Authors:
Bethany Bracken
1
;
Aaron Winder
1
;
Brandon Hager
1
;
Mica Endsley
2
and
Elena Festa
3
Affiliations:
1
Charles River Analytics, 625 Mount Auburn St., Cambridge, MA, 02138, U.S.A.
;
2
SA Technologies, LLC, 5301 S. Superstition Mountain Drive, Suite 104377, Gold Canyon, AZ, 85118 U.S.A.
;
3
Brown University, 190 Thayer St, Providence, RI 02912, U.S.A.
Keyword(s):
Cognitive Workload, Situation Awareness (SA), Training.
Abstract:
To operate effectively across a variety of environments, personnel (e.g., air traffic controllers, pilots, truck
drivers, emergency response crews) need to be trained to the point at which their responses are automatic. If
their responses require high mental effort when carried out in emergency situations, they may be unable to
perform or to establish situation awareness (SA) needed to perform and to keep themselves safe. We have
been developing a software application to assess cognitive workload (i.e., mental effort) during task
performance using functional near-infrared spectroscopy (fNIRS). Here we present our work toward
extending this human state assessment software to include SA. We used a driving task (Crundall & Kroll,
2018; Muela et al., 2021) in which participants saw a clip of someone driving from a first person perspective
followed by a Level 3 SA (prediction) question asking what hazard was about to occur. Participants were 22
Brown University undergraduate and
medical students (8 females) with an average age of 22.2 (SD=4.7) and
22 Army personnel in one of the U.S. Army installations with an average age of 49 (SD=11). We were able
to predict performance on the SA questions using the fNIRS data, at the group level (mean accuracy = 65%
in Brown students, 71% in Army personnel, and 65% in the combined datasets). We were also able to predict
SA performance of individual participants with a mean accuracy of 69% (range = .45-.88). This adds to the
growing literature indicating that neurophysiological information, even when data is acquired at a single
location, is useful for predicting individual SA.
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