The Multimodal Dataset of Negative Affect and Aggression

Abstract

Within the affective computing and social signal processing communities, increasing efforts are being made in order to collect data with genuine (emotional) content. When it comes to negative emotions and even aggression, ethical and privacy related issues prevent the usage of many emotion elicitation methods, and most often actors are employed to act out different scenarios. Moreover, for most databases, emotional arousal is not explicitly checked, and the footage is annotated by external raters based on observable behavior. In the attempt to gather data a step closer to real-life, previous work proposed an elicitation method for collecting the database of negative affect and aggression that involved unscripted role-plays between aggression regulation training actors (actors) and naive participants (students), where only short role descriptions and goals are given to the participants. In this paper we present a validation study for the database of negative affect and aggression by investigating whether the actors' behavior (e.g. becoming more aggressive) had a real impact on the students' emotional arousal. We found significant changes in the students' heart rate variability (HRV) parameters corresponding to changes in aggression level and emotional states of the actors, and therefore conclude that this method can be considered as a good candidate for emotion elicitation.