Research On Neural Network Modeling Of Infants' Phonetic Category Acquisition

Chomsky’s linguistic theory states that only by supposing that human has some innate neural structure built specifically for language learning tasks, could we explain the astonishing language learning abilities of infants. Infants’ phoneme acquisition is an essential topic among all researches focusing on infant language acquisition. In this study, by using neurocomputational modeling, we have explored and discussed the mechanisms of how infants process phonetic and semantic information, store those knowledge and build phoneme categories of their native languages.Based on theories of Duality of Patterning, Perception Reorganization, Functional Reorganization, Categorical Learning, Statistical Learning and Social Learning, and biological structures and neural functions of language functional cortices, and synaptic links, and modeling principles of Modular Theory and Connectionism, we have proposed the Interconnected Growing Self-organizing Map(I-GSOM) model and the Duality-based Interconnected Growing Self-organizing Map(DI-GSOM) model, on the foundation of Kr?ger’s language processing model. According to the characteristics of language learning task, we have also developed the improved growing self-organizing map and corresponding learning algorithms, and further simulated those learning mechanisms mentioned above.In this study, we have conducted two simulation experiments. In the first experiment, we simulated the phoneme acquisition process of Standard German based on I-GSOM model. Modeling results prove that the improved growing self-organizing map we proposed has the self-organizing ability to cluster auditory and semantic categories, and its network structure can help I-GSOM model to handle the knowledge growth during the process of learning. Although the model is highly abstract, it is plausible enough to model biological mechanisms like basic neural principles, self-organizing, associative learning, self-adaptation and neural plasticity. In the second experiment, we simulated the phoneme acquisition process of Standard Chinese based on DI-GSOM model. We have respectively simulated infants’ innate universal language perceiving ability, the perception reorganization process of infants between 12-18 months old, and the refining process of native phonemic perception of infants between 19-36 months old. By modeling the Duality of Patterning and the semantic-based top-down process, we have verified processes of perception reorganization and functional reorganization. Based on our modeling results, we conclude that the infants’ perception pattern shiftting from phonetic perception to phonemic perception is not a single-factor-determined process. Infant’s acquisition of native language phoneme categories and native language phonology needs, at least, the cooperation of semantic learning and phonetic learning. Therefore, we believe that semantic processing is essential for acquiring correct phonemic perception ability of infants’ native language.Based on the Duality of Patterning theory, the two models we proposed in this study have considered the interactive effects of phonetic learning and semantic learning during infants’ language acquisition process, and further detailed the modeling of phonetic processing. The proposed improved growing self-organizing map and corresponding learning algorithms are capable of modeling complex learning mechanisms. Therefore, this study is an important breakthrough on language acquisition algorithm design. Moreover, the modeling of tonal acquisition has particular characteristics of Standard Chinese study. All in all, as a cross-filed study that covers fields including linguistics, psycholinguistics and neurolinguistics, our work is very innovative and fills the gaps of related research areas.