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

Behavioral and kinematic outcomes of adaptive pauses in VR social cognition training for autistic children.

Frontiers in psychology·2026
Same author

Adaptive VR intervention on social-cognitive skills in children with ASD: a feasibility study.

International journal of clinical and health psychology : IJCHP·2026
Same author

[Autism detection based on computational analysis of parental language].

Medicina·2026
Same author

Detecting depression through speech and text from casual talks with fully automated virtual humans.

Artificial intelligence in medicine·2025
Same author

Integrating Low-Cost Eye-Trackers to Enhance Design Education: A Case Study in University Course.

Sensors (Basel, Switzerland)·2025
Same author

Enhancing Psychological Assessments With Open-Ended Questionnaires and Large Language Models: An ASD Case Study.

IEEE journal of biomedical and health informatics·2025

Related Experiment Video

Updated: Dec 5, 2025

Eye Tracking Young Children with Autism
09:03

Eye Tracking Young Children with Autism

Published on: March 27, 2012

46.2K

Recognizing Decision-Making Using Eye Movement: A Case Study With Children.

Juan-Carlos Rojas1, Javier Marín-Morales2, Jose Manuel Ausín Azofra2

  • 1Escuela de Arquitectura, Arte y Diseño, Tecnologico de Monterrey, Monterrey, Mexico.

Frontiers in Psychology
|October 19, 2020
PubMed
Summary
This summary is machine-generated.

Eye-tracking reveals children's preferences by analyzing visual attention. Fixation times accurately predict choices, offering insights into decision-making beyond self-reports.

Keywords:
childrendecision-makingeye movementsproductrecognizing

More Related Videos

Comparing Eye-tracking Data of Children with High-functioning ASD, Comorbid ADHD, and of a Control Watching Social Videos
05:32

Comparing Eye-tracking Data of Children with High-functioning ASD, Comorbid ADHD, and of a Control Watching Social Videos

Published on: December 7, 2018

9.4K
A Method to Quantify Visual Information Processing in Children Using Eye Tracking
09:47

A Method to Quantify Visual Information Processing in Children Using Eye Tracking

Published on: July 9, 2016

18.0K

Related Experiment Videos

Last Updated: Dec 5, 2025

Eye Tracking Young Children with Autism
09:03

Eye Tracking Young Children with Autism

Published on: March 27, 2012

46.2K
Comparing Eye-tracking Data of Children with High-functioning ASD, Comorbid ADHD, and of a Control Watching Social Videos
05:32

Comparing Eye-tracking Data of Children with High-functioning ASD, Comorbid ADHD, and of a Control Watching Social Videos

Published on: December 7, 2018

9.4K
A Method to Quantify Visual Information Processing in Children Using Eye Tracking
09:47

A Method to Quantify Visual Information Processing in Children Using Eye Tracking

Published on: July 9, 2016

18.0K

Area of Science:

  • Consumer Behavior Research
  • Cognitive Psychology
  • Human-Computer Interaction

Background:

  • Traditional methods like self-reports have limitations in understanding children's decision-making.
  • Visual attention is increasingly recognized as a key indicator of consumer preferences.
  • Eye-tracking offers a non-intrusive method to capture implicit consumer behavior.

Purpose of the Study:

  • To investigate the utility of eye-tracking for understanding consumer preferences in children aged 7-12.
  • To determine if visual attention patterns can predict children's choices between various stimuli (icons and toys).
  • To explore how stimulus design dimensions influence children's visual attention and preferences.

Main Methods:

  • An experiment involving 28 children (7-12 years) using an Alternative Forced-choice task.
  • Participants viewed sets of icons or toys and made preference-based selections.
  • Eye-tracking technology recorded visual attention, including fixation times and movement patterns.
  • A post-experiment Likert scale assessed liking/disliking of individual stimuli.

Main Results:

  • Fixation times during the final visits to stimuli predicted choices with 71.2% (icons) and 67.2% (toys) accuracy.
  • Stimulus design dimensions significantly influenced eye movement patterns and choice behavior.
  • Gender differences were observed in fixation times and response to specific design elements for both icons and toys.

Conclusions:

  • Eye-tracking serves as a valuable implicit tool for analyzing children's decision-making and preferences.
  • Visual attention metrics can predict choice likelihood, offering an alternative to subjective reporting.
  • This research provides empirical data on children's consumer behavior, highlighting the potential of eye-tracking in this domain.