Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Autism Spectrum Disorder

Autism Spectrum Disorder

Autism spectrum disorder (ASD) is a neurodevelopmental condition marked by persistent deficits in social communication and interaction alongside restrictive and repetitive behaviors or interests. ASD is sometimes accompanied by intellectual impairment.
These core symptoms manifest differently among individuals, ranging from mild to severe. The disorder's complexity extends beyond its clinical presentation, encompassing a diverse range of biological, cognitive, and sociocultural influences.

You might also read

Related Articles

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

Sort by

Same author

Kinematic signatures in reaching movements during spaceflight provide evidence that humans underestimate body mass in microgravity.

eLife·2026

Same author

Perceptual prediction error supports implicit process in motor learning.

PLoS computational biology·2026

Same author

Cognitive load suppresses explicit learning while sparing implicit learning in visuomotor adaptation.

Journal of neurophysiology·2025

Same author

Mirizzi syndrome masquerading as gallbladder carcinoma: a case report on diagnostic challenges and multidisciplinary management.

Frontiers in medicine·2025

Same author

Metabolic control of glycosylation forms for establishing glycan-dependent protein interaction networks.

Proceedings of the National Academy of Sciences of the United States of America·2025

Same author

Dual-Luciferase Reporter Assay to Investigate Virus-Host Interactions.

Methods in molecular biology (Clifton, N.J.)·2025

Search research articles

Related Experiment Video

Updated: Nov 9, 2025

Eye Tracking Young Children with Autism

Eye Tracking Young Children with Autism

Published on: March 27, 2012

Automatic classification of children with autism spectrum disorder by using a computerized visual-orienting task.

Qiao He¹, Qiandong Wang², Yaxue Wu^3,4

¹Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China.

|April 13, 2021

Summary

Machine learning algorithms analyzing eye-tracking data can help screen for autism spectrum disorder (ASD). This approach accurately differentiates children with ASD from typically developing peers, aiding early diagnosis.

Keywords:

autism diagnosis autism screening autism spectrum disorder automatic classification machine learning visual orienting

More Related Videos

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

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

Published on: December 7, 2018

Using the Visual World Paradigm to Study Sentence Comprehension in Mandarin-Speaking Children with Autism

Using the Visual World Paradigm to Study Sentence Comprehension in Mandarin-Speaking Children with Autism

Published on: October 3, 2018

Related Experiment Videos

Last Updated: Nov 9, 2025

Eye Tracking Young Children with Autism

Eye Tracking Young Children with Autism

Published on: March 27, 2012

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

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

Published on: December 7, 2018

Using the Visual World Paradigm to Study Sentence Comprehension in Mandarin-Speaking Children with Autism

Using the Visual World Paradigm to Study Sentence Comprehension in Mandarin-Speaking Children with Autism

Published on: October 3, 2018

Area of Science:

Neuroscience
Developmental Psychology
Computer Science

Background:

Early diagnosis of autism spectrum disorder (ASD) relies on behavioral observation.
Machine learning and sensor data can enhance clinical decision-making for ASD screening.

Purpose of the Study:

To develop and validate a machine learning model using eye-tracking data for classifying children with ASD (high-functioning and low-functioning) and typically developing children.
To investigate the utility of gaze-related and non-gaze-related directional cues in differentiating these groups.

Main Methods:

A computerized visual-orienting task was administered to children.
Eye-movement data was collected using an eye tracker during the task.
Machine learning algorithms were applied to classify high-functioning ASD (HFA), low-functioning ASD (LFA), and typically developing (TD) children.

Main Results:

TD children showed higher reward success rates than HFA children, who in turn had higher rates than LFA children.
The machine learning model achieved 81.1% accuracy in classifying the three groups using raw eye-tracking data.
Including both gaze and non-gaze cue data improved classification accuracy compared to using either alone.

Conclusions:

Eye-tracking data combined with machine learning shows promise for aiding the early screening of ASD.
Visual-orienting deficits are present in children with ASD, and both social and non-social cues provide valuable diagnostic information.
This approach offers a data-driven method to complement traditional behavioral assessments for ASD.