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Li Guo, Susan Courtney, Jason Fischer; Physical Properties Guide Visual Search for Real-world Objects. Journal of Vision 2017;17(10):82. doi: 10.1167/17.10.82.
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Finding a missing earring in a jewelry box can be a frustrating challenge, but it becomes easier if the earring differs in color, shape, or size from other items in the box. These visual attributes and many others help to guide our attention toward the items of interest. Does our knowledge of objects' physical properties – e.g., that the earring is hard, smooth, and dense – also guide our search? Would we be faster to locate the earring if it appeared among soft objects rather than hard ones? Here, we tested observers' ability to use their physical knowledge about everyday objects to guide their attention in visual search. We presented participants with search arrays comprising sixteen objects. The objects were rated on perceived hardness by a separate group of online participants, and in each search array a target object was paired either with 15 distractors of similar hardness (e.g., soft target among soft distractors) or 15 distractors of different hardness (e.g., soft target among hard distractors). Participants (n=24) were asked to find the target object among the distractors after viewing a word label of that target for 1s. They pressed a key after locating the target and then indicated the target location with a mouse click. We found that participants were faster to locate a target object when it appeared among distractors of different hardness (1.28s±0.05) vs. distractors of similar hardness (1.64s±0.08; t(23)=5.04; p< 0.001). Critically, this effect was intact after controlling for any influences of image luminance, contrast, color, shape, and semantic content. Our results indicate that observers can use their knowledge of objects' physical properties to guide their visual search toward likely targets. These findings point toward an important role of physical knowledge in guiding how we engage with visual scenes in daily life.
Meeting abstract presented at VSS 2017
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