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 Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Exploratory LC-MS/MS-Based Proteomic and Lipidomic Profiling of Plasma Samples from Premature Coronary Artery Disease Patients: A Pilot Study in a South Asian Population.

International journal of molecular sciences·2026
Same author

GTAttn-XC: Physically constrained attention for nonlocal density functionals.

The Journal of chemical physics·2026
Same author

Multi-omics integration identifies macrophage senescence driven by the RUNX1-P53 axis as a key mechanism in diabetic foot ulcer.

Functional & integrative genomics·2026
Same author

A Non-Canonical Role for Hepatocyte MLKL in Promoting Mitochondrial Dysfunction and Senescence in the Aging Liver.

Aging cell·2026
Same author

Single-Cell Mass Spectrometry Imaging Using Desorption Electrospray Ionization Coupled To Orbitrap Mass Spectrometers.

Journal of visualized experiments : JoVE·2026
Same author

Development of an Immunoassay Platform Targeting β-1,3- and β-1,6-Glucans for Rapid Detection of Fungi.

Journal of fungi (Basel, Switzerland)·2026

Related Experiment Video

Updated: Nov 8, 2025

Investigating the Deployment of Visual Attention Before Accurate and Averaging Saccades via Eye Tracking and Assessment of Visual Sensitivity
06:46

Investigating the Deployment of Visual Attention Before Accurate and Averaging Saccades via Eye Tracking and Assessment of Visual Sensitivity

Published on: March 18, 2019

7.3K

COCO-Search18 fixation dataset for predicting goal-directed attention control.

Yupei Chen1, Zhibo Yang2, Seoyoung Ahn1

  • 1Department of Psychology, Stony Brook University, New York, USA.

Scientific Reports
|April 23, 2021
PubMed
Summary

Researchers developed COCO-Search18, a novel dataset for studying goal-directed attention. This dataset enables advanced deep learning models to predict human visual search behavior, improving attention control models.

More Related Videos

Gaze in Action: Head-mounted Eye Tracking of Children's Dynamic Visual Attention During Naturalistic Behavior
07:09

Gaze in Action: Head-mounted Eye Tracking of Children's Dynamic Visual Attention During Naturalistic Behavior

Published on: November 14, 2018

11.2K
Using Rapid Serial Visual Presentation to Measure Set-Specific Capture, a Consequence of Distraction While Multitasking
05:58

Using Rapid Serial Visual Presentation to Measure Set-Specific Capture, a Consequence of Distraction While Multitasking

Published on: August 29, 2018

9.1K

Related Experiment Videos

Last Updated: Nov 8, 2025

Investigating the Deployment of Visual Attention Before Accurate and Averaging Saccades via Eye Tracking and Assessment of Visual Sensitivity
06:46

Investigating the Deployment of Visual Attention Before Accurate and Averaging Saccades via Eye Tracking and Assessment of Visual Sensitivity

Published on: March 18, 2019

7.3K
Gaze in Action: Head-mounted Eye Tracking of Children's Dynamic Visual Attention During Naturalistic Behavior
07:09

Gaze in Action: Head-mounted Eye Tracking of Children's Dynamic Visual Attention During Naturalistic Behavior

Published on: November 14, 2018

11.2K
Using Rapid Serial Visual Presentation to Measure Set-Specific Capture, a Consequence of Distraction While Multitasking
05:58

Using Rapid Serial Visual Presentation to Measure Set-Specific Capture, a Consequence of Distraction While Multitasking

Published on: August 29, 2018

9.1K

Area of Science:

  • Cognitive Science
  • Computer Vision
  • Neuroscience

Background:

  • Attention control is a fundamental cognitive process.
  • Current models of attention control primarily use deep networks trained on free-viewing data to predict saliency.
  • Predicting goal-directed attention remains a challenge due to limitations in existing datasets.

Purpose of the Study:

  • Introduce COCO-Search18, the first large-scale, laboratory-quality dataset of goal-directed visual search behavior.
  • Enable the training of deep-network models for predicting attention control in goal-directed tasks.
  • Benchmark machine learning models on this new dataset.

Main Methods:

  • Collected eye-movement data from 10 participants searching for 18 object categories in 6,202 natural images.
  • Generated over 300,000 search fixations for the COCO-Search18 dataset.
  • Benchmarked three machine learning models (ResNet50 object detector, ResNet50 on fixation maps, inverse reinforcement learning) on the dataset, including experiments with foveated image transformations.

Main Results:

  • Developed the COCO-Search18 dataset, comprising extensive goal-directed eye-movement data.
  • Established a new state-of-the-art in predicting goal-directed search fixations using benchmarked models.
  • Demonstrated model performance on both natural and foveated images, highlighting biological constraints.

Conclusions:

  • COCO-Search18 is a valuable resource for advancing attention control research.
  • Future work with this dataset is expected to yield significant advancements in understanding and predicting human visual attention.
  • Potential applications include human-computer interaction and early identification of attention-related clinical disorders.