Multimodal Cues of the Sense of Presence and Co-presence in Human-Virtual Agent Interaction

Abstract

A key challenge when studying human-agent interaction is the evaluation of user's experience. In virtual reality, this question is addressed by studying the sense of "presence'' and"co-presence'', generally assessed thanks to well-grounded subjective post-experience questionnaires. In this article, we aim at exploring behavioral measures of presence and co-presence by analyzing multimodal cues produced during an interaction both by the user and the virtual agent. In our study, we started from a corpus of human-agent interaction collected in a task-oriented context: a virtual environment aiming at training doctors to break bad news to a patient (played by a virtual agent). Based on this corpus, we have used machine learning algorithms to explore the possibility of predicting user's sense of presence and co-presence. In particular, we have applied and compared two techniques, Random forest and SVM, both showing very good results in predicting the level of presence and co-presence.