Recently, there has been great interest among vision researchers in developing computational models that predict the distribution of saccadic endpoints in naturalistic scenes. In many of these studies, subjects are instructed to view scenes without any particular task in mind so that stimulus-driven (bottom-up) processes guide visual attention. However, whenever there is a search task, goal-driven (top-down) processes tend to dominate guidance, as indicated by attention being systematically biased toward image features that resemble those of the search target. In the present study, we devise a top-down model of visual attention during search in complex scenes based on similarity between the target and regions of the search scene. Similarity is defined for several feature dimensions such as orientation or spatial frequency using a histogram-matching technique. The amount of attentional guidance across visual feature dimensions is predicted by a previously introduced informativeness measure. We use eye-movement data gathered from participants’ search of a set of naturalistic scenes to evaluate the model. The model is found to predict the distribution of saccadic endpoints in search displays nearly as accurately as do other observers’ eye-movement data in the same displays.
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