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Inference in the Wild: A Framework for Human Situation Assessment and a Case Study of Air Combat
Author(s) -
McAnally Ken,
Davey Catherine,
White Daniel,
Stimson Murray,
Mascaro Steven,
Korb Kevin
Publication year - 2018
Publication title -
cognitive science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.498
H-Index - 114
eISSN - 1551-6709
pISSN - 0364-0213
DOI - 10.1111/cogs.12636
Subject(s) - construct (python library) , computer science , inference , bayesian inference , causal inference , perception , bayesian network , process (computing) , causal reasoning , fidelity , identification (biology) , artificial intelligence , associative property , bayesian probability , machine learning , psychology , cognition , ecology , mathematics , pure mathematics , telecommunications , neuroscience , economics , econometrics , biology , programming language , operating system
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 ( BN s) 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 BN s for modeling human SA .