De¯ned as attentive process in presence of visual sequences, dynamic visual attention responds to static and motion features as well. For a computer model, a straightforward way to integrate these features is to combine all features in a competitive scheme: the saliency map contains a contribution of each feature, static and motion. Another way of integration is to combine the features in a motion priority scheme: in presence of motion, the saliency map is computed as the motion map, and in absence of motion, as the static map. In this paper, four models are considered: two models based on a competitive scheme and two models based on a motion priority scheme. The models are evaluated experimen- tally by comparing them with respect to the eye movement patterns of human subjects, while viewing a set of video sequences. Qualitative and quantitative evaluations, performed in the context of simple synthetic video sequences, show the highest performance of the motion priority scheme, compared to the competitive scheme.