Children with Autism Spectrum Disorder scan own-race faces differently from other-race faces

Yi, Li; Quinn, Paul C.; Fan, Yuebo; Huang, Dan; Feng, Cong; Joseph, Lisa; Li, Jiao; Lee, Kang

doi:10.1016/j.jecp.2015.09.011

Cited by 19 publications

(30 citation statements)

References 37 publications

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“…Children's eye movements during the study and test phases were recorded by a Tobii T60 eye‐tracker (sample rate: 60 Hz; both eyes were tracked) with the Tobii Studio software. More details of the participants, the material, and the experimental procedures were provided in the Yi et al [].…”

Section: Methodsmentioning

confidence: 99%

“…Yi et al [] analyzed the eye movement data based on the area of interest (AOI) approach to compare the fixation duration between groups within each predefined face region (e.g. eyes, nose, and mouth).…”

Section: Methodsmentioning

confidence: 99%

“…Children's eye movements during the study and test phases were recorded by a Tobii T60 eye-tracker (sample rate: 60 Hz; both eyes were tracked) with the Tobii Studio software. More details of the participants, the material, and the experimental procedures were provided in the Yi et al [2016]. Yi et al [2016] analyzed the eye movement data based on the area of interest (AOI) approach to compare the fixation duration between groups within each predefined face region (e.g.…”

Section: Description Of the Datasetmentioning

confidence: 99%

“…More details of the participants, the material, and the experimental procedures were provided in the Yi et al [2016]. Yi et al [2016] analyzed the eye movement data based on the area of interest (AOI) approach to compare the fixation duration between groups within each predefined face region (e.g. eyes, nose, and mouth).…”

Section: Description Of the Datasetmentioning

confidence: 99%

“…The present study examined whether their face scanning patterns could be potentially useful to identify children with ASD by adopting the machine learning algorithm for the classification purpose. Particularly, we applied the machine learning method to analyze an eye movement dataset from a face recognition task [Yi et al, 2016], to classify children with and without ASD. We evaluated the performance of our model in terms of its accuracy, sensitivity, and specificity of classifying ASD.…”

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confidence: 99%

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Identifying children with autism spectrum disorder based on their face processing abnormality: A machine learning framework

2016

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The atypical face scanning patterns in individuals with Autism Spectrum Disorder (ASD) has been repeatedly discovered by previous research. The present study examined whether their face scanning patterns could be potentially useful to identify children with ASD by adopting the machine learning algorithm for the classification purpose. Particularly, we applied the machine learning method to analyze an eye movement dataset from a face recognition task [Yi et al., 2016], to classify children with and without ASD. We evaluated the performance of our model in terms of its accuracy, sensitivity, and specificity of classifying ASD. Results indicated promising evidence for applying the machine learning algorithm based on the face scanning patterns to identify children with ASD, with a maximum classification accuracy of 88.51%. Nevertheless, our study is still preliminary with some constraints that may apply in the clinical practice. Future research should shed light on further valuation of our method and contribute to the development of a multitask and multimodel approach to aid the process of early detection and diagnosis of ASD. Autism Res 2016, 9: 888-898. © 2016 International Society for Autism Research, Wiley Periodicals, Inc.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Description Of the Datasetmentioning

confidence: 99%

Section: Description Of the Datasetmentioning

confidence: 99%

mentioning

confidence: 99%

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Identifying children with autism spectrum disorder based on their face processing abnormality: A machine learning framework

2016

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Atypical gaze patterns to facial feature areas in autism spectrum disorders reveal age and culture effects: A meta‐analysis of eye‐tracking studies

Zhao

2021

Autism Research

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Children and adults with autism spectrum disorder (ASD) often present with atypical gaze patterns to others' faces, a finding substantiated throughout the literature. Yet, a quantification of atypical gaze patterns to different facial regions (e.g., eyes versus mouth) in ASD remains controversial. Also few study has investigated how age and culture impacted the pattern of gaze abnormalities in ASD. This research therefore conducted a meta-analysis of eye-tracking studies to evaluate age and culture effect on atypical gaze patterns of face processing in ASD. A total of 75 articles (91 studies) and 4209 individuals (ASD: 2027; controls: 2182) across all age ranges (i.e., childhood through to adulthood) from both Eastern and Western cultures were included in this meta-analysis. Individuals with ASD yielded shorter fixation durations to the eyes than individuals without ASD. Group differences in the time spent fixating on the eyes were not modulated by age, but affected by culture. Effect size in the eastern culture was larger than that in the western culture. In contrast, group differences on time spent looking at the mouth were not significant, but changed with age and modulated by culture. Relative to the neurotypical controls, Western individuals with ASD spent more time looking at the mouth from school age, whereas Eastern individuals with ASD did not gaze longer on mouth until adulthood. These results add to the body of evidence supporting atypical gaze behaviors to eyes in ASD and provide new insights into a potential mouth compensation strategy that develops with age in ASD. Lay SummaryIndividuals with autism spectrum disorder (ASD) often show atypical gaze patterns when looking at others' faces compared to neurotypical individuals. This paper examines the role of age and culture on pattern of gaze abnormalities in individuals with ASD. Results show that reduction of gaze on eyes in ASD is stable across all ages and cultures, while increase of gaze on mouth emerges as individuals with ASD get older. The findings provide a developmental insight to the gaze patterns on the autism spectrum across culture.

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Brief Report: Sensitivity of Children with Autism Spectrum Disorders to Face Appearance in Selective Trust

Zhang

2016

J Autism Dev Disord

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The current study examined how children with Autism Spectrum Disorders (ASD) could selectively trust others based on three facial cues: the face race, attractiveness, and trustworthiness. In a computer-based hide-and-seek game, two face images, which differed significantly in one of the three facial cues, were presented as two cues for selective trust. Children had to selectively trust the own-race, attractive and trustworthy faces to get the prize. Our findings demonstrate an intact ability of selective trust based on face appearance in ASD compared to typical children: they could selectively trust the informant based on face race and attractiveness. Our results imply that despite their face recognition deficits, children with ASD are still sensitive to some aspects of face appearance.

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Children with Autism Spectrum Disorder scan own-race faces differently from other-race faces

Cited by 19 publications

References 37 publications

Identifying children with autism spectrum disorder based on their face processing abnormality: A machine learning framework

Identifying children with autism spectrum disorder based on their face processing abnormality: A machine learning framework

Atypical gaze patterns to facial feature areas in autism spectrum disorders reveal age and culture effects: A meta‐analysis of eye‐tracking studies

Brief Report: Sensitivity of Children with Autism Spectrum Disorders to Face Appearance in Selective Trust

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