Learning cognitive load models for developing team shared mental models

Cognitive studies indicate that members of a high performing team often develop shared mental models to predict others' needs and coordinate their behaviors. The concept of shared mental models is especially useful in the study of human-centered collaborative systems that require humans to team with autonomous agents in complex activities. We take the position that in a mixed human/agent team, agents empowered with cognitive load models of human team members can help humans develop better shared mental models. In this paper, we focus on the development of realistic cognitive load models. Cognitive experiments were conducted in team contexts to collect data about the observable secondary task performance of human participants. The data were used to train hidden Markov models (HMM) with varied number of hypothetical hidden states. The results indicate that the model spaces have a three-layer structure. Statistical analysis reveals some characteristics of top layer models, which can be used in guiding the selection of HMM-based cognitive load models.