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Robust Multimodal Cognitive Load Measurement

:	Fang Chen, Jianlong Zhou, Yang Wang, Kun Yu, Syed Z. Arshad, Ahmad Khawaji, Dan Conway
:	Robust Multimodal Cognitive Load Measurement
:	Springer-Verlag
:	9783319317007
:	1
:	CHF 86.90
:

:	Betriebssysteme, Benutzeroberflächen
:	English

:	253
:	Wasserzeichen/DRM
:	PC/MAC/eReader/Tablet
:	PDF

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.

Robu t 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.

	Preface	6
	Acknowledgements	8
	Contents	10
	Part I: Preliminaries	16
	Chapter 1: Introduction	17
	1.1 What Is Cognitive Load	18
	1.2 Background	19
	1.3 Multimodal Cognitive Load Measurement	20
	1.4 Structure of the Book	22
	References	26
	Chapter 2: The State-of-The-Art	27
	2.1 Working Memory and Cognitive Load	27
	2.2 Subjective Measures	29
	2.3 Performance Measures	30
	2.4 Physiological Measures	32
	2.5 Behavioral Measures	33
	2.6 Estimating Load from Interactive Behavior	37
	2.7 Measuring Different Types of Cognitive Load	38
	2.8 Differences in Cognitive Load	39
	2.8.1 Gender Differences in Cognitive Load	39
	2.8.2 Age Differences in Cognitive Load	39
	2.8.3 Static Graphics Versus Animated Graphics in Cognitive Load	40
	2.9 Summary	41
	References	41
	Chapter 3: Theoretical Aspects of Multimodal Cognitive Load Measures	47
	3.1 Load? What Load? Mental? Or Cognitive? Why Not Effort?	48
	3.2 Mental Load in Human Performance	48
	3.2.1 Mental Workload: The Early Years	49
	3.2.2 Subjective Mental Workload Scales and Curve	52
	3.2.3 Cognitive Workload and Physical Workload Redlines	53
	3.3 Cognitive Load in Human Learning	54
	3.3.1 Three Stages of CLT: The Additivity Hypothesis	56
	3.3.2 Schema Acquisition and First-in Method	57
	3.3.3 Modality Principle in CTML	58
	3.3.4 Has Measuring Cognitive Load Been a Means to Advancing Theory?	59
	3.3.5 Bridging Mental Workload and Cognitive Load Constructs	63
	3.3.6 CLT Continues to Evolve	64
	3.4 Multimodal Interaction and Cognitive Load	65
	3.4.1 Multimodal Interaction and Robustness	65
	3.4.2 Cognitive Load in Human Centred Design	69
	3.4.3 Dual Task Methodology for Inducing Load	69
	3.4.4 Workload Measurement in a Test and Evaluation Environment	70
	3.4.5 Working Memory´s Workload Capacity: Limited But Not Fixed	72
	3.4.6 Load Effort Homeostasis (LEH) and Interpreting Cognitive Load	73
	3.5 Multimodal Cognitive Load Measures (MCLM)	77
	3.5.1 Framework for MCLM	77
	3.5.2 MCLM and Cognitive Modelling	79
	3.5.3 MCLM and Decision Making	79
	3.5.4 MCLM and Trust Studies	80
	3.6 Summary	80
	References	81
	Part II: Physiological Measurement	86
	Chapter 4: Eye-Based Measures	87
	4.1 Pupillary Response for Cognitive Load Measurement	87
	4.2 Cognitive Load Measurement Under Luminance Changes	89
	4.2.1 Task Design	89
	4.2.2 Participants and Apparatus	90
	4.2.3 Subjective Ratings	90
	4.3 Pupillary Response Features	91
	4.4 Workload Classification	92
	4.4.1 Feature Generation for Workload Classification	93
	4.4.2 Feature Selection and Workload Classification	94
	4.4.3 Results on Pupillary Response	96
	4.5 Summary	96
	References	97
	Chapter 5: Galvanic Skin Response-Based Measures	98
	5.1 Galvanic Skin Response for Cognitive Load Measurement	98
	5.2 Cognitive Load Measurement in Arithmetic Tasks	99
	5.2.1 Task Design	99
	5.2.2 GSR Feature Extraction	100
	5.2.2.1 Time Domain Features	100
	5.2.2.2 Frequency Domain Features	101
	5.2.3 Feature Analyses	102
	5.3 Cognitive Load Measurement in Reading Tasks	104
	5.3.1 Task Design	104
	5.3.2 GSR Feature Extraction	105
	5.3.3 Feature Analyses	105
	5.4 Cognitive Load Classification in Arithmetic Tasks	106
	5.4.1 Features for Workload Classification	106
	5.4.2 Classification Results	107
	5.5 Summary	108
	References	109
	Part III: Behavioural Measurement	111
	Chapter 6: Linguistic Feature-Based Measures	112
	6.1 Linguistics	112
	6.2 Cognitive Load Measurement With Non-Word Linguistics	113
	6.3 Cognitive Load Measurement with Words	115