Related Experiment Video
Updated: Apr 11, 2026
Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments
Published on: January 23, 2017
Parametric modeling of visual search efficiency in real scenes
Xing Zhang1, Qingquan Li2, Qin Zou3
1College 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.
Searching for real objects in complex scenes is best measured by reaction time (RT) and object visibility, not just set size. Increased visible size and separation improve search efficiency.
Area of Science:
- Visual perception
- Cognitive psychology
- Human-computer interaction
Background:
- Traditional search efficiency models use reaction time (RT) × Set Size for artificial targets.
- The effectiveness of set size for measuring search efficiency in real-world scenes is unclear.
- Real scenes involve complex low-level and high-level features influencing search.
Purpose of the Study:
- To investigate how to effectively measure search efficiency for real objects in real scenes.
- To determine the influence of low-level (visible size, separation) and high-level (category, template) features on search.
- To propose a new function for predicting search efficiency in complex visual environments.
Main Methods:
- Observers searched for targets in urban scenes using picture templates.
- Measured reaction time (RT) in relation to target-flanker separation and visible object size.
- Analyzed the impact of set size, visible size, separation, category, and target template on search performance.
Main Results:
- The influence of set size on search efficiency in real scenes was diminished by other factors.
- Increased visible size and target-flanker separation significantly improved search efficiency.
- A novel RT × Visible Size × Separation function was proposed based on empirical data.
Conclusions:
- Search efficiency in real scenes is better predicted by a function incorporating visible size and separation, rather than solely set size.
- The proposed RT × Visible Size × Separation function offers a practical approach to quantifying search performance in complex visual environments.
- Understanding these factors is crucial for designing effective visual search interfaces and systems.
