Inference in the Wild: A Framework for Human Situation Assessment and a Case Study of Air Combat

Autor/inn/en	McAnally, Ken; Davey, Catherine; White, Daniel; Stimson, Murray; Mascaro, Steven; Korb, Kevin
Titel	Inference in the Wild: A Framework for Human Situation Assessment and a Case Study of Air Combat
Quelle	In: Cognitive Science, 42 (2018) 7, S.2181-2204 (24 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0364-0213
DOI	10.1111/cogs.12636
Schlagwörter	Bayesian Statistics; Models; Air Transportation; Flight Training; Military Training; Simulation; Weapons; Armed Forces; Foreign Countries; Australia + Suchen Sie Ihr Suchwort? Analogiemodell; Militärausbildung; Simulation program; Simulationsprogramm; Weapon; Waffe; Military; Militär; Ausland; Australien
Abstract	Situation awareness is a key construct in human factors and arises from a process of situation assessment (SA). SA comprises the perception of information, its integration with existing knowledge, the search for new information, and the prediction of the future state of the world, including the consequences of planned actions. Causal models implemented as Bayesian networks (BNs) are attractive for modeling all of these processes within a single, unified framework. We elicited declarative knowledge from two Royal Australian Air Force (RAAF) fighter pilots about the information sources used in the identification (ID) of airborne entities and the causal relationships between these sources. This knowledge was represented in a BN (the declarative model) that was evaluated against the performance of 19 RAAF fighter pilots in a low-fidelity simulation. Pilot behavior was well predicted by a simple associative model (the behavioral model) with only three attributes of ID. Search for information by pilots was largely compensatory and was near-optimal with respect to the behavioral model. The average revision of beliefs in response to evidence was close to Bayesian, but there was substantial variability. Together, these results demonstrate the value of BNs for modeling human SA. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01