Computational modeling of spatial attention

This book chapter examines the role of spatial attention from a computational perspective. It is intended as an overview for cognitive scientists interested in computational modeling of attentional phenomena. Because the function of attention can be understood only in its relation to visual information processing, we model not only the attentional system itself, but also the process of object recognition. We begin by presenting a basic model of object recognition, showing that interference prevents the system from reliably processing multiple, complex stimuli, and then we show how a simple mechanism of attentional selection can reduce this interference. Our first goal is to present a model that is computationally adequate, that is, a model that has the computational power to perform the sort of visual information processing tasks that people do. We then turn to simulations showing that the model can account for diverse experimental data, including: the benefit of attentional precuing, the time course of attention shifts, the effect of spatial uncertainty, the effect of irrelevant stimuli, the relation of object-based and location-based selection, and visual search. We conclude with a discussion of basic questions about computation modeling, including: Why build computational models? What makes a model compelling? When is a model right or wrong? Should one opt for depth or breadth in model coverage?

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