CS376 Introduction

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Human-Information Interaction
Scott Klemmertas Marcello Bastea-Forte, Joel
Brandt,Neil Patel, Leslie Wu, Mike Cammarano
06 November 2007
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Questions about the Project
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Engelbart Video
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Form Me to You
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ADAPTIVE BEHAVIOR
107 (months) SOCIAL Social Behavior 106
(weeks) 105 (days) 104 (hours) RATIONAL Adaptive
Behavior 103 102 (minutes) 101 COGNITIVE I
mmediate Behavior 100 (seconds) 10-1
10-2 BIOLOGICAL 10-3 (msec) 10-4
Meg Stewart
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HUMAN INFORMATION INTERACTION
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GOMS

• Routine cognitive skill
• Well-known path

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Information Search

• Problem solving
• Heuristic search
• Exponential if dont know what to do

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OPTIMALITY THEORY
Optimal Foraging Theory
Information Foraging Theory
Information
Energy




Useful info Time
Energy Time
Max
Max
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Information Foraging Theory
Microeconomics of information access. People are
information rate maximizers of benefits/costs Info
rmation has a cost structure
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INFORMATION PATCHES
e.g. desk piles, Alta vista search list unlike
animals foraging for food, humans can do patch
construction
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Well stay in a patch longer

• When a patch is highly profitable
• As distance between patches increases
• When the environment as a whole is less profitable

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WITHIN-PATCH ENRICHMENTINFORMATION SCENT
perception of value and cost of a path to a
source based on proximal cues
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Relevance-Enhanced Thumbnails

• Within-patch enrichment
• Emphasize text that is relevant to query
• Text callouts

Allison Woodruff
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PHASE TRANSITION IN NAVIGATION COSTS AS FUNCTION
OF INFORMATION SCENT
150
150
Probability of choosing wrong link (f)
.150
.150
100
100
Number of pages visited
.125
50
50
.100
.100
0
0
0
2
4
6
8
10
0
2
4
6
8
10
Depth
Notes Average branching factor 10
Depth 10
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IMPORTANCE FOR WEB DESIGN
Jarad Spool, UIE
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Peter Pirolli
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MACHINE MODELING OF INFORMATION SCENT
new
cell
Information Goal
medical
patient
Link Text
treatments
dose
procedures
beam
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PREDICTION OF LINK CHOICE
35
50
(b) Yahoo
(a)
ParcWeb
30
40
25
Predicted frequency
30
20
R2 0.72
Predicted frequency
15
20
10
R2 0.90
10
5
0
0
0
10
20
30
40
50
0
5
10
15
20
25
30
35
Observed frequency
Observed frequency
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USER FLOW MODEL
User need (vector of goal concepts)
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SENSE MAKING TASKS

• Characteristics
• Massive amounts of data
• Ill-structured task
• Organization, interpretation, insight needed
• Output, decision, solution required
• Examples
• Understanding a health problem and making a
  medical decision
• Buying a new laptop
• Weather forecasting
• Producing an intelligence report

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Importance of Sensemaking

• 75 of significant tasks on the Web are more
  than simple finding of information (Morrison et
  al., 2001)
• Understanding a topic (e.g., about health)
• Comparing/choosing products
• Information retrieval does not support these
  tasks (Bhavnani et al., 2002)
• E.g., Estimated that one must visit 25 Web pages
  in order to read about 12 basic concepts about
  skin cancer

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SENSEMAKING
Reevaluate
PRESENTATION
Search for Support
HYPOTHESES
Tell Story
Search for Evidence
SCHEMAS
Build Case
Search for Relations
EVIDENCE FILE
STRUCTURE
Schematize
Search for Information
SHOEBOX
Read Extract
EXTERNAL DATA SOURCES
Search Filter
TIME or EFFORT
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SENSEMAKING
Reevaluate
PRESENTATION
Search for Support
HYPOTHESES
Tell Story
Search for Evidence
SCHEMAS
Build Case
Search for Relations
EVIDENCE FILE
STRUCTURE
Schematize
Search for Information
SHOEBOX
Read Extract
EXTERNAL DATA SOURCES
Search Filter
TIME or EFFORT
25
Credits Further Reading

• This lecture draws heavily on Stu Cards slides
  on HII
• Peter Pirolli, Information Foraging