Controlling attention with noise The cue-combination model of visual search.pdf

Controlling Attention With Noise: The Cue-Combination Model of Visual Search David F. Baldwin College of Computer Science Northeastern University dfb@ccs.neu.edu Michael C. Mozer Institute of Cognitive Science University of Colorado at Boulder mozer@colorado.edu Abstract Visual search is a ubiquitous human activity. Individ- uals can perform a remarkable range of tasks involv- ing search for a target object in a cluttered environ- ment with ease and efficiency. Wolfe (1994) proposed a model called Guided Search to explain how attention can be directed to locations containing task-relevant visual features. Despite its attractive qualities, the model is complex with many arbitrary assumptions, and heuris- tic mechanisms that have no formal justification. We propose a new variant of the Guided Search model that treats selection of task-relevant features for attentional guidance as a problem of cue combination: each visual feature serves as an unreliable cue to the location of the target, and cues from different features must be combined to direct attention to a target. Attentional control involves modulating the level of additive noise on individual feature maps, which affects their reliabil- ity as cues, which in turn affects their ability to draw attention. We show that our Cue-Combination Guided Search model obtains results commensurate withWolfe’s Guided Search. Through its leverage of probabilistic formulations of optimal cue combination, the model achieves a degree of mathematical elegance and parsi- mony, and makes a novel claim concerning the compu- tational role of noise in attentional control. Introduction Visual search is a ubiquitous human activity. We search for our keys on a cluttered desk, a familiar face in a crowd, an exit sign on the highway, our favorite brand of cereal at the supermarket, and so forth. That the human visual system can perform such a diverse variety of tasks is truly remarkable. The flexibility of the human visual system stems from t