Maternal and adolescent depressive symptoms and family conflict: An autoregressive cross-lagged examination of competing models in multi-stressed mothers and adolescents

Maternal and adolescent depression are challenges that often co-occur. Many studies have drawn bivariate associations between maternal depressive symptoms, adolescent depressive symptoms, and family conflict, but few have examined reciprocal effects. Even among extant studies, there is a lack of clarity related to directionality of influence. Three competing theoretical models may explain the relationship between maternal depressive symptoms, adolescent depressive symptoms, and family conflict, and these processes may differ by adolescents’ sex. Using three time points of data from 187 diverse mother-adolescent dyads, we fit a taxonomy of autoregressive cross-lagged structural equation models to simultaneously evaluate the competing theoretical models and also examine differences by sex using multiple-group analyses. Results indicate a symptom-driven model whereby adolescent depressive symptoms predicted increases in family conflict. Sex differences were also found. For males, but not females, greater adolescent depressive symptoms predicted subsequent increases in maternal depressive symptoms, which then predicted lower family conflict—possibly indicating maternal disengagement/withdrawal. Our findings suggest addressing adolescent depressive symptoms in order to prevent family conflict and that distinctive targets for the prevention/intervention of family conflict should account for differences by adolescents’ sex.     

Reinforcer pathology,probabilistic choice,and medication adherence in patients with multiple sclerosis

The reinforcer pathology model posits that core behavioral economic mechanisms, including delay discounting and behavioral economic demand, underlie adverse health decisions and related clinical disorders. Extensions beyond substance use disorder and obesity, however, are limited. Using a reinforcer pathology framework, this study evaluates medical adherence decisions in patients with multiple sclerosis. Participants completed behavioral economic measures, including delay discounting, probability discounting, and a medication purchase task. A medical decision-making task was also used to evaluate how sensitivity to mild side effect risk and efficacy contributed to the likelihood of taking a hypothetical disease-modifying therapy. Less steep delay discounting and more intense (greater) medication demand were independently associated with greater adherence to the medication decision-making procedure. More generally, the pattern of interrelations between the medication-specific and general behavioral economic metrics was consistent with and contributes to the reinforcer pathology model. Additional research is warranted to expand these models to different populations and health behaviors, including those of a positive health orientation (i.e., medication adherence).     

Visual attention to own- versus other-race faces: Perspectives from learning mechanisms and task demands

Multiple factors have been proposed to contribute to the other-race effect in face recognition, including perceptual expertise and social-cognitive accounts. Here, we propose to understand the effect and its contributing factors from the perspectives of learning mechanisms that involve joint learning of visual attention strategies and internal representations for faces, which can be modulated by quality of contact with other-race individuals including emotional and motivational factors. Computational simulations of this process will enhance our understanding of interactions among factors and help resolve inconsistent results in the literature. In particular, since learning is driven by task demands, visual attention effects observed in different face-processing tasks, such as passive viewing or recognition, are likely to be task specific (although may be associated) and should be examined and compared separately. When examining visual attention strategies, the use of more data-driven and comprehensive eye movement measures, taking both spatial–temporal pattern and consistency of eye movements into account, can lead to novel discoveries in other-race face processing. The proposed framework and analysis methods may be applied to other tasks of real-life significance such as face emotion recognition, further enhancing our understanding of the relationship between learning and visual cognition.