Feedback delays can enhance anticipatory synchronization in human-machine interaction.

Research investigating the dynamics of coupled physical systems has demonstrated that small feedback delays can allow a dynamic response system to anticipate chaotic behavior. This counterintuitive phenomenon, termed anticipatory synchronization, has been observed in coupled electrical circuits, laser semi-conductors, and artificial neurons. Recent research indicates that the same process might also support the ability of humans to anticipate the occurrence of chaotic behavior in other individuals.

Human social motor solutions for human-machine interaction in dynamical task contexts.

Multiagent activity is commonplace in everyday life and can improve the behavioral efficiency of task performance and learning. Thus, augmenting social contexts with the use of interactive virtual and robotic agents is of great interest across health, sport, and industry domains. However, the effectiveness of human-machine interaction (HMI) to effectively train humans for future social encounters depends on the ability of artificial agents to respond to human coactors in a natural, human-like manner.

Intermittent coupling between grip force and load force during oscillations of a hand-held object.

Tightly coordinated grip force adaptations in response to changing load forces have been reported as continuous, stable, and proportional to the load force changes. Considering the existence of inherent sensorimotor feedback delays, current accounts of grip force-load force coupling invoke explicit predictive mechanisms in the form of internal models for feedforward control to account for anticipatory grip force modulations. However, recent findings suggest that the stability and regularity of grip force-load force coupling is less persistent than previously thought.

To Pass or Not to Pass: Modeling the Movement and Affordance Dynamics of a Pick and Place Task.

Humans commonly engage in tasks that require or are made more efficient by coordinating with other humans. In this paper we introduce a task dynamics approach for modeling multi-agent interaction and decision making in a pick and place task where an agent must move an object from one location to another and decide whether to act alone or with a partner. Our aims were to identify and model (1) the related dynamics that define an actor's choice to move an object alone or to pass it to their co-actor and (2) the trajectory dynamics of an actor's hand movements when moving to grasp, relocate, or pass the object.