Models of eye movements of an observer searching for human targets are helpful in developing accurate models of target acquisition times and false positive detections. We develop a new model describing the distribution of gaze positions for an observer which includes both bottom-up (salience) and top-down (task dependent) factors. We validate the combined model against a bottom-up model from the literature and against the bottom up and top down parts alone using human performance data on stationary targets. The new model is shown to be significantly better. The new model requires a large amount of data about the terrain and target that is obtained directly from the 3D simulation through an automated process.
Representation of search and target acquisition (STA) in military models and simulations arguably abstracts the most critical aspects of combat. This research focuses on the search aspect of STA for the unaided human eye. It is intuitive that an individual's environmental characteristics and interpretation of the environment in the context of all comprehended information, commonly summarized as their situational awareness (SA), influences attention and search. Current simulation models use a primitive sweeping search method that devotes an unbiased amount of time to every area in an entity's field of regard and neglects the effects of SA. The goal of this research is to provide empirical results and recommend modeling approaches that improve the representation of unaided search in military models and simulations. The major contributions towards this goal include novel empirical results from two incremental eye-tracking experiments, analysis and modeling of the eye-tracking data to illustrate the effect of the environment and SA on search, and a recommended model for unaided search for high-fidelity combat simulation models. The results of this work support soldier search models driven by metrics that summarize the threat based on environmental characteristics and contextual information.
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