title: Parametric modeling of visual search efficiency in real scenes.
abstract: How should the efficiency of searching for real objects in real scenes be measured? Traditionally, when searching for artificial targets, e.g., letters or rectangles, among distractors, efficiency is measured by a reaction time (RT) × Set Size function. However, it is not clear whether the set size of real scenes is as effective a parameter for measuring search efficiency as the set size of artificial scenes. The present study investigated search efficiency in real scenes based on a combination of low-level features, e.g., visible size and target-flanker separation factors, and high-level features, e.g., category effect and target template. Visible size refers to the pixel number of visible parts of an object in a scene, whereas separation is defined as the sum of the flank distances from a target to the nearest distractors. During the experiment, observers searched for targets in various urban scenes, using pictures as the target templates. The results indicated that the effect of the set size in real scenes decreased according to the variances of other factors, e.g., visible size and separation. Increasing visible size and separation factors increased search efficiency. Based on these results, an RT × Visible Size × Separation function was proposed. These results suggest that the proposed function is a practicable predictor of search efficiency in real scenes.
authors.affiliation: College of Information Engineering, Shenzhen University, Shenzhen, P.R. China; Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen, P.R. China; Key Laboratory for Geo-Environment Monitoring of Coastal Zone of the National Administration of Surveying, Mapping and GeoInformation, Shenzhen University, Shenzhen, P.R. China.
authors.forename: Xing
authors.initials: X
authors.lastname: Zhang
authors.affiliation: Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen, P.R. China; Key Laboratory for Geo-Environment Monitoring of Coastal Zone of the National Administration of Surveying, Mapping and GeoInformation, Shenzhen University, Shenzhen, P.R. China; State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan, P.R. China.
authors.forename: Qingquan
authors.initials: Q
authors.lastname: Li
authors.affiliation: School of Computer Science, Wuhan University, P.R. China.
authors.forename: Qin
authors.initials: Q
authors.lastname: Zou
authors.affiliation: State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan, P.R. China.
authors.forename: Zhixiang
authors.initials: Z
authors.lastname: Fang
authors.affiliation: Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen, P.R. China; State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan, P.R. China.
authors.forename: Baoding
authors.initials: B
authors.lastname: Zhou
mesh_terms: D000328:Adult; D001288:Attention / Q000502:physiology; D003463:Cues; D005133:Eye Movements / Q000502:physiology*; D005260:Female; D006801:Humans; D008297:Male; D010364:Pattern Recognition, Visual / Q000502:physiology*; D010775:Photic Stimulation / Q000379:methods; D011930:Reaction Time / Q000502:physiology; D014796:Visual Perception / Q000502:physiology*; D055815:Young Adult