scholarly journals Computer-aided autism diagnosis based on visual attention models using eye tracking

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jessica S. Oliveira ◽  
Felipe O. Franco ◽  
Mirian C. Revers ◽  
Andréia F. Silva ◽  
Joana Portolese ◽  
...  
Keyword(s):  
Visual Attention ◽  
Eye Tracking ◽  
Autism Spectrum ◽  
Computational Method ◽  
Video Frame ◽  
Tracking Data ◽  
Attention Model ◽  
Group Case ◽  
Computer Aided ◽  
Fixation Points

AbstractAn advantage of using eye tracking for diagnosis is that it is non-invasive and can be performed in individuals with different functional levels and ages. Computer/aided diagnosis using eye tracking data is commonly based on eye fixation points in some regions of interest (ROI) in an image. However, besides the need for every ROI demarcation in each image or video frame used in the experiment, the diversity of visual features contained in each ROI may compromise the characterization of visual attention in each group (case or control) and consequent diagnosis accuracy. Although some approaches use eye tracking signals for aiding diagnosis, it is still a challenge to identify frames of interest when videos are used as stimuli and to select relevant characteristics extracted from the videos. This is mainly observed in applications for autism spectrum disorder (ASD) diagnosis. To address these issues, the present paper proposes: (1) a computational method, integrating concepts of Visual Attention Model, Image Processing and Artificial Intelligence techniques for learning a model for each group (case and control) using eye tracking data, and (2) a supervised classifier that, using the learned models, performs the diagnosis. Although this approach is not disorder-specific, it was tested in the context of ASD diagnosis, obtaining an average of precision, recall and specificity of 90%, 69% and 93%, respectively.

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 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.

scholarly journals Visual Attention Software: A New Tool for Understanding the “Subliminal” Experience of the Built Environment

2021 ◽  
Vol 11 (13) ◽  
pp. 6197
Author(s):  
Alexandros A. Lavdas ◽  
Nikos A. Salingaros ◽  
Ann Sussman
Keyword(s):  
Visual Attention ◽  
Eye Tracking ◽  
Wearable Sensors ◽  
Computer Gaming ◽  
Geometric Patterns ◽  
Relative Design ◽  
Fixation Points ◽  
High Degree ◽  
Artificial Intelligence Application ◽  
Intelligence Application

Eye-tracking technology is a biometric tool that has found many commercial and research applications. The recent advent of affordable wearable sensors has considerably expanded the range of these possibilities to fields such as computer gaming, education, entertainment, health, neuromarketing, psychology, etc. The Visual Attention Software by 3M (3M-VAS) is an artificial intelligence application that was formulated using experimental data from eye-tracking. It can be used to predict viewer reactions to images, generating fixation point probability maps and fixation point sequence estimations, thus revealing pre-attentive processing of visual stimuli with a very high degree of accuracy. We have used 3M-VAS software in an innovative implementation to analyze images of different buildings, either in their original state or photographically manipulated, as well as various geometric patterns. The software not only reveals non-obvious fixation points, but also overall relative design coherence, a key element of Christopher Alexander’s theory of geometrical order. A more evenly distributed field of attention seen in some structures contrasts with other buildings being ignored, those showing instead unconnected points of splintered attention. Our findings are non-intuitive and surprising. We link these results to both Alexander’s theory and Neuroscience, identify potential pitfalls in the software’s use, and also suggest ways to avoid them.

scholarly journals A Unique Visual Attention Profile Associated With the FMR1 Premutation

2021 ◽  
Vol 12 ◽  
Author(s):  
Molly Winston ◽  
Kritika Nayar ◽  
Emily Landau ◽  
Nell Maltman ◽  
John Sideris ◽  
...  
Keyword(s):  
Visual Attention ◽  
Eye Tracking ◽  
Picture Book ◽  
Fragile X ◽  
Autism Spectrum ◽  
Social Stimuli ◽  
Fmr1 Premutation ◽  
Tracking Tasks ◽  
Neurocognitive Profile ◽  
Areas Of Interest

Atypical visual attention patterns have been observed among carriers of the fragile X mental retardation gene (FMR1) premutation (PM), with some similarities to visual attention patterns observed in autism spectrum disorder (ASD) and among clinically unaffected relatives of individuals with ASD. Patterns of visual attention could constitute biomarkers that can help to inform the neurocognitive profile of the PM, and that potentially span diagnostic boundaries. This study examined patterns of eye movement across an array of fixation measurements from three distinct eye-tracking tasks in order to investigate potentially overlapping profiles of visual attention among PM carriers, ASD parents, and parent controls. Logistic regression analyses were conducted to examine whether variables constituting a PM-specific looking profile were able to effectively predict group membership. Participants included 65PM female carriers, 188 ASD parents, and 84 parent controls. Analyses of fixations across the eye-tracking tasks, and their corresponding areas of interest, revealed a distinct visual attention pattern in carriers of the FMR1 PM, characterized by increased fixations on the mouth when viewing faces, more intense focus on bodies in socially complex scenes, and decreased fixations on salient characters and faces while narrating a wordless picture book. This set of variables was able to successfully differentiate individuals with the PM from controls (Sensitivity = 0.76, Specificity = 0.85, Accuracy = 0.77) as well as from ASD parents (Sensitivity = 0.70, Specificity = 0.80, Accuracy = 0.72), but did not show a strong distinction between ASD parents and controls (Accuracy = 0.62), indicating that this set of variables comprises a profile that is unique to PM carriers. Regarding predictive power, fixations toward the mouth when viewing faces was able to differentiate PM carriers from both ASD parents and controls, whereas fixations toward other social stimuli did not differentiate PM carriers from ASD parents, highlighting some overlap in visual attention patterns that could point toward shared neurobiological mechanisms. Results demonstrate a profile of visual attention that appears strongly associated with the FMR1 PM in women, and may constitute a meaningful biomarker.

scholarly journals Are Photographs Worth More Than a Thousand Words? Examining the Effects of Photographic–Textual and Textual-Only Frames on Public Attitude Toward Nuclear Energy and Nanotechnology

2018 ◽  
Vol 95 (4) ◽  
pp. 948-970 ◽  
Author(s):  
Edmund W. J. Lee ◽  
Shirley S. Ho
Keyword(s):  
Risk Perception ◽  
Visual Attention ◽  
Eye Tracking ◽  
Nuclear Energy ◽  
Public Support ◽  
Public Attitude ◽  
Theory And Practice ◽  
Tracking Data ◽  
Subject Design ◽  
The Impact

This study examines the impact of photographic–textual and risk–benefit frames on the level of visual attention, risk perception, and public support for nuclear energy and nanotechnology in Singapore. Using a 2 (photographic–textual vs. textual-only frames) × 2 (risk vs. benefit frames) × 2 (nuclear energy vs. nanotechnology) between-subject design with eye-tracking data, the results showed that photographic–textual frames elicited more attention and did have partial amplification effect. However, this was observable only in the context of nuclear energy, where public support was lowest when participants were exposed to risk frames accompanied by photographs. Implications for theory and practice were discussed.

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.

Gaze Following and Pupil Dilation as Objective Measures of Early Diagnosis of Children with ASD

2020 ◽  
Author(s):  
Raquel Camero ◽  
Verónica Martínez ◽  
Carlos Gallego
Keyword(s):  
Early Detection ◽  
Early Diagnosis ◽  
Visual Attention ◽  
Eye Tracking ◽  
Autism Spectrum ◽  
Pupil Dilation ◽  
Gaze Following ◽  
Objective Measures ◽  
Human Face ◽  
Children With Asd

(1) Background: Children with autism spectrum disorder (ASD) show certain characteristics in visual attention which generate difficulties in the integration of relevant social information to set the basis of communication. Gaze following and pupil dilation could be used to identify signs for the early detection of ASD. Eye-tracking methodology allows objective measurement of these anomalies in visual attention. The aim is to determine whether measurements of gaze following and pupillary dilation in a linguistic interaction task, captured using eye-tracking methodology, are objective for early diagnosis of ASD. (2) Methods: 20 children between 17 and 24 months of age, made up of 10 neurotypical children and 10 children with ASD were paired together according to chronological age. A human face on a monitor pronounced pseudowords associated with pseudo-objects. Gaze following and pupil dilation was registered during the task. (3) Results: Significant statistical differences were found in the time of gaze fixation on the human face and on the object, as well as in the number of gazes. Also, there were significant differences in the maximum peak of pupil dilation, this being found in the neurotypical group at the moment of processing of the pseudoword, and in the ASD group in the baseline prior to the task (4) Conclusions: The registration and the duration of gaze, and the measurement of pupil dilation with &lsquo;eye-tracker&rsquo; are objective measures for early detection of ASD.

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