Salient stimuli, such as color or motion contrasts, attract human attention, thus providing a fast heuristic for focusing limited neural resources on behaviorally relevant sensory inputs. Here we address the following questions: What types of saliency attract attention and how do they compare to each other during natural vision? We asked human participants to inspect scene-shuffled video clips, tracked their instantaneous eye-position, and quantified how well a battery of computational saliency models predicted overt attentional selections (saccades). Saliency effects were measured as a function of total viewing time, proximity to abrupt scene transitions (jump cuts), and inter-participant consistency. All saliency models predicted overall attentional selection well above chance, with dynamic models being equally predictive to each other, and up to 3.6 times more predictive than static models. The prediction accuracy of all dynamic models was twice higher than their average for saccad...