Modeling Cognitive Loads for Evolving Shared Mental Models in Human–Agent Collaboration

Recent research on human-centered teamwork highly demands the design of cognitive agents that can model and exploit human partners´ cognitive load to enhance team performance. In this paper, we focus on teams composed of human-agent pairs and develop a system called Shared Mental Models for all - SMMall. SMMall implements a hidden Markov model (HMM)-based cognitive load model for an agent to predict its human partner´s instantaneous cognitive load status. It also implements a user interface (UI) concept called shared belief map, which offers a synergic representation of team members´ information space and allows them to share beliefs. An experiment was conducted to evaluate the HMM-based load models. The results indicate that the HMM-based load models are effective in helping team members develop a shared mental model (SMM), and the benefit of load-based information sharing becomes more significant as communication capacity increases. It also suggests that multiparty communication plays an important role in forming/evolving team SMMs, and when a group of agents can be partitioned into subteams, splitting messages by their load status can be more effective for developing subteam SMMs.