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Bottom-Up Attention Orienting in Young Children with Autism / Dima AMSO in Journal of Autism and Developmental Disorders, 44-3 (March 2014)
[article]
Titre : Bottom-Up Attention Orienting in Young Children with Autism Type de document : Texte imprimé et/ou numérique Auteurs : Dima AMSO, Auteur ; Sara HAAS, Auteur ; Elena TENENBAUM, Auteur ; Julie MARKANT, Auteur ; Stephen J. SHEINKOPF, Auteur Article en page(s) : p.664-673 Langues : Anglais (eng) Mots-clés : Bottom-up attention Saliency Visual attention Autism Eye tracking Social attention Index. décimale : PER Périodiques Résumé : We examined the impact of simultaneous bottom-up visual influences and meaningful social stimuli on attention orienting in young children with autism spectrum disorders (ASDs). Relative to typically-developing age and sex matched participants, children with ASDs were more influenced by bottom-up visual scene information regardless of whether social stimuli and bottom-up scene properties were congruent or competing. This initial reliance on bottom-up strategies correlated with severity of social impairment as well as receptive language impairments. These data provide support for the idea that there is enhanced reliance on bottom-up attention strategies in ASDs, and that this may have a negative impact on social and language development. En ligne : http://dx.doi.org/10.1007/s10803-013-1925-5 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=225
in Journal of Autism and Developmental Disorders > 44-3 (March 2014) . - p.664-673[article] Bottom-Up Attention Orienting in Young Children with Autism [Texte imprimé et/ou numérique] / Dima AMSO, Auteur ; Sara HAAS, Auteur ; Elena TENENBAUM, Auteur ; Julie MARKANT, Auteur ; Stephen J. SHEINKOPF, Auteur . - p.664-673.
Langues : Anglais (eng)
in Journal of Autism and Developmental Disorders > 44-3 (March 2014) . - p.664-673
Mots-clés : Bottom-up attention Saliency Visual attention Autism Eye tracking Social attention Index. décimale : PER Périodiques Résumé : We examined the impact of simultaneous bottom-up visual influences and meaningful social stimuli on attention orienting in young children with autism spectrum disorders (ASDs). Relative to typically-developing age and sex matched participants, children with ASDs were more influenced by bottom-up visual scene information regardless of whether social stimuli and bottom-up scene properties were congruent or competing. This initial reliance on bottom-up strategies correlated with severity of social impairment as well as receptive language impairments. These data provide support for the idea that there is enhanced reliance on bottom-up attention strategies in ASDs, and that this may have a negative impact on social and language development. En ligne : http://dx.doi.org/10.1007/s10803-013-1925-5 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=225 Brief Report: Atypical Visual Exploration in Autism Spectrum Disorder Cannot be Attributed to the Amygdala / S. WANG in Journal of Autism and Developmental Disorders, 49-6 (June 2019)
[article]
Titre : Brief Report: Atypical Visual Exploration in Autism Spectrum Disorder Cannot be Attributed to the Amygdala Type de document : Texte imprimé et/ou numérique Auteurs : S. WANG, Auteur Article en page(s) : p.2605-2611 Langues : Anglais (eng) Mots-clés : Amygdala Attention Autism spectrum disorder Eye tracking Saliency Index. décimale : PER Périodiques Résumé : Prior studies have emphasized the contribution of aberrant amygdala structure and function in social aspects of autism. However, it remains largely unknown whether amygdala dysfunction directly impairs visual attention and exploration as has been observed in people with autism spectrum disorders (ASD). Here, gaze patterns were directly compared between a rare amygdala lesion patient and adults with ASD when they freely viewed static images of complex natural scenes. The amygdala lesion patient showed a gaze pattern that was more similar to controls rather than that of the ASD group, which was independent of image content (social vs. objects) or complexity. This finding was further corroborated by analysis of temporal aspects of the gaze patterns and semantic category analysis. Together, the present results suggest that abnormal visual exploration observed in people with ASD is not likely primarily attributed to the amygdala. En ligne : https://dx.doi.org/10.1007/s10803-019-04009-w Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=400
in Journal of Autism and Developmental Disorders > 49-6 (June 2019) . - p.2605-2611[article] Brief Report: Atypical Visual Exploration in Autism Spectrum Disorder Cannot be Attributed to the Amygdala [Texte imprimé et/ou numérique] / S. WANG, Auteur . - p.2605-2611.
Langues : Anglais (eng)
in Journal of Autism and Developmental Disorders > 49-6 (June 2019) . - p.2605-2611
Mots-clés : Amygdala Attention Autism spectrum disorder Eye tracking Saliency Index. décimale : PER Périodiques Résumé : Prior studies have emphasized the contribution of aberrant amygdala structure and function in social aspects of autism. However, it remains largely unknown whether amygdala dysfunction directly impairs visual attention and exploration as has been observed in people with autism spectrum disorders (ASD). Here, gaze patterns were directly compared between a rare amygdala lesion patient and adults with ASD when they freely viewed static images of complex natural scenes. The amygdala lesion patient showed a gaze pattern that was more similar to controls rather than that of the ASD group, which was independent of image content (social vs. objects) or complexity. This finding was further corroborated by analysis of temporal aspects of the gaze patterns and semantic category analysis. Together, the present results suggest that abnormal visual exploration observed in people with ASD is not likely primarily attributed to the amygdala. En ligne : https://dx.doi.org/10.1007/s10803-019-04009-w Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=400 Deep Neural Network Reveals the World of Autism From a First-Person Perspective / Mindi RUAN in Autism Research, 14-2 (February 2021)
[article]
Titre : Deep Neural Network Reveals the World of Autism From a First-Person Perspective Type de document : Texte imprimé et/ou numérique Auteurs : Mindi RUAN, Auteur ; Paula J. WEBSTER, Auteur ; Xin LI, Auteur ; Shuo WANG, Auteur Article en page(s) : p.333-342 Langues : Anglais (eng) Mots-clés : artificial intelligence attention autism spectrum disorder deep neural network faces photos saliency Index. décimale : PER Périodiques Résumé : People with autism spectrum disorder (ASD) show atypical attention to social stimuli and aberrant gaze when viewing images of the physical world. However, it is unknown how they perceive the world from a first-person perspective. In this study, we used machine learning to classify photos taken in three different categories (people, indoors, and outdoors) as either having been taken by individuals with ASD or by peers without ASD. Our classifier effectively discriminated photos from all three categories, but was particularly successful at classifying photos of people with >80% accuracy. Importantly, visualization of our model revealed critical features that led to successful discrimination and showed that our model adopted a strategy similar to that of ASD experts. Furthermore, for the first time we showed that photos taken by individuals with ASD contained less salient objects, especially in the central visual field. Notably, our model outperformed classification of these photos by ASD experts. Together, we demonstrate an effective and novel method that is capable of discerning photos taken by individuals with ASD and revealing aberrant visual attention in ASD from a unique first-person perspective. Our method may in turn provide an objective measure for evaluations of individuals with ASD. LAY SUMMARY: People with autism spectrum disorder (ASD) demonstrate atypical visual attention to social stimuli. However, it remains largely unclear how they perceive the world from a first-person perspective. In this study, we employed a deep learning approach to analyze a unique dataset of photos taken by people with and without ASD. Our computer modeling was not only able to discern which photos were taken by individuals with ASD, outperforming ASD experts, but importantly, it revealed critical features that led to successful discrimination, revealing aspects of atypical visual attention in ASD from their first-person perspective. En ligne : http://dx.doi.org/10.1002/aur.2376 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=441
in Autism Research > 14-2 (February 2021) . - p.333-342[article] Deep Neural Network Reveals the World of Autism From a First-Person Perspective [Texte imprimé et/ou numérique] / Mindi RUAN, Auteur ; Paula J. WEBSTER, Auteur ; Xin LI, Auteur ; Shuo WANG, Auteur . - p.333-342.
Langues : Anglais (eng)
in Autism Research > 14-2 (February 2021) . - p.333-342
Mots-clés : artificial intelligence attention autism spectrum disorder deep neural network faces photos saliency Index. décimale : PER Périodiques Résumé : People with autism spectrum disorder (ASD) show atypical attention to social stimuli and aberrant gaze when viewing images of the physical world. However, it is unknown how they perceive the world from a first-person perspective. In this study, we used machine learning to classify photos taken in three different categories (people, indoors, and outdoors) as either having been taken by individuals with ASD or by peers without ASD. Our classifier effectively discriminated photos from all three categories, but was particularly successful at classifying photos of people with >80% accuracy. Importantly, visualization of our model revealed critical features that led to successful discrimination and showed that our model adopted a strategy similar to that of ASD experts. Furthermore, for the first time we showed that photos taken by individuals with ASD contained less salient objects, especially in the central visual field. Notably, our model outperformed classification of these photos by ASD experts. Together, we demonstrate an effective and novel method that is capable of discerning photos taken by individuals with ASD and revealing aberrant visual attention in ASD from a unique first-person perspective. Our method may in turn provide an objective measure for evaluations of individuals with ASD. LAY SUMMARY: People with autism spectrum disorder (ASD) demonstrate atypical visual attention to social stimuli. However, it remains largely unclear how they perceive the world from a first-person perspective. In this study, we employed a deep learning approach to analyze a unique dataset of photos taken by people with and without ASD. Our computer modeling was not only able to discern which photos were taken by individuals with ASD, outperforming ASD experts, but importantly, it revealed critical features that led to successful discrimination, revealing aspects of atypical visual attention in ASD from their first-person perspective. En ligne : http://dx.doi.org/10.1002/aur.2376 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=441