During social interactions, understanding the intricacies of the context can be vital, particularly for socially anxious individuals. While previous research has found that the presence of a social interaction can be detected from ambient audio, the nuances within social contexts, which influence how anxiety provoking interactions are, remain largely unexplored. As an alternative to traditional, burdensome methods like self-report, this study presents a novel approach that harnesses ambient audio segments to detect social threat contexts. We focus on two key dimensions: number of interaction partners (dyadic vs. group) and degree of evaluative threat (explicitly evaluative vs. not explicitly evaluative). Building on data from a Zoom-based social interaction study (N=52 college students, of whom the majority N=45 are socially anxious), we employ deep learning methods to achieve strong detection performance. Under sample-wide 5-fold Cross Validation (CV), our model distinguished dyadic from group interactions with 90\% accuracy and detected evaluative threat at 83\%. Using a leave-one-group-out CV, accuracies were 82\% and 77\%, respectively. While our data are based on virtual interactions due to pandemic constraints, our method has the potential to extend to diverse real-world settings. This research underscores the potential of passive sensing and AI to differentiate intricate social contexts, and may ultimately advance the ability of context-aware digital interventions to offer personalized mental health support.

What makes people 'click' on a first date and become mutually attracted to one another? While understanding and predicting the dynamics of romantic interactions used to be exclusive to human judgment, we show that Large Language Models (LLMs) can detect romantic attraction during brief getting-to-know-you interactions. Examining data from 964 speed dates, we show that ChatGPT (and Claude 3) can predict both objective and subjective indicators of speed dating success (r=0.12-0.23). ChatGPT's predictions of actual matching (i.e., the exchange of contact information) were not only on par with those of human judges who had access to the same information but incremental to speed daters' own predictions. While some of the variance in ChatGPT's predictions can be explained by common content dimensions (such as the valence of the conversations) the fact that there remains a substantial proportion of unexplained variance suggests that ChatGPT also picks up on conversational dynamics. In addition, ChatGPT's judgments showed substantial overlap with those made by the human observers (mean r=0.29), highlighting similarities in their representation of romantic attraction that is, partially, independent of accuracy.

Solving the problem of cooperation is of fundamental importance to the creation and maintenance of functional societies, with examples of cooperative dilemmas ranging from navigating busy road junctions to negotiating carbon reduction treaties. As the use of AI becomes more pervasive throughout society, the need for socially intelligent agents that are able to navigate these complex cooperative dilemmas is becoming increasingly evident. In the natural world, direct punishment is an ubiquitous social mechanism that has been shown to benefit the emergence of cooperation within populations. However no prior work has investigated its impact on the development of cooperation within populations of artificial learning agents experiencing social dilemmas. Additionally, within natural populations the use of any form of punishment is strongly coupled with the related social mechanisms of partner selection and reputation. However, no previous work has considered the impact of combining multiple social mechanisms on the emergence of cooperation in multi-agent systems. Therefore, in this paper we present a comprehensive analysis of the behaviours and learning dynamics associated with direct punishment in multi-agent reinforcement learning systems and how it compares to third-party punishment, when both are combined with the related social mechanisms of partner selection and reputation. We provide an extensive and systematic evaluation of the impact of these key mechanisms on the dynamics of the strategies learned by agents. Finally, we discuss the implications of the use of these mechanisms on the design of cooperative AI systems.

Robots can support humans in tedious tasks, as well as provide social support. However, the decision-making and behavior of robots is not always clear to the human interaction partner. In this work, we discuss the opportunity of using thermal feedback as an additional modality to create transparent interactions. We then present scenarios where thermal feedback is incorporated into the interaction e.g. to unobtrusively communicate the behavior of the robot. We highlight the limitations and challenges of temperature-based feedback, which can be explored in future research.

In this context, a key issue is the semantic understanding of the environment in order to enable mobile robots more complex interactions and a facilitated communication with humans. Prerequisites are the vision-based registration of semantic objects and humans where the latter are further analyzed for potential interaction partners. Despite significant research achievements, the reliable and fast registration of semantic information still remains a challenging tasks for mobile robots in real-world scenarios. In this paper, we present a vision-based system for mobile assistive robots to enable a semantic-aware environment perception without additional a-priori knowledge. We deploy our system on a mobile humanoid robot that enables us to test our methods in real-world applications.

With increased applications of collaborative robots (cobots) in industrial workplaces, behavioural effects of human-cobot interactions need to be further investigated. This is of particular importance as nonverbal behaviours of collaboration partners in human-robot teams significantly influence the experience of the human interaction partners and the success of the collaborative task. During the Ars Electronica 2020 Festival for Art, Technology and Society (Linz, Austria), we invited visitors to exploratively interact with an industrial robot, exhibiting restricted interaction capabilities: extending and retracting its arm, depending on the movements of the volunteer. The movements of the arm were pre-programmed and telecontrolled for safety reasons (which was not obvious to the participants). We recorded video data of these interactions and investigated general nonverbal behaviours of the humans interacting with the robot, as well as nonverbal behaviours of people in the audience. Our results showed that people were more interested in exploring the robot's action and perception capabilities than just reproducing the interaction game as introduced by the instructors. We also found that the majority of participants interacting with the robot approached it up to a distance which would be perceived as threatening or intimidating, if it were a human interaction partner. Regarding bystanders, we found examples where people made movements as if trying out variants of the current participant's behaviour.

In this article, an enactive architecture is described that allows a humanoid robot to learn to compose simple actions into turn-taking behaviors while playing interaction games with a human partner. The robot's action choices are reinforced by social feedback from the human in the form of visual attention and measures of behavioral synchronization. We demonstrate that the system can acquire and switch between behaviors learned through interaction based on social feedback from the human partner. The role of reinforcement based on a short term memory of the interaction is experimentally investigated. Results indicate that feedback based only on the immediate state is insufficient to learn certain turn-taking behaviors. Therefore some history of the interaction must be considered in the acquisition of turn-taking, which can be efficiently handled through the use of short term memory.

This work describes an approach to time-series modeling of social interactions between human and robot, which is motivated by the social psychology concept of social grounding. In this model, the goal of the agents is to establish and use patterns of communication, rather than rely on existing patterns. Our goal is to allow an artifical agent to construct a pattern of shared meaning with a human or other agent through shared experience rather than relying a model provided A priori. We describe a preliminary human robot interaction study which illustrates the proposed approach.