Impact of emotion-laden acoustic stimuli on group synchronisation performance

The ability to synchronise with other people is a core socio-motor competence acquired during human development. In this study we aimed to understand the impact of individual emotional arousal on joint action performance. We asked 15 mixed-gender groups (of 4 individuals each) to participate in a digital, four-way movement synchronisation task. Participants shared the same physical space, but could not see each other during the task. In each trial run, every participant was induced with an emotion-laden acoustic stimulus (pre-selected from the second version of International Affective Digitized Sounds). Our data demonstrated that the human ability to synchronise is overall robust to fluctuations in individual emotional arousal, but performance varies in quality and movement speed as a result of valence of emotional induction (both on the individual and group level). We found that three negative inductions per group per trial led to a drop in overall group synchronisation performance (measured as the median and standard deviation of Kuramoto's order parameter-an index measuring the strength of synchrony between oscillators, in this study, players) in the 15 sec post-induction. We report that negatively-valenced inductions led to slower oscillations, whilst positive induction afforded faster oscillations. On the individual level of synchronisation performance we found an effect of empathetic disposition (higher competence linked to better performance during the negative induction condition) and of participant's sex (males displayed better synchronisation performance with others). We believe this work is a blueprint for exploring the frontiers of inextricably bound worlds of emotion and joint action, be it physical or digital.

Dynamic Input Deep Learning Control of Artificial Avatars in a Multi-Agent Joint Motor Task

In many real-word scenarios, humans and robots are required to coordinate their movements in joint tasks to fulfil a common goal. While several examples regarding dyadic human robot interaction exist in the current literature, multi-agent scenarios in which one or more artificial agents need to interact with many humans are still seldom investigated. In this paper we address the problem of synthesizing an autonomous artificial agent to perform a paradigmatic oscillatory joint task in human ensembles while exhibiting some desired human kinematic features. We propose an architecture based on deep reinforcement learning which is flexible enough to make the artificial agent interact with human groups of different sizes. As a paradigmatic coordination task we consider a multi-agent version of the mirror game, an oscillatory motor task largely used in the literature to study human motor coordination.