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Visualizing Causal Semantics Using Animations
November/December 2007 (vol. 13 no. 6)
pp. 1254-1261
Michotte's theory of ampliation suggests that causal relationships are perceived by objects animated under appropriate spatiotemporal conditions. We extend the theory of ampliation and propose that the immediate perception of complex causal relations is also dependent on a set of structural and temporal rules. We designed animated representations, based on Michotte's rules, for showing complex causal relationships or causal semantics. In this paper we describe a set of animations for showing semantics such as causal amplification, causal strength, causal dampening, and causal multiplicity. In a two part study we compared the effectiveness of both the static and animated representations. The first study (N=44) asked participants to recall passages that were previously displayed using both types of representations. Participants were 8% more accurate in recalling causal semantics when they were presented using animations instead of static graphs. In the second study (N=112) we evaluated the intuitiveness of the representations. Our results showed that while users were as accurate with the static graphs as with the animations, they were 9% faster in matching the correct causal statements in the animated condition. Overall our results show that animated diagrams that are designed based on perceptual rules such as those proposed by Michotte have the potential to facilitate comprehension of complex causal relations.

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Index Terms:
Causality, visualization, semantics, animated graphs, perception, visualizing cause and effect, graph semantics.
Citation:
Nivedita Kadaba, Pourang Irani, Jason Leboe, "Visualizing Causal Semantics Using Animations," IEEE Transactions on Visualization and Computer Graphics, vol. 13, no. 6, pp. 1254-1261, Nov.-Dec. 2007, doi:10.1109/TVCG.2007.70618
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