OBJECTIVE: A model of semisystematic search was sought that could account for both memory retrieval and other performance-shaping factors. BACKGROUND: 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 participant 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. METHODS: This model ultimately took the form of a discrete-time nonstationary Markov process. RESULTS: It yields both performance and process measures that include accuracy, time to perception, task time, and coverage while avoiding the statistical difficulties inherent to simulations. In particular, it was seen that as the search behavior becomes more systematic, expected coverage and accuracy increase while expected task time decreases. CONCLUSION: In addition to explaining these outcomes and their interrelationships from a theoretical standpoint, the model can predict these outcomes in practice to a certain extent as it can create an envelope defined by best- and worst-case search performance. APPLICATION: The model also has the capability of supporting assessment. That is, it can be used to assess the effectiveness of an individual's search performance, and to provide possible explanations for this performance, through the use of one or more of the output measures.
OBJECTIVE: A model of semisystematic search was sought that could account for both memory retrieval and other performance-shaping factors. BACKGROUND: 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 participant 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. METHODS: This model ultimately took the form of a discrete-time nonstationary Markov process. RESULTS: It yields both performance and process measures that include accuracy, time to perception, task time, and coverage while avoiding the statistical difficulties inherent to simulations. In particular, it was seen that as the search behavior becomes more systematic, expected coverage and accuracy increase while expected task time decreases. CONCLUSION: In addition to explaining these outcomes and their interrelationships from a theoretical standpoint, the model can predict these outcomes in practice to a certain extent as it can create an envelope defined by best- and worst-case search performance. APPLICATION: The model also has the capability of supporting assessment. That is, it can be used to assess the effectiveness of an individual's search performance, and to provide possible explanations for this performance, through the use of one or more of the output measures.