EAGER: Humans, Teams, and Technologies: Human-Team and Team-Machine Interaction in Emergency Response Grant uri icon


  • A critical challenge to emergency response in critical infrastructures is understanding how decisions are made by the incident management team (IMT), which serves as the nerve center guiding the overall response effort. At the core of decision-making in such large and dynamic teams is team cognition, which in emergency response can be viewed as the responders'' ability to form a "common operating picture." The goal of this EArly-concept Grant for Exploratory Research (EAGER) project is to examine the relationship of cognition to individual and team performance in IMTs. While it is difficult to study actual IMTs at work, high-fidelity simulation facilities place responders in exercises that replicate real-world emergency events. This research leverages one such facility, the Emergency Operations Training Center (EOTC) operated by the Texas A&M Engineering Extension Service (TEEX), a world leader in emergency responder training, to study IMTs without the need to collect data during actual incidents (which would be both impractical and undesirable given the urgency of real events). Based on observations and recordings, the researchers will conduct a qualitative analysis of team communication and build social networks representing interactions among team members, which collectively form cognition in the team, according to recent findings. The researchers will break the team''s work into its constituent elements and examine how each element makes use of key technologies. Because the work involves performance-oriented interventions, it will lead to improved responder training and thus will contribute to reduced loss of life and property during catastrophic events. The EOTC will be able to use research findings to guide redesign of software and training tools. As leaders in emergency management, EOTC instructors and staff are in regular communication with policymakers, enabling them to translate research outcomes into actionable information to improve the U.S. National Preparedness Guidelines, the National Incident Management System (NIMS), and state and local equivalents. This research seeks to characterize the influence of interactions among humans, teams, and technologies on individual and team performance in high-pressure, time-constrained environments such as emergency response. In these situations, the IMT supports first responders from a centralized incident command post (ICP). The IMT solves complex safety-critical problems under high time pressure and ubiquitous information flow, so the team''s ability to receive, process, and share information is essential to successful outcomes. Building on the view that team cognition is inseparable into individual cognition, the research focuses on the team as a singular entity, termed the "aggregate team artifact." Unlike traditional human-machine interaction (HMI) studies, this research examines two new complementary concepts: human-team interaction (HTI) and team-machine interaction (TMI). While HTI focuses on individuals'' interactions with the aggregate team artifact, TMI applies HMI methods to the team entity''s interactions with tools and technologies. The researchers will conduct a focused study to explore and validate HTI and TMI. During exercises involving actual emergency responders in high-fidelity ICP simulations, the work of the Planning subteam will be recorded and transcribed. Beginning with grounded theory analysis of transcripts, the researchers will categorize HTI and TMI content, build team communication networks to devise metrics of HTI, adapt task analysis techniques to assess TMI, and relate metrics of HTI and TMI to individual and team performance. This work will make fundamental contributions to human factors (HTI and TMI to complement HMI), social and organizational psychology (team cognition in real-world high-pressure settings), and systems engineering (characterization of teams as complex systems). By providing a proof-of-concept for HTI and TMI, this exploratory study will serve as an initial step toward the creation of an integrated predictive model for emergency response.

date/time interval

  • 2016 - 2019