Cognitive biases in visualizations
This book brings together the latest research in this new and exciting area of visualization, looking at classifying and modelling cognitive biases, together with user studies which reveal their undesirable impact on human judgement, and demonstrating how visual analytic techniques can provide effec...
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| Main Author | |
|---|---|
| Format | eBook Book |
| Language | English |
| Published |
Cham
Springer
2018
Springer International Publishing AG Springer International Publishing |
| Edition | 1 |
| Subjects | |
| Online Access | Get full text |
| ISBN | 9783319958309 3319958305 |
| DOI | 10.1007/978-3-319-95831-6 |
Cover
Table of Contents:
- 5.2.3 Process-Oriented Operationalization -- 5.3 Empirical Approaches - Behavioral Observation and Outcome-Oriented Operationalization -- 5.3.1 Behavioral Observation -- 5.3.2 Outcome-Oriented Operationalization -- 5.4 Automatic Cognitive Bias Detection Approach -- 5.5 Conclusion and Outlook -- References -- 6 Experts' Familiarity Versus Optimality of Visualization Design: How Familiarity Affects Perceived and Objective Task Performance -- 6.1 Introduction -- 6.2 Manifestations of the Familiarity Heuristic -- 6.3 Effects on Visualization Based Judgments -- 6.4 Critical Reflection -- 6.5 Conclusion -- References -- 7 Data Visualization Literacy and Visualization Biases: Cases for Merging Parallel Threads -- 7.1 Introduction -- 7.2 Background -- 7.2.1 Measuring Data Visualization Literacy and Quantifying Its Impact on Performance -- 7.2.2 Novices, Experts and Visualization Use -- 7.2.3 Biases and Data Visualization -- 7.3 Individual Differences and Bias: Guiding Results and Organizational Frameworks -- 7.4 Linking Data Visualization Literacy to Existing Studies of Visualization and Biases -- 7.4.1 Reversal: Augmenting Data Visualization Literacy Research with Biases -- 7.5 Conclusion -- References -- 8 The Biases of Thinking Fast and Thinking Slow -- 8.1 Introduction -- 8.2 Heuristics and Biases -- 8.3 Case studies -- 8.3.1 Causes Trump Statistics -- 8.3.2 Tom W's Specialty -- 8.3.3 The 3-D Heuristic -- 8.4 Implications for Visualization and Visual Analytics -- References -- Mitigation Strategies -- 9 Experimentally Evaluating Bias-Reducing Visual Analytics Techniques in Intelligence Analysis -- 9.1 Introduction -- 9.2 Basics of Intelligence Analysis -- 9.2.1 Intelligence as a Cognitive Process -- 9.2.2 Assessing Analytic Quality -- 9.2.3 Judging "Correctness" -- 9.3 Impact of Heuristics and Biases on Intelligence Analysis -- 9.3.1 Confirmation Bias
- 9.3.2 Illusory Correlation -- 9.3.3 Absence of Evidence -- 9.3.4 Irrelevant Evidence -- 9.3.5 Overconfidence Bias -- 9.4 Mitigating Bias Effects -- 9.4.1 Training Interventions -- 9.4.2 Procedural Interventions -- 9.5 Experimental Methods -- 9.5.1 Considerations -- 9.5.2 Prior Studies -- 9.5.3 Experimental Design Framework -- 9.5.4 Cautions -- 9.6 Conclusion -- References -- 10 Promoting Representational Fluency for Cognitive Bias Mitigation in Information Visualization -- 10.1 Introduction -- 10.2 Representational Fluency -- 10.3 Implications for Visualization Research and Practice -- 10.3.1 Developing Representational Fluency -- 10.3.2 Effect on Cognitive Processing -- 10.3.3 Preliminary Research Agenda -- 10.4 Summary -- References -- 11 Designing Breadth-Oriented Data Exploration for Mitigating Cognitive Biases -- 11.1 Introduction -- 11.2 The Information Space Model of Breadth-Oriented Exploration -- 11.3 Three Considerations for Designing Breadth-Oriented Data Exploration -- 11.3.1 Unit of Exploration -- 11.3.2 User-Driven Versus System-Driven Exploration -- 11.3.3 Related Versus Systematic Exploration -- 11.4 Application of the Three Design Considerations -- 11.4.1 Task Analysis -- 11.4.2 Usage Scenario -- 11.4.3 Designing Based on the Three Considerations -- 11.5 Discussion -- References -- 12 A Visualization Approach to Addressing Reviewer Bias in Holistic College Admissions -- 12.1 Introduction -- 12.2 Related Work -- 12.2.1 Reasoning Heuristics and Cognitive Biases -- 12.2.2 Using Visualizations to Mitigate Cognitive Biases -- 12.3 Characterization of the Holistic Review Process -- 12.4 System 1 and System 2 -- 12.5 Accuracy of Expert Intuition in Holistic Reviews -- 12.6 Possible Reviewer Biases -- 12.6.1 Coherence, Causal Associations, and Narrative Fallacy -- 12.6.2 Anchoring as Adjustment -- 12.6.3 The Halo Effect
- 12.6.4 Confirmation Bias -- 12.6.5 Availability -- 12.6.6 Representativeness -- 12.6.7 The Avoidance of Cognitive Dissonance -- 12.6.8 Time-Induced and Stress-Induced Biases -- 12.7 Proposed Visualization Strategies to Mitigate Biases -- 12.7.1 Easing Cognitive Load -- 12.7.2 Supporting Sensemaking -- 12.7.3 Decorrelating Error -- 12.7.4 Mobilizing System 2 -- 12.7.5 Combining Formulas with Intuition -- 12.8 Conclusion -- References -- 13 Cognitive Biases in Visual Analytics-A Critical Reflection -- 13.1 Introduction -- 13.2 Puzzle Problem Approach Versus Everyday Thinking and Reasoning -- 13.3 Bias Mitigation Strategies -- 13.4 Conclusion -- References
- Intro -- Preface -- DECISIVe 2017 Workshop -- Organizers -- Program Committee Members -- Contents -- Contributors -- 1 So, What Are Cognitive Biases? -- 1.1 Introduction -- 1.1.1 Examples -- 1.2 A Brief History of Cognitive Biases -- 1.3 Impact of Biases -- 1.4 Cognitive Biases in Visualization -- 1.4.1 Interpretation of Visualizations -- 1.4.2 Visualization Tools -- 1.5 Debiasing -- 1.6 Conclusion -- References -- Bias Definitions, Perspectives and Modeling -- 2 Studying Biases in Visualization Research: Framework and Methods -- 2.1 Introduction -- 2.2 A Framework to Study Biases -- 2.2.1 Perceptual Biases -- 2.2.2 Action Biases -- 2.2.3 Social Biases -- 2.3 Methodological Considerations When Studying Biases -- 2.3.1 Perceptual Biases -- 2.3.2 Action Biases -- 2.3.3 Social Biases -- 2.3.4 Application of the Framework to Derive a Model -- 2.3.5 Threats to Validity -- 2.4 Conclusion -- References -- 3 Four Perspectives on Human Bias in Visual Analytics -- 3.1 Introduction -- 3.2 Bias as a Cognitive Processing Error -- 3.3 Bias as a Filter for Information -- 3.4 Bias as a Preconception -- 3.5 Bias as a Model Mechanism -- 3.6 Discussion -- 3.6.1 Does Bias Endanger Mixed-Initiative Visual Analytics? -- 3.6.2 How to Keep the Machine ``Above the Bias''? -- 3.6.3 Could the Mixed-Initiative System Impart Bias to the User? -- 3.6.4 Is Bias Good or Bad? -- 3.7 Conclusion -- References -- 4 Bias by Default? -- 4.1 Introduction -- 4.2 Relationship to Analytic Provenance -- 4.3 Gapminder -- 4.4 Markov Models -- 4.5 Interface Models -- 4.6 Application: Gapminder Analysis -- 4.7 Discussion -- 4.8 Conclusion -- References -- Cognitive Biases in Action -- 5 Methods for Discovering Cognitive Biases in a Visual Analytics Environment -- 5.1 Introduction -- 5.2 Theory-Driven Approaches -- 5.2.1 Design Recommendations -- 5.2.2 Systematic Tool Analysis