Publications by authors named "Benjamin Listyg"
Sex differences in psychopathology are well-established, with females demonstrating higher rates of internalizing (INT) psychopathology and males demonstrating higher rates of externalizing (EXT) psychopathology. Using two waves of data from the Adolescent Brain Cognitive Development Study ( = 6,778 at each wave), the current study tested whether the relations between sex and psychopathology might be accounted for by structural brain differences. In general, we found robust, relatively consistent relations between sex and structural morphometry across waves.
View Article and Find Full Text PDFIntroduction: Males and females tend to exhibit small but reliable differences in personality traits and indices of psychopathology that are relatively stable over time and across cultures. Previous work suggests that sex differences in brain structure account for differences in domains of cognition.
Methods: We used data from the Human Connectome Project (N = 1098) to test whether sex differences in brain morphometry account for observed differences in the personality traits neuroticism and agreeableness, as well as symptoms of internalizing and externalizing psychopathology.
Recent personality neuroscience research in large samples suggests that personality traits tend to bear null-to-small relations to morphometric (i.e., brain structure) regions of interest (ROIs).
View Article and Find Full Text PDFArticle Synopsis
- Job satisfaction research traditionally follows a tripartite model based on cognitive and affective factors, but recent work integrates these ideas with network theory through the causal attitudes network (CAN) model, highlighting interconnectedness in attitudes.
- Using multiple datasets, the study demonstrates that job satisfaction is shaped differently by instrumental (e.g., pay) versus symbolic (e.g., supervisor) features, with instrumental factors forming stable clusters while symbolic ones evolve with experience.
- The CAN model reveals that central features in attitude networks can significantly influence overall job satisfaction and better predict employee turnover compared to traditional structural equation models, offering valuable insights for both research and workplace practices.