A theoretical review of the Proteus effect: understanding the underlying processes.

Humans' inherent fascination for stories can be observed throughout most of our documented history. If, for a long time, narratives were told through paintings, songs, or literature, recent technological advances such as immersive virtual reality have made it possible for us to interact with storylines and characters in a completely new manner. With these new technologies came the need to study how people interact with them and how they affect their users. Notably, research in this area has revealed that users of virtual environments tend to display behaviors/attitudes that are congruent with the appearance of the avatars they embody; a phenomenon termed the Proteus effect. Since its introduction in the literature, many studies have demonstrated the Proteus effect in various contexts, attesting to the robustness of the effect. However, beyond the first articles on the subject, very few studies have sought to investigate the social, affective, and cognitive mechanisms underlying the effect. Furthermore, the current literature appears somewhat disjointed with different schools of thought, using different methodologies, contributing to this research topic. Therefore, this work aims to give an overview of the current state of the literature and its shortcomings. It also presents a critical analysis of multiple theoretical frameworks that may help explain the Proteus effect. Notably, this work challenges the use of self-perception theory to explain the Proteus effect and considers other approaches from social psychology. Finally, we present new perspectives for upcoming research that seeks to investigate the effect of avatars on user behavior. All in all, this work aims to bring more clarity to an increasingly popular research subject and, more generally, to contribute to a better understanding of the interactions between humans and virtual environments.

Managing an Agent's Self-Presentational Strategies During an Interaction.

In this paper we present a computational model for managing the impressions of warmth and competence (the two fundamental dimensions of social cognition) of an Embodied Conversational Agent (ECA) while interacting with a human. The ECA can choose among four different self-presentational strategies eliciting different impressions of warmth and/or competence in the user, through its verbal and non-verbal behavior. The choice of the non-verbal behaviors displayed by the ECA relies on our previous studies. In our first study, we annotated videos of human-human natural interactions of an expert on a given topic talking to a novice, in order to find associations between the warmth and competence elicited by the expert's non-verbal behaviors (such as type of gestures, arms rest poses, smiling). In a second study, we investigated whether the most relevant non-verbal cues found in the previous study were perceived in the same way when displayed by an ECA. The computational learning model presented in this paper aims to learn in real-time the best strategy (i.e., the degree of warmth and/or competence to display) for the ECA, that is, the one which maximizes user's engagement during the interaction. We also present an evaluation study, aiming to investigate our model in a real context. In the experimental scenario, the ECA plays the role of a museum guide introducing an exposition about video games. We collected data from 75 visitors of a science museum. The ECA was displayed in human dimension on a big screen in front of the participant, with a Kinect on the top. During the interaction, the ECA could adopt one of 4 self-presentational strategies during the whole interaction, or it could select one strategy randomly for each speaking turn, or it could use a reinforcement learning algorithm to choose the strategy having the highest reward (i.e., user's engagement) after each speaking turn.