Dynamic data visualizations as events: effects of framing and change salience on segmenting dynamic maps

Area of Science:

• Cognitive Psychology
• Information Visualization
• Human-Computer Interaction

Background:

• Event segmentation theory explains how humans divide continuous experiences into discrete events.
• Previous research focused on naturalistic stimuli, leaving generalization to data visualizations unexplored.

Purpose of the Study:

• To investigate if event segmentation theory applies to dynamic thematic maps.
• To examine how perceptual and conceptual factors influence segmentation agreement in visualizations.

Main Methods:

• Three experiments using dynamic thematic maps depicting fictional insect population changes.
• Manipulation of trend direction, conceptual framing (endangered/invasive), and perceptual salience (color scales, spatial patterns).

Main Results:

• Inter-individual segmentation agreement in dynamic maps occurs above chance and is influenced by trend direction.
• A three-way interaction found: agreement was higher when framing matched trend direction, but only with high visual salience.
• Findings partially replicated, showing higher agreement for negative trends when decline was salient and framed as endangered.

Conclusions:

• Symbolic visualizations are subject to event segmentation processes.
• Both bottom-up perceptual features and top-down conceptual expectations are crucial for forming and updating internal event models in visualizations.