Cognitive Modeling of Human Memory and Learning

LIDIA GHOSH, PHD, is currently a post-doctoral research fellow on Brain Science and Memory Research, granted by Liverpool Hope University to Jadavpur University, India. AMIT KONAR, PHD, is currently a Professor in the dept. of Electronics and Tele-Communication Engineering (ETCE), Jadavpur University. He is an author of 15 books including a Wiley title: Emotion Recognition-A Pattern Analysis Approach. PRATYUSHA RAKSHIT, PHD, is an Assistant Professor of ETCE dept., Jadavpur University, India and is currently on lien to Basque Centre for Applied Mathematics, Bilbao, Spain.

• Preface xiAcknowledgments xviiAbout the Authors xix1 Introduction to Brain-Inspired Memory and Learning Models 11.1 Introduction 11.2 Philosophical Contributions to Memory Research 31.3 Brain-Theoretic Interpretation of Memory Formation 101.4 Cognitive Maps 161.5 Neural Plasticity 171.6 Modularity 181.7 The Cellular Process Behind STM Formation 181.8 LTM Formation 201.9 Brain Signal Analysis in the Context of Memory and Learning 201.10 Memory Modeling by Computational Intelligence Techniques 351.11 Scope of the Book 39References 432 Working Memory Modeling Using Inverse Fuzzy Relational Approach 512.1 Introduction 522.2 Problem Formulation and Approach 542.3 Experiments and Performance Analysis 702.4 Discussion 852.5 Conclusions 86References 883 Short-Term Memory Modeling in Shape-Recognition Task by Type-2 Fuzzy Deep Brain Learning 933.1 Introduction 943.2 System Overview 963.3 Brain Functional Mapping Using Type-2 Fuzzy DBLN 1013.4 Experiments and Results 1133.5 Biological Implications 1203.6 Performance Analysis 1223.7 Conclusions 127References 1304 EEG Analysis for Subjective Assessment of Motor Learning Skill in Driving Using Type-2 Fuzzy Reasoning 1374.1 Introduction 1384.2 System Overview 1404.3 Determining Type and Degree of Learning by Type-2 Fuzzy Reasoning 1474.4 Experiments and Results 1574.5 Performance Analysis and Statistical Validation 1644.6 Conclusions 169References 1695 EEG Analysis to Decode Human Memory Responses in Face Recognition Task Using Deep LSTM Network 1755.1 Introduction 1765.2 CSP Modeling 1795.3 Proposed LSTM Classifier with Attention Mechanism 1835.4 Experiments and Results 1885.5 Conclusions 196References 1976 Cognitive Load Assessment in Motor Learning Tasks by Near-Infrared Spectroscopy Using Type-2 Fuzzy Sets 2036.1 Introduction 2036.2 Principles and Methodologies 2066.3 Classifier Design 2116.4 Experiments and Results 2196.5 Biological Implications 2266.6 Performance Analysis 2266.7 Conclusions 232References 2327 Conclusions and Future Directions of Research on BCI-Based Memory and Learning 2397.1 Self-Review of the Works Undertaken in the Book 2397.2 Limitations of EEG BCI-Based Memory Experiments 2427.3 Further Scope of Future Research on Memory and Learning 242References 245Index 247

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