Atypical and variable attention patterns reveal reduced contextual priors in children with autism spectrum disorder / Rong CHENG ; Zhong ZHAO ; Haotian LIAO ; Jing LI in Autism Research, 17-8 (August 2024)  

Public ISBD [article]  inAutism Research > 17-8 (August 2024) . - p.1572-1585 Titre : Atypical and variable attention patterns reveal reduced contextual priors in children with autism spectrum disorder Type de document : Texte imprimé et/ou numérique Auteurs : Rong CHENG, Auteur ; Zhong ZHAO, Auteur ; Haotian LIAO, Auteur ; Jing LI, Auteur Article en page(s) : p.1572-1585 Langues : Anglais (eng) Index. décimale : PER Périodiques Résumé : Abstract Accumulating evidence suggests that individuals with autism spectrum disorder (ASD) show impairments in using contextual priors to predict others'actions and make intention inference. Yet less is known about whether and how children with ASD acquire contextual priors during action observation and how contextual priors relate to their action prediction and intention inference. To form proper contextual priors, individuals need to observe the social scenes in a reliable manner and focus on socially relevant information. By employing a data-driven scan path method and areas of interest (AOI)-based analysis, the current study investigated how contextual priors would relate to action prediction and intention understanding in 4-to-9-year-old children with ASD (N = 56) and typically developing (TD) children (N = 50) during free viewing of dynamic social scenes with different intentions. Results showed that children with ASD exhibited higher intra-subject variability when scanning social scenes and reduced attention to socially relevant areas. Moreover, children with high-level action prediction and intention understanding showed lower intra-subject variability and increased attention to socially relevant areas. These findings suggest that altered fixation patterns might restrain children with ASD from acquiring proper contextual priors, which has cascading downstream effects on their action prediction and intention understanding. En ligne : https://doi.org/10.1002/aur.3194 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=533 [article] Atypical and variable attention patterns reveal reduced contextual priors in children with autism spectrum disorder [Texte imprimé et/ou numérique] / Rong CHENG, Auteur ; Zhong ZHAO, Auteur ; Haotian LIAO, Auteur ; Jing LI, Auteur . - p.1572-1585. Langues : Anglais (eng) in Autism Research > 17-8 (August 2024) . - p.1572-1585 Index. décimale : PER Périodiques Résumé : Abstract Accumulating evidence suggests that individuals with autism spectrum disorder (ASD) show impairments in using contextual priors to predict others'actions and make intention inference. Yet less is known about whether and how children with ASD acquire contextual priors during action observation and how contextual priors relate to their action prediction and intention inference. To form proper contextual priors, individuals need to observe the social scenes in a reliable manner and focus on socially relevant information. By employing a data-driven scan path method and areas of interest (AOI)-based analysis, the current study investigated how contextual priors would relate to action prediction and intention understanding in 4-to-9-year-old children with ASD (N = 56) and typically developing (TD) children (N = 50) during free viewing of dynamic social scenes with different intentions. Results showed that children with ASD exhibited higher intra-subject variability when scanning social scenes and reduced attention to socially relevant areas. Moreover, children with high-level action prediction and intention understanding showed lower intra-subject variability and increased attention to socially relevant areas. These findings suggest that altered fixation patterns might restrain children with ASD from acquiring proper contextual priors, which has cascading downstream effects on their action prediction and intention understanding. En ligne : https://doi.org/10.1002/aur.3194 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=533

Correction to: Random and ShortTerm Excessive Eye Movement in Children with Autism During FacetoFace Conversation / Zhong ZHAO in Journal of Autism and Developmental Disorders, 52-8 (August 2022)  

Public ISBD [article]  inJournal of Autism and Developmental Disorders > 52-8 (August 2022) . - p.3711 Titre : Correction to: Random and ShortTerm Excessive Eye Movement in Children with Autism During FacetoFace Conversation Type de document : Texte imprimé et/ou numérique Auteurs : Zhong ZHAO, Auteur ; Jiayi XING, Auteur ; Xiaobin ZHANG, Auteur ; Xingda QU, Auteur ; Xinyao HU, Auteur ; Jianping LU, Auteur Article en page(s) : p.3711 Langues : Anglais (eng) Index. décimale : PER Périodiques En ligne : http://dx.doi.org/10.1007/s10803-021-05294-0 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=485 [article] Correction to: Random and ShortTerm Excessive Eye Movement in Children with Autism During FacetoFace Conversation [Texte imprimé et/ou numérique] / Zhong ZHAO, Auteur ; Jiayi XING, Auteur ; Xiaobin ZHANG, Auteur ; Xingda QU, Auteur ; Xinyao HU, Auteur ; Jianping LU, Auteur . - p.3711. Langues : Anglais (eng) in Journal of Autism and Developmental Disorders > 52-8 (August 2022) . - p.3711 Index. décimale : PER Périodiques En ligne : http://dx.doi.org/10.1007/s10803-021-05294-0 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=485

Identifying Autism with Head Movement Features by Implementing Machine Learning Algorithms / Zhong ZHAO in Journal of Autism and Developmental Disorders, 52-7 (July 2022)  

Public ISBD [article]  inJournal of Autism and Developmental Disorders > 52-7 (July 2022) . - p.3038-3049 Titre : Identifying Autism with Head Movement Features by Implementing Machine Learning Algorithms Type de document : Texte imprimé et/ou numérique Auteurs : Zhong ZHAO, Auteur ; Zhipeng ZHU, Auteur ; Xiaobin ZHANG, Auteur ; Haiming TANG, Auteur ; Jiayi XING, Auteur ; Xinyao HU, Auteur ; Jianping LU, Auteur ; Xingda QU, Auteur Article en page(s) : p.3038-3049 Langues : Anglais (eng) Mots-clés : Algorithms  Autism Spectrum Disorder/diagnosis  Autistic Disorder  Child  Head Movements  Humans  Machine Learning  Autism  Biomarkers  Diagnosis  Head movement Index. décimale : PER Périodiques Résumé : Our study investigated the feasibility of using head movement features to identify individuals with autism spectrum disorder (ASD). Children with ASD and typical development (TD) were required to answer ten yes-no questions, and they were encouraged to nod/shake head while doing so. The head rotation range (RR) and the amount of rotation per minute (ARPM) in the pitch (head nodding direction), yaw (head shaking direction) and roll (lateral head inclination) directions were computed, and further fed into machine learning classifiers as the input features. The maximum classification accuracy of 92.11% was achieved with the decision tree classifier with two features (i.e., RR_Pitch and ARPM_Yaw). Our study suggests that head movement dynamics contain objective biomarkers that could identify ASD. En ligne : http://dx.doi.org/10.1007/s10803-021-05179-2 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=477 [article] Identifying Autism with Head Movement Features by Implementing Machine Learning Algorithms [Texte imprimé et/ou numérique] / Zhong ZHAO, Auteur ; Zhipeng ZHU, Auteur ; Xiaobin ZHANG, Auteur ; Haiming TANG, Auteur ; Jiayi XING, Auteur ; Xinyao HU, Auteur ; Jianping LU, Auteur ; Xingda QU, Auteur . - p.3038-3049. Langues : Anglais (eng) in Journal of Autism and Developmental Disorders > 52-7 (July 2022) . - p.3038-3049 Mots-clés : Algorithms  Autism Spectrum Disorder/diagnosis  Autistic Disorder  Child  Head Movements  Humans  Machine Learning  Autism  Biomarkers  Diagnosis  Head movement Index. décimale : PER Périodiques Résumé : Our study investigated the feasibility of using head movement features to identify individuals with autism spectrum disorder (ASD). Children with ASD and typical development (TD) were required to answer ten yes-no questions, and they were encouraged to nod/shake head while doing so. The head rotation range (RR) and the amount of rotation per minute (ARPM) in the pitch (head nodding direction), yaw (head shaking direction) and roll (lateral head inclination) directions were computed, and further fed into machine learning classifiers as the input features. The maximum classification accuracy of 92.11% was achieved with the decision tree classifier with two features (i.e., RR_Pitch and ARPM_Yaw). Our study suggests that head movement dynamics contain objective biomarkers that could identify ASD. En ligne : http://dx.doi.org/10.1007/s10803-021-05179-2 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=477

Random and Short-Term Excessive Eye Movement in Children with Autism During Face-to-Face Conversation / Zhong ZHAO in Journal of Autism and Developmental Disorders, 52-8 (August 2022)  

Public ISBD [article]  inJournal of Autism and Developmental Disorders > 52-8 (August 2022) . - p.3699-3710 Titre : Random and Short-Term Excessive Eye Movement in Children with Autism During Face-to-Face Conversation Type de document : Texte imprimé et/ou numérique Auteurs : Zhong ZHAO, Auteur ; Jiayi XING, Auteur ; Xiaobin ZHANG, Auteur ; Xingda QU, Auteur ; Xinyao HU, Auteur ; Jianping LU, Auteur Article en page(s) : p.3699-3710 Langues : Anglais (eng) Mots-clés : Autism Spectrum Disorder  Autistic Disorder  Child  Communication  Eye Movements  Humans  Autism  Entropy  Eye movement quantity  Eye tracking  Face-to-face interaction  Oculomotor Index. décimale : PER Périodiques Résumé : This study investigated the oculomotor performance in children with autism spectrum disorder (ASD) during a face-to-face conversation. A head mounted eye tracker recorded the eye movements in 20 children with ASD and 23 children with typical development (TD). Group comparisons were conducted on the randomness and the quantity of eye movement. The amount of time needed to reveal group difference was also examined. Results showed that the randomness of eye movement was significantly higher at all examined time durations, and the amount of eye movement was significantly greater within 3 s in the ASD group. These findings demonstrated an atypical pattern of oculomotor dynamics in children ASD, which might facilitate the objective identification of ASD during daily social interaction. En ligne : http://dx.doi.org/10.1007/s10803-021-05255-7 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=485 [article] Random and Short-Term Excessive Eye Movement in Children with Autism During Face-to-Face Conversation [Texte imprimé et/ou numérique] / Zhong ZHAO, Auteur ; Jiayi XING, Auteur ; Xiaobin ZHANG, Auteur ; Xingda QU, Auteur ; Xinyao HU, Auteur ; Jianping LU, Auteur . - p.3699-3710. Langues : Anglais (eng) in Journal of Autism and Developmental Disorders > 52-8 (August 2022) . - p.3699-3710 Mots-clés : Autism Spectrum Disorder  Autistic Disorder  Child  Communication  Eye Movements  Humans  Autism  Entropy  Eye movement quantity  Eye tracking  Face-to-face interaction  Oculomotor Index. décimale : PER Périodiques Résumé : This study investigated the oculomotor performance in children with autism spectrum disorder (ASD) during a face-to-face conversation. A head mounted eye tracker recorded the eye movements in 20 children with ASD and 23 children with typical development (TD). Group comparisons were conducted on the randomness and the quantity of eye movement. The amount of time needed to reveal group difference was also examined. Results showed that the randomness of eye movement was significantly higher at all examined time durations, and the amount of eye movement was significantly greater within 3 s in the ASD group. These findings demonstrated an atypical pattern of oculomotor dynamics in children ASD, which might facilitate the objective identification of ASD during daily social interaction. En ligne : http://dx.doi.org/10.1007/s10803-021-05255-7 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=485

Use of Oculomotor Behavior to Classify Children with Autism and Typical Development: A Novel Implementation of the Machine Learning Approach / Zhong ZHAO in Journal of Autism and Developmental Disorders, 53-3 (March 2023)  

Public ISBD [article]  inJournal of Autism and Developmental Disorders > 53-3 (March 2023) . - p.934-946 Titre : Use of Oculomotor Behavior to Classify Children with Autism and Typical Development: A Novel Implementation of the Machine Learning Approach Type de document : Texte imprimé et/ou numérique Auteurs : Zhong ZHAO, Auteur ; Jiwei WEI, Auteur ; Jiayi XING, Auteur ; Xiaobin ZHANG, Auteur ; Xingda QU, Auteur ; Xinyao HU, Auteur ; Jianping LU, Auteur Article en page(s) : p.934-946 Langues : Anglais (eng) Index. décimale : PER Périodiques Résumé : This study segmented the time series of gaze behavior from nineteen children with autism spectrum disorder (ASD) and 20 children with typical development in a face-to-face conversation. A machine learning approach showed that behavior segments produced by these two groups of participants could be classified with the highest accuracy of 74.15%. These results were further used to classify children using a threshold classifier. A maximum classification accuracy of 87.18% was achieved, under the condition that a participant was considered as 'ASD' if over 46% of the child?s 7-s behavior segments were classified as ASD-like behaviors. The idea of combining the behavior segmentation technique and the threshold classifier could maximally preserve participants' data, and promote the automatic screening of ASD. En ligne : https://doi.org/10.1007/s10803-022-05685-x Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=500 [article] Use of Oculomotor Behavior to Classify Children with Autism and Typical Development: A Novel Implementation of the Machine Learning Approach [Texte imprimé et/ou numérique] / Zhong ZHAO, Auteur ; Jiwei WEI, Auteur ; Jiayi XING, Auteur ; Xiaobin ZHANG, Auteur ; Xingda QU, Auteur ; Xinyao HU, Auteur ; Jianping LU, Auteur . - p.934-946. Langues : Anglais (eng) in Journal of Autism and Developmental Disorders > 53-3 (March 2023) . - p.934-946 Index. décimale : PER Périodiques Résumé : This study segmented the time series of gaze behavior from nineteen children with autism spectrum disorder (ASD) and 20 children with typical development in a face-to-face conversation. A machine learning approach showed that behavior segments produced by these two groups of participants could be classified with the highest accuracy of 74.15%. These results were further used to classify children using a threshold classifier. A maximum classification accuracy of 87.18% was achieved, under the condition that a participant was considered as 'ASD' if over 46% of the child?s 7-s behavior segments were classified as ASD-like behaviors. The idea of combining the behavior segmentation technique and the threshold classifier could maximally preserve participants' data, and promote the automatic screening of ASD. En ligne : https://doi.org/10.1007/s10803-022-05685-x Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=500

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