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DTSTART:19700308T020000
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DTSTART:19701101T020000
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DTSTAMP:20251016T135126Z
LOCATION:Grand  E/F
DTSTART;TZID=America/Chicago:20251016T163000
DTEND;TZID=America/Chicago:20251016T165000
UID:HFESAM_ASPIRE 2025_sess229_LECT509@linklings.com
SUMMARY:Measuring Multidimensional Team Adaptation
DESCRIPTION:Matthew Peel, Jayci Landfair, Polemnia Amazeen, and Nancy Cook
 e (Arizona State University)\n\nRecognizing their adaptive nature, recent 
 research has shifted toward viewing teams as dynamical systems, in which t
 eam states emerge through ongoing interactions rather than being predefine
 d at the individual level (Gorman et al., 2010; Gorman et al., 2017; Grimm
  et al., 2023; Ramos-Villagrasa et al., 2017). Most studies, however, focu
 s on single measurement modalities, limiting insights into system-wide ada
 ptation. This study addresses this gap by using Collective Systems Adaptat
 ion (CSA) analysis, a multivariate time-series method designed to detect s
 ynchronized changes across multiple dimensions of team interaction. We app
 lied CSA to three complementary measures of team interaction: Communicatio
 n Flow (information distribution), Geospatial Coordination (spatial organi
 zation), and Workload Synchrony (pupillometry-based measure of cognitive s
 train). These dimensions were integrated to examine how adaptive processes
  emerge across multiple measures of team interaction. Teams were observed 
 during high-fidelity simulated combat missions, and adaptation events iden
 tified through CSA were used to predict combat effectiveness. Our findings
  show that multidimensional measures of adaptation accounted for 11% to 55
 % of the variability in performance outcomes, supporting the value of mult
 i-modal analysis for understanding team dynamics. These results demonstrat
 e the potential for real-time assessment of team adaptation, with applicat
 ions for training, monitoring, and decision support in complex domains.\n\
 nTrack: Human Performance Modeling\n\nSession Chairs: Andrew Abbate (Pacif
 ic Science and Engineering Group) and Yaohan Ding (University of Pittsburg
 h)\n\n
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