An inhibitory beam for attentional selection
Proceedings of the 1991 York conference on Spacial vision in humans and robots
Modeling visual attention via selective tuning
Artificial Intelligence - Special volume on computer vision
Vision: A Computational Investigation into the Human Representation and Processing of Visual Information
Computer Vision and Image Understanding - Special issue: Attention and performance in computer vision
Feature conjunctions in visual search
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
Selective tuning: feature binding through selective attention
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
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Many think attention needs an executive to allocate resources. Although the cortex exhibits substantial plasticity, dynamic allocation of neurons seems outside its capability. Suppose instead that the processing structure is fixed, but can be 'tuned' to task needs. The only resource that can be allocated is time. How can this fixed structure be used over periods of time longer than one feed-forward pass? Can the Selective Tuning model provide the answer? This short paper has one goal, that of explaining a single figure (Fig.1), that puts forward the proposal that by using multiple passes of the visual processing hierarchy, both bottom-up and top-down, and using task information to tune the processing prior to each pass, we can explain the different recognition behaviors that human vision exhibits. To accomplish this, four different kinds of binding processes are introduced and are tied directly to specific recognition tasks and their time course.