A two module computational model of situation awareness is presented. One module, characterizing stage 1 (noticing) SA is based on the SEEV model of selective attention in complex environments, and consists of components of Salience (capturing attention), Effort (inhibiting attention movement), Expectancy (for events along a channel) and Value (of attending those events). These are combined additively, and accurately predict visual scanning on the flight deck and in driving. The second module characterizing stage 2 (understanding) SA, results from the integration of noticed information, and its decay if unattended. We describe briefly the application and validation of the attention module to pilot scanning of the synthetic vision system display suite in aviation, and in more detail, the application to predicting differences in situation awareness supported by three formats of a wake vortex display, designed to alert aircraft pilots to dangers in the flight path ahead.
The Efficacy of Using Synthetic Vision Terrain-Textured Images to Improve Pilot Situation Awareness
