Debugging and Hacking the User in Visual Analytics
description
Transcript of Debugging and Hacking the User in Visual Analytics
![Page 1: Debugging and Hacking the User in Visual Analytics](https://reader035.fdocuments.us/reader035/viewer/2022062301/568161ac550346895dd169a9/html5/thumbnails/1.jpg)
Intro Reasoning Waldo Priming Application1/54 DisFunc
Debugging and Hacking the User in Visual Analytics
Remco Chang
Assistant ProfessorTufts University
![Page 2: Debugging and Hacking the User in Visual Analytics](https://reader035.fdocuments.us/reader035/viewer/2022062301/568161ac550346895dd169a9/html5/thumbnails/2.jpg)
Intro Reasoning Waldo Priming Application2/54 DisFunc
“The computer is incredibly fast, accurate, and stupid. Man is unbelievably slow, inaccurate, and
brilliant. The marriage of the two is a force beyond calculation.”
-Leo Cherne, 1977 (often attributed to Albert Einstein)
![Page 3: Debugging and Hacking the User in Visual Analytics](https://reader035.fdocuments.us/reader035/viewer/2022062301/568161ac550346895dd169a9/html5/thumbnails/3.jpg)
Intro Reasoning Waldo Priming Application3/54 DisFunc
Which Marriage?
![Page 4: Debugging and Hacking the User in Visual Analytics](https://reader035.fdocuments.us/reader035/viewer/2022062301/568161ac550346895dd169a9/html5/thumbnails/4.jpg)
Intro Reasoning Waldo Priming Application4/54 DisFunc
Which Marriage?
![Page 5: Debugging and Hacking the User in Visual Analytics](https://reader035.fdocuments.us/reader035/viewer/2022062301/568161ac550346895dd169a9/html5/thumbnails/5.jpg)
Intro Reasoning Waldo Priming Application5/54 DisFunc
Work Distribution
Crouser et al., Balancing Human and Machine Contributions in Human Computation Systems. Human Computation Handbook, 2013Crouser et al., An affordance-based framework for human computation and human-computer collaboration. IEEE VAST, 2012
CreativityPerception
Domain Knowledge
Data ManipulationStorage and Retrieval
Bias-Free Analysis
LogicPrediction
![Page 6: Debugging and Hacking the User in Visual Analytics](https://reader035.fdocuments.us/reader035/viewer/2022062301/568161ac550346895dd169a9/html5/thumbnails/6.jpg)
Intro Reasoning Waldo Priming Application6/54 DisFunc
Visual Analytics = Human + Computer
• Visual analytics is “the science of analytical reasoning facilitated by visual interactive interfaces.”1
1. Thomas and Cook, “Illuminating the Path”, 2005.2. Keim et al. Visual Analytics: Definition, Process, and Challenges. Information Visualization, 2008
Interactive Data Exploration
Automated Data Analysis
Feedback Loop
![Page 7: Debugging and Hacking the User in Visual Analytics](https://reader035.fdocuments.us/reader035/viewer/2022062301/568161ac550346895dd169a9/html5/thumbnails/7.jpg)
Intro Reasoning Waldo Priming Application7/54 DisFunc
Example Visual Analytics Systems
• Political Simulation– Agent-based analysis– With DARPA
• Wire Fraud Detection– With Bank of America
• Bridge Maintenance – With US DOT– Exploring inspection
reports
• Biomechanical Motion– Interactive motion
comparisonCrouser et al., Two Visualization Tools for Analysis of Agent-Based Simulations in Political Science. IEEE CG&A, 2012
![Page 8: Debugging and Hacking the User in Visual Analytics](https://reader035.fdocuments.us/reader035/viewer/2022062301/568161ac550346895dd169a9/html5/thumbnails/8.jpg)
Intro Reasoning Waldo Priming Application8/54 DisFunc
Example Visual Analytics Systems
R. Chang et al., WireVis: Visualization of Categorical, Time-Varying Data From Financial Transactions, VAST 2008.
• Political Simulation– Agent-based analysis– With DARPA
• Wire Fraud Detection– With Bank of America
• Bridge Maintenance – With US DOT– Exploring inspection
reports
• Biomechanical Motion– Interactive motion
comparison
![Page 9: Debugging and Hacking the User in Visual Analytics](https://reader035.fdocuments.us/reader035/viewer/2022062301/568161ac550346895dd169a9/html5/thumbnails/9.jpg)
Intro Reasoning Waldo Priming Application9/54 DisFunc
Example Visual Analytics Systems
R. Chang et al., An Interactive Visual Analytics System for Bridge Management, Journal of Computer Graphics Forum, 2010.
• Political Simulation– Agent-based analysis– With DARPA
• Wire Fraud Detection– With Bank of America
• Bridge Maintenance – With US DOT– Exploring inspection
reports
• Biomechanical Motion– Interactive motion
comparison
![Page 10: Debugging and Hacking the User in Visual Analytics](https://reader035.fdocuments.us/reader035/viewer/2022062301/568161ac550346895dd169a9/html5/thumbnails/10.jpg)
Intro Reasoning Waldo Priming Application10/54 DisFunc
Example Visual Analytics Systems
R. Chang et al., Interactive Coordinated Multiple-View Visualization of Biomechanical Motion Data , IEEE Vis (TVCG) 2009.
• Political Simulation– Agent-based analysis– With DARPA
• Wire Fraud Detection– With Bank of America
• Bridge Maintenance – With US DOT– Exploring inspection
reports
• Biomechanical Motion– Interactive motion
comparison
![Page 11: Debugging and Hacking the User in Visual Analytics](https://reader035.fdocuments.us/reader035/viewer/2022062301/568161ac550346895dd169a9/html5/thumbnails/11.jpg)
Intro Reasoning Waldo Priming Application11/54 DisFunc
How does Visual Analytics work?
• Types of Human-Visualization Interactions– Word editing (input heavy, little output)– Browsing, watching a movie (output heavy, little input)– Visual Analysis (collaboration, closer to 50-50)
• Question: • Can I hack the user’s brain by analyzing the interactions?
Visualization HumanOutput
Input
Keyboard, Mouse, etc
Images (monitor)
![Page 12: Debugging and Hacking the User in Visual Analytics](https://reader035.fdocuments.us/reader035/viewer/2022062301/568161ac550346895dd169a9/html5/thumbnails/12.jpg)
Intro Reasoning Waldo Priming Application12/54 DisFunc
Research Statement
“Reverse engineer” the human cognitive black box
A. Debugging the User1. Reasoning and intent2. Individual differences and analysis behavior
B. Hacking the User3. Extract user’s knowledge4. Influencing a user’s behavior (priming)
C. Use these techniques for “good”5. Adaptive and augmented visualizations
R. Chang et al., Science of Interaction, Information Visualization, 2009.
![Page 13: Debugging and Hacking the User in Visual Analytics](https://reader035.fdocuments.us/reader035/viewer/2022062301/568161ac550346895dd169a9/html5/thumbnails/13.jpg)
Intro Reasoning Waldo Priming Application13/54 DisFunc
1. Debugging the UserWhat is in a User’s Interactions?
![Page 14: Debugging and Hacking the User in Visual Analytics](https://reader035.fdocuments.us/reader035/viewer/2022062301/568161ac550346895dd169a9/html5/thumbnails/14.jpg)
Intro Reasoning Waldo Priming Application14/54 DisFunc
What is in a User’s Interactions?
• Goal: determine if a user’s reasoning and intent are reflected in a user’s interactions.
Analysts
GradStudents(Coders)
Logged(semantic) Interactions
Compare!(manually)
StrategiesMethodsFindings
Guesses ofAnalysts’ thinking
WireVis Interaction-Log Vis
![Page 15: Debugging and Hacking the User in Visual Analytics](https://reader035.fdocuments.us/reader035/viewer/2022062301/568161ac550346895dd169a9/html5/thumbnails/15.jpg)
Intro Reasoning Waldo Priming Application15/54 DisFunc
What’s in a User’s Interactions
• From this experiment, we find that interactions contains at least:– 60% of the (high level) strategies– 60% of the (mid level) methods– 79% of the (low level) findings
R. Chang et al., Recovering Reasoning Process From User Interactions. CG&A, 2009.R. Chang et al., Evaluating the Relationship Between User Interaction and Financial Visual Analysis. VAST, 2009.
![Page 16: Debugging and Hacking the User in Visual Analytics](https://reader035.fdocuments.us/reader035/viewer/2022062301/568161ac550346895dd169a9/html5/thumbnails/16.jpg)
Intro Reasoning Waldo Priming Application16/54 DisFunc
What’s in a User’s Interactions
• Why are these so much lower than others?
• (recovering “methods” at about 15%)
• Only capturing a user’s interaction in this case is insufficient.
![Page 17: Debugging and Hacking the User in Visual Analytics](https://reader035.fdocuments.us/reader035/viewer/2022062301/568161ac550346895dd169a9/html5/thumbnails/17.jpg)
Intro Reasoning Waldo Priming Application17/54 DisFunc
2. Learning about a User in Real-TimeWho is the user,
and what is she doing?
![Page 18: Debugging and Hacking the User in Visual Analytics](https://reader035.fdocuments.us/reader035/viewer/2022062301/568161ac550346895dd169a9/html5/thumbnails/18.jpg)
Intro Reasoning Waldo Priming Application18/54 DisFunc
Task: Find Waldo
• Google-Maps style interface– Left, Right, Up, Down, Zoom In, Zoom Out, Found
![Page 19: Debugging and Hacking the User in Visual Analytics](https://reader035.fdocuments.us/reader035/viewer/2022062301/568161ac550346895dd169a9/html5/thumbnails/19.jpg)
Intro Reasoning Waldo Priming Application19/54 DisFunc
User Modeling
• Collect three types of data about the user in real-time
• Physical mouse movement– Mouse position, velocity, acceleration, angle change, distance, etc.
• Interaction sequences– Sequences of button clicks– 7 possible symbols
• Data state information– Which “chunk” of data the user looked at– Transitioning between the data chunks
• Goal: Predict if a user will find Waldo within 500 seconds
Helen Zhao et al., Modeling user interactions for complex visual search tasks. Poster, IEEE VAST , 2013.Brown and Ottley et al., Title: TDB. IEEE VAST, In Preparation.
![Page 20: Debugging and Hacking the User in Visual Analytics](https://reader035.fdocuments.us/reader035/viewer/2022062301/568161ac550346895dd169a9/html5/thumbnails/20.jpg)
Intro Reasoning Waldo Priming Application20/54 DisFunc
Pilot Visualization – Completion Time
Fast completion time Slow completion time
![Page 21: Debugging and Hacking the User in Visual Analytics](https://reader035.fdocuments.us/reader035/viewer/2022062301/568161ac550346895dd169a9/html5/thumbnails/21.jpg)
Intro Reasoning Waldo Priming Application21/54 DisFunc
Analysis 1: Mouse Movement
![Page 22: Debugging and Hacking the User in Visual Analytics](https://reader035.fdocuments.us/reader035/viewer/2022062301/568161ac550346895dd169a9/html5/thumbnails/22.jpg)
Intro Reasoning Waldo Priming Application22/54 DisFunc
Analysis 2: Interaction Sequences
• Uses a combination of n-grams and decision tree
0 100 200 300 400 500 600 700 8000
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
Number of Interactions
Accu
racy
![Page 23: Debugging and Hacking the User in Visual Analytics](https://reader035.fdocuments.us/reader035/viewer/2022062301/568161ac550346895dd169a9/html5/thumbnails/23.jpg)
Intro Reasoning Waldo Priming Application23/54 DisFunc
Pilot Visualization – Locus of Control*
External Locus of Control Internal Locus of Control
Ottley et al., How locus of control influences compatibility with visualization style. IEEE VAST , 2011.Ottley et al., Understanding visualization by understanding individual users. IEEE CG&A, 2012.
![Page 24: Debugging and Hacking the User in Visual Analytics](https://reader035.fdocuments.us/reader035/viewer/2022062301/568161ac550346895dd169a9/html5/thumbnails/24.jpg)
Intro Reasoning Waldo Priming Application24/54 DisFunc
Detecting User’s Characteristic
• We can detect a faint signal on the user’s personality traits…
0 100 200 300 400 500 600 700 8000
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
Neuroticism
Number of Interactions
Accu
racy
![Page 25: Debugging and Hacking the User in Visual Analytics](https://reader035.fdocuments.us/reader035/viewer/2022062301/568161ac550346895dd169a9/html5/thumbnails/25.jpg)
Intro Reasoning Waldo Priming Application25/54 DisFunc
Implications
• Allows prediction in real-time
• N-gram + DT gives us a glimpse into what makes a user [fast|slow], [neurotic|not], etc.
![Page 26: Debugging and Hacking the User in Visual Analytics](https://reader035.fdocuments.us/reader035/viewer/2022062301/568161ac550346895dd169a9/html5/thumbnails/26.jpg)
Intro Reasoning Waldo Priming Application26/54 DisFunc
3. Hacking the UserWhat information can I
extract out of the user’s brain?
![Page 27: Debugging and Hacking the User in Visual Analytics](https://reader035.fdocuments.us/reader035/viewer/2022062301/568161ac550346895dd169a9/html5/thumbnails/27.jpg)
Intro Reasoning Waldo Priming Application27/54 DisFunc
1. Richard Heuer. Psychology of Intelligence Analysis, 1999. (pp 53-57)
![Page 28: Debugging and Hacking the User in Visual Analytics](https://reader035.fdocuments.us/reader035/viewer/2022062301/568161ac550346895dd169a9/html5/thumbnails/28.jpg)
Intro Reasoning Waldo Priming Application28/54 DisFunc
Metric Learning
• Finding the weights to a linear distance function
• Instead of a user manually give the weights, can we learn them implicitly through their interactions?
![Page 29: Debugging and Hacking the User in Visual Analytics](https://reader035.fdocuments.us/reader035/viewer/2022062301/568161ac550346895dd169a9/html5/thumbnails/29.jpg)
Intro Reasoning Waldo Priming Application29/54 DisFunc
Metric Learning
• In a projection space (e.g., MDS), the user directly moves points on the 2D plane that don’t “look right”…
• Until the expert is happy (or the visualization can not be improved further)
• The system learns the weights (importance) of each of the original k dimensions
• Short Video (play)
![Page 30: Debugging and Hacking the User in Visual Analytics](https://reader035.fdocuments.us/reader035/viewer/2022062301/568161ac550346895dd169a9/html5/thumbnails/30.jpg)
Intro Reasoning Waldo Priming Application30/54 DisFunc
Dis-Function
Brown et al., Find Distance Function, Hide Model Inference. IEEE VAST Poster 2011Brown et al., Dis-function: Learning Distance Functions Interactively. IEEE VAST 2012.
Optimization:
![Page 31: Debugging and Hacking the User in Visual Analytics](https://reader035.fdocuments.us/reader035/viewer/2022062301/568161ac550346895dd169a9/html5/thumbnails/31.jpg)
Intro Reasoning Waldo Priming Application31/54 DisFunc
Results• Used the “Wine” dataset (13 dimensions, 3
clusters)– Assume a linear (sum of squares) distance
function
• Added 10 extra dimensions, and filled them with random values
Blue: original data dimensionRed: randomly added dimensionsX-axis: dimension numberY-axis: final weights of the distance function
• Shows that the user doesn’t care about many of the features (in this case, only 5 dimensions matter)
• Reveals the user’s knowledge about the data (often in a way that the user isn’t even aware)
![Page 32: Debugging and Hacking the User in Visual Analytics](https://reader035.fdocuments.us/reader035/viewer/2022062301/568161ac550346895dd169a9/html5/thumbnails/32.jpg)
Intro Reasoning Waldo Priming Application32/54 DisFunc
4. Influencing the UserCan we manipulate the user’s
interactions?
![Page 33: Debugging and Hacking the User in Visual Analytics](https://reader035.fdocuments.us/reader035/viewer/2022062301/568161ac550346895dd169a9/html5/thumbnails/33.jpg)
Intro Reasoning Waldo Priming Application33/54 DisFunc
Why Studying Interactions is Hard
Visualization HumanOutput
Input
Keyboard, Mouse, etc
Images (monitor)
![Page 34: Debugging and Hacking the User in Visual Analytics](https://reader035.fdocuments.us/reader035/viewer/2022062301/568161ac550346895dd169a9/html5/thumbnails/34.jpg)
Intro Reasoning Waldo Priming Application34/54 DisFunc
Observations
• Given a complex task, no two users produce the same interaction trails
• In fact, at two different times, the same user does not repeat the exact same sequence of actions
• Makes sense… but these changes are not purely random
![Page 35: Debugging and Hacking the User in Visual Analytics](https://reader035.fdocuments.us/reader035/viewer/2022062301/568161ac550346895dd169a9/html5/thumbnails/35.jpg)
Intro Reasoning Waldo Priming Application35/54 DisFunc
Individual Differences and Interaction Pattern
• Existing research shows that all the following factors affect how someone uses a visualization:
Peck et al., ICD3: Towards a 3-Dimensional Model of Individual Cognitive Differences. BELIV 2012Peck et al., Using fNIRS Brain Sensing To Evaluate Information Visualization Interfaces. CHI 2013
– Spatial Ability– Cognitive Workload/Mental
Demand*
– Perceptual Speed– Experience (novice vs. expert)– Emotional State– Personality*
– … and more
![Page 36: Debugging and Hacking the User in Visual Analytics](https://reader035.fdocuments.us/reader035/viewer/2022062301/568161ac550346895dd169a9/html5/thumbnails/36.jpg)
Intro Reasoning Waldo Priming Application36/54 DisFunc
Cognitive Priming
![Page 37: Debugging and Hacking the User in Visual Analytics](https://reader035.fdocuments.us/reader035/viewer/2022062301/568161ac550346895dd169a9/html5/thumbnails/37.jpg)
Intro Reasoning Waldo Priming Application37/54 DisFunc
Priming Emotion on Visual Judgment
Harrison et al., Influencing Visual Judgment Through Affective Priming, CHI 2013
![Page 38: Debugging and Hacking the User in Visual Analytics](https://reader035.fdocuments.us/reader035/viewer/2022062301/568161ac550346895dd169a9/html5/thumbnails/38.jpg)
Intro Reasoning Waldo Priming Application38/54 DisFunc
Priming Inferential Judgment
• The personality factor, Locus of Control* (LOC), is a predictor for how a user interacts with the following visualizations:
Ottley et al., How locus of control influences compatibility with visualization style. IEEE VAST , 2011.
![Page 39: Debugging and Hacking the User in Visual Analytics](https://reader035.fdocuments.us/reader035/viewer/2022062301/568161ac550346895dd169a9/html5/thumbnails/39.jpg)
Intro Reasoning Waldo Priming Application39/54 DisFunc
Locus of Control vs. Visualization Type
• When with list view compared to containment view, internal LOC users are:– faster (by 70%)– more accurate (by 34%)
• Only for complex (inferential) tasks• The speed improvement is about 2 minutes (116 seconds)
![Page 40: Debugging and Hacking the User in Visual Analytics](https://reader035.fdocuments.us/reader035/viewer/2022062301/568161ac550346895dd169a9/html5/thumbnails/40.jpg)
Intro Reasoning Waldo Priming Application40/54 DisFunc
Priming LOC - Stimulus
• Borrowed from Psychology research: reduce locus of control (to make someone have a more external LOC)
“We know that one of the things that influence how well you can do everyday tasks is the number of obstacles you face on a daily basis. If you are having a particularly bad day today, you may not do as well as you might on a day when everything goes as planned. Variability is a normal part of life and you might think you can’t do much about that aspect. In the space provided below, give 3 examples of times when you have felt out of control and unable to achieve something you set out to do. Each example must be at least 100 words long.”
![Page 41: Debugging and Hacking the User in Visual Analytics](https://reader035.fdocuments.us/reader035/viewer/2022062301/568161ac550346895dd169a9/html5/thumbnails/41.jpg)
Intro Reasoning Waldo Priming Application41/54 DisFunc
Results: Averages Primed More Internal
Visual Form
List-View Containment
Performance
Poor
Good
Internal LOC
External LOC
Average ->Internal
Average LOC
Ottley et al., Manipulating and Controlling for Personality Effects on Visualization Tasks, Information Visualization, 2013
![Page 42: Debugging and Hacking the User in Visual Analytics](https://reader035.fdocuments.us/reader035/viewer/2022062301/568161ac550346895dd169a9/html5/thumbnails/42.jpg)
Intro Reasoning Waldo Priming Application42/54 DisFunc
Results
![Page 43: Debugging and Hacking the User in Visual Analytics](https://reader035.fdocuments.us/reader035/viewer/2022062301/568161ac550346895dd169a9/html5/thumbnails/43.jpg)
Intro Reasoning Waldo Priming Application43/54 DisFunc
5. Work In Progress:Implications and Applications
How do I use these techniques for “good”?
![Page 44: Debugging and Hacking the User in Visual Analytics](https://reader035.fdocuments.us/reader035/viewer/2022062301/568161ac550346895dd169a9/html5/thumbnails/44.jpg)
Intro Reasoning Waldo Priming Application44/54 DisFunc
Human
Two Example Applications
Visualization HumanOutput
Input• Adaptive System
VisualizationOutput
Input• Augmented System
![Page 45: Debugging and Hacking the User in Visual Analytics](https://reader035.fdocuments.us/reader035/viewer/2022062301/568161ac550346895dd169a9/html5/thumbnails/45.jpg)
Intro Reasoning Waldo Priming Application45/54 DisFunc
Adaptive System: Big Data Problem
Visualization on aCommodity Hardware
Large Data in aData Warehouse
![Page 46: Debugging and Hacking the User in Visual Analytics](https://reader035.fdocuments.us/reader035/viewer/2022062301/568161ac550346895dd169a9/html5/thumbnails/46.jpg)
Intro Reasoning Waldo Priming Application46/54 DisFunc
Problem Statement
• Constraint: Data is too big to fit into the memory or hard drive of the personal computer– Note: Ignoring various database technologies (OLAP, Column-Store,
No-SQL, Array-Based, etc)
• Classic Computer Science Problem…
![Page 47: Debugging and Hacking the User in Visual Analytics](https://reader035.fdocuments.us/reader035/viewer/2022062301/568161ac550346895dd169a9/html5/thumbnails/47.jpg)
Intro Reasoning Waldo Priming Application47/54 DisFunc
Work in Progress…
• However, exploring large DB (usually) means high degrees of freedom
• Goal: Predictive Pre-Fetching from large DB
• Collaboration with MIT Big Data Center• Teams:
– MIT: Based on data characteristic– Brown: Based on past SQL queries– Tufts: Based on user’s analysis profile
• Current progress: developed middleware (ScalaR)
Battle et al., Dynamic Reduction of Result Sets for Interactive Visualization. IEEE BigData, 2013.
![Page 48: Debugging and Hacking the User in Visual Analytics](https://reader035.fdocuments.us/reader035/viewer/2022062301/568161ac550346895dd169a9/html5/thumbnails/48.jpg)
Intro Reasoning Waldo Priming Application48/54 DisFunc
Augmented System: Bayes Reasoning
The probability that a woman over age 40 has breast cancer is 1%. However, the probability that mammography accurately detects the disease is 80% with a false positive rate of 9.6%.
If a 40-year old woman tests positive in a mammography exam, what is the probability that she indeed has breast cancer?
Answer: Bayes’ theorem states that P(A|B) = P(B|A) * P(A) / P(B). In this case, A is having breast cancer, B is testing positive with mammography. P(A|B) is the probability of a person having breast cancer given that the person is tested positive with mammography. P(B|A) is given as 80%, or 0.8, P(A) is given as 1%, or 0.01. P(B) is not explicitly stated, but can be computed as P(B,A)+P(B,˜A), or the probability of testing positive and the patient having cancer plus the probability of testing positive and the patient not having cancer. Since P(B,A) is equal 0.8*0.01 = 0.008, and P(B,˜A) is 0.093 * (1-0.01) = 0.09207, P(B) can be computed as 0.008+0.09207 = 0.1007. Finally, P(A|B) is therefore 0.8 * 0.01 / 0.1007, which is equal to 0.07944.
![Page 49: Debugging and Hacking the User in Visual Analytics](https://reader035.fdocuments.us/reader035/viewer/2022062301/568161ac550346895dd169a9/html5/thumbnails/49.jpg)
Intro Reasoning Waldo Priming Application49/54 DisFunc
Visualization Aids
Ottley et al., Visually Communicating Bayesian Statistics to Laypersons. Tufts CS Tech Report, 2012.
![Page 50: Debugging and Hacking the User in Visual Analytics](https://reader035.fdocuments.us/reader035/viewer/2022062301/568161ac550346895dd169a9/html5/thumbnails/50.jpg)
Intro Reasoning Waldo Priming Application50/54 DisFunc
Spatial Aptitude Score
• High spatial aptitude -> higher accuracy in solving Bayes problems (with visualization)
• Could priming help?• Adaptive visual representation?
Ottley et al., Title: TBD. IEEE InfoVis, In Preparation
![Page 51: Debugging and Hacking the User in Visual Analytics](https://reader035.fdocuments.us/reader035/viewer/2022062301/568161ac550346895dd169a9/html5/thumbnails/51.jpg)
Intro Reasoning Waldo Priming Application51/54 DisFunc
Summary
![Page 52: Debugging and Hacking the User in Visual Analytics](https://reader035.fdocuments.us/reader035/viewer/2022062301/568161ac550346895dd169a9/html5/thumbnails/52.jpg)
Intro Reasoning Waldo Priming Application52/54 DisFunc
Summary• “Interaction is the analysis”1
• A user’s interactions in a visual analytics system encodes a large amount of data
• Successful analysis can lead to a better understanding of the user
• The future of visual analytics lies in better human-computer collaboration
• That future starts by enabling the computer to better understand the user
1. R. Chang et al., Science of Interaction, Information Visualization, 2009.
![Page 53: Debugging and Hacking the User in Visual Analytics](https://reader035.fdocuments.us/reader035/viewer/2022062301/568161ac550346895dd169a9/html5/thumbnails/53.jpg)
Intro Reasoning Waldo Priming Application53/54 DisFunc
Summary
• “Reverse engineer” the human cognitive black box!
A. Debugging the User:1. Reasoning and intent2. Analysis behaviors and
individual differences
B. Hacking the User:1. Extract domain knowledge2. Influence the user’s behaviors
C. With great power comes great responsibility…