A fundamental feature of social behavior is the face-to-face or co-present interactions that characterize everyday social activity. The success of such interactions, whether measured in terms of social connection, goal achievement, or the ability of an individual or group of individuals to understand and predict the meaningful intentions and behaviors of others, is not only dependent on the processes of social cognition and perception, but also on the between-person motor coordination that makes such face-to-face and co-present interactions possible. Understanding and modeling the dynamics of social motor coordination, including how it emerges and is maintained over time, as well as how its stable states are activated, dissolved, transformed, and exchanged over time, is therefore an extremely important endeavor. The overall aim of the proposed project is to develop a dynamical modeling strategy for capturing the self-organized behavioral dynamics of goal-directed physical activity among socially coordinated human agents and how the dynamics of such tasks are influenced by physical, information, and task-goal properties. More specifically, the proposed project will build differential equation models of the temporal and spatial patterns that dynamically emerge during a number of different movement based multi-agent action tasks: social rhythmic or repetitive movement and targeting tasks, social object-moving tasks, structured conversation tasks, and a competitive sport task. Employing a systems identification approach to formulate candidate behavioral dynamics models, we will not only capture the steady-state dynamics of the joint and social behaviors investigated, but will also formulate models that capture how parameter tuning and symmetry breaking events fundamentally modify the dynamics of social interaction, including movement sub-roles (e.g., leader-follower) and action sequencing. By recording the limb or whole body movements of participants during the real-world performance of social action tasks, we will evaluate and refine the proposed models using a range of parameter estimation techniques. For some of the social action tasks, we will also test and refine the developed models by implementing them into real-time human-computer interfaces and investigate whether the behavior of real participants is modulated in a qualitatively similarly manner when interacting with model-controlled versus other-participant-controlled task stimuli. By developing a detailed strategy for modeling the dynamics of social action tasks, the proposed project will have a transformative impact on the fields which study social coordination such as cognitive science, social and clinical psychology and robotics by providing researchers with empirical modeling strategies for better apprehending the self-organizing dynamics of goal-directed social activity.
Dynamical modeling is a powerful way of understanding the dynamics and self-organization of social motor coordination, and the models and modeling strategy that will be developed through the course of the project will provide a deep and rigorous understanding of how coordinated patterns of social activity emerge from the functional relationships and interactions that exist between agents, environmental objects, and events. Stable and effective social motor coordination increases rapport and cooperation between individuals, reduces perceived social differences and prejudice, is intrinsically important to how we share time with others, and has been shown to break down in pathologies such as premature birth, autism, and schizophrenia. Given its broad ranging impact, modeling the processes by which social action and multi-agent movement coordination evolve, and identifying the constraints on its formation, will both integrate knowledge across a wide range of disciplines (e.g., cognitive science, perception-action psychology, social psychology, clinical psychology, movement science, human-computer and robot interaction, and complex systems) and broaden our understanding of social motor coordination's role in social cognition, social pathologies, and in creating behaviorally flexible and mutually responsive artificial human-machine systems.
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