CRII: CHS: RUI: Computational models of humans for studying and improving Human-AI interaction

Understanding the interactions between humans and systems utilizing artificial intelligence (AI) requires an understanding of how human physiological changes impact memory and other mental processes. To realize the beneficial societal outcomes on, for example, interactions between humans and intelligent robots, it is important to develop simulations to test a variety of situations where memory under arousal states will function. In this project, the investigator articulates a research plan to use a simulation of the human mind and body to understand effects of physiological arousal on human memory and cognition and the consequences for human interaction with AI agents. Human-subject studies will be used to introduce stimuli to induce human arousal by using selected stressors and collect physiological and behavioral data during tasks that require cooperation between humans and AI agents. Societal benefits include an architecture to simulate a variety of human-AI interactions under various levels of arousal and stress. This architecture can be used to explore new ways to co-team humans with AI agents and set expectations for positive and/or negative behaviors that might occur in such collaborations. Undergraduate students at Bucknell University will be heavily involved as research assistants on this project. The investigator plans to develop simulations and elicit arousal states in human operators performing collaborative tasks with agents enabled by artificial intelligence (AI) to understand human-AI interaction. The goal is to better understand, through simulation, how algorithms for intelligent agents can be advanced and expanded to respond to human variations in behavior and memory processes under different levels of arousal, including levels that mimic stress. The objectives include developing simulations to examine contexts and tasks; understanding how environmental stimuli affect interactions between humans and intelligent agents; and determining how to advance algorithms to optimize human-AI cooperation and avoid maladaptive interactions. The investigator has already extended Adaptive Control of Thought-Rational (ACT-R) architecture to account for physiological influences on declarative and procedural memory. Physio-cognitive agents will be developed based on Bayesian and reinforcement learning to acquire knowledge of their environment. Human-AI task simulations will be implemented in a virtual environment to understand how arousal mediates intelligent behavior and how interaction with external environments that include AI agents may change performance, including through maladaptive behavior. The project seeks to discover computationally-enabled processes and contexts to amplify human capabilities. The resulting revised models and open-source code will be made available to the public for further explorations in human-AI interaction. The university is an undergraduate institution allowing significant participation of undergraduates in 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.