Controlling Attention with Noise: The Cue-Combination Model of Visual Search
David Baldwin, Michael C. Mozer · eScholarship (California Digital Library) · 2006
Visual search is a ubiquitous human activity.Individuals can perform a remarkable range of tasks involving search for a target object in a cluttered environment 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 heuristic 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 reliability as cues, which in turn affects their ability to draw attention.We show that our Cue-Combination Guided Search model obtains results commensurate with Wolfe's Guided Search.Through its leverage of probabilistic formulations of optimal cue combination, the model achieves a degree of mathematical elegance and parsimony, and makes a novel claim concerning the computational role of noise in attentional control.