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The Resource Modeling Human Behaviors in Psychology Using Engineering Methods

Modeling Human Behaviors in Psychology Using Engineering Methods

Label
Modeling Human Behaviors in Psychology Using Engineering Methods
Title
Modeling Human Behaviors in Psychology Using Engineering Methods
Creator
Contributor
Subject
Genre
Language
eng
Member of
Cataloging source
MiAaPQ
Literary form
non fiction
Nature of contents
dictionaries
Series statement
River Publishers Series in Information Science and Technology
Label
Modeling Human Behaviors in Psychology Using Engineering Methods
Link
http://ebookcentral.proquest.com/lib/multco/detail.action?docID=4509464
Instantiates
Publication
Copyright
Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
txt
Content type MARC source
rdacontent
Contents
  • Cover -- Half Title -- Series Page -- Title Page -- Copy Right -- Contents -- Part I - Modeling Human Behaviors:An Engineering Approach -- Chapter 1 - Behavioral Signal Processing (BSP): Behavioral Informatics -- 1.1 BSP: Introduction -- 1.1.1 BSP: Technical Challenges and Complexities -- 1.2 BSP: Computational Methods for Dyadic InteractionDynamics -- 1.2.1 BSP: Further Complexities in Modeling Interaction Dynamics -- Chapter 2 - Applications in Modeling Human Behaviors Computationally -- 2.1 BSP Application Domains -- 2.2 Case Study I: Emotion Recognition from Speech -- 2.3 Case Study II: Quantifying Implicit Vocal Entrainment inHuman Interactions -- 2.4 Case Study III: Data-driven Perceptual Experiment -- Part II - Affective Computing fromSpeech -- Chapter 3 - Individual Utterance Emotion Recognition -- 3.1 Introduction -- 3.2 Emotion Databases and Acoustic Feature Extraction -- 3.2.1 The AIBO Database -- 3.2.2 The USC IEMOCAP Database -- 3.2.3 Acoustic Feature Extraction -- 3.2.4 Feature Selection and Normalization -- 3.3 Emotion Classification Framework -- 3.3.1 Building the Hierarchical Decision Tree -- 3.3.2 Building the Hierarchical Decision Tree for the AIBO Database andthe USC IEMOCAP Database -- 3.3.3 Classifier for Binary Classification Tasks -- 3.3.3.1 Bayesian Logistic Regression -- 3.4 Emotion Recognition Experiment Setup and Results -- 3.4.1 The AIBO Database -- 3.4.1.1 Results of Experiment I on the AIBO database -- 3.4.1.2 Results of Experiment II on the AIBO database -- 3.4.2 The USC IEMOCAP Database -- 3.4.2.1 Experiment Result of the USC IEMOCAP Database -- 3.5 Conclusions and Future Work -- Chapter 4 - Dialog-based Emotion Recognition -- 4.1 Introduction -- 4.2 Emotion Database and Annotation -- 4.2.1 The USC IEMOCAP Database -- 4.2.2 Emotion Annotation -- 4.3 Dynamic Bayesian Network Model
  • 4.4 Experimental Results and Discussion -- 4.4.1 Acoustic Feature Extraction -- 4.4.2 Experiment Setup -- 4.4.3 Experiment Results and Discussion -- 4.5 Conclusions and Future Work -- Part III Quantifying Human Behavior in Psychology -- Chaper 5 - Implicit Vocal Synchrony Quantification -- 5.1 Introduction -- 5.2 BSP Database: The Couple Therapy Corpus -- 5.2.1 Pre-processing and Audio Feature Extraction -- 5.2.2 Behavioral Codes of Interest -- 5.3 Signal-derived Vocal Entrainment Quantification -- 5.3.1 PCA-based Similarity Measures -- 5.3.1.1 Symmetric Similarity Measures -- 5.3.1.2 Directional Similarity Measures -- 5.3.2 Representative Vocal Features -- 5.3.3 Vocal Entrainment Measures in Dialogs -- 5.4 Analysis of Vocal Entrainment Measures -- 5.4.1 Natural Cohesiveness of Dialogs -- 5.4.2 Entrainment in Affective Interactions -- 5.5 Affect Classification using Entrainment Measures -- 5.5.1 Classification Framework -- 5.5.1.1 Factorial Hidden Markov Model -- 5.5.2 Classification Setup -- 5.5.3 Classification Results and Discussions -- 5.6 Conclusions and Future Work -- Chapter 6 - Analysis of Vocal Synchrony in Couples Therapy -- 6.1 Introduction -- 6.2 BSP Database: The Couple Therapy Corpus -- 6.3 PCA-based Vocal Entrainment Measures -- 6.3.1 Symmetric Entrainment Measures -- 6.3.2 Directional Entrainment Measures -- 6.3.2.1 Acoustic Features -- 6.3.3 Canonical Correlation Analysis -- 6.4 Analysis of Results and Discussions -- 6.4.1 Correlation Analysis: The Four Behavioral Dimensions -- 6.4.2 Canonical Correlation Analysis: Withdrawal -- 6.5 Lessons Learnt from Correlation Analysis -- 6.6 Vocal Entrainment and Demand-and-Withdraw in CoupleConflict -- 6.6.1 Demand and Withdraw -- 6.6.2 Behavioral Influence and Polarization of Demand and Withdraw -- 6.6.3 Data Analysis -- 6.6.4 Results and Discussions
  • Part IV Data-driven Perceptual Experiment -- Chapter 7 - Multiple Instance Learning Framework for Perceptual Experiment -- 7.1 Introduction -- 7.2 BSP Database: The Couple Therapy Corpus -- 7.3 Computational Framework -- 7.3.1 Multiple Instance Learning -- 7.3.2 Sequential Probability Ratio Test -- 7.4 Analysis Setup -- 7.4.1 Lexical Feature Extraction -- 7.4.2 Classification Setup -- 7.5 Detection Results and Discussions -- 7.6 Isolated-Saliency vs. Causal-Integration -- 7.7 Conclusions and Future Work -- Part V Outlook of BSP -- Chapter 8 - Continuously Emerging Importance of Modeling Human Behavior -- Bibliography -- Author Biography
Control code
EBC4509464
Dimensions
unknown
Extent
1 online resource (131 pages)
Form of item
online
Isbn
9788793102613
Media category
computer
Media MARC source
rdamedia
Media type code
c
Note
Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2017. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.
Sound
unknown sound
Specific material designation
remote
System control number
  • (MiAaPQ)EBC4509464
  • (Au-PeEL)EBL4509464
  • (CaPaEBR)ebr11247304
  • (OCoLC)957125024
Label
Modeling Human Behaviors in Psychology Using Engineering Methods
Link
http://ebookcentral.proquest.com/lib/multco/detail.action?docID=4509464
Publication
Copyright
Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
txt
Content type MARC source
rdacontent
Contents
  • Cover -- Half Title -- Series Page -- Title Page -- Copy Right -- Contents -- Part I - Modeling Human Behaviors:An Engineering Approach -- Chapter 1 - Behavioral Signal Processing (BSP): Behavioral Informatics -- 1.1 BSP: Introduction -- 1.1.1 BSP: Technical Challenges and Complexities -- 1.2 BSP: Computational Methods for Dyadic InteractionDynamics -- 1.2.1 BSP: Further Complexities in Modeling Interaction Dynamics -- Chapter 2 - Applications in Modeling Human Behaviors Computationally -- 2.1 BSP Application Domains -- 2.2 Case Study I: Emotion Recognition from Speech -- 2.3 Case Study II: Quantifying Implicit Vocal Entrainment inHuman Interactions -- 2.4 Case Study III: Data-driven Perceptual Experiment -- Part II - Affective Computing fromSpeech -- Chapter 3 - Individual Utterance Emotion Recognition -- 3.1 Introduction -- 3.2 Emotion Databases and Acoustic Feature Extraction -- 3.2.1 The AIBO Database -- 3.2.2 The USC IEMOCAP Database -- 3.2.3 Acoustic Feature Extraction -- 3.2.4 Feature Selection and Normalization -- 3.3 Emotion Classification Framework -- 3.3.1 Building the Hierarchical Decision Tree -- 3.3.2 Building the Hierarchical Decision Tree for the AIBO Database andthe USC IEMOCAP Database -- 3.3.3 Classifier for Binary Classification Tasks -- 3.3.3.1 Bayesian Logistic Regression -- 3.4 Emotion Recognition Experiment Setup and Results -- 3.4.1 The AIBO Database -- 3.4.1.1 Results of Experiment I on the AIBO database -- 3.4.1.2 Results of Experiment II on the AIBO database -- 3.4.2 The USC IEMOCAP Database -- 3.4.2.1 Experiment Result of the USC IEMOCAP Database -- 3.5 Conclusions and Future Work -- Chapter 4 - Dialog-based Emotion Recognition -- 4.1 Introduction -- 4.2 Emotion Database and Annotation -- 4.2.1 The USC IEMOCAP Database -- 4.2.2 Emotion Annotation -- 4.3 Dynamic Bayesian Network Model
  • 4.4 Experimental Results and Discussion -- 4.4.1 Acoustic Feature Extraction -- 4.4.2 Experiment Setup -- 4.4.3 Experiment Results and Discussion -- 4.5 Conclusions and Future Work -- Part III Quantifying Human Behavior in Psychology -- Chaper 5 - Implicit Vocal Synchrony Quantification -- 5.1 Introduction -- 5.2 BSP Database: The Couple Therapy Corpus -- 5.2.1 Pre-processing and Audio Feature Extraction -- 5.2.2 Behavioral Codes of Interest -- 5.3 Signal-derived Vocal Entrainment Quantification -- 5.3.1 PCA-based Similarity Measures -- 5.3.1.1 Symmetric Similarity Measures -- 5.3.1.2 Directional Similarity Measures -- 5.3.2 Representative Vocal Features -- 5.3.3 Vocal Entrainment Measures in Dialogs -- 5.4 Analysis of Vocal Entrainment Measures -- 5.4.1 Natural Cohesiveness of Dialogs -- 5.4.2 Entrainment in Affective Interactions -- 5.5 Affect Classification using Entrainment Measures -- 5.5.1 Classification Framework -- 5.5.1.1 Factorial Hidden Markov Model -- 5.5.2 Classification Setup -- 5.5.3 Classification Results and Discussions -- 5.6 Conclusions and Future Work -- Chapter 6 - Analysis of Vocal Synchrony in Couples Therapy -- 6.1 Introduction -- 6.2 BSP Database: The Couple Therapy Corpus -- 6.3 PCA-based Vocal Entrainment Measures -- 6.3.1 Symmetric Entrainment Measures -- 6.3.2 Directional Entrainment Measures -- 6.3.2.1 Acoustic Features -- 6.3.3 Canonical Correlation Analysis -- 6.4 Analysis of Results and Discussions -- 6.4.1 Correlation Analysis: The Four Behavioral Dimensions -- 6.4.2 Canonical Correlation Analysis: Withdrawal -- 6.5 Lessons Learnt from Correlation Analysis -- 6.6 Vocal Entrainment and Demand-and-Withdraw in CoupleConflict -- 6.6.1 Demand and Withdraw -- 6.6.2 Behavioral Influence and Polarization of Demand and Withdraw -- 6.6.3 Data Analysis -- 6.6.4 Results and Discussions
  • Part IV Data-driven Perceptual Experiment -- Chapter 7 - Multiple Instance Learning Framework for Perceptual Experiment -- 7.1 Introduction -- 7.2 BSP Database: The Couple Therapy Corpus -- 7.3 Computational Framework -- 7.3.1 Multiple Instance Learning -- 7.3.2 Sequential Probability Ratio Test -- 7.4 Analysis Setup -- 7.4.1 Lexical Feature Extraction -- 7.4.2 Classification Setup -- 7.5 Detection Results and Discussions -- 7.6 Isolated-Saliency vs. Causal-Integration -- 7.7 Conclusions and Future Work -- Part V Outlook of BSP -- Chapter 8 - Continuously Emerging Importance of Modeling Human Behavior -- Bibliography -- Author Biography
Control code
EBC4509464
Dimensions
unknown
Extent
1 online resource (131 pages)
Form of item
online
Isbn
9788793102613
Media category
computer
Media MARC source
rdamedia
Media type code
c
Note
Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2017. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.
Sound
unknown sound
Specific material designation
remote
System control number
  • (MiAaPQ)EBC4509464
  • (Au-PeEL)EBL4509464
  • (CaPaEBR)ebr11247304
  • (OCoLC)957125024

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