The FeatureGate model is designed to account for the results from a number of studies in visual attention, including parallel feature searches and serial conjunction searches, variations in search slope with variations in feature contrast and individual subject differences, attentional gradients triggered by cueing, feature-driven spatial selection, split attention, inhibition of distractor locations, and flanking inhibition. The model is implemented in a neural network consisting of a hierarchy of spatial maps. Attentional gates control the flow of information from each level of the hierarchy to the next. The gates are jointly controlled by a bottom-up system favoring locations with unique features and a top-down system favoring locations with features designated as target features. The gating of each location depends on the features present there, hence the name FeatureGate.
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