A target attractiveness model describes field-of-view search as a two-step process. The observer first decides to move to some location based on its attraction and size. These attractive points are further divided by a second decision, which determines whether an observer will perform a detailed examination of the area or will choose a new attractive point. Simple metrics based on preattentive vision processes such as a probability of edge (POE) metric or the peak signal are used to represent the attractiveness. The model is tested on human performance experiments performed by the Night Vision and Electronic Sensors Directorate, which provided eyetracker data for a series of static field-of- view images. The model predicts the statistical properties of the eye- tracker points associated with the saccades, attractive points, and the examinations. The model predicts a clear statistical distinction between these points based on the distribution of the attractive metric in the image. In the simplest case considered, the saccade points are shown to be random with respect to the image (distribution of attractiveness equivalent to whole image) while the attractive points and examinations have distributions weighted by the first and second power of the attractiveness, respectively.