Innovation in Language Learning

This piece of research adopts an interdisciplinary conceptual approach that incorporates Artificial Neural Networks (ANN) with learning, neurological and cognitive sciences. Specifically, it considers modeling of associative memorization to introduce optimal analysis for development of reading brain performance via the role of ensemble Number of highly specialized neurons. By more details, this brain performance realistically simulated using ANNs self-organized modeling paradigm. In other words, presented simulation adopts Sanger’s Rule Approach which equivalent to Generalized Hebian Algorithm (GHA). Furthermore, it inspired by functioning of highly specialized biological neurons in reading brain based on the organization the brain's structures/substructures.  In accordance with the prevailing concept of individual intrinsic characterized properties of highly specialized neurons. Presented models have been in close correspondence with set of neurons’ performance for developing reading brain in a significant way. More specifically, herein, introduced model concerned with their important role played in carrying out cognitive reading brain function's outcomes. Accordingly, the cognitive goal for reading brain is to translate that seen word (orthographic word-from) into a spoken word (phonological word-form). In this context herein, the presented work illustrates via ANN simulation results the following. : How a number of highly specialized neurons corresponding to visual brain area contribute to the perceived sight (seen) signal is in direct proportionality with the correctness of identified depicted / printed images. These images represent the orthographic word-from has to be transferred subsequently into a phonological word-form during reading process. Furthermore, individual intrinsic characteristics   of such highly specialized neurons (in visual brain area) influence directly on the correctness of identified images associated with orthographic word-from