Coadaptation Principles that Govern Emergent Behavior During Human-Agent and Human-Human Interactions

Humans have a remarkable ability to seamlessly interact with synthetic agents (e.g., control brain machine interfaces, active prosthetics, and assistive exoskeletons) and other humans. Despite its ubiquity and importance, the mechanisms of co-adaptation that govern and lead to the emergence of these seamless interactions remain unknown. Understanding how humans interact with other adaptive entities is highly relevant across many facets of society, such as medical rehabilitation, human and artificial intelligence interactions, military, and economics.

This award supports research towards the development of improved synthetic agents that co-adapt alongside humans, with the potential to enable development of more effective biologically-inspired robot-guided neurorehabilitation systems that can seamlessly interact with humans.

The investigators leverage a state-space approach that has been successful in explaining how an individual learns, but has not been utilized to capture co-adaptation. Humans will be immersed in a state-of-the-art virtual reality and robotics suite that allows individuals to sense their synthetic agent partner or human partner during a learning task.

In addition to healthy participants, the investigators also consider a stroke population to find the balance between assisting and resisting when attempting to improve long-term overall performance (human plus agent) or individual performance (human only). The blend of computational modeling and experimental work involving both healthy and clinical populations will allow for better understanding of the mechanisms that underpin emergent interactive behavior.

The investigators also have a strong commitment to diversity, equity, and inclusion, and plan to include undergraduates and high school students in the research.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.