Combining Trending Scan Paths with Arousal to Model Visual Behaviour on the Web

Matthews, Oludamilare; Eraslan, Şükrü; Yaneva, Victoria; Davies, Alan; Yeşi̇lada, Yeli̇z; Vigo, Markel; Harper, Simon

doi:10.1145/3320435.3320446

Cited by 3 publications

(1 citation statement)

References 40 publications

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“…For example, Balestra et al 62 showed that it can be used to study language impairments, text comprehension and production deficits. In addition, it can be used to identify fixation and saccades 63 , recognise affective states 64 and even reveal early biomarkers associated with ASD 65,66 . Furthermore, eye tracking can be used to detect saliency differences between ASD and TD children.…”

Section: Eye Gaze Datamentioning

confidence: 99%

Computer vision in autism spectrum disorder research: a systematic review of published studies from 2009 to 2019

et al. 2020

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The current state of computer vision methods applied to autism spectrum disorder (ASD) research has not been well established. Increasing evidence suggests that computer vision techniques have a strong impact on autism research. The primary objective of this systematic review is to examine how computer vision analysis has been useful in ASD diagnosis, therapy and autism research in general. A systematic review of publications indexed on PubMed, IEEE Xplore and ACM Digital Library was conducted from 2009 to 2019. Search terms included [‘autis*’ AND (‘computer vision’ OR ‘behavio* imaging’ OR ‘behavio* analysis’ OR ‘affective computing’)]. Results are reported according to PRISMA statement. A total of 94 studies are included in the analysis. Eligible papers are categorised based on the potential biological/behavioural markers quantified in each study. Then, different computer vision approaches that were employed in the included papers are described. Different publicly available datasets are also reviewed in order to rapidly familiarise researchers with datasets applicable to their field and to accelerate both new behavioural and technological work on autism research. Finally, future research directions are outlined. The findings in this review suggest that computer vision analysis is useful for the quantification of behavioural/biological markers which can further lead to a more objective analysis in autism research.

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Section: Eye Gaze Datamentioning

confidence: 99%

Computer vision in autism spectrum disorder research: a systematic review of published studies from 2009 to 2019

et al. 2020

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Optical Character Recognition (OCR)-Based and Gaussian Mixture Modeling-OCR-Based Slide-Level “With-Me-Ness”: Automated Measurement and Feedback of Learners’ Attention State during Video Lectures

Lyu

Chen

Peng

et al. 2023

International Journal of Human–Computer Interaction

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Effect of Adaptive Guidance and Visualization Literacy on Gaze Attentive Behaviors and Sequential Patterns on Magazine-Style Narrative Visualizations

BarralOswald

Lallé

IranpourAlireza

et al. 2021

ACM Trans. Interact. Intell. Syst.

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We study the effectiveness of adaptive interventions at helping users process textual documents with embedded visualizations, a form of multimodal documents known as Magazine-Style Narrative Visualizations (MSNVs). The interventions are meant to dynamically highlight in the visualization the datapoints that are described in the textual sentence currently being read by the user, as captured by eye-tracking. These interventions were previously evaluated in two user studies that involved 98 participants reading excerpts of real-world MSNVs during a 1-hour session. Participants’ outcomes included their subjective feedback about the guidance, and well as their reading time and score on a set of comprehension questions. Results showed that the interventions can increase comprehension of the MSNV excerpts for users with lower levels of a cognitive skill known as visualization literacy. In this article, we aim to further investigate this result by leveraging eye-tracking to analyze in depth how the participants processed the interventions depending on their levels of visualization literacy. We first analyzed summative gaze metrics that capture how users process and integrate the key components of the narrative visualizations. Second, we mined the salient patterns in the users’ scanpaths to contextualize how users sequentially process these components. Results indicate that the interventions succeed in guiding attention to salient components of the narrative visualizations, especially by generating more transitions between key components of the visualization (i.e., datapoints, labels, and legend), as well as between the two modalities (text and visualization). We also show that the interventions help users with lower levels of visualization literacy to better map datapoints to the legend, which likely contributed to their improved comprehension of the documents. These findings shed light on how adaptive interventions help users with different levels of visualization literacy, informing the design of personalized narrative visualizations.

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Combining Trending Scan Paths with Arousal to Model Visual Behaviour on the Web

Cited by 3 publications

References 40 publications

Computer vision in autism spectrum disorder research: a systematic review of published studies from 2009 to 2019

Computer vision in autism spectrum disorder research: a systematic review of published studies from 2009 to 2019

Optical Character Recognition (OCR)-Based and Gaussian Mixture Modeling-OCR-Based Slide-Level “With-Me-Ness”: Automated Measurement and Feedback of Learners’ Attention State during Video Lectures

Effect of Adaptive Guidance and Visualization Literacy on Gaze Attentive Behaviors and Sequential Patterns on Magazine-Style Narrative Visualizations

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