Readings 08: Why Does (or doesn't) Vis Work?

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For this week’s readings, we’ll have a grab bag of papers that don’t necessarily fit in well elsewhere. The first two are general background about perception and cognition - things I would have had you read in week 2. The other readings are about statistics and exploration - which is important since we’ll be doing it in the design exercises.

  1. (required)  The Dance of Meaning (Chapter 9 of Visual Thinking for Design) (Ware-9-Meaning.pdf 2.7mb)

    Yes, we’re reading the last chapter first. It’s basically a summary of the book, followed by the implications - which makes it a pretty self-contained introduction to the perceptual motivations of visualization. It points out some things about how we see, and then tells us how that can help us make effective visualizations. It’s an unusual, informal book (see the discussion), we’ll read more of it later in the semester.

  2. (required)  Information Visualization (The first 17 pages of the Introduction to “Information Visualization: Using Visualization to Think” by Card, Mackinlay, and Schneiderman) (01-InfoVis-CardMackinlaySchneid-Chap1.pdf 77.4mb).

    This is a 1999 book that consists of this intro, and a bunch of seminal papers. The examples are old, but the main points are timeless. It is the best thing I know of that gets at Vis from the cognitive science perspective. The rest of the chapter (past page 17) is good too, but more redundant with other things we’ll read – so it’s optional. Although, every time I go back to it, I am amazed how good this is - despite being old. The authors are the founders of the field.

    The section “How Visualization Amplifies Cognition” (starting on page 15), with Table 1.3 is particularly important. It really gets at why visualizations help us do things.

  3. (required)  J. T. Leek and R. D. Peng. 2015. What is the question? Science 347, 6228 (March 2015), 1314–1315. (DOI) (pdf)

    This is only two pages, but it gives a great introduction to the ways we should think about using data, and the terminology statisticians recommend. There’s another paper I like (below), but its too much statistics to require.

  4. (required)  Emanuel Zgraggen, Zheguang Zhao, Robert Zeleznik, and Tim Kraska. 2018. Investigating the Effect of the Multiple Comparisons Problem in Visual Analysis. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems - CHI ’18, ACM Press, New York, New York, USA, 1–12. (DOI) [(open pdf)] ( https://dspace.mit.edu/handle/1721.1/137892)

    This paper brings up a somewhat scary point about interactive exploration: we need to be careful about its statistical validity. You don’t need to worry about the second half of the paper (the experiment) - although, it is pretty interesting.

  5. (optional)  Galit Shmueli. 2010. To Explain or to Predict? Statistical Science 25, 3 (January 2010), 289–310. DOI: https://doi.org/10.1214/10-STS330

    This paper fundamentally changed the way I think about data. However, it is a bit too statistically involved to require for class. Actually, watching the video on her web page is the best thing to do as it really gets the point across.