Visualizing Causal Semantics

Project Description


In this project we are designing simple animations to depict causal relations. Causal (or cause-effect) relations are defined when an event causes another event to occur. Causal events are abundant in the environment and play an important role in decision making. Our investigations focus on achieving two goals: ascertaining the semantics that comprise a causal relation and designing simple animations to illustrate these semantics. We are comparing our representations to a textual description and to a static (graph) representation of the relations in order to determine if animations are more effective in depicting the causal semantics. Results of our studies show that participants respond faster and more accurately when viewing the animations as stand-alone representations or along with textual descriptions.


Project Publications

Nivedita R. Kadaba, Pourang P. Irani and Jason Leboe. 2009. Analyzing Animated Representations of Complex Causal Semantics. In Proceedings of the 6th Symposium on Applied Perception in Graphics and Visualization (APGV '09). Chania, Crete, Greece. ACM, 77-84.
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Nivedita Kadaba, Pourang Irani and Jason Leboe. 2007. Visualizing Causal Semantics using Animations. IEEE Transactions on Visualization and Computer Graphics, 13(6), 1254-1261.
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Nivedita Kadaba. 2006. Perceptual based visualizations for time-dependent semantics. Master's thesis, University of Manitoba.

Nivedita Kadaba, Pourang Irani and Michel Toulouse. 2005. Visualizing Time Dependent Semantics: An Application to Quantum Algorithms. In Proceedings of the IEEE Conference on Information Visualization (IV '05), 182-187.
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