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DTSTAMP:20261012T210903Z
LOCATION:Summit Pavilion
DTSTART;TZID=America/Los_Angeles:20261021T173000
DTEND;TZID=America/Los_Angeles:20261021T183000
UID:HFESAM_ASPIRE 2026_sess257_POST128@linklings.com
SUMMARY:An Exploration of Statistically Derived Individual Differences in 
 Collaborative Combat Aircraft Operators
DESCRIPTION:Jade Driggs, Megan Morris, and Christopher Myers (U.S. Air For
 ce Research Laboratory) and Margaret Ugolini (BAE Systems Inc.)\n\nIndivid
 ual differences play a prominent role in shaping human responses to task d
 emands. Individual differences span any variable that differs across peopl
 e – ranging from decision style to cognitive abilities and personality. Wi
 thin the literature, individual differences have also been found to influe
 nce trust in automation. Given that humans are continually leveraging auto
 mation to address task demands (i.e., reduce cognitive load) in operationa
 l settings, understanding how individual differences influence automation 
 management is a ripe area for research. The role of individual differences
  in operational and military settings is well-documented, with the militar
 y endeavoring to optimally assign personnel to various job functions. As t
 he United States Air Force (USAF) gradually transitions to operations posi
 tioning a singular operator overseeing a team of autonomous collaborative 
 platforms (such as Collaborative Combat Aircraft; CCAs), understanding the
  role of individual differences in this new human-machine teaming context 
 becomes increasingly important. In the current study, statistically derive
 d individual differences in performance are examined against operators’ ba
 ckgrounds via mixed-effects models. Specifically, we sought to address the
  following research question (RQ): What do model-estimated individual diff
 erences in performance reveal about potential distinctions between operato
 r backgrounds (e.g., fighter pilots and weapons systems officers; WSOs)?\n
 \n
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