Universität Stuttgart

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Institute: search.filters.UBSInstitut.Fakultätsübergreifend / Sonstige EinrichtungAuthor: search.filters.author.Wang, Yao

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    VisRecall++: analysing and predicting visualisation recallability from gaze behaviour
    (2024) Wang, Yao; Jiang, Yue; Hu, Zhiming; Ruhdorfer, Constantin; Bâce, Mihai; Bulling, Andreas
    Question answering has recently been proposed as a promising means to assess the recallability of information visualisations. However, prior works are yet to study the link between visually encoding a visualisation in memory and recall performance. To fill this gap, we propose VisRecall++ - a novel 40-participant recallability dataset that contains gaze data on 200 visualisations and five question types, such as identifying the title, and finding extreme values.We measured recallability by asking participants questions after they observed the visualisation for 10 seconds.Our analyses reveal several insights, such as saccade amplitude, number of fixations, and fixation duration significantly differ between high and low recallability groups.Finally, we propose GazeRecallNet - a novel computational method to predict recallability from gaze behaviour that outperforms several baselines on this task.Taken together, our results shed light on assessing recallability from gaze behaviour and inform future work on recallability-based visualisation optimisation.
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    SalChartQA: question-driven saliency on information visualisations
    (2024) Wang, Yao; Wang, Weitian; Abdelhafez, Abdullah; Elfares, Mayar; Hu, Zhiming; Bâce, Mihai; Bulling, Andreas
    Understanding the link between visual attention and user’s needs when visually exploring information visualisations is under-explored due to a lack of large and diverse datasets to facilitate these analyses. To fill this gap, we introduce SalChartQA - a novel crowd-sourced dataset that uses the BubbleView interface as a proxy for human gaze and a question-answering (QA) paradigm to induce different information needs in users. SalChartQA contains 74,340 answers to 6,000 questions on 3,000 visualisations. Informed by our analyses demonstrating the tight correlation between the question and visual saliency, we propose the first computational method to predict question-driven saliency on information visualisations. Our method outperforms state-of-the-art saliency models, improving several metrics, such as the correlation coefficient and the Kullback-Leibler divergence. These results show the importance of information needs for shaping attention behaviour and paving the way for new applications, such as task-driven optimisation of visualisations or explainable AI in chart question-answering.
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    VisRecall: quantifying information visualisation recallability via question answering
    (2022) Wang, Yao; Jiao, Chuhan; Bâce, Mihai; Bulling, Andreas
    Despite its importance for assessing the effectiveness of communicating information visually, fine-grained recallability of information visualisations has not been studied quantitatively so far. In this work, we propose a question-answering paradigm to study visualisation recallability and present VisRecall - a novel dataset consisting of 200 visualisations that are annotated with crowd-sourced human (N = 305) recallability scores obtained from 1,000 questions of five question types. Furthermore, we present the first computational method to predict recallability of different visualisation elements, such as the title or specific data values. We report detailed analyses of our method on VisRecall and demonstrate that it outperforms several baselines in overall recallability and FE-, F-, RV-, and U-question recallability. Our work makes fundamental contributions towards a new generation of methods to assist designers in optimising visualisations.
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    Saliency3D: a 3D saliency dataset collected on screen
    (2024) Wang, Yao; Dai, Qi; Bâce, Mihai; Klein, Karsten; Bulling, Andreas
    While visual saliency has recently been studied in 3D, the experimental setup for collecting 3D saliency data can be expensive and cumbersome. To address this challenge, we propose a novel experimental design that utilizes an eye tracker on a screen to collect 3D saliency data. Our experimental design reduces the cost and complexity of 3D saliency dataset collection. We first collect gaze data on a screen, then we map them to 3D saliency data through perspective transformation. Using this method, we collect a 3D saliency dataset (49,276 fixations) comprising 10 participants looking at sixteen objects. Moreover, we examine the viewing preferences for objects and discuss our findings in this study. Our results indicate potential preferred viewing directions and a correlation between salient features and the variation in viewing directions.