Cognitive Modeling of Human Memory and Learning

Name: Cognitive Modeling of Human Memory and Learning | A Non-invasive Brain-Computer Interfacing Approach
Brand: Wiley
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A Non-invasive Brain-Computer Interfacing Approach

Lidia Ghosh Amit Konar Pratyusha Rakshit(Autor*in)

Wiley (Verlag)

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Erschienen am 21. August 2020

272 Seiten

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Beschreibung

Proposes computational models of human memory and learning using a brain-computer interfacing (BCI) approach

Human memory modeling is important from two perspectives. First, the precise fitting of the model to an individual's short-term or working memory may help in predicting memory performance of the subject in future. Second, memory models provide a biological insight to the encoding and recall mechanisms undertaken by the neurons present in active brain lobes, participating in the memorization process. This book models human memory from a cognitive standpoint by utilizing brain activations acquired from the cortex by electroencephalographic (EEG) and functional near-infrared-spectroscopic (f-NIRs) means.

Cognitive Modeling of Human Memory and Learning A Non-invasive Brain-Computer Interfacing Approach begins with an overview of the early models of memory. The authors then propose a simplistic model of Working Memory (WM) built with fuzzy Hebbian learning. A second perspective of memory models is concerned with Short-Term Memory (STM)-modeling in the context of 2-dimensional object-shape reconstruction from visually examined memorized instances. A third model assesses the subjective motor learning skill in driving from erroneous motor actions. Other models introduce a novel strategy of designing a two-layered deep Long Short-Term Memory (LSTM) classifier network and also deal with cognitive load assessment in motor learning tasks associated with driving. The book ends with concluding remarks based on principles and experimental results acquired in previous chapters.

* Examines the scope of computational models of memory and learning with special emphasis on classification of memory tasks by deep learning-based models

* Proposes two algorithms of type-2 fuzzy reasoning: Interval Type-2 fuzzy reasoning (IT2FR) and General Type-2 Fuzzy Sets (GT2FS)

* Considers three classes of cognitive loads in the motor learning tasks for driving learners

Cognitive Modeling of Human Memory and Learning A Non-invasive Brain-Computer Interfacing Approach will appeal to researchers in cognitive neuro-science and human/brain-computer interfaces. It is also beneficial to graduate students of computer science/electrical/electronic engineering.

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Inhalt

Cover
Title Page
Copyright
Contents
Preface
Acknowledgments
About the Authors
Chapter 1 Introduction to Brain-Inspired Memory and Learning Models
1.1 Introduction
1.2 Philosophical Contributions to Memory Research
1.2.1 Atkinson and Shiffrin's Model
1.2.2 Tveter's Model
1.2.3 Tulving's Model
1.2.4 The Parallel and Distributed Processing (PDP) Approach
1.2.5 Procedural and Declarative Memory
1.3 Brain-Theoretic Interpretation of Memory Formation
1.3.1 Coding for Memory
1.3.2 Memory Consolidation
1.3.3 Location of Stored Memories
1.3.4 Isolation of Information in Memory
1.4 Cognitive Maps
1.5 Neural Plasticity
1.6 Modularity
1.7 The Cellular Process Behind STM Formation
1.8 LTM Formation
1.9 Brain Signal Analysis in the Context of Memory and Learning
1.9.1 Association of EEG and Band with Memory Performances
1.9.2 Oscillatory and Frequency Band Activation in STM Performance
1.9.3 Change in EEG Band Power with Changing Working Memory Load
1.9.4 Effects of Electromagnetic Field on the EEG Response of Working Memory
1.9.5 EEG Analysis to Discriminate Focused Attention and WM Performance
1.9.6 EEG Power Changes in Memory Repetition Effect
1.9.7 Correlation Between LTM Retrieval and EEG Features
1.9.8 Impact of Math Anxiety on WM Response: An EEG Study
1.10 Memory Modeling by Computational Intelligence Techniques
1.11 Scope of the Book
References
Chapter 2 Working Memory Modeling Using Inverse Fuzzy Relational Approach
2.1 Introduction
2.2 Problem Formulation and Approach
2.2.1 Independent Component Analysis as a Source Localization Tool
2.2.2 Independent Component Analysis vs. Principal Component Analysis
2.2.3 Feature Extraction
2.2.4 Phase 1: WM Modeling
2.2.4.1 Step I: WM Modeling of Subject Using EEG Signals During Full Face Encoding and Recall from Specific Part of Same Face
2.2.4.2 Step II: WM Modeling of Subject Using EEG Signals During Full Face Encoding and Recall from All Parts of Same Face
2.2.5 Phase 2: WM Analysis
2.2.6 Finding Max-Min Compositional Inverse of Weight Matrix Wkc
2.3 Experiments and Performance Analysis
2.3.1 Experimental Set-up
2.3.2 Source Localization Using eLORETA
2.3.3 Pre-processing
2.3.4 Selection of EEG Features
2.3.5 WM Model Consistency Across Partial Face Stimuli
2.3.6 Inter-person Variability of W
2.3.7 Variation in Imaging Attributes
2.3.8 Comparative Analysis with Existing Fuzzy Inverse Relations
2.4 Discussion
2.5 Conclusions
References
Chapter 3 Short-Term Memory Modeling in Shape-Recognition Task by Type-2 Fuzzy Deep Brain Learning
3.1 Introduction
3.2 System Overview
3.3 Brain Functional Mapping Using Type-2 Fuzzy DBLN
3.3.1 Overview of Type-2 Fuzzy Sets
3.3.2 Type-2 Fuzzy Mapping and Parameter Adaptation by Perceptron-Like Learning
3.3.2.1 Construction of the Proposed Interval Type-2 Fuzzy Membership Function (IT2MF)
3.3.2.2 Construction of IT2FS-Induced Mapping Function
3.3.2.3 Secondary Membership Function Computation of Proposed GT2FS
3.3.2.4 Proposed General Type-2 Fuzzy Mapping
3.3.3 Perceptron-Like Learning for Weight Adaptation
3.3.4 Training of the Proposed Shape-Reconstruction Architecture
3.3.5 The Test Phase of the Memory Model
3.4 Experiments and Results
3.4.1 Experimental Set-up
3.4.2 Experiment 1: Validation of the STM Model with Respect to Error Metric ?
3.4.3 Experiment 2: Similar Encoding by a Subject for Similar Input Object Shapes
3.4.4 Experiment 3: Study of Subjects' Learning Ability with Increasing Complexity in Object Shape
3.4.5 Experiment 4: Convergence Time of the Weight Matrix G for Increased Complexity of the Input Shape Stimuli
3.4.6 Experiment 5: Abnormality in G matrix for the Subjects with Brain Impairment
3.5 Biological Implications
3.6 Performance Analysis
3.6.1 Performance Analysis of the Proposed T2FS Methods
3.6.2 Computational Performance Analysis of the Proposed T2FS Methods
3.6.3 Statistical Validation Using Wilcoxon Signed-Rank Test
3.6.4 Optimal Parameter Selection and Robustness Study
3.7 Conclusions
References
Chapter 4 EEG Analysis for Subjective Assessment of Motor Learning Skill in Driving Using Type-2 Fuzzy Reasoning
4.1 Introduction
4.2 System Overview
4.2.1 Rule Design to Determine the Degree of Learning
4.2.2 Single Trial Detection of Brain Signals
4.2.2.1 Feature Extraction
4.2.2.2 Feature Selection
4.2.2.3 Classification
4.2.3 Type-2 Fuzzy Reasoning
4.2.4 Training and Testing of the Classifiers
4.3 Determining Type and Degree of Learning by Type-2 Fuzzy Reasoning
4.3.1 Preliminaries on IT2FS and GT2FS
4.3.2 Proposed Reasoning Method 1: CIT2FS-Based Reasoning
4.3.3 Computation of Percentage Normalized Degree of Learning
4.3.4 Optimal ? Selection in IT2FS Reasoning
4.3.5 Proposed Reasoning Method 2: Triangular Vertical Slice (TVS)-Based CGT2FS Reasoning
4.3.5.1 Closed General Type-2 Fuzzy Inference Generation
4.3.5.2 Time complexity
4.3.6 Proposed Reasoning Method 3: CGT2FS Reasoning with Gaussian Secondary MF
4.3.6.1 Time-Complexity
4.4 Experiments and Results
4.4.1 The Experimental Set-up
4.4.2 Stimulus Presentation
4.4.3 Experiment 1: Source Localization Using eLORETA
4.4.4 Experiment 2: Validation of the Rules
4.4.5 Experiment 3: Pre-processing and Artifact Removal Using ICA
4.4.6 Experiment 4: N400 Old/New Effect Observation over the Successive Trials
4.4.7 Experiment 5: Selection of the Discriminating EEG Features Using PCA
4.5 Performance Analysis and Statistical Validation
4.5.1 Performance Analysis of the LSVM Classifiers
4.5.2 Robustness Study
4.5.3 Performance Analysis of the Proposed T2FS Reasoning Methods
4.5.4 Computational Performance Analysis of the Proposed T2FS Reasoning Methods
4.5.5 Statistical Validation Using Wilcoxon Signed-Rank Test
4.6 Conclusions
References
Chapter 5 EEG Analysis to Decode Human Memory Responses in Face Recognition Task Using Deep LSTM Network
5.1 Introduction
5.2 CSP Modeling
5.2.1 The Standard CSP Algorithm
5.2.2 The Proposed CSP Algorithm
5.3 Proposed LSTM Classifier with Attention Mechanism
5.3.1 Attention Mechanism in Each LSTM Unit
5.4 Experiments and Results
5.4.1 Experimental Set-up
5.4.2 Experiment 1: Activated Brain Region Selection Using eLORETA
5.4.3 Experiment 2: Detection of the ERP Signals Associated with the Familiar and Unfamiliar Face Discrimination
5.4.4 Experiment 3: Performance Analysis of the Proposed CSP Algorithm as a Feature Extraction Technique
5.4.5 Experiment 4: Performance Analysis of the Proposed LSTM-Based Classifier
5.4.6 Experiment 5: Classifier Performance Analysis with Varying EEG Time-Window Length
5.4.7 Statistical Validation of the Proposed LSTM Classifier Using McNemar's Test
5.5 Conclusions
References
Chapter 6 Cognitive Load Assessment in Motor Learning Tasks by Near-Infrared Spectroscopy Using Type-2 Fuzzy Sets
6.1 Introduction
6.2 Principles and Methodologies
6.2.1 Normalization of the Raw Data
6.2.2 Pre-processing
6.2.3 Feature Extraction
6.2.4 Training Instance Generation for Offline Training
6.2.5 Feature Selection Using Evolutionary Algorithm
6.2.6 Classifier Training and Testing
6.3 Classifier Design
6.3.1 Preliminaries on IT2FS and GT2FS
6.3.2 IT2FS-Induced Classifier Design
6.3.3 GT2FS-Induced Classifier Design
6.4 Experiments and Results
6.4.1 Experimental Set-up
6.4.2 Participants
6.4.3 Stimulus Presentation for Online Classification
6.4.4 Experiment 1: Demonstration of Decreasing Cognitive Load with Increasing Learning Epochs for Similar Stimulus
6.4.5 Experiment 2: Automatic Extraction of Discriminating fNIRs Features
6.4.6 Experiment 3: Optimal Parameter Setting of Feature Selection and Classifier Units
6.5 Biological Implications
6.6 Performance Analysis
6.6.1 Performance Analysis of the Proposed IT2FS and GT2FS Classifier
6.6.2 Statistical Validation of the Classifier Using McNemar's Test
6.7 Conclusions
References
Chapter 7 Conclusions and Future Directions of Research on BCI-Based Memory and Learning
7.1 Self-Review of the Works Undertaken in the Book
7.2 Limitations of EEG BCI-Based Memory Experiments
7.3 Further Scope of Future Research on Memory and Learning
References
Index
EULA

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