Causviz: visual representations of complex causal semantics based on theories of perception

Michotte's theory of ampliation suggests that causal relationships are perceived by objects animated under appropriate spatiotemporal conditions. In this thesis I extend the theory of ampliation and propose that the immediate perception of complex causal relations is also dependent upon a set of structural and temporal rules. The thesis aims at achieving two main goals. The first goal is to define a taxonomy of semantics that describe different causal events in the environment. Ten semantics are defined in this thesis and divided into two main groups; simple causal semantics and complex causal semantics. Simple causal semantics describe basic semantics, which form the building blocks for more complex information and include causal amplification, causal dampening, causal strength, and causal multiplicity. Complex causal semantics are built by enhancing or combining one or more simple semantics and include additive causality, contradictive causality, fully-mediated causality, partially-mediated causality, threshold causality, and bidirectional causality. The second goal of this thesis is to design simple visual representations to describe the causal information. Three representation types were designed during the course of this research; static-graph, static-sequence, and animation. Nine experiments were also conducted to test the effectiveness of these representations. The first five experiments compared the static-graph and the animated representations through Memory Recall and Intuitiveness Evaluations tests. Results of these experiments suggest that animations were ∼8% more accurate and performed ∼9% faster than the static-graph representations. The last four experiments compared an enhanced static representation, called static-sequence, to the animations to test if sequential animation of causal relations had any influence on the superior performance of the animations in the previous experiments. Results of these experiments suggest that there was no significant difference in the performance of the static-sequence representations when compared to the static-graph representations. The results also suggest that the animations performed more accurately than their static counterparts mainly due to their intuitiveness. 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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