In feature integration theory (FIT; A. Treisman & S. Sato, 1990), feature detection is driven by independent dimensional modules, and other searches are driven by a master map of locations that integrates dimensional information into salience signals. Although recent theoretical models have largely abandoned this distinction, some observed results are difficult to explain in its absence. The present study measured dimension-specific performance during detection and localization, tasks that require operation of dimensional modules and the master map, respectively. Results showed a dissociation between tasks in terms of both dimension-switching costs and cross-dimension attentional capture, reflecting a dimension-specific nature for detection tasks and a dimension-general nature for localization tasks. In a feature-discrimination task, results precluded an explanation based on response mode. These results are interpreted to support FIT's postulation that different mechanisms are involved in parallel and focal attention searches. This indicates that the FIT architecture should be adopted to explain the current results and that a variety of visual attention findings can be addressed within this framework.

Visual search is the act of looking for a predefined target among other objects. This task has been widely used as an experimental paradigm to study visual attention, and because of its influence has also become a subject of research itself. When used as a paradigm, visual search studies address questions including the nature, function, and limits of preattentive processing and focused attention. As a subject of research, visual search studies address the role of memory in search, the procedures involved in search, and factors that affect search performance. In this article, we review major theories of visual search, the ways in which preattentive information is used to guide attentional allocation, the role of memory, and the processes and decisions involved in its successful completion. We conclude by summarizing the current state of knowledge about visual search and highlight some unresolved issues. WIREs Cogn Sci 2013, 4:415-429. doi: 10.1002/wcs.1235 The authors have declared no conflicts of interest for this article. For further resources related to this article, please visit the WIREs website.

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A fundamental task for the visual system is to determine where to attend next. In general, attention is guided by visual saliency. Computational models suggest that saliency values are estimated through an iterative process in which each visual item suppresses each other item's saliency, especially for those with close proximity. To investigate this proposal, we tested the effect of two salient distractors on visual search for a size target. While fixing the target-to-distractor distance, we manipulated the distance between two distractors. If two salient distractors suppressed each other when they were close together, they should interfere with search less; this was exactly what we found. However, we observed such a distance effect only for distractors of the same dimension (e.g., both defined in color) but not for those of different dimensions (e.g., one defined in color and the other in shape), displaying specificity to a perceptual dimension. Therefore, we conclude that saliency in visual search is calculated through a surround suppression process that occurs at a dimension-specific level.