Due Date: Friday, March 6 – with a free extension
Turn-in link: Modern Graphical Perception on Canvas
Note: I want to make this be due on Friday, to keep the class pattern uniform. However, we won’t count your initial posting as late providing you make it by Wednesday, March 11th. But be warned, there is a lot of other stuff coming up.
From the week’s readings, you already saw how people were doing experiments in graphical perception in order to develop the guidelines to inform our design choices. This quest to understand these basic issues is continuing. The goal of this assignment is to give you a sense of what the more current graphical perception literature is like, both in terms of its style and its results.
I would like you to pick 2 papers from the following list. If you’re a Statistics student, I strongly recommend that you read 2 (since this issue comes up a lot). Also, pick either 4 or 5 but not both.
- Javed, W., McDonnel, B., & Elmqvist, N. (2010). Graphical perception of multiple time series. IEEE Transactions on Visualization and Computer Graphics, 16(6), 927–34. doi:10.1109/TVCG.2010.162 (author link)
- Talbot, J., Gerth, J., & Hanrahan, P. (2011). Arc length-based aspect ratio selection. IEEE Transactions on Visualization and Computer Graphics, 17(12), 2276–82. doi:10.1109/TVCG.2011.167 (author link)
- Talbot, J., Setlur, V., & Anand, A. (2014). Four Experiments on the Perception of Bar Charts. IEEE Transactions on Visualization and Computer Graphics, 20(12), 2152–2160. doi:10.1109/TVCG.2014.2346320 (author link)
- Albers, D., Correll, M., & Gleicher, M. (2014). Task-Driven Evaluation of Aggregation in Time Series Visualization. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems CHI Conference, 2014, 551–560. doi:10.1145/2556288.2557200 (author link)
- Correll, M. A., Alexander, E. C., & Gleicher, M. (2013). Quantity estimation in visualizations of tagged text. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems – CHI ’13 (p. 2697). New York, New York, USA: ACM Press. doi:10.1145/2470654.2481373 (author link)
When you read, don’t worry about all the gory details. But try to figure out (and write in your post):
- What is the question they are asking?
- What is the short version of the answer? (what did the paper find?)
- How might that finding influence visualization design? What does this paper tell you that you might use in your own visualizations? (assuming that you believe the results)
You may or may not want to think about the details of how the experiments were run (depending on your experimental methodologies background). But as a basic version of the question:
- How compelling is the evidence in the paper that the findings are true enough that you should believe them enough to have them influence your visualization designs?
In other words, those top questions are “what does this paper suggest that you do” and the bottom one is “do you trust the paper’s advice.” For the bottom question, it might be easier to consider the two papers together (which one do you find more convincing? why? how do their strategies of proving their point and explaining their evidence vary?)
Create a posting on the Canvas discussion that answers these questions for the two papers.
