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Monica S. Castelhano; Where in the world?: Explaining Scene Context Effects during Visual Search through Object-Scene Spatial Associations. Journal of Vision 2017;17(10):9. doi: https://doi.org/10.1167/17.10.9.
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© ARVO (1962-2015); The Authors (2016-present)
The spatial relationship between objects and scenes and its effects on visual search performance has been well-established. Here, we examine how object-scene spatial associations support scene context effects on eye movement guidance and search efficiency. We reframed two classic visual search paradigms (set size and sudden onset) according to the spatial association between the target object and scene. Using the recently proposed Surface Guidance Framework, we operationalize target-relevant and target-irrelevant regions. Scenes are divided into three regions (upper, mid, lower) that correspond with possible relevant surfaces (wall, countertop, floor). Target-relevant regions are defined according to the surface on which the target is likely to appear (e.g., painting, toaster, rug). In the first experiment, we explored how spatial associations affect search by manipulating search size in either target-relevant or target-irrelevant regions. We found that only set size increases in target-relevant regions adversely affected search performance. In the second experiment, we manipulated whether a suddenly-onsetting distractor object appeared in a target-relevant or target-irrelevant region. We found that fixations to the distractor were significantly more likely and search performance was negatively affected in the target-relevant condition. The Surface Guidance Framework allows for further exploration of how object-scene spatial associations can be used to quickly narrow processing to specific areas of the scene and largely ignore information in other areas. Viewing effects of scene context through the lens of target-relevancy allows us to develop new understanding of how the spatial associations between objects and scenes can affect performance.
Meeting abstract presented at VSS 2017
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