[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ skip to main content
10.1145/3204493.3207423acmconferencesArticle/Chapter ViewAbstractPublication PagesetraConference Proceedingsconference-collections
abstract

Eye-tracking measures in audiovisual stimuli in infants at high genetic risk for ASD: challenging issues

Published: 14 June 2018 Publication History

Abstract

Individuals with autism spectrum disorder (ASD) have shown difficulties to integrate auditory and visual sensory modalities. Here we aim to explore if very young infants at genetic risk of ASD show atypicalities in this ability early in development. We registered visual attention of 4-month-old infants in a task using audiovisual natural stimuli (speaking faces). The complexity of this information and the attentional features of this population, among others, involves a great amount of challenges regarding data quality obtained with an eye-tracker. Here we discuss some of them and draw possible solutions.

References

[1]
Lorraine E. Bahrick, Robert Lickliter, and Robert Flom. 2004. Intersensory redundancy guides the development of selective attention, perception, and cognition in infancy. Current Directions in Psychological Science 13, 99--102 (2004), 1245--1260.
[2]
Jeff M. Bebko, Jonathan A. Weiss, Jenny L. Demark, and Pamela Gómez. 2006. Discrimination of temporal synchrony in intermodal events by children with autism and children with developmental disabilities without autism. Journal of Child Psychology and Psychiatry 47, 1 (2006), 88--98.
[3]
Andrew L. Bremner, David J. Lewkowicz, and Charles Spence. 2012a. Multisensory development. Oxford University Press: Oxford, England.
[4]
Andrew L. Bremner, David J. Lewkowicz, and Charles Spence. 2012b. Multisensory development. Oxford University Press: Oxford, England.
[5]
Anne Hillairet de Boisferon, Amy Hansen Tift, Nicholas Minar, and David Lewkowicz. 2016. Selective attention to a talker's mouth in infancy: role of audiovisual temporal synchrony and linguistic experience. Developmental Science 20, 3 (2016).
[6]
Irati R. Saez de Urabain, Mark H. Johnson, and Tim J. Smith. 2015. GraFIX: A semiautomatic approach for parsing low- and high-quality eye-tracking data. Behavior Research Methods 47, 1 (2015), 53--72.
[7]
Jean A. Guiraud, Przemysław Tomalski, Elena Kushnerenko, Helena Ribeiro, Kim Davies, Tony Charman, and BASIS Team. 2012. Atypical audiovisual speech integration in infants at risk for autism. PloS one 7, 5 (2012), e36428.
[8]
Roy S. Hessels, Richard Andersson, Ignace T.C. Hooge, Marcus Nyström, and Chantal Kemner. 2015. Consequences of eye color, positioning, and head movement for eye-tracking data quality in infant research. Infancy 20, 6 (2015), 601--633.
[9]
Roy S. Hessels, Diederick Niehorster, Chantal Kemner, and Ignace T.C. Hooge. 2016. Noise-robust fixation detection in eye-movement data: Identification by two-means clustering (I2MC). Behavior Research Methods 49, 5 (2016).
[10]
David J. Lewkowicz. 2010. Infant perception of audio-visual speech synchrony. Developmental Psychology 46, 1 (2010), 66--77.
[11]
Ferran Pons, David J. Lewkowicz, Salvador Soto-Faraco, and Nuria Sebastián-Gallés. 2009. Narrowing of intersensory speech perception in infancy. Proceedings of the National Academy of Sciences of the United States of America 106, 26 (2009), 10598--10602.
[12]
Jessica Prior and Jo van Herwegen. 2016. Practical Research With Children. Routledge Editors.
[13]
Giulia Righi, Elena Tenenbaum, Carolyn McCormick, Megan Blossom, Dima Amso, and Stephen J. Sheinkopf. 2018. Sensitivity to Audio-Visual Synchrony and Its Relation to Language Abilities in Children with and without ASD. Autism Research (2018).
[14]
Elizabeth G. Smith and Loisa Bennetto. 2007. Audiovisual speech integration and lipreading in autism. Journal of Child Psychology and Psychiatry 48, 8 (2007), 813--821.
[15]
Barry E. Stein. 2012. The new handbook of multisensory processes. Cambridge, MA: MIT.
[16]
Sam Wass, Linda Forssman, and Jukka M. Leppänen. 2014. Robustness and Precision: How Data Quality May Influence Key Dependent Variables in Infant Eye-Tracker Analyses. Infancy 19, 5 (2014), 427--460.
[17]
Sam Wass, Tim J. Smith, and Mark H. Johnson. 2012. Parsing eye-tracking data of variable quality to provide accurate fixation duration estimates in infants and adults. Behavior Research Methods 45 (2012), 229--250.
[18]
Terje Falck-Ytter, Pär Nyström, Gustaf Gredebäck, Teodora Gliga, and Sven Bölte. 2018. Reduced orienting to audiovisual synchrony in infancy predicts autism diagnosis at 3 years of age. Journal of Child Psychology and Psychiatry (2018).

Cited By

View all
  • (2021)Eye tracking algorithms, techniques, tools, and applications with an emphasis on machine learning and Internet of Things technologiesExpert Systems with Applications10.1016/j.eswa.2020.114037166(114037)Online publication date: Mar-2021

Index Terms

  1. Eye-tracking measures in audiovisual stimuli in infants at high genetic risk for ASD: challenging issues

    Recommendations

    Comments

    Please enable JavaScript to view thecomments powered by Disqus.

    Information & Contributors

    Information

    Published In

    cover image ACM Conferences
    ETRA '18: Proceedings of the 2018 ACM Symposium on Eye Tracking Research & Applications
    June 2018
    595 pages
    ISBN:9781450357067
    DOI:10.1145/3204493
    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

    Sponsors

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 14 June 2018

    Check for updates

    Author Tags

    1. audiovisual multimodal processing
    2. autism spectrum disorder
    3. eye-tracking
    4. high genetic risk
    5. infants
    6. quality of data

    Qualifiers

    • Abstract

    Conference

    ETRA '18

    Acceptance Rates

    Overall Acceptance Rate 69 of 137 submissions, 50%

    Upcoming Conference

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)3
    • Downloads (Last 6 weeks)1
    Reflects downloads up to 09 Jan 2025

    Other Metrics

    Citations

    Cited By

    View all
    • (2021)Eye tracking algorithms, techniques, tools, and applications with an emphasis on machine learning and Internet of Things technologiesExpert Systems with Applications10.1016/j.eswa.2020.114037166(114037)Online publication date: Mar-2021

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Media

    Figures

    Other

    Tables

    Share

    Share

    Share this Publication link

    Share on social media