A model for aspects of visual attention based on the concept of selective tuning is presented. It provides for a solution to the problems of selection in an image, information routing through the visual processing hierarchy and task-specific attentional bias. The central thesis is that attention acts to optimize the search procedure inherent in a solution to vision. It does so by selectively tuning the visual processing network which is accomplished by a top-down hierarchy of winner-take-all processes embedded within the visual processing pyramid. Comparisons to other major computational models of attention and to the relevant neurobiology are included in detail throughout the paper. The model has been implemented; several examples of its performance are shown. This model is a hypothesis for primate visual attention, but it also outperforms existing computational solutions for attention in machine vision and is highly appropriate to solving the problem in a robot vision system.