Activity-Based Target Acquisition Methods for Use in Urban Environments

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Virginia Tech

Many military conflicts are fought in urban environments that subject the U.S. soldier to a number of challenges not otherwise found in traditional battle. In the urban environment, the soldier is subject to threatening attacks not only from the organized army but also from civilians who harbor hostility. U.S. enemies use the civilian crowd as an unconventional tactic to blend in and look like civilians, and in response to this growing trend, soldiers must detect and identify civilians as a threat or non-threat. To identify a civilian as a threat, soldiers must familiarize themselves with behavioral cues that implicate threatening individuals. This study elicited expert strategies regarding how to use nonverbal cues to detect a threat and evaluated the best medium for distinguishing a threat from a non-threat to develop a training guide of heuristics for training novices (i.e., soldiers) in the threat detection domain. Forty experts from the threat detection domain were interviewed to obtain strategies regarding how to use nonverbal cues to detect a threat (Phase 1). The use of nonverbal cues in context and learning from intuitive individuals in the domain stood out as strategies that would promote the efficient use of nonverbal cues in detecting a threat. A new group of 14 experts judged scenarios presented in two media (visual, written) (Phase 2). Expert detection accuracy rates of 61% for the visual medium and 56% for the written medium were not significantly different, F (1, 13) = .44, p = .52. For Phase 3 of the study, a training development guide of heuristics was developed and eight different experts in the threat detection domain subjectively rated the heuristics for their importance and relevance in training novices. Nine heuristics were included in the training guide, and overall, experts gave all heuristics consistently high ratings for importance and relevance. The results of this study can be used to improve accuracy rates in the threat detection domain and other populations: 1) the soldier, 2) the average U.S. citizen, and 3) employees of the Transportation Security Administration.

covert mischievous intentions, threat detection, urban environment, MOUT, judgment and decision making, nonverbal behavior