Preattentive Segmentation of Figures from Ground in Visual Search

Serena Butcher, Aude Oliva, & Jeremy M. Wolfe

Purpose: Evidence suggests in visual search tasks for a target item among distractors attention is effectively guided to objects. However in many laboratory search tasks blank backgrounds are used, while in the real world, objects must be segmented from complex heterogeneous backgrounds. How does background composition and complexity effect search performance? Is each item in a search display extracted in series from the background, or does a single “preattentive” process separate all possible target items before search proceeds? If each search object must be separately extracted from the background, increasing background complexity should increase RT x set size slope because there will be an added cost for each item in the display. If all search items are separated in one “preattentive” step, mean RT should increase with background complexity, but search slope should not. Methods: In each experiment we kept the search task the same (target = T distractor = L), while changing the composition and complexity of the search backgrounds. Backgrounds ranged from homogenous textures composed of spatial frequencies varying in similarity to the target, to patterns composed of the same T and L junctions as the search stimuli, to realistic scenes. Results: We found an additive mean RT cost with more complex background producing greater costs. The complexity of the background did not effect search slopes unless the background it self was a texture of distractors. Conclusions: The results suggest an initial preattentive process that parses potential targets from other visual information in the display, so that attention can be guided to the set of task relevant objects.