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

Saccadic selectivity in complex visual search displays.

Marc Pomplun1

  • 1Department of Computer Science, University of Massachusetts at Boston, 02125-3393, USA. marc@cs.umb.edu

Vision Research
|February 1, 2006
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

OMAMA-DB: the Oregon-Massachusetts Mammography Database.

Journal of medical imaging (Bellingham, Wash.)·2026
Same author

Deep Learning-Based Visual Complexity Analysis of Electroencephalography Time-Frequency Images: Can It Localize the Epileptogenic Zone in the Brain?

Algorithms·2024
Same author

Meningioma brain tumor detection and classification using hybrid CNN method and RIDGELET transform.

Scientific reports·2023
Same author

Lung_PAYNet: a pyramidal attention based deep learning network for lung nodule segmentation.

Scientific reports·2022
Same author

Mood-Driven Colorization of Virtual Indoor Scenes.

IEEE transactions on visualization and computer graphics·2022
Same author

Eye movement control during visual pursuit in Parkinson's disease.

PeerJ·2018
Same journal

Complex interactions between lightness, chroma, and hue in color ensemble perception.

Vision research·2026
Same journal

Driving with autism spectrum disorder: Exploring the impact of tactile hazard warnings on gaze behavior and hazard responses.

Vision research·2026
Same journal

Early visual processing in adults with ADHD: evidence from contrast sensitivity, spatial integration, and external noise.

Vision research·2026
Same journal

Pupil reflexes generate the peripheral drift illusion due to ON/OFF motion responses.

Vision research·2026
Same journal

Perceived direction of glass patterns can flip by 90°: A neural model.

Vision research·2026
Same journal

Effects of color-enhancing filters on color salience in normal trichromats.

Vision research·2026
See all related articles

This study reveals that low-level visual features guide attention even in complex scenes, challenging purely stimulus-driven models. Understanding visual search in natural settings is key to explaining attentional mechanisms.

Area of Science:

  • Cognitive Psychology
  • Neuroscience
  • Visual Perception

Background:

  • Visual search is crucial for daily tasks and understanding attention.
  • Previous studies used artificial displays, limiting naturalistic insights.
  • Investigating attentional mechanisms requires more complex visual environments.

Purpose of the Study:

  • To examine task-driven (top-down) visual guidance by low-level features in complex search displays.
  • To analyze the properties of feature guidance under more naturalistic conditions.
  • To compare findings with stimulus-driven (bottom-up) attention models.

Main Methods:

  • Employed complex search displays and targets.
  • Analyzed saccadic eye movement selectivity for low-level features.

Related Experiment Videos

  • Investigated guidance by intensity, contrast, spatial frequency, and orientation.

    • Main Results:

    • Found significant top-down guidance by low-level features like intensity and contrast.
    • Quantified the magnitude and resolution of feature guidance across dimensions.
    • Detected feature-ratio effects, similar to distractor-ratio effects in simpler searches.

    Conclusions:

    • Low-level features provide substantial guidance in complex visual search.
    • Current bottom-up attention models are insufficient for explaining scene perception.
    • Task-driven guidance plays a critical role in naturalistic visual search.