Emotion socialization in mothers with mood disorders: Affective modeling and recollected responses to childhood emotion

Growing evidence suggests that emotion socialization may be disrupted by maternal depression. However, little is known about emotion-related parenting by mothers with bipolar disorder or whether affective modeling in early childhood is linked to young adults’ recollections of emotion socialization practices. The current study investigates emotion socialization by mothers with histories of major depression, bipolar disorder, or no mood disorder. Affective modeling was coded from parent–child interactions in early childhood and maternal responses to negative emotions were recollected by young adult offspring (n = 131, 59.5% female, M age = 22.16, SD = 2.58). Multilevel models revealed that maternal bipolar disorder was associated with more neglecting, punishing, and magnifying responses to children's emotions, whereas maternal major depression was associated with more magnifying responses; links between maternal diagnosis and magnifying responses were robust to covariates. Young adult recollections of maternal responses to emotion were predicted by affective modeling in early childhood, providing preliminary validity evidence for the Emotions as a Child Scale. Findings provide novel evidence that major depression and bipolar disorder are associated with altered emotion socialization and that maternal affective modeling in early childhood prospectively predicts young adults’ recollections of emotion socialization in families with and without mood disorder.

Two-Way Affective Modeling for Hidden Movie Highlights’ Extraction

Movie highlights are composed of video segments that induce a steady increase of the audience’s excitement. Automatic movie highlights’ extraction plays an important role in content analysis, ranking, indexing, and trailer production. To address this challenging problem, previous work suggested a direct mapping from low-level features to high-level perceptual categories. However, they only considered the highlight as intense scenes, like fighting, shooting, and explosions. Many hidden highlights are ignored because their low-level features’ values are too low. Driven by cognitive psychology analysis, combined top-down and bottom-up processing is utilized to derive the proposed two-way excitement model. Under the criteria of global sensitivity and local abnormality, middle-level features are extracted in excitement modeling to bridge the gap between the feature space and the high-level perceptual space. To validate the proposed approach, a group of well-known movies covering several typical types is employed. Quantitative assessment using the determined excitement levels has indicated that the proposed method produces promising results in movie highlights’ extraction, even if the response in the low-level audio-visual feature space is low.

Computational Analytical Framework for Affective Modeling

Computational affective models are being developed both to elucidate affective mechanisms, and to enhance believability of synthetic agents and robots. Yet in spite of the rapid growth of computational affective modeling, no systematic guidelines exist for model design and analysis. Lack of systematic guidelines contributes to ad hoc design practices, hinders model sharing and re-use, and makes systematic comparison of existing models and theories challenging. Lack of a common computational terminology also hinders cross-disciplinary communication that is essential to advance our understanding of emotions. In this chapter the author proposes a computational analytical framework to provide a basis for systematizing affective model design by: (1) viewing emotion models in terms of two core types: emotion generation and emotion effects, and (2) identifying the generic computational tasks necessary to implement these processes. The chapter then discusses how these computational ‘building blocks' can support the development of design guidelines, and a systematic analysis of distinct emotion theories and alternative means of their implementation.

Computational Analytical Framework for Affective Modeling

Computational affective models are being developed both to elucidate affective mechanisms, and to enhance believability of synthetic agents and robots. Yet in spite of the rapid growth of computational affective modeling, no systematic guidelines exist for model design and analysis. Lack of systematic guidelines contributes to ad hoc design practices, hinders model sharing and re-use, and makes systematic comparison of existing models and theories challenging. Lack of a common computational terminology also hinders cross-disciplinary communication that is essential to advance our understanding of emotions. In this chapter the author proposes a computational analytical framework to provide a basis for systematizing affective model design by: (1) viewing emotion models in terms of two core types: emotion generation and emotion effects, and (2) identifying the generic computational tasks necessary to implement these processes. The chapter then discusses how these computational ‘building blocks' can support the development of design guidelines, and a systematic analysis of distinct emotion theories and alternative means of their implementation.