Robust Multimodal Cognitive Load Measurement

This book explores robust multimodal cognitive load measurement with physiological and behavioural modalities, which involve the eye, Galvanic Skin Response, speech, language, pen input, mouse movement and multimodality fusions. Factors including stress, trust, and environmental factors such as illu...

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Main Author Chen, Fang
Format eBook Book
LanguageEnglish
Published Cham Springer Nature 2016
Springer International Publishing AG
Springer International Publishing
Springer
Edition1
SeriesHuman–Computer Interaction Series
Subjects
Biological Psychology
Biometrics
Computer programming, programs, data
Computer Science
User Interfaces and Human Computer Interaction
Online AccessGet full text
ISBN3319317008
9783319317007
3319316982
9783319316987
ISSN1571-5035
DOI10.1007/978-3-319-31700-7

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Abstract This book explores robust multimodal cognitive load measurement with physiological and behavioural modalities, which involve the eye, Galvanic Skin Response, speech, language, pen input, mouse movement and multimodality fusions. Factors including stress, trust, and environmental factors such as illumination are discussed regarding their implications for cognitive load measurement. Furthermore, dynamic workload adjustment and real-time cognitive load measurement with data streaming are presented in order to make cognitive load measurement accessible by more widespread applications and users. Finally, application examples are reviewed demonstrating the feasibility of multimodal cognitive load measurement in practical applications. This is the first book of its kind to systematically introduce various computational methods for automatic and real-time cognitive load measurement and by doing so moves the practical application of cognitive load measurement from the domain of the computer scientist and psychologist to more general end-users, ready for widespread implementation. Robust Multimodal Cognitive Load Measurement is intended for researchers and practitioners involved with cognitive load studies and communities within the computer, cognitive, and social sciences. The book will especially benefit researchers in areas like behaviour analysis, social analytics, human-computer interaction (HCI), intelligent information processing, and decision support systems.
AbstractList This book explores robust multimodal cognitive load measurement with physiological and behavioural modalities, which involve the eye, Galvanic Skin Response, speech, language, pen input, mouse movement and multimodality fusions. Factors including stress, trust, and environmental factors such as illumination are discussed regarding their implications for cognitive load measurement. Furthermore, dynamic workload adjustment and real-time cognitive load measurement with data streaming are presented in order to make cognitive load measurement accessible by more widespread applications and users. Finally, application examples are reviewed demonstrating the feasibility of multimodal cognitive load measurement in practical applications. This is the first book of its kind to systematically introduce various computational methods for automatic and real-time cognitive load measurement and by doing so moves the practical application of cognitive load measurement from the domain of the computer scientist and psychologist to more general end-users, ready for widespread implementation. Robust Multimodal Cognitive Load Measurement is intended for researchers and practitioners involved with cognitive load studies and communities within the computer, cognitive, and social sciences. The book will especially benefit researchers in areas like behaviour analysis, social analytics, human-computer interaction (HCI), intelligent information processing, and decision support systems.
Author Wang, Yang
Khawaji, Ahmad
Chen, Fang
Conway, Dan
Zhou, Jianlong
Yu, Kun
Arshad, Syed Z.
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ISBN 3319317008
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Notes Includes bibliographical references
Other authors: Jianlong Zhou, Yang Wang, Kun Yu, Syed Z. Arshad, Ahmad Khawaji, Dan Conway
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Snippet This book explores robust multimodal cognitive load measurement with physiological and behavioural modalities, which involve the eye, Galvanic Skin Response,...
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SubjectTerms Biological Psychology
Biometrics
Computer programming, programs, data
Computer Science
User Interfaces and Human Computer Interaction
TableOfContents 6.4 Cognitive Load Measurement Based on Personal Pronouns -- 6.5 Language Complexity as Indices of Cognitive Load -- 6.5.1 Lexical Density -- 6.5.2 Complex Word Ratio -- 6.5.3 Gunning Fog Index -- 6.5.4 Flesch-Kincaid Grade -- 6.5.5 SMOG Grade -- 6.5.6 Summary of Language Measurements -- 6.6 Summary -- References -- Chapter 7: Speech Signal Based Measures -- 7.1 Basics of Speech -- 7.2 Cognitive Load Experiments -- 7.2.1 Reading Comprehension Experiment -- 7.2.2 Stroop Test -- 7.2.3 Reading Span Experiment -- 7.2.4 Time Constraint -- 7.2.5 Experiment Validation -- 7.3 Speech Features and Cognitive Load -- 7.3.1 Source-Based Features -- 7.3.2 Filter-Based Features -- 7.4 A Comparison of Features for Cognitive Load Classification -- 7.4.1 Pitch and Intensity Features -- 7.4.2 EGG Features -- 7.4.3 Glottal Flow Features -- 7.5 Cognitive Load Classification System via Speech -- 7.6 Summary -- References -- Chapter 8: Pen Input Based Measures -- 8.1 Writing Based Measures -- 8.2 Datasets for Writing-Based Cognitive Load Examination -- 8.2.1 CLTex Dataset -- 8.2.2 CLSkt Dataset -- 8.2.3 CLDgt Dataset -- 8.3 Stroke-, Substroke- and Point-Level Features -- 8.4 Cognitive Load Implications on Writing Shapes -- 8.5 Cognitive Load Classification System -- 8.6 Summary -- References -- Chapter 9: Mouse Based Measures -- 9.1 User Mouse Activity -- 9.2 Mouse Features for Cognitive Load Change Detection -- 9.2.1 Temporal Features -- 9.2.2 Spatial Features -- 9.2.2.1 Spatial Features with Straight Lines -- 9.2.2.2 Straight Line Results -- 9.3 Limitations of Mouse Feature Measurements -- 9.4 Mouse Interactivity in Multimodal Measures -- 9.5 Summary -- References -- Part IV: Multimodal Measures and Affecting Factors -- Chapter 10: Multimodal Measures and Data Fusion -- 10.1 Multimodal Measurement of Cognitive Load -- 10.2 An Abstract Model for Multimodal Assessment
Intro -- Preface -- Acknowledgements -- Contents -- Part I: Preliminaries -- Chapter 1: Introduction -- 1.1 What Is Cognitive Load -- 1.2 Background -- 1.3 Multimodal Cognitive Load Measurement -- 1.4 Structure of the Book -- References -- Chapter 2: The State-of-The-Art -- 2.1 Working Memory and Cognitive Load -- 2.2 Subjective Measures -- 2.3 Performance Measures -- 2.4 Physiological Measures -- 2.5 Behavioral Measures -- 2.6 Estimating Load from Interactive Behavior -- 2.7 Measuring Different Types of Cognitive Load -- 2.8 Differences in Cognitive Load -- 2.8.1 Gender Differences in Cognitive Load -- 2.8.2 Age Differences in Cognitive Load -- 2.8.3 Static Graphics Versus Animated Graphics in Cognitive Load -- 2.9 Summary -- References -- Chapter 3: Theoretical Aspects of Multimodal Cognitive Load Measures -- 3.1 Load? What Load? Mental? Or Cognitive? Why Not Effort? -- 3.2 Mental Load in Human Performance -- 3.2.1 Mental Workload: The Early Years -- 3.2.2 Subjective Mental Workload Scales and Curve -- 3.2.3 Cognitive Workload and Physical Workload Redlines -- 3.3 Cognitive Load in Human Learning -- 3.3.1 Three Stages of CLT: The Additivity Hypothesis -- 3.3.2 Schema Acquisition and First-in Method -- 3.3.3 Modality Principle in CTML -- 3.3.4 Has Measuring Cognitive Load Been a Means to Advancing Theory? -- 3.3.5 Bridging Mental Workload and Cognitive Load Constructs -- 3.3.6 CLT Continues to Evolve -- 3.4 Multimodal Interaction and Cognitive Load -- 3.4.1 Multimodal Interaction and Robustness -- 3.4.2 Cognitive Load in Human Centred Design -- 3.4.3 Dual Task Methodology for Inducing Load -- 3.4.4 Workload Measurement in a Test and Evaluation Environment -- 3.4.5 Working Memory´s Workload Capacity: Limited But Not Fixed -- 3.4.6 Load Effort Homeostasis (LEH) and Interpreting Cognitive Load -- 3.5 Multimodal Cognitive Load Measures (MCLM)
3.5.1 Framework for MCLM -- 3.5.2 MCLM and Cognitive Modelling -- 3.5.3 MCLM and Decision Making -- 3.5.4 MCLM and Trust Studies -- 3.6 Summary -- References -- Part II: Physiological Measurement -- Chapter 4: Eye-Based Measures -- 4.1 Pupillary Response for Cognitive Load Measurement -- 4.2 Cognitive Load Measurement Under Luminance Changes -- 4.2.1 Task Design -- 4.2.2 Participants and Apparatus -- 4.2.3 Subjective Ratings -- 4.3 Pupillary Response Features -- 4.4 Workload Classification -- 4.4.1 Feature Generation for Workload Classification -- 4.4.2 Feature Selection and Workload Classification -- 4.4.3 Results on Pupillary Response -- 4.5 Summary -- References -- Chapter 5: Galvanic Skin Response-Based Measures -- 5.1 Galvanic Skin Response for Cognitive Load Measurement -- 5.2 Cognitive Load Measurement in Arithmetic Tasks -- 5.2.1 Task Design -- 5.2.2 GSR Feature Extraction -- 5.2.2.1 Time Domain Features -- 5.2.2.2 Frequency Domain Features -- 5.2.3 Feature Analyses -- 5.3 Cognitive Load Measurement in Reading Tasks -- 5.3.1 Task Design -- 5.3.2 GSR Feature Extraction -- 5.3.3 Feature Analyses -- 5.4 Cognitive Load Classification in Arithmetic Tasks -- 5.4.1 Features for Workload Classification -- 5.4.2 Classification Results -- 5.5 Summary -- References -- Part III: Behavioural Measurement -- Chapter 6: Linguistic Feature-Based Measures -- 6.1 Linguistics -- 6.2 Cognitive Load Measurement With Non-Word Linguistics -- 6.3 Cognitive Load Measurement with Words -- 6.3.1 Word Count and Words per Sentence -- 6.3.2 Long Words -- 6.3.3 Positive and Negative Emotion Words -- 6.3.4 Swear Words -- 6.3.5 Cognitive Words -- 6.3.6 Perceptual Words -- 6.3.7 Inclusive Words -- 6.3.8 Achievement Words -- 6.3.9 Agreement and Disagreement Words -- 6.3.10 Certainty and Uncertainty Words -- 6.3.11 Summary of Measurements
10.3 Basketball Skills Training -- 10.4 Subjective Ratings and Performance Results -- 10.5 Individual Modalities -- 10.6 Multimodal Fusion -- 10.7 Summary -- References -- Chapter 11: Emotion and Cognitive Load -- 11.1 Emotional Arousal and Physiological Response -- 11.2 Cognitive Load Measurement with Emotional Arousal -- 11.2.1 Task Design -- 11.2.2 Pupillary Response Based Measurement -- 11.2.3 Skin Response Based Measurement -- 11.3 Cognitive Load Classification with Emotional Arousal -- 11.3.1 Cognitive Load Classification Based on Pupillary Response -- 11.3.2 Cognitive Load Classification Based on GSR -- 11.3.3 Cognitive Load Classification Based on the Fusion -- 11.4 Summary -- References -- Chapter 12: Stress and Cognitive Load -- 12.1 Stress and Galvanic Skin Response -- 12.2 Cognitive Load Measurement Under Stress Conditions -- 12.2.1 Task Design -- 12.2.2 Procedures -- 12.2.3 Subjective Ratings -- 12.3 GSR Features Under Stress Conditions -- 12.3.1 Mean GSR Under Stress Conditions -- 12.3.2 Peak Features Under Stress Conditions -- 12.4 Summary -- References -- Chapter 13: Trust and Cognitive Load -- 13.1 Definition of Trust -- 13.2 Related Work -- 13.2.1 Trust -- 13.2.2 Trust and Cognitive Load -- 13.3 Trust of Information and Cognitive Load -- 13.3.1 Task Design -- 13.3.2 Data Collection -- 13.3.2.1 Survey Responses -- 13.3.2.2 Behavioral Measures -- 13.3.2.3 Performance Measures -- 13.4 Data Analyses -- 13.5 Analysis Results -- 13.5.1 Subjective Ratings of Mental Effort -- 13.5.2 Linguistic Analysis of Think-Aloud Speech -- 13.5.2.1 Pause Analysis -- 13.5.2.2 Linguistic Category Analysis -- 13.5.2.3 Other Behavioral Features -- 13.6 Interpersonal Trust and Cognitive Load -- 13.6.1 Task Design -- 13.6.2 Results -- 13.7 Summary -- References -- Part V: Making Cognitive Load Measurement Accessible
Chapter 14: Dynamic Cognitive Load Adjustments in a Feedback Loop -- 14.1 Dynamic Cognitive Load Adjustments -- 14.2 Dynamic Workload Adaptation Feedback Loop -- 14.2.1 Task Design -- 14.2.2 Procedures -- 14.3 GSR Features -- 14.3.1 Signal Processing -- 14.3.2 Feature Extraction -- 14.4 Cognitive Load Classification -- 14.4.1 Offline Cognitive Load Classifications -- 14.4.2 Online Cognitive Load Classifications -- 14.5 Dynamic Workload Adjustment -- 14.5.1 Adaptation Models -- 14.5.2 Performance Evaluation of Adaptation Models -- 14.6 Summary -- References -- Chapter 15: Real-Time Cognitive Load Measurement: Data Streaming Approach -- 15.1 Sliding Window Implementation -- 15.2 Streaming Mouse Activity Features -- 15.3 Lessons Learnt -- 15.4 Summary -- References -- Chapter 16: Applications of Cognitive Load Measurement -- 16.1 User Interface Design -- 16.2 Emergency Management -- 16.3 Driving and Piloting -- 16.4 Education and Training -- 16.5 Other Applications -- 16.6 Future Applications -- References -- Part VI: Conclusions -- Chapter 17: Cognitive Load Measurement in Perspective -- References
Title Robust Multimodal Cognitive Load Measurement
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