Muutke küpsiste eelistusi

E-raamat: Robust Multimodal Cognitive Load Measurement

, , , , , ,
Teised raamatud teemal:
  • Formaat - PDF+DRM
  • Hind: 110,53 €*
  • * hind on lõplik, st. muud allahindlused enam ei rakendu
  • Lisa ostukorvi
  • Lisa soovinimekirja
  • See e-raamat on mõeldud ainult isiklikuks kasutamiseks. E-raamatuid ei saa tagastada.
Robust Multimodal Cognitive Load MeasurementSuurem pilt
Teised raamatud teemal:

DRM piirangud

  • Kopeerimine (copy/paste):

    ei ole lubatud

  • Printimine:

    ei ole lubatud

  • Kasutamine:

    Digitaalõiguste kaitse (DRM)
    Kirjastus on väljastanud selle e-raamatu krüpteeritud kujul, mis tähendab, et selle lugemiseks peate installeerima spetsiaalse tarkvara. Samuti peate looma endale  Adobe ID Rohkem infot siin. E-raamatut saab lugeda 1 kasutaja ning alla laadida kuni 6'de seadmesse (kõik autoriseeritud sama Adobe ID-ga).

    Vajalik tarkvara
    Mobiilsetes seadmetes (telefon või tahvelarvuti) lugemiseks peate installeerima selle tasuta rakenduse: PocketBook Reader (iOS / Android)

    PC või Mac seadmes lugemiseks peate installima Adobe Digital Editionsi (Seeon tasuta rakendus spetsiaalselt e-raamatute lugemiseks. Seda ei tohi segamini ajada Adober Reader'iga, mis tõenäoliselt on juba teie arvutisse installeeritud )

    Seda e-raamatut ei saa lugeda Amazon Kindle's. 

This book explores robust multimodal cognitiveload measurement with physiological and behavioural modalities, which involve theeye, Galvanic Skin Response, speech, language, pen input, mouse movement andmultimodality fusions. Factors including stress, trust, and environmentalfactors such as illumination are discussed regarding their implications forcognitive load measurement. Furthermore, dynamic workload adjustment andreal-time cognitive load measurement with data streaming are presented in orderto make cognitive load measurement accessible by more widespread applicationsand users. Finally, application examples are reviewed demonstrating thefeasibility of multimodal cognitive load measurement in practical applications.This is thefirst book of its kind to systematically introduce various computationalmethods for automatic and real-time cognitive load measurement and by doing somoves the practical application of cognitive load measurement from the domainof the compute

r scientist and psychologist to more general end-users, ready forwidespread implementation.Robust Multimodal CognitiveLoad Measurement is intended for researchers and practitioners involved with cognitiveload studies and communities within the computer, cognitive, and socialsciences. The book will especially benefit researchers in areas like behaviouranalysis, social analytics, human-computer interaction (HCI), intelligentinformation processing, and decision support systems.

Preface.- Part I: Preliminaries.-Introduction.- The State-of-The-Art.- Theoretical Aspects of MultimodalCognitive Load Measures.- Part II: Physiological Measurement.- Eye BasedMeasures. Galvanic Skin Response Based Measures.- Part III: BehaviouralMeasurement.- Linguistic Feature Based Measures.- Speech Signal BasedMeasures.- Pen Input Based Measures.- Mouse Based Measures.- Part IV:Multimodal Measures and Affecting Factors.- Multimodal Measures and DataFusion.- Emotion and Cognitive Load.- Stress and Cognitive Load.- Trust andCognitive Load.- Part V: Making Cognitive Load Measurement Accessible.- DynamicCognitive Load Adjustments in A Feedback Loop.- Real-Time Cognitive LoadMeasurement: Data Streaming Approach.- Applications of Cognitive LoadMeasurement.- Part VI: Conclusions.- Cognitive Load Measurement in Perspectives.

Arvustused

The content of this volume is a fair reference source for researchers and stakeholders interested in the investigation and application of the construct of cognitive load. this is a good reference for those who are interested in the application of cognitive load and its measurement, with the aim of designing multimodal human-computer interactive systems better aligned with limited mental capacities of humans. (Luca Longo, Computing Reviews, March, 2017)

Part I Preliminaries
1 Introduction
3(10)
1.1 What Is Cognitive Load
4(1)
1.2 Background
5(1)
1.3 Multimodal Cognitive Load Measurement
6(2)
1.4 Structure of the Book
8(5)
References
12(1)
2 The State-of-The-Art
13(20)
2.1 Working Memory and Cognitive Load
13(2)
2.2 Subjective Measures
15(1)
2.3 Performance Measures
16(2)
2.4 Physiological Measures
18(1)
2.5 Behavioral Measures
19(4)
2.6 Estimating Load from Interactive Behavior
23(1)
2.7 Measuring Different Types of Cognitive Load
24(1)
2.8 Differences in Cognitive Load
25(2)
2.8.1 Gender Differences in Cognitive Load
25(1)
2.8.2 Age Differences in Cognitive Load
25(1)
2.8.3 Static Graphics Versus Animated Graphics in Cognitive Load
26(1)
2.9 Summary
27(6)
References
27(6)
3 Theoretical Aspects of Multimodal Cognitive Load Measures
33(42)
3.1 Load? What Load? Mental? Or Cognitive? Why Not Effort?
34(1)
3.2 Mental Load in Human Performance
34(6)
3.2.1 Mental Workload: The Early Years
35(3)
3.2.2 Subjective Mental Workload Scales and Curve
38(1)
3.2.3 Cognitive Workload and Physical Workload Redlines
39(1)
3.3 Cognitive Load in Human Learning
40(11)
3.3.1 Three Stages of CLT: The Additivity Hypothesis
42(1)
3.3.2 Schema Acquisition and First-in Method
43(1)
3.3.3 Modality Principle in CTML
44(1)
3.3.4 Has Measuring Cognitive Load Been a Means to Advancing Theory?
45(4)
3.3.5 Bridging Mental Workload and Cognitive Load Constructs
49(1)
3.3.6 CLT Continues to Evolve
50(1)
3.4 Multimodal Interaction and Cognitive Load
51(12)
3.4.1 Multimodal Interaction and Robustness
51(4)
3.4.2 Cognitive Load in Human Centred Design
55(1)
3.4.3 Dual Task Methodology for Inducing Load
55(1)
3.4.4 Workload Measurement in a Test and Evaluation Environment
56(2)
3.4.5 Working Memory's Workload Capacity: Limited But Not Fixed
58(1)
3.4.6 Load Effort Homeostasis (LEH) and Interpreting Cognitive Load
59(4)
3.5 Multimodal Cognitive Load Measures (MCLM)
63(3)
3.5.1 Framework for MCLM
63(2)
3.5.2 MCLM and Cognitive Modelling
65(1)
3.5.3 MCLM and Decision Making
65(1)
3.5.4 MCLM and Trust Studies
66(1)
3.6 Summary
66(9)
References
67(8)
Part II Physiological Measurement
4 Eye-Based Measures
75(12)
4.1 Pupillary Response for Cognitive Load Measurement
75(2)
4.2 Cognitive Load Measurement Under Luminance Changes
77(2)
4.2.1 Task Design
77(1)
4.2.2 Participants and Apparatus
78(1)
4.2.3 Subjective Ratings
78(1)
4.3 Pupillary Response Features
79(1)
4.4 Workload Classification
80(4)
4.4.1 Feature Generation for Workload Classification
81(1)
4.4.2 Feature Selection and Workload Classification
82(2)
4.4.3 Results on Pupillary Response
84(1)
4.5 Summary
84(3)
References
85(2)
5 Galvanic Skin Response-Based Measures
87(16)
5.1 Galvanic Skin Response for Cognitive Load Measurement
87(1)
5.2 Cognitive Load Measurement in Arithmetic Tasks
88(5)
5.2.1 Task Design
88(1)
5.2.2 GSR Feature Extraction
89(2)
5.2.3 Feature Analyses
91(2)
5.3 Cognitive Load Measurement in Reading Tasks
93(2)
5.3.1 Task Design
93(1)
5.3.2 GSR Feature Extraction
94(1)
5.3.3 Feature Analyses
94(1)
5.4 Cognitive Load Classification in Arithmetic Tasks
95(2)
5.4.1 Features for Workload Classification
95(1)
5.4.2 Classification Results
96(1)
5.5 Summary
97(6)
References
98(5)
Part III Behavioural Measurement
6 Linguistic Feature-Based Measures
103(12)
6.1 Linguistics
103(1)
6.2 Cognitive Load Measurement With Non-Word Linguistics
104(2)
6.3 Cognitive Load Measurement with Words
106(4)
6.3.1 Word Count and Words per Sentence
106(1)
6.3.2 Long Words
106(1)
6.3.3 Positive and Negative Emotion Words
106(1)
6.3.4 Swear Words
107(1)
6.3.5 Cognitive Words
107(1)
6.3.6 Perceptual Words
107(1)
6.3.7 Inclusive Words
107(1)
6.3.8 Achievement Words
108(1)
6.3.9 Agreement and Disagreement Words
108(1)
6.3.10 Certainty and Uncertainty Words
108(1)
6.3.11 Summary of Measurements
108(2)
6.4 Cognitive Load Measurement Based on Personal Pronouns
110(1)
6.5 Language Complexity as Indices of Cognitive Load
111(2)
6.5.1 Lexical Density
111(1)
6.5.2 Complex Word Ratio
111(1)
6.5.3 Gunning Fog Index
112(1)
6.5.4 Flesch-Kincaid Grade
112(1)
6.5.5 SMOG Grade
112(1)
6.5.6 Summary of Language Measurements
113(1)
6.6 Summary
113(2)
References
114(1)
7 Speech Signal Based Measures
115(18)
7.1 Basics of Speech
116(1)
7.2 Cognitive Load Experiments
116(4)
7.2.1 Reading Comprehension Experiment
116(2)
7.2.2 Stroop Test
118(1)
7.2.3 Reading Span Experiment
118(1)
7.2.4 Time Constraint
119(1)
7.2.5 Experiment Validation
120(1)
7.3 Speech Features and Cognitive Load
120(3)
7.3.1 Source-Based Features
121(1)
7.3.2 Filter-Based Features
121(2)
7.4 A Comparison of Features for Cognitive Load Classification
123(6)
7.4.1 Pitch and Intensity Features
123(1)
7.4.2 EGG Features
124(2)
7.4.3 Glottal Flow Features
126(3)
7.5 Cognitive Load Classification System via Speech
129(1)
7.6 Summary
129(4)
References
130(3)
8 Pen Input Based Measures
133(14)
8.1 Writing Based Measures
133(2)
8.2 Datasets for Writing-Based Cognitive Load Examination
135(4)
8.2.1 CLTex Dataset
136(1)
8.2.2 CLSkt Dataset
137(1)
8.2.3 CLDgt Dataset
138(1)
8.3 Stroke-, Substroke-and Point-Level Features
139(2)
8.4 Cognitive Load Implications on Writing Shapes
141(2)
8.5 Cognitive Load Classification System
143(1)
8.6 Summary
144(3)
References
145(2)
9 Mouse Based Measures
147(14)
9.1 User Mouse Activity
147(1)
9.2 Mouse Features for Cognitive Load Change Detection
148(7)
9.2.1 Temporal Features
148(3)
9.2.2 Spatial Features
151(4)
9.3 Limitations of Mouse Feature Measurements
155(1)
9.4 Mouse Interactivity in Multimodal Measures
156(1)
9.5 Summary
156(5)
References
157(4)
Part IV Multimodal Measures and Affecting Factors
10 Multimodal Measures and Data Fusion
161(12)
10.1 Multimodal Measurement of Cognitive Load
161(1)
10.2 An Abstract Model for Multimodal Assessment
162(2)
10.3 Basketball Skills Training
164(1)
10.4 Subjective Ratings and Performance Results
165(2)
10.5 Individual Modalities
167(2)
10.6 Multimodal Fusion
169(2)
10.7 Summary
171(2)
References
171(2)
11 Emotion and Cognitive Load
173(12)
11.1 Emotional Arousal and Physiological Response
173(1)
11.2 Cognitive Load Measurement with Emotional Arousal
174(3)
11.2.1 Task Design
174(1)
11.2.2 Pupillary Response Based Measurement
175(1)
11.2.3 Skin Response Based Measurement
176(1)
11.3 Cognitive Load Classification with Emotional Arousal
177(5)
11.3.1 Cognitive Load Classification Based on Pupillary Response
178(1)
11.3.2 Cognitive Load Classification Based on GSR
179(1)
11.3.3 Cognitive Load Classification Based on the Fusion
180(2)
11.4 Summary
182(3)
References
182(3)
12 Stress and Cognitive Load
185(10)
12.1 Stress and Galvanic Skin Response
185(1)
12.2 Cognitive Load Measurement Under Stress Conditions
186(2)
12.2.1 Task Design
186(1)
12.2.2 Procedures
187(1)
12.2.3 Subjective Ratings
188(1)
12.3 GSR Features Under Stress Conditions
188(5)
12.3.1 Mean GSR Under Stress Conditions
188(2)
12.3.2 Peak Features Under Stress Conditions
190(3)
12.4 Summary
193(2)
References
194(1)
13 Trust and Cognitive Load
195(22)
13.1 Definition of Trust
195(1)
13.2 Related Work
196(3)
13.2.1 Trust
196(1)
13.2.2 Trust and Cognitive Load
197(2)
13.3 Trust of Information and Cognitive Load
199(4)
13.3.1 Task Design
199(2)
13.3.2 Data Collection
201(2)
13.4 Data Analyses
203(1)
13.5 Analysis Results
204(7)
13.5.1 Subjective Ratings of Mental Effort
204(1)
13.5.2 Linguistic Analysis of Think-Aloud Speech
204(7)
13.6 Interpersonal Trust and Cognitive Load
211(1)
13.6.1 Task Design
211(1)
13.6.2 Results
211(1)
13.7 Summary
212(5)
References
213(4)
Part V Making Cognitive Load Measurement Accessible
14 Dynamic Cognitive Load Adjustments in a Feedback Loop
217(12)
14.1 Dynamic Cognitive Load Adjustments
217(1)
14.2 Dynamic Workload Adaptation Feedback Loop
218(2)
14.2.1 Task Design
218(1)
14.2.2 Procedures
219(1)
14.3 GSR Features
220(2)
14.3.1 Signal Processing
220(1)
14.3.2 Feature Extraction
221(1)
14.4 Cognitive Load Classification
222(3)
14.4.1 Offline Cognitive Load Classifications
222(1)
14.4.2 Online Cognitive Load Classifications
223(2)
14.5 Dynamic Workload Adjustment
225(2)
14.5.1 Adaptation Models
225(1)
14.5.2 Performance Evaluation of Adaptation Models
226(1)
14.6 Summary
227(2)
References
227(2)
15 Real-Time Cognitive Load Measurement: Data Streaming Approach
229(6)
15.1 Sliding Window Implementation
230(1)
15.2 Streaming Mouse Activity Features
231(1)
15.3 Lessons Learnt
232(2)
15.4 Summary
234(1)
References
234(1)
16 Applications of Cognitive Load Measurement
235(16)
16.1 User Interface Design
235(3)
16.2 Emergency Management
238(2)
16.3 Driving and Piloting
240(1)
16.4 Education and Training
241(2)
16.5 Other Applications
243(1)
16.6 Future Applications
244(7)
References
245(6)
Part VI Conclusions
17 Cognitive Load Measurement in Perspective
251(3)
References 254
Lisainfo e-raamatute kohta Lisainfo e-raamatute kohta
Püsilink: https://www.kriso.ee/db/97833193170072e.html
Märksõnad: