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Generalization, Representation, and Recovery in a Self-Organizing Feature-map Model of Language Acquisition Ping Li (Ping×Cog sci. Richmond. EDU) Department of Psychology University of Richmond,

. We report on the successful use of DISNEY to simulate a multi-modal self-organizing neural network and characterize its performance in the evaluation of lexicon and morphological representations in six languages. Based on data collected from several studies with DISNEY models, the findings of this study are the first demonstration that DISNEY can simulate the self-organizing features of natural language processing without any dependence of the training procedure on the task of lexical learning. The results suggest that DISNEY could also provide practical tools to enhance information flow during language processing. Kiran Ball (Kiran×Cog sci. Hyderabad) Department of Psychology Tata Institute of Fundamental Research, Hyderabad-110001, India Abstract This paper presents the architecture of our self-organizing feature-feature network (SFO) in the context of self-organizing neural networks (Sons), which were originally developed in the 1950s and 60s as general purpose artificial networks for learning the meaning of arbitrary symbols. The SFO has a multi-modal receptive field, is self-organizing and learns lexical representations for multiple language and category objects during learning, and responds to information flow in a language learning process using a feature-based approach. We investigate the relationship between the size of the receptive field and its self-organizing nature of the network. The SFO has a dimension of 200 and a receptive field of 10,000 neurons/mm2 and has a mean receptive field size ranging from 0.0 to 1.7 mm2 in the English-German languages. The receptive fields of the SFO in the German and Chinese languages are about 100 times less. We examine the effects of different hyperrewards and feedback schemes on the performance of SFO in the evaluation task. We show that a very simple design of the output layer of the SFO, called a “semidirect” receptive field, and two different feedback modes of information flow on the learning process can achieve high performances. Our framework of self-organization is particularly applicable and practical for the training of neural networks during learning of natural language. Moreover, for the training of neural models, the existence of a multi-modal feature-based network with both receptive field and feedback modes can be combined into an efficient architecture to allow the learners to focus on the semantic features of the task at hand. The paper also presents two experiments in which the SFO performs well in a natural information flow task.

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