Cognitive Issues &
Human Tasks
CS 7450 - Information Visualization January 13, 2004
John Stasko
Outline
• Overview
• 1. Role – How visualizations aid cognition?
• 2. Tasks – What does the visualization assist?
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Basic Premise
• Understanding (the cognitive aspects) is the crucial part of InfoVis
• Visualization is simply a tool useful for aiding comprehension and understanding
• Discussed the role of external cognition aids briefly last time
How Are Graphics Used?
• What does a visualization or graphic image provide for us?
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How Are Graphics Used?
• Larkin & Simon ‘87 investigated usefulness of graphical displays
• Graphical visualization could support more efficient task performance by:
−Allowing substitution of rapid perceptual influences for difficult logical inferences
−Reducing search for information required for task completion
• Sometimes text is better
Cognitive Map
• What is it?
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Understanding
• People utilize an internal model that is generated based on what is observed
• Tversky calls the internal model a cognitive map
−Think about that term
Example
• You’re taking the MARTA train to get to Georgia State University
−You have some existing internal model of the system, stops, how to get there
−On train, you glance at MARTA map for help
−Refines your internal model, clarifying items and extending it
−Note that it’s still not perfect, no internal model ever is
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Cognitive Map
• Just don’t have one big one
• Have large number of these for all different kinds of things
• Collection of cognitive maps --> Cognitive collage
1. Process Models
• (Recall the user and cognitive models from HCI?)
• Process by which a person looks at a graphic and makes some use of it
−A number of substeps probably exist
• Can you describe process?
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Process Model 1
• Spence
• Navigation - Creation and interpretation of an internal mental model
Navigation
Content
Browsing
strategy Internal
model
Interpretation
Browse Model
Interpret Formulate a
browsing strategy
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Interpretation
• Can someone explain that?
Interpretation
• Content is the display on screen.
Modeling of that pattern results in cognitive map. Interpretation (ah, variables x and y are related) leads to new view, that generates an idea for a new browsing strategy. Look at the display again with that.
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Process Model 2
• Card, Mackinlay, Shneiderman
• Knowledge crystallization task
−Gather info for some purpose, make sense of it by constructing a representational
framework, and package it into a form for communication or action
Knowledge Crystallization
• Information foraging
• Search for schema (representation)
• Instantiate schema
• Problem solve to trade off features
• Search for a new schema that reduces problem to a simple trade-off
• Package the patterns found in some output product
From CMS ‘98
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How Vis Amplifies Cognition
• Increasing memory and processing resources available
• Reducing search for information
• Enhancing the recognition of patterns
• Enabling perceptual inference operations
• Using perceptual attention mechanisms for monitoring
• Encoding info in a manipulable medium
Process
task
Raw data
Data tables
Visual
Structures Views
Data
transformations Visual
mappings View
transformations
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Knowledge Crystallization
Instantiate schema Search for
schema Forage for data
Problem-solve Author,
decide or act Task
Extract Compose
Read fact Read comparison Read pattern Manipulate Create Delete Instantiate
Reorder Cluster Class Average Promote Detect pattern Abstract Overview ZoomFilter
Details-on-demand Browse
Search query
Intermission
• HW 1
−Challenging?
−Interesting ones?
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2. User Tasks
• What things will people want to accomplish using information visualizations?
• Last time, we discussed
−search vs. browsing
Browsing vs. Search
• Important difference in activities
• Appears that information visualization may have more to offer to browsing
• But…browsing is a softer, fuzzier activity
• So, how do we articulate utility?
−Maybe describe when it’s useful
−When is browsing useful?
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Browsing
• Useful when
− Good underlying structure so that items close to one another can be inferred to be similar
− Users are unfamiliar with collection contents
− Users have limited understanding of how system is organized and prefer less cognitively loaded method of exploration
− Users have difficulty verbalizing underlying information need
− Information is easier to recognize than describe
Lin ‘97
Tasks
• OK, but browsing and search are very high level
• Let’s be more specific…
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Example from Last Time
Example courtesy of Chris North
Which state has the highest income?
Is there a relationship between income and education?
Are there any outliers?
Questions:
Exercise
• What are the (types of) tasks being done here?
• Can you think of others?
−Let’s develop a list
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Task Taxonomies
• Number of different ones exist, important to understand what process they focus on
−Creating an artifact
−Human tasks
−Tasks using visualization system
−...
User Tasks
• Wehrend & Lewis ‘90 created a low-level, domain independent taxonomy of user tasks in visualization environments
• Eleven basic actions
−identify, locate, distinguish, categorize, cluster, distribution, rank, compare within relations, compare between relations, associate, correlate
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W&L 1
• Locate
• Finding something that one knows about already
W&L 2
• Identify
• Describe an object not necessarily known previously
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W&L 3
• Distinguish
• Detecting different values of same variable
W&L 4
• Categorize
• Define divisions that visual objects can be sorted by
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W&L 5
• Cluster
• Determining whether data items are clustered or not
W&L 6
• Distribution
• Describe overall pattern of data
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W&L 7
• Rank
• Finding best and worst, for example
W&L 8
• Compare within entities
• Decide something based on attributes of similar objects
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W&L 9
• Compare between relations
• Different or sets of entities used as basis of comparison
W&L 10
• Associate
• Form relationship between objects on display
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W&L 11
• Correlate
• Determine which objects share similar attributes
Another Taxonomy
• Zhou and Feiner ’98
• Discuss
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Visual Task Taxonomy
• Zhou and Feiner developed a hierarchical taxonomy/model of visual tasks
# Relational tasks Associate -- Collocate -- Connect -- Unite -- Attach Background Categorize -- MarkDistribute Cluster
-- Outline -- Individualize Compare -- Differentiate -- Intersect
Correlate -- Plot
-- MarkCompose Distinguish -- MarkDistribue -- Isolate Emphasize -- Focus -- Isolate -- Reinforce Generalize -- Merge Identify -- Name -- Portray -- Individualize -- Profile
Locate -- Position -- Situate -- Pinpoint -- Outline Rank -- Time Reveal -- Expose -- Itemize -- Specify -- Separate Switch
# Direct visual
# organizing and
# encoding tasks Encode
-- Label -- Symbolize -- -- Quantify -- -- Iconify -- Portray -- Tabulate -- Plot -- Structure -- Trace -- Map
Interpretation
• The nested items are refinements of particular ways of achieving task
• E.g., To locate an item, we might use the more specific visual task pinpoint
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Dimensions
• Visual tasks have two main dimensions
−1. Visual accomplishments - describe
presentation intents that task might help to achieve
−2. Visual implications - particular type of visual action that visual task may carry out
1. Visual Accomplishments
• All about presentation intent
• Classified into two categories:
−Tasks that inform the user (e.g., make a presentation with ppt)
−Tasks that enable user to explore or compute (e.g., decide which stock to buy)
• Each of these can be broken down further
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Visual Accomplishments
Inform Enable
Elaborate Summarize Explore Compute
Search Verify Sum Differentiate Emphasize
Reveal
Associate Background Categorize Cluster Compare Correlate Distinguish Generalize Identify Locate Rank
Correlate Locate Rank Correlate Locate Rank Categorize
Cluster Compare Correlate Distinguish Emphasize Identify Locate Rank Reveal
Categorize Compare Correlate Distinguish Identify Locate Rank Reveal
2. Visual Implications
• Categorize various visual tasks by whether they imply
−Certain types of visual organization
−Certain ways of visual signaling
−Certain paths of visual transformation
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HW 2
• Design a visualization
• Minivan data
• Start now – next class will help some too
Odds-n-Ends
• Questionnaires
• Co-web
• Pictures
• Office hours
• Logo
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Upcoming
• Multivariate Data
−The “DB session”
−Reading:
Chapter 3 Inselberg paper
• Multivariate visualization tools
• Graphical principles
Be reading Tufte
References
• Spence & CMS texts
• All referred to papers