LECTURE 02:
MENTAL AND VISUALIZATION
MODELS
January 21, 2015
COMP 150-04
Topics in Visual Analytics
Note: slide deck adapted from R. Chang, Fall 2010
Announcements (1 / 2 )
• Course website has moved:
http://www.cs.tufts.edu/comp/150VAN/
• Piazza Q&A is now live:
https://piazza.com/tufts/spring2015/15004/home
• 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!
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
Outline
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Mental Models
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Illustration: the 9-dot problem
Properties of mental models
Visualization Models
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Reference model (Card, Mackinlay, & Shneiderman)
Data state reference model (Chi)
Model of visualization (van Wijk)
Visual analytics model (Keim)
Sensemaking Loop (Pirolli & Card)
The 9-dot Problem
Task 1: Connect all 9 dots using only straight lines
The 9-dot Problem
Task 2: Connect all 9 dots using 4 straight lines
The 9-dot Problem
Task 3: Connect all 9 dots using 3 straight lines
The 9-dot Problem
Task 4: Connect all 9 dots using 1 straight line
Mental Models: a Sketch
Mental Models: Formalization
A person’s decision-making process is bounded1 by:
1. the (incomplete) information they have available
2. the (finite) processing power of their brain
3. 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.
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 refitting the model
• Expectation is at least as strong as perception
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
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
4. Initial exposure interferes with
accurate perception
Blur
50
40
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10
0
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
The good, the bad, and the ugly…
The good:
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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:
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People (esp. experts) tend not to notice information that
contradicts their mental model
A “fresh pair of eyes” can be beneficial
The ugly:
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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
Questions?
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!
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, 1999. pp 17.
Discussion
What’s missing in this model?
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.
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 2000. IEEE Symposium on. IEEE, 2000.
Discussion
What’s missing in this model?
2005: Van Wijk’s Model of Visualization
Image source: Van Wijk, Jarke J. "The value of visualization." Visualization, 2005. VIS 05. IEEE. IEEE, 2005.
2005: Van Wijk’s model of visualization
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(2)
(3)
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D = Data
V = visualization
S = specification (params)
I = image
P = perception
K = knowledge
E = exploration
(4)
(5)
Discussion
What’s missing in this model?
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.
Discussion
What’s missing in this model?
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
Pirolli-Card Sensemaking Loop
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Bottom up:
Search and filter
Read and extract
Schematize
Build case
Tell story
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Top down:
Re-evaluate
Search for support
Search for evidence
Search for relations
Search for information
Discussion
What’s missing in this model?
Questions / Comments?
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