1. Lecture 02: Mental and visualization models
January 21, 2015
COMP
Topics in Visual Analytics
Note: slide deck adapted from R. Chang, Fall 2010

2. https://piazza.com/tufts/spring2015/15004/home
Announcements (1 / 2 )
Course website has moved:
Piazza Q&A is now live:
Piazza > Resources:
Posted 5 (potentially) interesting datasets (many with nice APIs)
I’ll continue to add more as I find them, feel free to post your own!

3. Announcements (2 / 2 ) Drag-and-drop visualization tool suite
Tableau for Teaching has donated license keys (good for one year) for this course
Want one? Instructions posted on Piazza

4. Outline Mental Models Visualization Models
Illustration: the 9-dot problem
Properties of mental models
Visualization Models
Reference model (Card, Mackinlay, & Shneiderman)
Data state reference model (Chi)
Model of visualization (van Wijk)
Visual analytics model (Keim)
Sensemaking Loop (Pirolli & Card)

5. The 9-dot Problem
Task 1: Connect all 9 dots using only straight lines

6. The 9-dot Problem
Task 2: Connect all 9 dots using 4 straight lines

7. The 9-dot Problem
Task 3: Connect all 9 dots using 3 straight lines

8. The 9-dot Problem
Task 4: Connect all 9 dots using 1 straight line

9. Mental Models: a Sketch

10. Mental Models: Formalization
A person’s decision-making process is bounded1 by:
the (incomplete) information they have available
the (finite) processing power of their brain
the (limited) amount of time they have to decide / act
To cope with this, we construct mental models: abstracted, simplified versions of the world that are more tractable
So how do mental models work?
1 Simon, Herbert (1957). "A Behavioral Model of Rational Choice", in Models of Man, Social and
Rational: Mathematical Essays on Rational Human Behavior in a Social Setting. New York: Wiley.

11. 1. We tend to see what we expect to see
Mental models are constructed from prior experience
We expect new input to “fit” the existing model
Recalibration is costly: given input that almost fits, we are willing to distort information in order to avoid re-fitting the model
Expectation is at least as strong as perception

12. 2. Mental models form quickly, and update slowly:
“First impressions matter”
The first pieces of information can have the highest impact
The order in which we present pieces of information can shape how a person comes to understand the whole
Once a mental model is formed, it takes effort to alter it

13. 3. New information gets incorporated into the existing model
Integrating competing perspectives into a single model can be challenging
Switching between two or more perspectives (visually or mentally) is also difficult
Real-world analysis (e.g. good guys vs. bad guys) often requires such perspective switching

14. 4. Initial exposure interferes with accurate perception
Blur 50 40 30 20 10

15. 4. Initial exposure interferes with accurate perception
Note: the images in the blurry pictures may not have directly contradicted your initial mental model
The longer someone is exposed to ambiguous data, the more confident they become in their initial model (even if new data presents strong evidence it is wrong)
Incremental information can be misleading
Important to be aware of this when designing overviews

16. The good, the bad, and the ugly…
Well-tuned mental models make experts capable of processing huge amounts of information quickly
This frees up more processing power, compares with having to build a new mental model from scratch
The bad:
People (esp. experts) tend not to notice information that contradicts their mental model
A “fresh pair of eyes” can be beneficial
The ugly:
Mental models are unavoidable: everyone has them, and no two are exactly alike
The key is to be aware of how mental models form, how they shape perception, and how to support (or challenge) them

17. Questions?

18. Models in Visualization and Visual Analytics
Mental models = abstractions of how the world works
Visualization / VA models = abstractions of how vis works:
Provide a way of picturing/talking about how humans interact with visualizations
Common language for describing different parts of the visual analytic process
Every model is an (over) simplification: reader beware!

19. 1999: A reference model for visualization
Raw Data: Idiosyncratic formats
Data Tables: Relations (cases by variables) + metadata
Visual Structures: Spatial substrates + marks + graphical properties
View: graphical parameters (position, scaling, clipping, …)
Image source: Card, Stuart K., Jock D. Mackinlay, and Ben Shneiderman, eds. Readings in information visualization: using vision to think.
Morgan Kaufmann, pp 17.

20. What’s missing in this model?
Discussion
What’s missing in this model?

21. 2000: Chi’s Data State Reference Model
Value: The raw data
Analytical Abstraction: Data about data, or information (aka, metadata)
Visualization Abstraction: Information that is visualizable on the screen using a visualization technique
View: The end-product of a visualization mapping, where the user sees and interprets the picture presented
Data Transformation: Generates some form of analytical abstraction from the value (usually by extraction)
Visualization Transformation: Takes an analytical abstraction and further reduces it into some form of visualization abstraction, which is visualizable content.
Visual Mapping Transformation: Takes information that is in a visualizable format and presents a graphical view.

22. 2000: Chi’s Data State Reference Model
Model applied to visualizing web sites
Image source: Chi, Ed H. "A taxonomy of visualization techniques using the data state reference model." Information Visualization, 2000.
InfoVis IEEE Symposium on. IEEE, 2000.

23. What’s missing in this model?
Discussion
What’s missing in this model?

24. 2005: Van Wijk’s Model of Visualization
Image source: Van Wijk, Jarke J. "The value of visualization." Visualization, VIS 05. IEEE. IEEE, 2005.

25. 2005: Van Wijk’s model of visualization
(1)
(2)
(3)
D = Data
V = visualization
S = specification (params)
I = image
P = perception
K = knowledge
E = exploration
(4)
(5)

26. What’s missing in this model?
Discussion
What’s missing in this model?

27. 2008: Keim’s Visual Analytics Model
interactions
Pre-process
input
interactions
Image source: Keim, Daniel, et al. Visual analytics: Definition, process, and challenges. Springer Berlin Heidelberg, 2008.

28. What’s missing in this model?
Discussion
What’s missing in this model?

29. Pirolli-Card Sensemaking Loop
Model: Pirolli, Peter, and Stuart Card. "The sensemaking process and leverage points for analyst technology as identified through cognitive task analysis." Proceedings of International Conference on Intelligence Analysis. Vol. 5. McLean, VA: Mitre, 2005.
Image source: Thomas, James J. and Kristin A. Cook "Illuminating The Path" (2005): pp. 44

30. Pirolli-Card Sensemaking Loop
Bottom up:
Search and filter
Read and extract
Schematize
Build case
Tell story
Top down:
Re-evaluate
Search for support
Search for evidence
Search for relations
Search for information

31. What’s missing in this model?
Discussion
What’s missing in this model?

32. Questions / Comments?

33. For next class
Please read the Executive Summary and Ch.1 of [R1] (available on the course website)
Next week, we’ll be doing two crash courses
Monday: data wrangling with Python
Wednesday: statistical analysis with R
If you have a laptop and want to follow along, please install python and Rstudio before you come to lecture