Visualization Psychology

Foreword
Preface
Contents
Contributors
Part I Visualization Psychology from a Psychology Perspective
	1 Color Semantics for Visual Communication
		1.1 Introduction
			1.1.1 Visual Semantics from Multiple Perspectives
			1.1.2 Chapter Overview
		1.2 Color Semantics for Categorical Information
			1.2.1 Color–Concept Associations vs. Inferred Mappings
				1.2.1.1 Color–Concept Associations
				1.2.1.2 Inferred Mappings
			1.2.2 Assignment Inference
			1.2.3 Semantic Discriminability
			1.2.4 Assignment Inference for Abstract Concepts?
			1.2.5 Semantic Discriminability Theory
			1.2.6 Summary and Open Questions for Visualizations of Categorical Information
		1.3 Color Semantics for Continuous Data
			1.3.1 Relational Associations for Colormaps
				1.3.1.1 Structure Preservation
				1.3.1.2 Dark-is-More Bias
				1.3.1.3 Opaque-is-More Bias
				1.3.1.4 Hotspot-is-More Bias
				1.3.1.5 High-is-More Bias
			1.3.2 Assignment Inference for Visualizations of Continuous Data
			1.3.3 Summary and Open Questions for Visualizations of Continuous Data
		1.4 Conclusion
		References
	2 Theories and Models in Graph Comprehension
		2.1 Introduction
			2.1.1 What Kind of Graph Is a Graph?
		2.2 An Abridged History of Theory in Graph Comprehension
			2.2.1 A Semiology of Graphics: Bertin
			2.2.2 Elementary Structures in Graphical Perception: From Cleveland and McGill to Simkin and Hastie
			2.2.3 The Rise of Process Theories
				2.2.3.1 A Theory of Graph Comprehension: Steven Pinker
				2.2.3.2 A Construction-Integration Model: Shah and Colleagues
		2.3 The Landscape of Contemporary Research
		2.4 What Remains to Be Discovered
		References
	3 Mental Models and Visualization
		3.1 Introduction
		3.2 Internal Representations
			3.2.1 Distributed Cognition
			3.2.2 Mental Models
		3.3 Designing Visualizations from a Mental Models Perspective
			3.3.1 Supporting the Initial Construction of Mental Models
				3.3.1.1 Structural Construction Support: Advance Organizer
				3.3.1.2 Behavioral Construction Support: Onboarding Techniques
			3.3.2 Supporting the Integration of Information from Multiple Views
				3.3.2.1 Synchronous Integration: Coordination and Linkage of Views
				3.3.2.2 Sequential Integration: Narration, Storytelling, and Seamless Transitions
		3.4 Discussion
			3.4.1 Macro Models
			3.4.2 Model Quality, Stability, and Depth of Internalization
			3.4.3 Advancement of Story Models
			3.4.4 Modality
			3.4.5 Sharing Mental Models
		References
	4 Improving Evaluation Using Visualization Decision-Making Models: A Practical Guide
		4.1 Introduction
			4.1.1 Evaluation Methods for Decision-Making
		4.2 The Science of Making Decisions
		4.3 The Utility-Optimal Perspective
			4.3.1 Using Utility-Optimality to Evaluate visualizations
				4.3.1.1 A Fantasy Football Study
				4.3.1.2 A Classic Lottery Game
			4.3.2 Outlook on Using Utility-Optimal Theories for Visualization Evaluation
		4.4 The Dual-Process Perspective
			4.4.1 Dual-Process in Decision-Making
			4.4.2 Dual-Processes and Visualization Evaluation
			4.4.3 Outlook on Using the Dual-Processing Approach for Visualization Evaluation
		4.5 Cognitive Models of Decision-Making with Visualization
			4.5.1 Padilla's Dual-Process Model and the Importance of Working Memory
			4.5.2 Outlook on Using Cognitive Models in Visualization
		4.6 Conclusion
		References
	5 Supporting Diverse Research Methods for Observing Huge Variable Space in Empirical Studies for Visualization
		5.1 Introduction
		5.2 Observations
			5.2.1 More Experimental Scientists
			5.2.2 More Studies on the ``Mind''
			5.2.3 Progressive Approaches
		5.3 The Diversity of Publications in Studying the ``Mind''
			5.3.1 The Types of Empirical Research Papers in Visualization
			5.3.2 A Survey of Paper Types in Psychology Journals
			5.3.3 A High-level Categorization
			5.3.4 Further Categorization of ``Articles''
			5.3.5 Further Categorization of ``Commentaries and Responses''
			5.3.6 Further Categorization of ``Reviews''
			5.3.7 Further Categorization of ``Reports''
			5.3.8 Further Categorization of ``Others''
			5.3.9 Observations and Discussions
		5.4 Conclusions
		References
Part II Visualization Psychology from a Visualization Perspective
	6 Visualization Onboarding Grounded in Educational Theories
		6.1 Introduction
		6.2 Related Work
			6.2.1 Visualization Onboarding
			6.2.2 Educational Theories in Visualization and Cognitive Science
			6.2.3 Knowledge Integration for Onboarding
		6.3 Descriptive Design Space
			6.3.1 Construction of Design Space
			6.3.2 Design Space Dimensions
				6.3.2.1 WHO Is the User?
				6.3.2.2 HOW Is Visualization Onboarding Provided?
			6.3.3 WHERE is Visualization Onboarding Provided?
			6.3.4 WHEN Is Visualization Onboarding Used?
		6.4 Survey on Visualization Onboarding
			6.4.1 Method
			6.4.2 Results
				6.4.2.1 WHO: Who Is the User? Which Knowledge Gap Does the User Have?
				6.4.2.2 HOW: How Is Visualization Onboarding Provided?
				6.4.2.3 WHERE: Where Is Visualization Onboarding Provided?
				6.4.2.4 WHEN: When Is Visualization Onboarding Used?
			6.4.3 Summary
			6.4.4 Existing Design Considerations for Visualization Onboarding
		6.5 Discussion and Conclusion
		References
	7 Adaptive Visualization of Health Information Based on Cognitive Psychology: Scenarios, Concepts, and Research Opportunities
		7.1 From Static to Adaptive Visual Health Information Systems
			7.1.1 Interactive Data Visualization for Health Data Visualization
			7.1.2 Evidence-Based Consumer Health Information as Information Basis
			7.1.3 Cognitive Psychology Principles for Adaptive Health Information
		7.2 Scenario: Adapting Health Information for Diabetes Type II
			7.2.1 Consumer Health Information System Scenario 1
			7.2.2 Consumer Health Information System Scenario 2
		7.3 Visual Health Information and Visual Analytics for Healthcare
			7.3.1 Previous Work
				7.3.1.1 Interactive Data Visualization and Health Data Visualization
				7.3.1.2 Visual Abstractions and Visual Literacy
				7.3.1.3 Adaptive Visualization for General and Medical Data
				7.3.1.4 Knowledge Technologies and Medical Health Information
			7.3.2 Research Challenges
		7.4 Evidence-Based Health Information and Systems
			7.4.1 Previous Work
				7.4.1.1 Health Literacy
				7.4.1.2 Consumer Health Information Systems
				7.4.1.3 Quality of CHIS
			7.4.2 Research Challenges
		7.5 Cognitive Psychology of Health Information
			7.5.1 Previous Work
				7.5.1.1 Knowledge Representation
				7.5.1.2 Adaptive Assessment
				7.5.1.3 Interactivity
				7.5.1.4 Identification and Mitigation of Cognitive Biases
				7.5.1.5 Instructional Design
				7.5.1.6 Evaluation of Adaptive Systems
			7.5.2 Research Challenges
		7.6 Architecture and Machine Learning Methods for an Adaptive Visual Consumer Health Information System
			7.6.1 Overview of Proposed Architectures
			7.6.2 Machine Learning Approaches for Adaptation
				7.6.2.1 Main Methods and Application Possibilities in an Adaptive CHIS
				7.6.2.2 Discussion of Machine Learning Approaches
		7.7 Conclusion
		References
	8 Design Cognition in Data Visualization
		8.1 Introduction
			8.1.1 Why Study Visualization Design Cognition?
			8.1.2 Methods for Studying Design Cognition
		8.2 Two Paradigms of Design Cognition
			8.2.1 Design as Rational Problem Solving
			8.2.2 Design as Reflective Practice
		8.3 Attempts at Integration
			8.3.1 Philosophical Considerations
		8.4 Implications for Data Visualization
			8.4.1 Defining Design for Data Visualization
			8.4.2 Automated Visualization Design
			8.4.3 Visualization Design Models and Frameworks
			8.4.4 Visualization Education
		8.5 Summary
		References
	9 Visualization Psychology: Foundations for an Interdisciplinary Research Program
		9.1 Introduction
		9.2 Why Visualization Needs Psychology
		9.3 Elements of a Framework
			9.3.1 Visualization is External Representation
				9.3.1.1 On Visualization
				9.3.1.2 On External Representation
			9.3.2 Meaning Is Constructed
			9.3.3 Information Is Processed
			9.3.4 Cognition Is Distributed
		9.4 On Doing Visualization Psychology
		9.5 The History and Future of Visualization Psychology
		References
	10 Visualization Psychology for Eye Tracking Evaluation
		10.1 Introduction
		10.2 Study Designs
			10.2.1 Controlled Experiments
			10.2.2 In-the-Wild Studies
			10.2.3 Bridging Between Quantitative and Qualitative Research
		10.3 Explainability of Observations
		10.4 Cognitive Architectures
		10.5 Example Scenarios
			10.5.1 Overview of Scenarios
			10.5.2 Potential Extensions
		10.6 Call for Actions
		References
Part III Visualization Psychology from an Experimental Perspective
	11 Task Matters When Scanning Data Visualizations
		11.1 Introduction
		11.2 An Experiment on the Impact of Task
			11.2.1 Materials
			11.2.2 Procedure
		11.3 Results
		11.4 Discussion
		References
	12 Perceptual Biases in Scatterplot Interpretation
		12.1 Introduction
		12.2 Bottom-Up and Top-Down Attention in Data Visualizations
		12.3 Expanding the Effectiveness of Saliency Models as a Visualization Evaluation Tool
		12.4 Visual–Spatial Biases with Scatterplots
		12.5 Experiment: Interpretation of Clusters in a Scatterplot
		12.6 Experiment: Method
			12.6.1 Participants
			12.6.2 Design
			12.6.3 Materials
			12.6.4 Procedure
		12.7 Experiment: Results
		12.8 Cluster Membership Task: Behavioral Results
			12.8.1 Density
			12.8.2 Dispersion
			12.8.3 Nearest Neighbor
		12.9 Cluster Height Task: Behavioral Results
			12.9.1 Density
			12.9.2 Dispersion
			12.9.3 Highest Point
			12.9.4 Eye Movement Results
		12.10 Experiment: Discussion
		References
	13 Leveraging Conscientiousness-Based Preferences in Information Visualization Design
		13.1 Introduction
		13.2 Fundamentals of Personality Psychology
		13.3 Related Work
		13.4 Methodology Overview
		13.5 Assessment of Personality and Design Preferences
			13.5.1 Data Collection
			13.5.2 Data Analysis
				13.5.2.1 Clustering Personality Variables
				13.5.2.2 Extracting Association Rules
				13.5.2.3 Finding Preferences for Clusters
		13.6 Evaluation
			13.6.1 Visualizations
			13.6.2 Tasks
			13.6.3 Measures
			13.6.4 Expected Findings
			13.6.5 Procedure
			13.6.6 Data Analysis
		13.7 Results
			13.7.1 Performance Metrics
			13.7.2 Self-assessment Metrics
			13.7.3 Discussion
				13.7.3.1 Research Implications
				13.7.3.2 Limitations and Future Work
		13.8 Conclusions
		References
	14 Visualizing Uncertainty in Different Domains: Commonalities and Potential Impacts on HumanDecision-Making
		14.1 Introduction
		14.2 Uncertainty and Human Decision-Making
			14.2.1 How Do Different Representations of Uncertainty Impact Decision-Making?
		14.3 Why Is Visualization of Uncertainty Difficult?
			14.3.1 We Do Not Really Know What Uncertainty Is
			14.3.2 Why Should We Bother?
		14.4 Design Considerations for Uncertainty Visualizations
			14.4.1 Why Do Users Need Information About Uncertainty?
			14.4.2 How Will Uncertainty Impact Users' Interactions with the Data Visualization?
			14.4.3 What Kinds of Visual Representations are Appropriate?
		14.5 Common Methods for Visualizing Uncertainty
			14.5.1 Intrinsic Representations of Uncertainty: Modifying Visual Attributes
			14.5.2 Extrinsic Representations of Uncertainty: Adding Graphical Elements
			14.5.3 Creating Multiple Visualizations
			14.5.4 Summary
		14.6 Applications of Uncertainty Visualization Techniques in Different Domains
			14.6.1 Intrinsic Representations of Uncertainty
				14.6.1.1 Hue
				14.6.1.2 Transparency and Texture
				14.6.1.3 Summary
			14.6.2 Extrinsic Representations of Uncertainty
				14.6.2.1 Summary
			14.6.3 Multiple Visualizations
			14.6.4 Statistical Graphs
		14.7 Discussion
		References
	15 Analysis of Sensemaking Strategies: Psychological Theories in Practice
		15.1 Introduction
		15.2 Related Work
		15.3 System Description
		15.4 Study
			15.4.1 Methodology
			15.4.2 Participants
			15.4.3 Dataset and Task
		15.5 Results
			15.5.1 Sensemaking Strategies
				15.5.1.1 Pattern: Looking for Similarities Across Several (Groups of) Actors
				15.5.1.2 Trend: Looking for Trends in the Data
				15.5.1.3 Profiling: Characterizing Crimes or Criminals Based on Features
				15.5.1.4 Pattern Incl. Profiling: Combination of Pattern and Profiling
				15.5.1.5 Elimination: Generating New Understanding by Eliminating Data Considered as Not Relevant
				15.5.1.6 Elimination Incl. Trend: Reducing the Search Space Due to Time
				15.5.1.7 Storytelling: Constructing a Story by Explaining the Behavior of Crimes and Relationships
				15.5.1.8 Creative Desperation: Not Knowing What to Do Next and the Feeling of Being Stuck in an Impasse
				15.5.1.9 Verification: Consulting Both Representations for Verification
				15.5.1.10 Contradiction: Realizing a Mismatch of What Was Hypothesized
				15.5.1.11 Coincidental Aha's: Seemingly Coincidental Insights That Are Not Conscious
			15.5.2 Reported Insights
			15.5.3 Employment of Strategies and the Number of Insights
			15.5.4 The Quality of Insights
		15.6 Discussions
		References
Index