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.
... 165 Thomson , C. , 52 , 59 Thurman , S. K. , 230 Thyer , B. A. , 308 Timberlake , W. , 165 Webster - Stratton , 237 , 251 , 253 , 366 Author Index.
Haberstick, B.C., Lessem, J. M., Hopfer, C. J., Smolen, A., Ehringer, M.A., Timberlake, D., et al. (2005). Monoamine oxidase A (MAOA) and antisocial ...
Some, like the “behavior systems” approach of Timberlake(1994)assume thatbehavior can be explained by a system of interactingmodules thatareeither built ...
However, there is clear evidence that this constant ratio does not always produce reinforcement (Timberlake & Allison, 1974). Second and, as we shall see ...
... 30, 32 Thomae, H., 40 Thompson, L., 23-24 Timberlake, E. M., 16 Tobin, S. S., ... E, 33 Wolfe, S. M., 81 Wolinsky, M. A., 85 Zarit, J., 11, 30, 31, 32, ...
La Crisi Mondiale e Saggi Critici di Marxiano e Socialismo. Bologna, N. Zanichelli. ... TIMBERLAKE (P. H.): 1912. Experimental Parasitism, a Study of the ...
... 143 Tharp, R. G., 80 Thompson, R. H., 250 Timberlake, W., 308,309 Tingey, ... B. W., 70 Ries, B.J., 268 Robins, E.,298 Robinson, S. L., 91,244 Roper, ...
... R.L., McGrath, Joseph E. McKeachie McPhail, Clark Miller, J.G. Mitchell, ... Jerry 469 Taylor 39 Timberlake, William 464 Tolman 72, 140, 142 Tucker, ...
... 247 Fromme, H., 523 Frost, P., 106 Frost, R., 161 Fryer, R., 291 Fuhrer, D., 4 Fukuyama, H., 408 Fulbright, R. K., 486 Fulero, S., 440 Fuligni, A. J., ...
... C. 638 Ernst, D. 704 Ernst, E. 278 Esch, T. 110 Eslinger, P.J. 448 Esposito-Smythers, ... E. 197 Frontera, W. R. 408 Frost, J. 332 Frost, R. 699 Frost, ...