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

Lidia Ghosh; Amit Konar; Pratyusha Rakshit

Informasi Umum

Kode

23.21.312

Klasifikasi

153.12 - Conscious mental processes and intelligence- Memory

Jenis

Buku - Elektronik (E-Book)

Subjek

Memory

No. Rak

Tel-U Bandung - Gedung Manterawu Lantai 5 : Rak 5
Tel-U Purwokerto : Rak 3

Dilihat

214 kali

Informasi Lainnya

Abstraksi

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 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.