Learning cognitive load models for developing team shared mental models

Xiaocong Fan, Po Chun Chen, John Yen

Research output: Contribution to conferencePaperpeer-review

1 Scopus citations

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. 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.

Original languageEnglish (US)
Pages145-150
Number of pages6
StatePublished - 2007
Event8th International Conference on Cognitive Modeling, ICCM 2007 - Ann Arbor, United States
Duration: Jul 26 2007Jul 29 2007

Conference

Conference8th International Conference on Cognitive Modeling, ICCM 2007
CountryUnited States
CityAnn Arbor
Period7/26/077/29/07

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Modeling and Simulation

Fingerprint Dive into the research topics of 'Learning cognitive load models for developing team shared mental models'. Together they form a unique fingerprint.

Cite this