Learning HMM-based cognitive load models for supporting human-agent teamwork

  • Authors:

  • Xiaocong Fan;Po-Chun Chen;John Yen

  • Affiliations:

  • School of Engineering, The Behrend College, The Pennsylvania State University, Erie, PA 16563, USA;Department of Computer Science and Engineering, The Pennsylvania State University, University Park, PA 16802, USA;College of Information Sciences and Technology, The Pennsylvania State University, University Park, PA 16802, USA

  • Venue:
  • Cognitive Systems Research
  • Year:
  • 2010

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Abstract

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 to enhance team performance. Inspired by human information processing system, we here propose a HMM-based load model for members of human-agent teams, and investigate the development of realistic cognitive load models. A cognitive experiment was 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 numbers of hypothetical hidden states. The result indicates that the model spaces have a three-layer structure. Statistical analysis also reveals some characteristics of the models at the top-layer. This study can be used in guiding the selection of HMM-based cognitive load models for agents in human-centered multi-agent systems.