Since its design in 2003, the joint Simon task and corollary joint Simon effect (JSE) have been invaluable tools towards the study of joint action and the understanding of how individuals represent the action/task of a co-actor. The purpose of this meta-analysis was to systematically and quantitatively review the sizeable behavioural evidence for the JSE. Google Scholar was used to identify stu...

Vision plays a crucial role in human interaction by facilitating the coordination of one's own actions with those of others in space and time. While previous findings have demonstrated that vision determines the default use of reference frames, little is known about the role of visual experience in coding action-space during joint action. Here, we tested if and how visual experience influences ...

How does the individual behavior of a musician change in solo Vs. creative joint action? In this paper we consider music performance, an ideal ecological test bed to investigate non-verbal social behavior, to compare the expressive movement of violinists when playing solo or in a string quartet ensemble. In the presented study, by measuring its Sample Entropy, we observe that the movement of a ...

Recent research has shown that joint-action effects in a social Simon task provide a good index of action co-representation. The present study aimed to specify the mechanisms underlying joint action by considering trial-to-trial transitions. Using non-social stimuli, we assigned a Simon task to two participants. Each was responsible for only one of two possible responses. This task was performe...

Automatic imitation tasks measuring motor priming effects showed that we directly map observed actions of other agents onto our own motor repertoire (direct matching). A recent joint action study using a social dual-task paradigm provided evidence for task monitoring. In the present study, we aimed to test (a) if automatic imitation is disturbed during joint action and (b) if task monitoring is...

We present Consensus Action Games (CAGs), a novel approach to modelling consensus action in multi-agent systems inspired by quorum sensing and other forms of decision making found in biological systems. In a consensus action game, each agent’s degree of commitment to the joint actions in which it may participate is expressed as a quorum function, and an agent is willing to participate in a join...

Joint segmentation and classification of fine-grained actions is important for applications in human-robot interaction, video surveillance, and human skill evaluation. However, despite substantial recent progress in large scale action classification, the performance of state-ofthe-art fine-grained action recognition approaches remains low. In this paper, we propose a new spatio-temporal CNN mod...

This paper proposes statistic learning based Q-learning algorithm for Multi-Agent System, the agent can learn other agents’ action policies through observing and counting the joint action, a concise but useful hypothesis is adopted to denote the optimal policies of other agents, the full joint probability of policies distribution guarantees the optimal action choice to the learning agent. The a...

Learning automata are reinforcement learners belonging to the category of policy iterators. They have already been shown to exhibit nice convergence properties in discrete action games. Recently, a new formulation for a Continuous Action Reinforcement Learning Automaton (CARLA) was proposed. In this paper we study the behavior of these CARLA in continuous action games and propose a novel method...