  We propose Unified Model of Saliency and Scanpaths (UMSS) -- a model that
learns to predict visual saliency and scanpaths (i.e. sequences of eye
fixations) on information visualisations. Although scanpaths provide rich
information about the importance of different visualisation elements during the
visual exploration process, prior work has been limited to predicting
aggregated attention statistics, such as visual saliency. We present in-depth
analyses of gaze behaviour for different information visualisation elements
(e.g. Title, Label, Data) on the popular MASSVIS dataset. We show that while,
overall, gaze patterns are surprisingly consistent across visualisations and
viewers, there are also structural differences in gaze dynamics for different
elements. Informed by our analyses, UMSS first predicts multi-duration
element-level saliency maps, then probabilistically samples scanpaths from
them. Extensive experiments on MASSVIS show that our method consistently
outperforms state-of-the-art methods with respect to several, widely used
scanpath and saliency evaluation metrics. Our method achieves a relative
improvement in sequence score of 11.5% for scanpath prediction, and a relative
improvement in Pearson correlation coefficient of up to 23.6% for saliency
prediction. These results are auspicious and point towards richer user models
and simulations of visual attention on visualisations without the need for any
eye tracking equipment.
