Editorial: Everyday Beliefs About Emotion: Their Role in Subjective Experience, Emotion as an Interpersonal Process, and Emotion Theory

Emotion beliefs: conceptual review and compendium

Laypeople hold richly divergent beliefs about emotion, and these beliefs are consequential. Specific forms of belief that have been investigated include the usefulness, contagiousness, duration, dependence upon intersubjective experience, cognitively mediated properties, malleability, and hindering properties of emotion, just to name a few. Progress in this emerging sub-field of research has been hampered by the lack of a widely accepted definition of emotion belief able to capture all of these dimensions. Correspondingly, there has been a proliferation of different terminologies, constructs, and measures. The present review aims to address these obstacles by defining emotion belief, and subsequently re-considering existing constructs and measures that align with this definition. The latter is presented in the form of a comprehensive compendium of 21 different constructs and associated self-report measures that assess varying components of one's beliefs about emotions in general and/or about their own emotions, and an additional 5 scales that were designed to measure one's beliefs about another's emotions. From the more unified conceptualization of emotion belief presented here, critical areas of future research are highlighted.

Modeling Subjective Affect Annotations with Multi-Task Learning

In supervised learning, the generalization capabilities of trained models are based on the available annotations. Usually, multiple annotators are asked to annotate the dataset samples and, then, the common practice is to aggregate the different annotations by computing average scores or majority voting, and train and test models on these aggregated annotations. However, this practice is not suitable for all types of problems, especially when the subjective information of each annotator matters for the task modeling. For example, emotions experienced while watching a video or evoked by other sources of content, such as news headlines, are subjective: different individuals might perceive or experience different emotions. The aggregated annotations in emotion modeling may lose the subjective information and actually represent an annotation bias. In this paper, we highlight the weaknesses of models that are trained on aggregated annotations for modeling tasks related to affect. More concretely, we compare two generic Deep Learning architectures: a Single-Task (ST) architecture and a Multi-Task (MT) architecture. While the ST architecture models single emotional perception each time, the MT architecture jointly models every single annotation and the aggregated annotations at once. Our results show that the MT approach can more accurately model every single annotation and the aggregated annotations when compared to methods that are directly trained on the aggregated annotations. Furthermore, the MT approach achieves state-of-the-art results on the COGNIMUSE, IEMOCAP, and SemEval_2007 benchmarks.