Robots often incorporate computational models of visual attention to streamline processing. Even though the number of visual attention systems employed on robots has increased dramatically in recent years, the evaluation of these systems has remained primarily qualitative and subjective. We introduce quantitative methods for evaluating computational models of visual attention by direct comparison with gaze trajectories acquired from humans. In particular, we focus on the need for metrics based not on distances within the image plane, but that instead operate at the level of underlying features. We present a framework, based on dimensionality-reduction over the features of human gaze trajectories, that can simultaneously be used for both optimizing a particular computational model of visual attention and for evaluating its performance in terms of similarity to human behavior. We use this framework to evaluate the Itti et al. (1998) model of visual attention, a computational model that serves as the basis for many robotic visual attention systems.