  Within the set of the many complex factors driving gaze placement, the
properities of an image that are associated with fixations under free viewing
conditions have been studied extensively. There is a general impression that
the field is close to understanding this particular association. Here we frame
saliency models probabilistically as point processes, allowing the calculation
of log-likelihoods and bringing saliency evaluation into the domain of
information. We compared the information gain of state-of-the-art models to a
gold standard and find that only one third of the explainable spatial
information is captured. We additionally provide a principled method to show
where and how models fail to capture information in the fixations. Thus,
contrary to previous assertions, purely spatial saliency remains a significant
challenge.
