Multi-disciplinary Characterization of Embarrassment: Behavioral and Acoustic Modeling

Introduction: Embarrassment is a social emotion that shares many characteristics with social anxiety (SA). Most people experience embarrassment in their daily lives, but it is quite unattended in research. We characterized embarrassment in an interdisciplinary approach, introducing a behavioral paradigm and applying machine learning approaches, including acoustic analyses. Methods: 33 participants had to write about an embarrassing experience and had then, without knowing it prior, to read it out loud to the conductor. Embarrassment was then examined with two different approaches: Firstly, from a subjective view, with self-report measures from the participants. Secondly, from an objective, machine-learning approach, where trained models tested the robustness of our embarrassment data set (i.e., prediction accuracy), and then described embarrassment in a dimensional (i.e., dimension: valence, activation, dominance; VAD) and categorical (i.e., comparing embarrassment to other emotional states) way. Results: The subjective rating of embarrassment was increased after participants read their stories out loud, and participants with higher SA scores experienced higher embarrassment than participants with lower SA scores. The state of embarrassment was predicted at 86.4% at the best of the unweighted average recall rate. While the simple VAD dimensional analyses did not differentiate between the state of embarrassment and the references, the complex emotional category analyses characterized embarrassment as closer to boredom, a neutral state, and less of sadness. Conclusion: Combining an effective behavioral paradigm and advanced acoustic modeling, we characterized the emotional state of embarrassment, and the identified characteristics could be used as a biomarker to assess SA.