Classification of Autism Spectrum Disorder Severity Using Eye Tracking Data Based on Visual Attention Model

2021 ◽  
Author(s):  
Mirian C. Revers ◽  
Jessica S. Oliveira ◽  
Felipe O. Franco ◽  
Joana Portolese ◽  
Thiago V. Cardoso ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Visual Attention ◽  
Eye Tracking ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Tracking Data ◽  
Visual Attention Model ◽  
Attention Model

scholarly journals Context Modulates Attention to Faces in Dynamic Social Scenes in Children and Adults with Autism Spectrum Disorder

2021 ◽  
Author(s):  
Dzmitry A. Kaliukhovich ◽  
Nikolay V. Manyakov ◽  
Abigail Bangerter ◽  
Gahan Pandina
Keyword(s):  
Autism Spectrum Disorder ◽  
Visual Attention ◽  
Eye Tracking ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Social Orientation ◽  
Typically Developing ◽  
Social Stimuli ◽  
Adults With Autism ◽  
Social Scenes

AbstractIndividuals with autism spectrum disorder (ASD) have been found to view social scenes differently compared to typically developing (TD) peers, but results can vary depending on context and age. We used eye-tracking in children and adults (age 6–63) to assess allocation of visual attention in a dynamic social orientation paradigm previously used only in younger children. The ASD group (n = 94) looked less at the actor’s face compared to TD (n = 38) when they were engaged in activity (mean percentage of looking time, ASD = 30.7% vs TD = 34.9%; Cohen’s d = 0.56; p value < 0.03) or looking at a moving toy (24.5% vs 33.2%; d = 0.65; p value < 0.001). Findings indicate that there are qualitative differences in allocation of visual attention to social stimuli across ages in ASD.ClinicalTrials.gov identifier: NCT02668991.

scholarly journals Computer-Aided Screening of Autism Spectrum Disorder Using Eye-Tracking, Data Visualization and Deep Learning (Preprint)

10.2196/27706 ◽  
2021 ◽  
Author(s):  
Federica Cilia ◽  
Romuald Carette ◽  
Mahmoud Elbattah ◽  
Gilles Dequen ◽  
Jean-Luc Guérin ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Deep Learning ◽  
Eye Tracking ◽  
Data Visualization ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Tracking Data ◽  
Computer Aided

Markov chain based computational visual attention model that learns from eye tracking data

2014 ◽  
Vol 49 ◽  
pp. 1-10 ◽  
Author(s):  
Ma Zhong ◽  
Xinbo Zhao ◽  
Xiao-chun Zou ◽  
James Z. Wang ◽  
Wenhu Wang
Keyword(s):  
Markov Chain ◽  
Visual Attention ◽  
Eye Tracking ◽  
Tracking Data ◽  
Visual Attention Model ◽  
Attention Model

scholarly journals Using Eye Tracking to Explore Visual Attention in Adolescents With Autism Spectrum Disorder

2021 ◽  
Vol 10 (1) ◽  
pp. 1-18
Author(s):  
Anne M. P. Michalek ◽  
Jonna Bobzien ◽  
Victor A. Lugo ◽  
Chung Hao Chen ◽  
Ann Bruhn ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Visual Attention ◽  
Eye Tracking ◽  
Eye Gaze ◽  
Visual Image ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Social Story ◽  
Adolescents With Autism ◽  
Adolescents With Asd

Video social stories are used to facilitate understanding of social situations for adolescents with autism spectrum disorder (ASD). This study explored the use of eye tracking technology to understand how adolescents with and without ASD visually attend to video social story content and whether visual attention is related to content comprehension. Six adolescents, with and without ASD, viewed a video social story of visiting a dental office. Eye gaze metrics, including fixation duration and count, and visit duration were collected to measure visual attention, and a knowledge assessment was administered for comprehension. Results indicated adolescents with ASD fixated and maintained visual attention at rates lower than peers without ASD. Adolescents with ASD scored higher (M=77.78) than peers without ASD (M=72.22) on the assessment indicating no relationship between eye gaze metrics and knowledge accuracy. Impact and implications of visual image type on frequency and duration of visual attention generated by participants is discussed.

scholarly journals A Visual Attention Model Based on Eye Tracking in 3D Scene Maps

2021 ◽  
Vol 10 (10) ◽  
pp. 664
Author(s):  
Bincheng Yang ◽  
Hongwei Li
Keyword(s):  
Visual Attention ◽  
Eye Tracking ◽  
Goodness Of Fit ◽  
Quantitative Relationship ◽  
Visual Saliency ◽  
Tracking Data ◽  
Visual Attention Model ◽  
Attention Model ◽  
Map Reading ◽  
3D Scene

Visual attention plays a crucial role in the map-reading process and is closely related to the map cognitive process. Eye-tracking data contains a wealth of visual information that can be used to identify cognitive behavior during map reading. Nevertheless, few researchers have applied these data to quantifying visual attention. This study proposes a method for quantitatively calculating visual attention based on eye-tracking data for 3D scene maps. First, eye-tracking technology was used to obtain the differences in the participants’ gaze behavior when browsing a street view map in the desktop environment, and to establish a quantitative relationship between eye movement indexes and visual saliency. Then, experiments were carried out to determine the quantitative relationship between visual saliency and visual factors, using vector 3D scene maps as stimulus material. Finally, a visual attention model was obtained by fitting the data. It was shown that a combination of three visual factors can represent the visual attention value of a 3D scene map: color, shape, and size, with a goodness of fit (R2) greater than 0.699. The current research helps to determine and quantify the visual attention allocation during map reading, laying the foundation for automated machine mapping.

A Statistical Physics Perspective to Understand Social Visual Attention in Autism Spectrum Disorder

Perception ◽  
2017 ◽  
Vol 46 (8) ◽  
pp. 889-913 ◽  
Author(s):  
Alessio Liberati ◽  
Roberta Fadda ◽  
Giuseppe Doneddu ◽  
Sara Congiu ◽  
Marco A. Javarone ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Eye Movements ◽  
Visual Attention ◽  
Eye Tracking ◽  
Statistical Physics ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Visual Exploration ◽  
Subjective Expectations ◽  
Children With Asd

This study investigated social visual attention in children with Autism Spectrum Disorder (ASD) and with typical development (TD) in the light of Brockmann and Geisel’s model of visual attention. The probability distribution of gaze movements and clustering of gaze points, registered with eye-tracking technology, was studied during a free visual exploration of a gaze stimulus. A data-driven analysis of the distribution of eye movements was chosen to overcome any possible methodological problems related to the subjective expectations of the experimenters about the informative contents of the image in addition to a computational model to simulate group differences. Analysis of the eye-tracking data indicated that the scanpaths of children with TD and ASD were characterized by eye movements geometrically equivalent to Lévy flights. Children with ASD showed a higher frequency of long saccadic amplitudes compared with controls. A clustering analysis revealed a greater dispersion of eye movements for these children. Modeling of the results indicated higher values of the model parameter modulating the dispersion of eye movements for children with ASD. Together, the experimental results and the model point to a greater dispersion of gaze points in ASD.

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