Emotion Dynamics as Predictors of Drinking Behavior: Idiographic and Pooled Approaches

The specific factors driving alcohol-related behavior and cognition likely vary from person to person. Many theories suggest emotions are pertinent to alcohol use. Emotions and how they change over time may provide an opportunity for more precise prediction of alcohol consumption. The present study applied statistical classification methods to idiographic time series data of emotions and emotion dynamics in order to identify person-specific and between-subjects predictors of future drinking-relevant behavior, affect, and cognition (N = 33). Participants were sent eight mobile phone surveys per day for 15 days. Each survey assessed the number of drinks consumed since the previous survey, as well as emotions, alcohol craving, and the desire to drink. Each participant’s EMA data were prepared for analysis separately. To estimate emotion dynamics, we utilized the Generalized Local Linear Approximation. The data collected from each individual were split into training and testing sets for out-of-sample, person-specific validation. Elastic net regularization was used to select a subset of emotion and emotion dynamic variables to be used in models that predicted either alcohol consumption, craving, or wanting to drink roughly two hours in the future. To compare predictive performance, we tested both person-specific and between-subject prediction models. Averaging across participants, out-of-sample predictions of future drinking using idiographic models were 69% accurate. For craving, the mean out-of-sample R² value was .13. For wanting to drink, the mean out-of-sample R² value was .16. Idiographic prediction models exceeded nomothetic models in prediction accuracy. Using person-specific emotion and emotion dynamics can help predict future drinking behaviors.

Investigating Emotion Dynamics and Its Association with Multimedia Content During Video Watching Through EEG Microstate Analysis

Emotions dynamically change in response to ever-changing environments. It is of great importance, both clinically and scientifically, to investigate the neural representation and evoking mechanism of emotion dynamics. But, there are many unknown places in this stream of research, such as consistent and conclusive findings are still lacking. In this work, we perform an in-depth investigation of emotion dynamics under a video-watching task by gauging the dynamic associations among evoked emotions, electroencephalography (EEG) responses, and multimedia stimulation. Here, we introduce EEG microstate analysis to study emotional EEG signals, which provides a spatial-temporal neural representation of emotion dynamics. To investigate the temporal characteristics of evoking emotions during video watching with its neural mechanism, we conduct two studies from the perspective of EEG microstates. In Study 1, the dynamic microstate activities under different emotion states and emotion levels are explored to identify EEG spatial-temporal correlates of emotion dynamics. In Study 2, the stimulation effects of multimedia content (visual and audio) on EEG microstate activities are examined to learn about the involved affective information and investigate the emotion-evoking mechanism. The results show that emotion dynamics could be well reflected by four EEG microstates (MS1, MS2, MS3, and MS4). Specifically, emotion tasks lead to an increase in MS2 and MS4 coverage but a decrease in MS3 coverage, duration, and occurrence. Meanwhile, there exists a negative association between valence and MS4 occurrence as well as a positive association between arousal and MS3 coverage and occurrence. Further, we find that MS4 and MS3 activities are significantly affected by visual and audio content, respectively. In this work, we verify the possibility to reveal emotion dynamics through EEG microstate analysis from sensory and stimulation dimensions, where EEG microstate features are found to be highly correlated to different emotion states (emotion task effect and level effect) and different affective information involved in the multimedia content (visual and audio). Our work deepens the understanding of the neural representation and evoking mechanism of emotion dynamics, which can be beneficial for future development in the applications of emotion decoding and regulation.

Emotion dynamics in movie dialogues

Emotion dynamics is a framework for measuring how an individual’s emotions change over time. It is a powerful tool for understanding how we behave and interact with the world. In this paper, we introduce a framework to track emotion dynamics through one’s utterances. Specifically we introduce a number of utterance emotion dynamics (UED) metrics inspired by work in Psychology. We use this approach to trace emotional arcs of movie characters. We analyze thousands of such character arcs to test hypotheses that inform our broader understanding of stories. Notably, we show that there is a tendency for characters to use increasingly more negative words and become increasingly emotionally discordant with each other until about 90% of the narrative length. UED also has applications in behavior studies, social sciences, and public health.

Using the Ball-in-Bowl Metaphor to Outline an Integrative Framework for Understanding Dysregulated Emotion

Dysregulated emotion plays an important role for mental health problems. To elucidate the underlying mechanisms, researchers have focused on the domains of strategy-based emotion regulation, psychophysiological self-regulation, emotion evaluations, and resulting emotion dynamics. So far, these four domains have been looked at in relative isolation from each other, and their reciprocal influences and interactive effects have seldom been considered. This domain-specific focus constrains the progress the field is able to make. Here, we aim to pave the way towards more cross-domain, integrative research focused on understanding the raised reciprocal influences and interactive effects of strategy-based emotion-regulation, psychophysiological self-regulation, emotion evaluations, and emotion dynamics. To this aim, we first summarize for each of these domains the most influential theoretical models, the research questions they have stimulated, and their strengths and weaknesses for research and clinical practice. We then introduce the metaphor of a ball in a bowl that we use as a basis for outlining an integrative framework of dysregulated emotion. We illustrate how such a framework can inspire new research on the reciprocal influences and interactions between the different domains of dysregulated emotion and how it can help to theoretically explain a broader array of findings, such as the high levels of negative affect in clinical populations that have not been fully accounted for by deficits in strategy-based emotion regulation and the positive long-term consequences of accepting and tolerating emotions. Finally, we show how it can facilitate individualized emotion regulation interventions that are tailored to the specific regulatory impairments of the individual patient.