Yao Wang
University of Stuttgart, Stuttgart, Germany
Understanding the link between visual attention and users' information 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 to track user attention 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 close correlation between information needs and visual saliency, we propose the first computational method to predict question-driven saliency on visualisations. Our method outperforms state-of-the-art saliency models for several metrics, such as the correlation coefficient and the Kullback-Leibler divergence. These results show the importance of information needs for shaping attentive behaviour and pave the way for new applications, such as task-driven optimisation of visualisations or explainable AI in chart question-answering.
doi.org/10.1145/3613904.3642942
The ACM CHI Conference on Human Factors in Computing Systems (https://chi2024.acm.org/)