International audience

The Proteus effect describes how users of virtual environments adjust their attitudes to match stereotypes associated with their avatar's appearance. While numerous studies have demonstrated this phenomenon's reliability, its underlying processes remain poorly understood. This work investigates deindividuation's hypothesized but unproven role within the Proteus effect. Deindividuated individuals tend to follow situational norms rather than personal ones. Therefore, together with high embodiment and identification processes, deindividuation may lead to a stronger Proteus effect. We present two experimental studies. First, we demonstrated the emergence of the Proteus effect in a real-world academic context: engineering students got better scores in a statistical task when embodying Albert Einstein's avatar compared to a control one. In the second study, we tested the role of deindividuation by manipulating participants' exposure to different identity cues during the task. While we could not find a significant effect of deindividuation on the participants' performance, our results highlight an unexpected pattern, with embodiment as a negative predictor and identification as a positive predictor of performance. These results open avenues for further research on the processes involved in the Proteus effect, particularly those focused on the relation between the avatar and the nature of the task to be performed. All supplemental materials are available at https://osf.io/au3wk/.

Othello, also known as Reversi, is a popular 2-players board game. Olivaw is an intelligent agent playing Othello. Compared to the most famous ones (such as Saio), it exploits limited resources by autonomously learning how to improve its gameplay by playing against itself. In previous occasions, Othello players reported the impression of a sort of human-likeness in how Olivaw plays. We designed and ran an experimental study to better investigate these impressions in a controlled setting. Participants were asked to watch the moves of pre-recorded Othello games played by a human expert player against either another agent (i.e., Olivaw, Saio) or another human. The identity of the opponent, the outcome of the game (i.e., whether the human expert or the opponent player won), and the color of the players (i.e., black or white, black always playing first) were manipulated. We then asked participants to evaluate the human-likeness of the opponent player. Results confirm that the outcome of the match affects the perception of human-likeness of the players.

Olivaw is an AI Othello playing agent which autonomously learns how to improve its gameplay by playing against itself. Some top-notch players (including former World Champions) reported that they had the impression that Olivaw's gameplay was human-like. To better investigate the processes related to these impressions, we conducted a pilot study using the Othello Game Evaluation App, a computer application we developed to evaluate pre-recorded Othello games in a controlled setting while assuring an adequate user experience. An exploratory analysis of the results shows that the participants mostly evaluated Olivaw as a human. When asked for a motivation for their choice, some of them reported that they evaluate poor game moves (and, consequently, losing the game) as an indication of the human-likeness of the player.

Transactive Memory System (TMS) is a mental representation of the distribution of knowledge between the members of a team. Can an Embodied Conversational Agent perform as well as a Human when intervening as a leader to support the development of the team’s TMS? And, if yes, are there differences in the way the team perceives their respective interventions? In this paper, a perceptive online study is conducted on how Human leader interventions affect the perception of a team’s TMS. The results are compared to the ones from a previous study evaluating an Embodied Conversational agent leader rather than a human one. Both the agent and the human adopt nonverbal behaviors characterizing 2 leadership styles: Transformational (TFL) and Transactional (TAL). TFL is expected to stimulate team members curiosity and creativity in problem-solving; instead, TAL emphasizes the role of the leader in supervising the team, providing it with feedback when needed. The results show that the intervention from both the agent and the human are perceived to potentially improve the perceived TMS of a team. Another interesting insight is that the TFL style works better when performed by the Human, where both the TAL and TFL style perform well when realized by the agent.

This Workshop aims at stimulating multi-disciplinary discussions about the challenges related to corpus creation and annotation for social skills behavior analysis. Contributions from computational, psychological and psychometrics perspectives, as well as applications including platforms to share corpora and annotations, are welcomed. The main challenges related to corpus creation include the choice of the best setup and sensors, finding a trade-off between eliciting natural interactions, limiting invasiveness and collecting precise information. The second issue in this context regards the process of annotation. The choice of the type of annotators (experts vs. nonexperts), the type of annotations (automatic vs. manual, continue vs. discrete), the temporal segmentation (windowed vs. holistic) is crucial for a correct measure of the phenomenon of interest and getting significant results. The topics of CATS2021 will have a strong impact on researchers and stakeholders across different disciplines, such as Computer Science, Social Signal Processing, Psychology, Statistics. Leveraging the opportunities offered by such a multidisciplinary environment, the participants could enrich their perspective, strengthen their practices and methodologies and draw together a research roadmap tackling the discussed challenges, which might be taken up in future collaborations.

We present WoNoWa, a novel multi-modal dataset of small group interactions in collaborative tasks. The dataset is explicitly designed to elicit and to study over time a Transactive Memory System (TMS), a group's emergent state characterizing the group's meta-knowledge about "who knows what". A rich set of automatic features and manual annotations, extracted from the collected audio-visual data, is available on request for research purposes. Features include individual descriptors (e.g., position, Quantity of Motion, speech activity) and group descriptors (e.g., F-formations). Additionally, participants' self-assessments are available. Preliminary results from exploratory analyses show that the WoNoWa design allowed groups to develop a TMS that increased across the tasks. These results encourage the use of the WoNoWa dataset for a better understanding of the relationship between behavioural patterns and TMS, that in turn could help to improve group performance.

This paper presents a computational model for managing an Embodied Conversational Agent's first impressions of warmth and competence towards the user. These impressions are important to manage because they can impact users' perception of the agent and their willingness to continue the interaction with the agent. The model aims at detecting user's impression of the agent and producing appropriate agent's verbal and nonverbal behaviours in order to maintain a positive impression of warmth and competence. User's impressions are recognized using a machine learning approach with facial expressions (action units) which are important indicators of users' affective states and intentions. The agent adapts in real-time its verbal and nonverbal behaviour, with a reinforcement learning algorithm that takes user's impressions as reward to select the most appropriate combination of verbal and non-verbal behaviour to perform. A user study to test the model in a contextualized interaction with users is also presented. Our hypotheses are that users' ratings differs when the agents adapts its behaviour according to our reinforcement learning algorithm, compared to when the agent does not adapt its behaviour to user's reactions (i.e., when it randomly selects its behaviours). The study shows a general tendency for the agent to perform better when using our model than in the random condition. Significant results shows that user's ratings about agent's warmth are influenced by their a-priori about virtual characters, as well as that users' judged the agent as more competent when it adapted its behaviour compared to random condition.

When interacting with others, we form an impression that can be declined along the two psychological dimensions of warmth and competence. By managing them, high level of engagement in an interaction can be maintained and reinforced. Our aim is to develop a virtual agent that can form and maintain a positive impression on the user that can help in improving the quality of the interaction and the user's experience. In this paper, we present an interactive system in which a virtual agent adopts a dynamic communication strategy during the interaction with a user, aiming at forming and maintaining a positive impression of warmth and competence. The agent continuously analyzes user's non-verbal signals to determine user's engagement level and adapts its communication strategy accordingly. We present a study in which we manipulate the communication strategy of the agent and we measure user's experience and user's perception of the agent's warmth and competence.

This paper presents a plan towards a computational model of first impressions generation and its integration in an embodied conversational agent (ECA). The goal is to endow an ECA with the ability to manage the impressions elicited in the user by adapting its behaviour in order to impact the interaction. Our approach starts from studying first impressions mechanisms in human-human interaction, with the goal of investigating whether the same processes occur in the interaction with a virtual agent. We present results obtained from the analysis of a corpus of natural human-human interactions, and our future steps intended to build the impression generation and the adaptation modules for the computational model of the agent.

In this paper we present an analysis from a corpus of dyadic expert-novice knowledge sharing interactions. The analysis aims at investigating the relationship between observed non-verbal cues and first impressions formation of warmth and competence. We first obtained both discrete and continuous annotations of our data. Discrete descriptors include non-verbal cues such as type of gestures, arms rest poses, head movements and smiles. Continuous descriptors concern annotators' judgments of the expert's warmth and competence during the observed interaction with the novice. Then we computed Odds Ratios between those descriptors. Results highlight the role of smiling in warmth and competence impressions. Smiling is associated with increased levels of warmth and decreasing competence. It also affects the impact of others non-verbal cues (e.g. self-adaptors gestures) on warmth and competence. Moreover, our findings provide interesting insights about the role of rest poses, that are associated with decreased levels of warmth and competence impressions.

Laughter is a social signal capable of facilitating interaction in groups of people: it communicates interest, helps to improve creativity, and facilitates sociability. This article focuses on: endowing virtual characters with computational models of laughter synthesis, based on an expressivity-copying paradigm; evaluating how the physically co-presence of the laughing character impacts on the user’s perception of an audio stimulus and mood. We adopt music as a means to stimulate laughter. Results show that the character presence influences the user’s perception of music and mood. Expressivity-copying has an influence on the user’s perception of music, but does not have any significant impact on mood.

In our demo, LoL, a user interacts with a virtual agentable to copy and to adapt its laughing and expressive behaviorson-the-fly. Our aim is to study copying capabilitiesparticipate in enhancing user’s experience in the interaction.User listens to funny audio stimuli in the presenceof a laughing agent: when funniness of audio increases, theagent laughs and the quality of its body movement (directionand amplitude of laughter movements) is modulated on-theflyby user’s body features.