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 Interval Type-2 fuzzy sets (IT2FS) and General Type-2 Fuzzy Sets (GT2FS) based reasoning in the context of memory modeling and learning
Employs Brain-Computer Interfaces for memory modeling and also cognitive load classification in 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.