In natural vision, shifts in spatial attention are associated with shifts of gaze. Computational models of such overt attention typically use the concept of a saliency map: Normalized maps of center-surround differences are computed for individual stimulus features and added linearly to obtain the saliency map. Although the predictions of such models correlate with fixated locations better than chance, their mechanistic assumptions are less well investigated. Here, we tested one key assumption: Do the effects of different features add linearly or according to a max-type of interaction? We measured the eye position of observers viewing natural stimuli whose luminance contrast and/or color contrast (saturation) increased gradually toward one side. We found that these feature gradients biased fixations toward regions of high contrasts. When two contrast gradients (color and luminance) were superimposed, linear summation of their individual effects predicted their combined effect. This demonstrated that the interaction of color and luminance contrast with respect to human overt attention is--irrespective of the precise model--consistent with the assumption of linearity, but not with a max-type interaction of these features.