Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Predicting visual search performance by quantifying stimuli similarities.

Tamar Avraham1, Yaffa Yeshurun, Michael Lindenbaum

  • 1Computer Science Department, Technion I.I.T., Haifa, Israel. tammya@cs.technion.ac.il

Journal of Vision
|May 20, 2008
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Examining the diametric model of autistic and psychotic traits through temporal perception.

Scientific reports·2026
Same author

Attentional modulation of size perception in peripheral vision.

Journal of vision·2026
Same author

Temporal dynamics of integration and individuation: Insights from temporal averaging and crowding.

Cognition·2025
Same author

Large-scale examination of the benefit and cost of spatial attention and their individual variability.

Cognition·2025
Same author

Attentional modulation of peripheral pointing hypometria in healthy participants: An insight into optic ataxia?

Neuropsychologia·2025
Same author

Dataset of polarimetric images of mechanically generated water surface waves coupled with surface elevation records by wave gauges linear array.

Data in brief·2025
Same journal

Analysis of human visual experience data.

Journal of vision·2026
Same journal

Pyramid-based Bayesian modeling for high-resolution behavioral analysis.

Journal of vision·2026
Same journal

Sensation without perception: The white whale effect and perceptual blindness in autonomous vehicles.

Journal of vision·2026
Same journal

Gaze behavior during closed-captioned movie viewing adapts to absent audio through more frequent switching between text and scene.

Journal of vision·2026
Same journal

In pursuit of saccade awareness: Limited volitional control and minimal conscious access to catch-up saccades during smooth pursuit eye movements.

Journal of vision·2026
Same journal

Dissociable effects of element-lifetime and stimulus-duration on local and global motion processing: An equivalent noise study.

Journal of vision·2026
See all related articles

This study enhances computer vision models for visual search tasks by incorporating internal noise to better predict human performance. The improved models demonstrated superior accuracy in predicting search behavior compared to existing quantitative models.

Area of Science:

  • Cognitive Psychology
  • Computer Vision
  • Human Performance Modeling

Background:

  • Previous computer vision models explored visual search but lacked human performance prediction capabilities.
  • Internal noise is a critical factor in human visual perception and search tasks.

Purpose of the Study:

  • To extend existing computer vision models of visual search by incorporating internal noise.
  • To evaluate the enhanced models' ability to predict human visual search performance accurately.

Main Methods:

  • Four experiments were conducted involving orientation and color visual search tasks.
  • Distractor homogeneity and target-distractor similarity were systematically manipulated.
  • Human search performance data were collected and compared against model predictions.

Related Experiment Videos

Main Results:

  • The enhanced models, accounting for internal noise, provided predictions closer to human performance.
  • The models showed improved accuracy in predicting search behavior across different visual search conditions.
  • Performance comparison indicated superiority over other prominent quantitative models.

Conclusions:

  • Incorporating internal noise significantly improves the predictive power of computer vision models for human visual search.
  • The enhanced models offer a more accurate quantitative account of visual search, bridging computer vision and cognitive psychology.