Motion-based visual encoding can improve performance on perceptual tasks with dynamic time series

TL;DR

The paper investigates motion-based encoding and animation design for dynamic time-series by evaluating staging, tracing, and history across multiple lines, and by treating animation speed as a data-encoding channel. Using four experiments with animated line charts and static controls, it reveals that synchronous displays with tracing/history offer the best perceptual accuracy for mean, variance, and outlier tasks, while data-speed congruence can enhance performance but misalignment can hurt it. The work provides design guidelines balancing cognitive load, engagement, and interpretability, and demonstrates the practical potential and limits of speed-encoding in dynamic visualizations. Limitations include scope to small-line-count line charts and a need to generalize to broader chart types, larger datasets, and individual differences in perception and preference.

Abstract

Dynamic data visualizations can convey large amounts of information over time, such as using motion to depict changes in data values for multiple entities. Such dynamic displays put a demand on our visual processing capacities, yet our perception of motion is limited. 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 improve processing by reducing the cognitive load. In this paper, We examine the effectiveness of staging and tracing in dynamic displays. We showed participants animated line charts depicting the movements of lines and asked them to identify the line with the highest mean and variance. We manipulated the animation to display the lines with or without staging, tracing and history, and compared the results to a static chart as a control. Results showed that tracing and staging are preferred by participants, and improve their performance in mean and variance tasks respectively. They also preferred display time 3 times shorter when staging is used. Also, encoding animation speed with mean and variance in congruent tasks is associated with higher accuracy. These findings help inform real-world best practices for building dynamic displays. The supplementary materials can be found at https://osf.io/8c95v/

Figures (7)

• Figure 1: Set-up for Experiments 1-3.
• Figure 2: Recalling mean value has the highest accuracy in all conditions.
• Figure 3: Accuracy drops with more lines in all conditions.
• Figure 4: Performance and preference rankings of 4-line dynamic series across conditions. The conditions on top mean higher performance or stronger preference. We observed asymmetry between subjective preference and objective performance.
• Figure 5: Experiment 2, Figure (a) shows one participant's response for condition sequential, trace, history. The mean value of speeds where “OK” is reported is taken as the preferred speed for that participant. Figure (b) shows the distribution of the preferred speed across 100 participants, where the speed derived in Figure (a) is marked as orange. Figure (c) shows the distribution of preferred speed for different designs in each column with strip plots like figure (b). It also shows the mean and standard deviation of the preferred speed distribution in each design.