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

Focusing of Light in the Eye01:16

Focusing of Light in the Eye

Light rays enter the eye through the cornea, a transparent dome-shaped tissue that is the eye's outermost layer. The cornea bends or refracts, light rays traveling to the pupil. The shape of the cornea determines how much of the light is bent and whether the image will be focused correctly on the retina at the back of the eye. Once the light has passed through both refraction layers, it converges into a single focal point onto a small area. This is where photoreceptors start transforming...
Relative Motion Analysis using Rotating Axes01:25

Relative Motion Analysis using Rotating Axes

Consider a component AB undergoing a linear motion. Along with a linear motion, point B also rotates around point A. To comprehend this complex movement, position vectors for both points A and B are established using a stationary reference frame.
However, to express the relative position of point B relative to point A, an additional frame of reference, denoted as x'y', is necessary. This additional frame not only translates but also rotates relative to the fixed frame, making it instrumental in...

You might also read

Related Articles

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

Sort by
Same author

GazePlotter: An open-source solution for the automatic generation of scarf plots from eye-tracking data.

Behavior research methods·2026
Same author

Visual Strategies of Avoidantly Attached Individuals: Attachment Avoidance and Gaze Behavior in Deceptive Interactions.

Journal of eye movement research·2026
Same author

[Our Experience with Trabecular Metal Total Ankle System].

Acta chirurgiae orthopaedicae et traumatologiae Cechoslovaca·2026
Same author

Acidity Is an Excellent Marker of Infection in Hip and Knee Arthroplasty.

Journal of clinical medicine·2024
Same author

Retraction Note: Eye tracking: empirical foundations for a minimal reporting guideline.

Behavior research methods·2023
Same author

Compositional cubes: a new concept for multi-factorial compositions.

Statistical papers (Berlin, Germany)·2022

Related Experiment Video

Updated: May 24, 2026

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

Advancing eye movement analysis through compositional modeling: A new perspective on Yarbus' classic study.

Kamila Fačevicová1, Jaroslav Vymazal2, Stanislav Popelka3

  • 1Department of Mathematical Analysis and Applications of Mathematics, Palacký University Olomouc, 17. listopadu, 12, Olomouc, 779 00, Czech Republic. kamila.facevicova@upol.cz.

Behavior Research Methods
|May 22, 2026
PubMed
Summary

Compositional data analysis (CoDA) offers a robust framework for analyzing eye-tracking data, revealing how task demands influence visual attention allocation across Areas of Interest (AOIs). This method enhances understanding of eye movement patterns.

Keywords:
Areas of interestCompositional dataEye-trackingMultivariate analysis

More Related Videos

Methods to Explore the Influence of Top-down Visual Processes on Motor Behavior
09:49

Methods to Explore the Influence of Top-down Visual Processes on Motor Behavior

Published on: April 16, 2014

Eye Tracking During Visually Situated Language Comprehension: Flexibility and Limitations in Uncovering Visual Context Effects
07:36

Eye Tracking During Visually Situated Language Comprehension: Flexibility and Limitations in Uncovering Visual Context Effects

Published on: November 30, 2018

Related Experiment Videos

Last Updated: May 24, 2026

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

Methods to Explore the Influence of Top-down Visual Processes on Motor Behavior
09:49

Methods to Explore the Influence of Top-down Visual Processes on Motor Behavior

Published on: April 16, 2014

Eye Tracking During Visually Situated Language Comprehension: Flexibility and Limitations in Uncovering Visual Context Effects
07:36

Eye Tracking During Visually Situated Language Comprehension: Flexibility and Limitations in Uncovering Visual Context Effects

Published on: November 30, 2018

Area of Science:

  • Cognitive Psychology
  • Data Science
  • Neuroscience

Background:

  • Eye-tracking studies commonly use Areas of Interest (AOIs) to measure visual attention distribution.
  • Traditional analysis of AOI data may not fully capture the relative nature of attention allocation.
  • Compositional data analysis (CoDA) offers a mathematically sound approach for analyzing proportional data.

Purpose of the Study:

  • To demonstrate the application and utility of CoDA in analyzing Area of Interest (AOI)-based eye-tracking data.
  • To explore how task demands influence the relative distribution of visual attention.
  • To provide a tutorial introduction to CoDA for eye-tracking researchers.

Main Methods:

  • Utilized a large-scale replication of Yarbus's 'Unexpected Visitor' experiment with 144 participants.
  • Recorded eye movements using a high-precision eye-tracker while participants viewed a painting under seven different tasks.
  • Analyzed total fixation durations within seven AOIs using both classical absolute measures and CoDA log-ratio coordinates, employing descriptive and multivariate statistical methods.

Main Results:

  • CoDA methods successfully reproduced known patterns of visual attention, confirming that task demands significantly shape attention allocation across AOIs.
  • Multivariate analyses in log-ratio coordinates (PCA, clustering, MANOVA) revealed underlying structures in the compositional eye-tracking data.
  • Linear discriminant analysis demonstrated that tasks could be accurately inferred from eye-movement patterns.

Conclusions:

  • CoDA provides a powerful and principled framework for analyzing AOI-based eye-tracking data, especially when dealing with proportions or fixed total viewing time.
  • The compositional approach offers a complementary perspective to classical analyses, enhancing the understanding of visual attention dynamics.
  • This study validates CoDA's effectiveness in eye-tracking research and encourages its adoption for deeper insights into visual behavior.