FeatureGate model of visual selection

The FeatureGate model of visual selection was developed by Kyle Cave asan attempt to explain the results from a number of different studies in visual attention. The model follows a neural network consisting of a hierarchy of spatial maps.

Attentional gates control the flow of information at each level of the hierarchy. They 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 model is called FeatureGate because the gating of each location depends on the features present.

The model helps integrate a number of findings related to:
 * Visual search: both parallel feature searches and serial conjunction searches; variations in search slope with variations in feature contrast etc
 * Individual differences in attention performance,
 * Attentional gradients triggered by cuing,
 * feature-driven spatial selection,
 * split attention,
 * Object-based attention:both the inhibition of distractor locations, and flanking inhibition.

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Papers

 * Cave, K.R. (1999). The FeatureGate Model of Visual Selection. Psychological Research, 62, 182-194.

Papers

 * Google Scholar