Design and Validation of a Library of Active Affective Tasks for Emotion Elicitation in VR

Emotion recognition models require datasets of physiological responses to stimuli designed to elicit targeted emotions, preferably stimuli similar to the experience during which the models will be used. Many libraries of such stimuli have been created to ease this data collection process, most of which involve passive media such as images or videos. Virtual Reality, however, offers an opportunity to investigate uniquely active emotion elicitation stimuli that directly center the user in the experience with an increased feeling of presence and potential to elicit stronger emotions. We leverage this to introduce a set of four active affective tasks in VR designed to quickly elicit targeted emotions without need for narrative understanding common to passive stimuli. We compare our tasks with selections from an existing affective library of passive 360° videos and validate our approach by comparing self-reported emotional responses to the stimuli. Results indicate that these types of active task stimuli can reliably elicit strong emotions comparable to other passive media and provide the basis for building a larger library of training- and education-relevant tasks.