CHS: Small: Optimizing Human-Machine Performance via Neurofeedback and Adaptive Autonomy

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Our society is being fundamentally transformed by increased interaction between humans and autonomous artificial intelligence (AI) systems. However, the addition of autonomy to our lives will not be successful unless we understand how smart machines and humans should best interact and communicate. Human-machine communication today is almost entirely linguistic, using spoken language for systems such as Siri or Alexa, or typed text for chatbots. However, humans communicate extremely efficiently with each other byu sing much more than just words; for example by being sensitive to facial expression, gestures, gait, and intonation. In fact, great teams, whether sports teams or military combat teams, are excellent at predicting teammates behavior and state of mind. In this project, the investigators consider both basic science and technology questions with respect to how to communicate that cognitive and physiological state of a human that is co-operating with an autonomous AI. The project has very broad implications since it addresses fundamental questions related to the interactions between humans and smart machines.The project investigates the hypothesis that adaptive autonomy together with coordinated neurofeedback can be employed in the same system to optimize human-machine performance. Investigators will develop a framework and investigate the hypothesis within the context of boundary avoidance tasks, or BAT, which is a class of tasks in which task critical boundaries surround the optimal operating point of the control system. These tasks are particularly interesting when considering human control because they typically result in a positive feedback loop that systematically increases the arousal state of the human subject, resulting in increasingly poor task performance and ultimate task failure, consistent with the Yerkes-Dodson law. Our framework uses a brain-computer interface (BCI) to both engage autonomy as well as being a source for neurofeedback that can shift human subjects to their performance 'sweet-spot'. This project will advance the science and technology development of how human-machine systems can be optimally integrated, specifically when both 1) the machine has access to ongoing changes in human cognitive and physiological state during performance of the task and 2) the human is made aware of their own state via appropriate neurofeedback.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.