RALPH » Controlling Attention With Noise: The Cue-Combination Model of Visual Research
Presentation date: March 15th, 2006
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 the top-down control of attention, which allows for processing resources to be directed to task-relevant regions and objects in the visual field. How is attention directed based on an individual's goals? To what sort of features of the visual environment can attention be directed? These two questions are central to the study of how humans interact with their environment.
With a burgeoning experimental literature, models of visual search have been proposed to explain data within a mechanistic framework. Perhaps the most influential and thoroughly developed model is Guided Search 2.0 (Wolfe, 1994) - a model that has been used as a theoretical framework for explaining visual search data for over a decade.
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 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.