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?