“Cognitive computer vision is concerned with integration and control of vision systems using explicit but not necessarily symbolic models of context, situation and goaldirected behaviour” (Vernon 2003 [473]). This paper discusses one small but critical slice of a cognitive computer vision system, that of visual attention. The presentation begins with a brief discussion on a definition for attention followed by an enumeration of the different ways in which attention should play a role in computer vision and cognitive vision systems in particular. The Selective Tuning Model is then overviewed with an emphasis on its components that are most relevant for cognitive vision, namely the winner-take-all processing, the use of distributed saliency and feature binding as a link to recognition.