Multidisciplinary characterization of embarrassment through behavioral and acoustic modeling.

Abstract

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 overlooked in research. We characterized embarrassment through an interdisciplinary approach, introducing a behavioral paradigm and applying machine learning approaches, including acoustic analyses. 33 participants wrote about an embarrassing experience and then, without knowing it prior, had to read it out loud to the conductor. Embarrassment was then examined using two different approaches: Firstly, from a subjective view, with self-report measures from the participants. Secondly, from an objective, machine-learning approach, in which 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, arousal, dominance; VAD) and categorical (i.e., comparing embarrassment to other emotional states) way. 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 with 86.4% as the best of the unweighted average recall rates. 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. 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.