Visual search is an important aspect of many examination and monitoring tasks. As a result, visual search performance has been the topic of many empirical investigations. These investigations have reported that individual search performance depends on subject factors such as search behavior, which has motivated the development of models of visual search that incorporate this behavior. Search behavior ranges from random to strictly systematic; variation in behavior is commonly assumed to be caused by differences in memory retrieval and search strategy. Accordingly, a mathematical model of semi-systematic search is proposed here that can account for both memory retrieval and other performance shaping factors.

In visual search task, search can stop after successfully finding a defect or it can stop randomly without finding a defect. This is also semi-systematic search, but its stopping condition is also influenced by a stopping distribution along with the search time distribution. Therefore, next model is introduced to measure performance for this type of search task. Because goal of a search task can be to find a single target or multiple targets, models have also developed to describe search task with multiple target detection.

All the above three models assume search tasks on homogeneous search field whereas next two models explain searches on heterogeneous search field. This type of search field is built with more than homogeneous search region. Here also the searches within a region are semi-systematic. Fourth model describes search with fixed time limit for each region whereas last model illustrates search with random stopping for an individual region.

All four models yield both performance and process measures that include accuracy, time to perception, task time and coverage, while avoiding the statistical difficulties inherent to simulations. The random stopping model in homogeneous search field, second model, can also calculate stopping time without a success. Lastly, all proposed models have the capability not only to represent the search behavior of individuals, but to support its assessment as well.