Using Neural Computational Model In Second Language Learning

Over the past 30 years, neural computational model, which metwith a huge success in the field of language cognition, has brought remarkable changes to study individual differences of individual language. However, current research on neural computational model is mostly about native language cognitive development, and is not involved with individual differences in the study of phonological awareness, short-term memory ability and long-term memory ability of connection model. So the suggestions also cannot achieve the anticipate target in the second language learning disorder.The results of neural computational model in the field of language cognition and the related research of neural cognition can provide new study idea andtheoretical foundation for language teaching.This paperadopts research framework and facts related to neurology, studied by professor Thomas in Centre for Brain and Cognitive Development,and proposes a connection model between phonological awareness and memory ability in the second language learning, Using ANN in model, in order to achieve the following goals:(1)To establish connection hypothesis between learners’ phonological awareness and memory ability;(2)To testify the hypothesis by using ANN computational model;(3)To provide suggestions to promote the second language learning for learners.Fromthe neural network simulation and T-TEST,we can see what influence the learners’ English achievements not phonological awareness,but short-term memory and long-term memory. Remarkablefunction of working memory can be seen from the simulation of individual ANN. The conclusion agrees with the theory of bilingual brain control in neuroscience. Individual ANN could providethe basis to predict learners’ learning ability and disorder throughsimulating learners’ learning styles, and at the same time it provides more accurate clues and direction for the subject teachers’ personalized teaching.