When analyzing a visualization, the user must often find or compare important objects. This analysis suffers from a fundamental problem: data sets are becoming larger and larger, leading to more visual clutter. This makes it very hard to find the objects the user is interested in. Part of this problem originates in the human visual system, which is limited through the bandwith of visual light, visual resolution, and the processing capabilities of the human mind. In this thesis, three methods are shown that adapt to these limitations,and use them to the advantage. The first method targets people with color vision deficiency (CVD), such as red-green blindness. People with CVD have difficulty discerning colors. The aim of this method is to adapt a color map to the individual and maximize the use of their personal color space. The second method offers a dynamic use of the color space for large hierarchical data. During interactive exploration of the data, the color mapping adapts on-the-fly to the current view position. We make use of "inattentional blindness'' -i.e., not noticing changes that are not focused on- in order to make the change in color very subtle. The third method uses flicker in order to subtly draw attention to parts of a scene. We use the fact that the "critical fusion frequency''-the frequency at which flickering becomes a stable signal-varies across the retina. Using a high frequency monitor and empirical measurements, we created a method that can draw attention to objects and can only be seen in the peripheral vision, but not in the foveal vision.