Visual attention is the ability to dynamically restrict processing to a subset of the visual field. Researchers have long argued that such a mechanism is necessary to efficiently perform many intermediate level visual tasks. This paper describes VISIT, a novel neural network model of visual attention. The current system models the search for target objects in scenes contain(cid:173) ing multiple distractors. This is a natural task for people, it is studied extensively by psychologists, and it requires attention. The network's be(cid:173) havior closely matches the known psychophysical data on visual search and visual attention. VISIT also matches much of the physiological data on attention and provides a novel view of the functionality of a number of visual areas. This paper concentrates on the biological plausibility of the model and its relationship to the primary visual cortex, pulvinar, superior colliculus and posterior parietal areas.