A Study of Emotion-triggered Adaptation Methods for Interactive Visualization

As the size and complexity of datasets increases, both visualization systems and their users are put under more pressure to offer quick and thorough insights about patterns hidden in this ocean of data. While novel visualization techniques are being developed to better cope with the various data contexts, users find themselves increasingly often under mental bottlenecks that can induce a variety of emotions. In this paper, we execute a study to investigate the effectiveness of various emotiontriggered adaptation methods for visualization systems. The emotions considered are boredom and frustration, and are measured by means of brain-computer interface technology. Our findings suggest that less intrusive adaptive methods perform better at supporting users in overcoming emotional states with low valence or arousal, while more intrusive ones tend to be misinterpreted or perceived as irritating.