Visualizing Causal Semantics Using Animations

Abstract

Abstract-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.Index Terms-Causality, visualization, semantics, animated graphs, perception, visualizing cause and effect, graph semantics. 1INTRODUCTION Causal relations are deeply rooted in human reasoning and appear in many contexts. Cause-and-effect relationships are used for explaining natural phenomena (the iron will become red under the influence of fire) and for specifying and resolving research questions (do horror movies lead to aggressive behaviour?). In most cases such relationships are intermeshed in the collection of information and data available to the user. To better comprehend cause-and-effect relationships, many visual representations, typically in the form of diagrams, have been developed and are being used extensively.Causal graphs constitute the most common representation of cause-and-effect relationships. These are directed acyclic graphs, in which vertices denote variable features of a phenomenon and edges denote a direct causal claim between these features (Fig. 1). These graphs have appeared in many forms: Feynman diagrams in physics [18], Lombardi diagrams to explain secret deals and suspect relations [6], and influence diagrams to represent the essential elements of a decision problem such as decisions, uncertainties, and objectives, and how they influence each other [16]. In all these variations, the causal graphs replace long verbose descriptions or complex mathematical formulations that describe events with their causes and effects.Although, node-link causal graphs provide information about cause-and-effect, in certain cases it can be very difficult to make credible causal inferences...

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