scholarly journals Visual Noise Effect on Contour Integration and Gaze Allocation in Autism Spectrum Disorder

2021 ◽  
Vol 15 ◽  
Author(s):  
Milena Slavcheva Mihaylova ◽  
Nadejda Bogdanova Bocheva ◽  
Tsvetalin Totev Totev ◽  
Svetla Nikolaeva Staykova

Contradictory results have been obtained in the studies that compare contour integration abilities in Autism Spectrum Disorders (ASDs) and typically developing individuals. The present study aimed to explore the limiting factors of contour integration ability in ASD and verify the role of the external visual noise by a combination of psychophysical and eye-tracking approaches. To this aim, 24 children and adolescents with ASD and 32 age-matched participants with typical development had to detect the presence of contour embedded among similar Gabor elements in a Yes/No procedure. The results obtained showed that the responses in the group with ASD were not only less accurate but also were significantly slower compared to the control group at all noise levels. The detection performance depended on the group differences in addition to the effect of the intellectual functioning of the participants from both groups. The comparison of the agreement and accuracy of the responses in the double-pass experiment showed that the results of the participants with ASD are more affected by the increase of the external noise. It turned out that the internal noise depends on the level of the added external noise: the difference between the two groups was non-significant at the low external noise and significant at the high external noise. In accordance with the psychophysical results, the eye-tracking data indicated a larger gaze allocation area in the group with autism. These findings may imply higher positional uncertainty in ASD due to the inability to maintain the information of the contour location from previous presentations and interference from noise elements in the contour vicinity. Psychophysical and eye-tracking data suggest lower efficiency in using stimulus information in the ASD group that could be caused by fixation instability and noisy and unstable perceptual template that affects noise filtering.

2015 ◽  
Vol 19 (3) ◽  
pp. 520-532 ◽  
Author(s):  
GLORIA CHAMORRO ◽  
ANTONELLA SORACE ◽  
PATRICK STURT

The recent hypothesis that L1 attrition affects the ability to process interface structures but not knowledge representations (Sorace, 2011) is tested by investigating the effects of recent L1 re-exposure on antecedent preferences for Spanish pronominal subjects, using offline judgements and online eye-tracking measures. Participants included a group of native Spanish speakers experiencing L1 attrition (‘attriters’), a second group of attriters exposed exclusively to Spanish before they were tested (‘re-exposed’), and a control group of Spanish monolinguals. The judgement data shows no significant differences between the groups. Moreover, the monolingual and re-exposed groups are not significantly different from each other in the eye-tracking data. The results of this novel manipulation indicate that attrition effects decrease due to L1 re-exposure, and that bilinguals are sensitive to input changes. Taken together, the findings suggest that attrition affects online sensitivity with interface structures rather than causing a permanent change in speakers’ L1 knowledge representations.


2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Woon Ju Park ◽  
Kimberly B. Schauder ◽  
Ruyuan Zhang ◽  
Loisa Bennetto ◽  
Duje Tadin

10.2196/27706 ◽  
2021 ◽  
Author(s):  
Federica Cilia ◽  
Romuald Carette ◽  
Mahmoud Elbattah ◽  
Gilles Dequen ◽  
Jean-Luc Guérin ◽  
...  

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jessica S. Oliveira ◽  
Felipe O. Franco ◽  
Mirian C. Revers ◽  
Andréia F. Silva ◽  
Joana Portolese ◽  
...  

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.


2019 ◽  
Author(s):  
J. Galli ◽  
F. Gitti ◽  
M. Lanaro ◽  
A. Rizzi ◽  
M.A. Pavlova ◽  
...  

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