Lecture 02: Models

1. Lecture 02:Models September 9, 2010 COMP 150-12Topics in Visual Analytics

2. Lecture Outline • Two types of models: • Mental models • The 9-dot problem • Properties of mental models • Are mental models good or bad? • Visualization models • Reference model for visualization (Card, Mackinlay, Shneiderman) • Ed Chi’s data state reference model • Van Wijk’s model of visualization • Keim’s visual analytics model • Pirolli-Card Sensemaking Loop

3. The 9 Dot Problem

4. The 9 Dot Problem Task 1: Without lifting the pencil from paper, draw no more than 4 straight lines that will cross through all nine dots. Task 2: Repeat the same process as before, this time using 3 straight lines. Task 3: Repeat the same process as before, this time using 1 straight line.

5. What happened?

6. Mental Models • Herbert Simon: “bounded” or “limited rationality” • The mind cannot cope directly with the complexity of the world. Rather, we construct a simplified mental model of reality and then work with this model. • Everyone has these mental models, but few are aware of what these models are for different situations. • Sometimes referred to as “common sense” • But what’s “common sense” for one might not be obvious to another

7. Some Properties of Mental Models We tend to perceive what we expect to perceive Mind-sets are quick to form, but difficult to change New information is assimilated to existing mind-set Initial exposure to blurred or ambiguous stimuli interferes with accurate perception and better information that is available later

8. 1. We tend to perceive what we expect to perceive

9. 1. We tend to perceive what we expect to perceive • Implications: • Our mind-set is created based on prior experience and knowledge • We expect new input to fit that mind-set (wishful thinking) • We are willing to go as far as “distorting” accurate information that is presented to us • Expectation > Perception • Seeing is believing?

10. 2. Mind-sets are quick to form, but difficult to change

11. 2. Mind-sets are quick to form, but difficult to change • Implications: • The first bit of information can have the highest impact • The presenting sequence of information matters • Once the mind-set is formed, it takes a great deal more effort to alter it • Going first, or going last?

12. 3. New information is assimilated to existing mind-set

13. 3. New information is assimilated to existing mind-set • Implications: • Integrating two perspectives into a single mind-set is difficult • Switching the two perspective (visually or mentally) is difficult • Real-world analysis of conflicts (good guys vs. bad guys) require such perspective switching. • What tricks do you use when switching? Original title: “My Wife and My Mother-in-Law”

14. 4. Initial exposure interferes with accurate perception Blur 50 40 30 20 10 0

15. 4. Initial exposure interferes with accurate perception Blur 20 10 0

16. 4. Initial exposure interferes with accurate perception • Implications: • An extension to properties 2 (mind-sets don’t change) and 3 (information assimilation into existing mind-set), but more explicit. • The images in the blurry pictures might not directly contradict the initial mind-set, therefore the assumption persists longer • The longer someone is exposed to such ambiguous input, the more confident they become in their mind-set. • The danger of designing a bad overview • Incremental information could be misleading…

17. Are Mental Models Good or Bad? • The good, the bad, and the ugly… • The good: • Experts build and refine their mental models and are capable to processing great deal of information quickly • Allows someone to free up more cognitive capability when operating on a (good) mental model • The bad: • Experts might be “blind” to some information that contradict their mental model • A “fresh pair of eyes” from a novice might be beneficial • The ugly: • There is no good or bad. Mental models are unavoidable. • The key is to be aware of the existence of mental models

18. Questions / Comments?

19. Models in Visualization and Visual Analytics • Reference model for visualization (Card, Mackinlay, Shneiderman) • Ed Chi’s data state reference model • Van Wijk’smodel of visualization • Keim’s visual analytics model • Pirolli-Card Sensemaking Loop

20. 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: Readings in Information Visualization: Using Vision To Think. P. 17

21. Discussion – what’s missing here?

22. 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 visualizableformat and presents a graphical view.

23. Data state reference model Model applied to visualizing web sites Image source: A Taxonomy of Visualization Techniques using the Data State Reference Model. Ed Chi, InfoVis, 2000

24. Discussion – what’s missing here?

25. Van Wijk’smodel of visualization Image source: The Value of Visualization. Jarke van Wijk, InfoVis, 2005

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

27. Discussion – what’s missing here?

28. Keim’s visual analytics model interactions Pre-process input interactions Image source: Visual Analytics Definition, Process, and Challenges, Keim et al, LNCS vol 4950, 2008

29. Discussion – what’s missing here?

30. Pirolli-Card Sensemaking Loop Image source: Illuminating the Path, Thomas and Cook, p. 44

31. 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

32. Discussion – what’s missing here?

33. Questions / Comments?