Impact of interaction technique in interactive data visualisations: A study on lookup, comparison, and relation-seeking tasks

This paper presents an analysis of different interaction techniques used in interactive data visualisations to support end-users in visual analytics tasks. Our selection of interaction techniques is based on prior work and consists of the interaction techniques Select, Explore, Reconfigure, Encode, Filter, Abstract/Elaborate, and Connect. Through a within-subject study, we assessed participants’ abilities to utilise these techniques when faced with three distinct types of data-driven tasks; lookup, comparison, and Relation-seeking. Our research investigates the impact of these interaction techniques on the correctness, confidence, perceived difficulty, and cognitive load of N = 80 self-identified data scientists and N = 80 non-experts. We find that interaction technique significantly impacts answer correctness and participant confidence. Participants performed best across those interaction techniques that allow for information that is deemed least relevant to be concealed, which is reflected in lower intrinsic and extraneous cognitive load. Interestingly, participants’ expertise affected their confidence but not their accuracy. Our results provide insights useful for a more targeted and informed design and usage of interactive data visualisations.