Visual Exploration of Dynamic or Static Joint Attention Bids in Children With Autism Syndrome Disorder

Cilia, Federica; Aubry, Alexandre; Driant, Barbara Le; Bourdin, Béatrice; Vandromme, Luc

doi:10.3389/fpsyg.2019.02187

Cited by 14 publications

(11 citation statements)

References 63 publications

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“…All eye-tracking data can be publicly accessible at the website https://doi. org/10.6084/m9.figshare.20113592 (Cilia et al, 2019;Cilia et al, 2021;Cilia et al, 2022).…”

Section: Eye-tracking Proceduresmentioning

confidence: 99%

Nonlinear features of gaze behavior during joint attention in children with autism spectrum disorder

Wang,

Zhao,

2023

Autism Research

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Since children with autism spectrum disorder (ASD) might exhibit a variety of aberrant response to joint attention (RJA) behaviors, there is growing interest in identifying robust, reliable and valid eye‐tracking metrics for determining differences in RJA behaviors between typically developing (TD) children and those with ASD. Previous eye‐tracking studies have not been deeply investigated nonlinear features of gaze time‐series during RJA. As a main motivation, this study aimed to extract three nonlinear features (i.e., complexity, long‐range correlation, and local instability) of gaze time‐series during RJA in children with ASD, which can be measured by fractal dimension (FD), Hurst exponent (H), and largest Lyapunov exponent (LLE), respectively. To illustrate our idea, this study adopted a publicly accessible database, including eye‐tracking data collected during RJA from 19 children with ASD (7.74 ± 2.73) and 30 TD children (8.02 ± 2.89), and conducted a battery of nonparametric analysis of covariance (ANCOVA), where gender was used as covariable. Findings showed that gaze time‐series during RJA in autistic children may generally have greater FD but lower H than that in TD controls. This implies that children with ASD possess more complex and unpredictable gaze behaviors during RJA than TD children. Furthermore, nonlinear metrics outperformed traditional eye‐tracking metrics in obtaining higher identification performance with an accuracy of 82% and an AUC value of 0.81, distinguishing the differences between successful and failed RJA trails, and predicting the severity of ASD symptoms. Findings might bring some new insights into the understanding of the impairments in RJA behaviors for children with ASD.

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“…All eye-tracking data can be publicly accessible at the website https://doi. org/10.6084/m9.figshare.20113592 (Cilia et al, 2019;Cilia et al, 2021;Cilia et al, 2022).…”

Section: Eye-tracking Proceduresmentioning

confidence: 99%

Nonlinear features of gaze behavior during joint attention in children with autism spectrum disorder

Wang,

Zhao,

2023

Autism Research

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“…Chevallier et al (2015) compared static and dynamic visual tasks involving both social and nonsocial stimuli and found that autistic children spent less time looking at social than nonsocial stimuli compared with TD children. Finally, a recent study by Cilia et al (2019) showed that both static and dynamic stimuli were relevant in distinguishing autistic from TD children using eye gaze. On the one hand, static stimuli enabled AOIs to be identified with greater precision, showing similar patterns in the two populations.…”

Section: Eye Gaze To Social Attentional Cue Recognition In Asdmentioning

confidence: 99%

“…Such differences might be dependent on the study methodology and the type of social stimuli involved. Using either static or dynamic social stimuli might yield different results, and only a few studies have compared these two conditions (e.g., Chevallier et al, 2015; Cilia et al, 2019; Saitovich et al, 2013; Shic et al, 2014; Speer et al, 2007). Shic et al (2014) compared a neutral female face image, a video with a woman smiling, and a video with a woman smiling and speaking and found that autistic children looked less often at the eyes in the social stimuli condition and in response to the dynamic stimulus of the woman speaking.…”

Section: Introductionmentioning

confidence: 99%

Eye gaze as a biomarker in the recognition of autism spectrum disorder using virtual reality and machine learning: A proof of concept for diagnosis

et al. 2021

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The core symptoms of autism spectrum disorder (ASD) mainly relate to social communication and interactions. ASD assessment involves expert observations in neutral settings, which introduces limitations and biases related to lack of objectivity and does not capture performance in real-world settings. To overcome these limitations, advances in technologies (e.g., virtual reality) and sensors (e.g., eye-tracking tools) have been used to create realistic simulated environments and track eye movements, enriching assessments with more objective data than can be obtained via traditional measures. This study aimed to distinguish between autistic and typically developing children using visual attention behaviors through an eye-tracking paradigm in a virtual environment as a measure of attunement to and extraction of socially relevant information. The 55 children participated. Autistic children presented a higher number of frames, both overall and per scenario, and showed higher visual preferences for adults over children, as well as specific preferences for adults' rather than children's faces on which looked more at bodies. A set of multivariate supervised machine learning models were developed using recursive feature selection to recognize ASD based on extracted eye gaze features. The models achieved up to 86% accuracy (sensitivity = 91%) in recognizing autistic children. Our results should be taken as preliminary due to the relatively small sample size and the lack of an external replication dataset. However, to our knowledge, this constitutes a first proof of concept in the combined use of virtual reality, eye-tracking tools, and machine learning for ASD recognition. Lay SummaryCore symptoms in children with ASD involve social communication and interaction. ASD assessment includes expert observations in neutral settings, which show limitations and biases related to lack of objectivity and do not capture performance in real settings. To overcome these limitations, this work aimed to distinguish between autistic and typically developing children in visual attention behaviors through an eye-tracking paradigm in a virtual environment as a measure of attunement to, and extraction of, socially relevant information.

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“…The counterbalancing of stimuli for participants and the number of participants included allowed the artificial intelligence and psychology teams to collaborate on the basis of this predefined research protocol. The results of these tests have been partly exploited, presented, and published [31][32][33].…”

Section: Methodsmentioning

confidence: 99%

Computer-Aided Screening of Autism Spectrum Disorder: Eye-Tracking Study Using Data Visualization and Deep Learning

Cilia¹,

Carette²,

Elbattah³

et al. 2021

JMIR Hum Factors

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Background The early diagnosis of autism spectrum disorder (ASD) is highly desirable but remains a challenging task, which requires a set of cognitive tests and hours of clinical examinations. In addition, variations of such symptoms exist, which can make the identification of ASD even more difficult. Although diagnosis tests are largely developed by experts, they are still subject to human bias. In this respect, computer-assisted technologies can play a key role in supporting the screening process. Objective This paper follows on the path of using eye tracking as an integrated part of screening assessment in ASD based on the characteristic elements of the eye gaze. This study adds to the mounting efforts in using eye tracking technology to support the process of ASD screening Methods The proposed approach basically aims to integrate eye tracking with visualization and machine learning. A group of 59 school-aged participants took part in the study. The participants were invited to watch a set of age-appropriate photographs and videos related to social cognition. Initially, eye-tracking scanpaths were transformed into a visual representation as a set of images. Subsequently, a convolutional neural network was trained to perform the image classification task. Results The experimental results demonstrated that the visual representation could simplify the diagnostic task and also attained high accuracy. Specifically, the convolutional neural network model could achieve a promising classification accuracy. This largely suggests that visualizations could successfully encode the information of gaze motion and its underlying dynamics. Further, we explored possible correlations between the autism severity and the dynamics of eye movement based on the maximal information coefficient. The findings primarily show that the combination of eye tracking, visualization, and machine learning have strong potential in developing an objective tool to assist in the screening of ASD. Conclusions Broadly speaking, the approach we propose could be transferable to screening for other disorders, particularly neurodevelopmental disorders.

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Visual Exploration of Dynamic or Static Joint Attention Bids in Children With Autism Syndrome Disorder

Cited by 14 publications

References 63 publications

Nonlinear features of gaze behavior during joint attention in children with autism spectrum disorder

Nonlinear features of gaze behavior during joint attention in children with autism spectrum disorder

Eye gaze as a biomarker in the recognition of autism spectrum disorder using virtual reality and machine learning: A proof of concept for diagnosis

Computer-Aided Screening of Autism Spectrum Disorder: Eye-Tracking Study Using Data Visualization and Deep Learning

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