Abstract
Our visual system has evolved to process a dynamic world. We can rapidly summarize the average speeds and trajectories of moving objects at high accuracies (Haberman & Whitney, 2012; Watamaniuk & Duchon, 1992; Williams & Sekuler, 1984). Dynamic data visualizations can convey large amounts of information across time, such as using movements to depict changes in data values for multiple entities. Such dynamic displays demand our visual processing capacities, yet our visual system is limited when perceiving motion. When tracking multiple objects across space and time, we can typically track up to four objects, and the capacity is even lower if we also need to remember the history of the objects’ features (Horowitz et al., 2007). Several techniques have been shown to improve the processing of dynamic displays. Staging the animation to sequentially show steps in a transition and tracing object movement by displaying trajectory histories can increase processing by reducing the cognitive load (Fisher, 2010; Robertson et al., 2008). We examine the effectiveness of staging and tracing in dynamic displays. We showed participants animated line charts depicting the movements of four lines and asked them to identify the line with the highest mean and variance. We manipulated the animation to display the lines sequentially or synchronously, either with or without tracing, and compared the results to a static chart as control. Preliminary results showed that, for the mean comparison task, displaying lines sequentially without tracing is associated with increased accuracy, despite participants expressing low preferences for this animation style. For the variance comparison task, tracing is associated with higher accuracy, with the static chart outperforming most dynamic displays. Overall, the benefit of dynamic displays can be task-dependent and misaligned with human preferences. These findings help inform real-world best practices for constructing dynamic displays to harness our visual processing.