitive and emotional self-regulation. Participants ( N = 47) completed a Stroop task, from which error-related brain potentials and behavioral measures of error correction were computed. Participants subsequently reported on daily stressors and anxiety over a 2-week period. As predicted by the common-systems view, a physiological marker of error monitoring and a behavioral measure of error correction predicted emotion regulation in daily life. Specifically, participants higher in cognitive control, as assessed neurally and behaviorally, were less reactive to stress in daily life. The results support the notion that cognitive control and emotion regulation depend on common or interacting systems.     

Social Relations and Cyberbullying: The Influence of Individual and Structural Attributes on Victimization and Perpetration via the Internet

Current research indicates that an alarming number of students are affected by cyberbullying. However, most of the empirical research has focused on psychological explanations of the phenomenon. In an explorative survey study based on the reconstruction of 2 complete school networks (N_P = 408), we expand the explanation strategies of cyberbullying to higher levels of social abstraction. Using statistical and structural analysis, and visual inspection of network environments, we compare explanations on individual and structural levels. In line with previous research, the findings support traditional explanations via sociodemographic and personality factors. However, the findings also reveal network positioning to be a comparably strong predictor for cyberbullying. Therefore, we argue that without taking structural factors into account, individual explanations will remain insufficient.