Yi-Wen Wang
ABSTRACT
Data loss in eye-tracking studies is often considered a nuisance variable or noise. This study examined the value of data loss in eye tracking and proposed a new method to utilize lost data in predicting the clinical characteristics of autism spectrum disorder (ASD). We used eye tracking to confirm previous findings on atypical attention patterns and further utilized behavior coding to examine the three types of causes of data loss including blinks, non-compliant behaviors, and technical errors. We discovered that data loss due to blinking was associated with a lack of interest in social cues, and data loss due to non-compliance predicted a greater severity of ASD symptoms. These results suggest that the loss of data in eye tracking is meaningful as a measure of diminished social attention and a reflection of clinical characteristics in ASD.
- American Psychiatric Association. 2013. Diagnostic and statistical manual of mental disorders: DSM-5.American Psychiatric Association, Arlington, VA.Google Scholar
- Kamila Borowiak, Stefanie Schelinski, and Katharina von Kriegstein. 2018. Recognizing visual speech: Reduced responses in visual-movement regions, but not other speech regions in autism. NeuroImage. Clinical 20 (2018), 1078–1091. https://doi.org/10.1016/j.nicl.2018.09.019Google ScholarCross Ref
- Daniel J. Campbell, Frederick Shic, Suzanne Macari, and Katarzyna Chawarska. 2014. Gaze response to dyadic bids at 2 years related to outcomes at 3 years in autism spectrum disorders: a subtyping analysis. Journal of Autism and Developmental Disorders 44, 2 (Feb 2014), 431–442. https://doi.org/10.1007/s10803-013-1885-9Google ScholarCross Ref
- Katarzyna Chawarska, Suzanne Macari, and Frederick Shic. 2013. Decreased spontaneous attention to social scenes in 6-month-old infants later diagnosed with autism spectrum disorders. Biological Psychiatry 74, 3 (Aug 2013), 195–203. https://doi.org/10.1016/j.biopsych.2012.11.022Google ScholarCross Ref
- P.N. Jadhav, D. Shanamugan, A. Chourasia, A.R. Ghole, A.A. Acharyya, and G. Naik. 2014. Automated detection and correction of eye blink and muscular artefacts in EEG signal for analysis of Autism Spectrum Disorder. In 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 1881–1884. https://doi.org/10.1109/EMBC.2014.6943977Google ScholarCross Ref
- Frederick Shic, Suzanne Macari, and Katarzyna Chawarska. 2014. Speech disturbs face scanning in 6-month-old infants who develop autism spectrum disorder. Biological Psychiatry 75, 3 (Feb 2014), 231–237. https://doi.org/10.1016/j.biopsych.2013.07.009Google ScholarCross Ref
Recommendations
Comparing Attention to Biological Motion in Autism across Age Groups Using Eye-Tracking
ETRA '23: Proceedings of the 2023 Symposium on Eye Tracking Research and ApplicationsThis study tracked eye movement in children with and without autism spectrum disorder (ASD) watching emotional biological and non-biological motion point-light-displays (PLDs). Older children with ASD focused on extremities while older typically ...
Impact of mainstream classroom setting on attention of children with autism spectrum disorder: an eye-tracking study
AbstractIt has long been reported that children with autism spectrum disorder (ASD) exhibit attention difficulties while learning. They tend to focus on irrelevant information and can easily be distracted. As a result, they are often confined to a one-to-...
An Eye Movement Study of Joint Attention Deficits in Children with Autism Spectrum Disorders
Intelligent Robotics and ApplicationsAbstractUsing eye tracking technology to explore the underlying processing mechanisms of joint attention in children with autism spectrum disorders (ASD). The experiment selected 32 ASD children and 34 IQ-matched typically developing (TD) children. By ...
Comments