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This multimethod, prospective study examined the nature of pathways between interparental hostility and withdrawal, parental emotional unavailability, and subsequent changes in children's internalizing and externalizing behaviors, and school adjustment difficulties over a 3-year period in a sample of 210 mothers, fathers, and 6-year-old children. The results of autoregressive structural equation models indicated that interparental withdrawal had a detrimental impact on all areas of children's adjustment, whereas interparental hostility had an indirect effect on subsequent changes in child adjustment. An intermediary role of parental emotional unavailability in links between interparental withdrawal and hostility and child outcomes was indicated, with specific, differential effects observed for fathers and mothers.

This study examined the nature of pathways between marital hostility and withdrawal, parental disagreements about child rearing issues, and subsequent changes in parental emotional unavailability and inconsistent discipline in a sample of 225 mothers, fathers, and 6-year-old children. Results of autoregressive, structural equation models indicated that marital withdrawal and hostility were associated with increases in parental emotional unavailability over the one-year period, whereas marital hostility and withdrawal did not predict changes in parental inconsistency in discipline. Additional findings supported the role of child rearing disagreements as an intervening or mediating mechanism in links between specific types of marital conflict and parenting practices. Implications for clinicians and therapists working with maritally distressed parents and families are discussed.

This multi-method study sought to identify parameters of developmental change and stability of child reaction patterns to interparental conflict in the context of family relations in a sample of 223 6-year-old children and their parents followed over the course of one year. Consistent with the sensitization hypothesis, interparental withdrawal and hostility each consistently and uniquely predicted child distress reactions to conflict even after analytically controlling for parental warmth. Associations were found across multiple domains of child responding (i.e., overt negative affect, subjective negative affect, internal representations) and both concurrent and prospective, autoregressive analyses. Results of the autoregressive path analyses indicated moderate stability in each of the domains of conflict reactivity over the 1-year longitudinal period.

We examined the nature and directionality of associations between interparental discord and parenting practices in a sample of 227 mothers and fathers of kindergarten children over two measurement waves spaced 1 year apart. Cross-lagged models indicated that associations between interparental discord and maternal and paternal parenting practices (i.e., acceptance, discipline) were generally negligible with one exception: interparental discord at Time 1 predicted decreases in maternal acceptance from Time 1 to Time 2. However, consistent with the family systems conceptualization of the family as an open system, prospective associations among interparental and parent-child subsystems varied significantly as a function of parental depressive symptoms, interparental relationship insecurity, and child-rearing disagreements. The moderating role of adult characteristics commonly varied across the type of adult vulnerability (e.g., depressive symptoms, interparental relationship insecurity, child-rearing disagreements), dimension of parenting practices (e.g., maternal and paternal acceptance and inconsistent discipline), and the directionality of the paths between interparental and parent-child subsystems.

Emotion classification is essential for understanding human interactions and hence is a vital component of behavioral studies. Although numerous algorithms have been developed, the emotion classification accuracy is still short of what is desired for the algorithms to be used in real systems. In this paper, we evaluate an approach where basic acoustic features are extracted from speech samples, and the One-Against-All (OAA) Support Vector Machine (SVM) learning algorithm is used. We use a novel hybrid kernel, where we choose the optimal kernel functions for the individual OAA classifiers. Outputs from the OAA classifiers are normalized and combined using a thresholding fusion mechanism to finally classify the emotion. Samples with low ‘relative confidence’ are left as ‘unclassified’ to further improve the classification accuracy. Results show that the decision-level recall of our approach for six-class emotion classification is 80.5%, outperforming a state-of-the-art approach that uses the same dataset.