Eye movements (EMs) are an important aspect of human visual behavior. The temporal and space-variant nature of sampling a visual scene requires frequent attentional gaze shifts (saccades) to fixate onto different parts of an image. Fixations are often directed toward the most informative regions in the visual scene. We introduce a model and its simulation that can select such regions based on prior knowledge of similar scenes. Having representations of scenes as a probabilistic combination of regions with certain properties, it is possible to assess the likely contribution of each region in the successive recognition process. Using Bayesian conditional probabilities for each region given the scene category, the model can then predict the informative value of that region and initiate a spatial information-gathering algorithm analogous to an EM saccade to a new fixation.