Coevolution of Mind and Language
نویسندگان
چکیده
We consider the complexity of a discourse in human language that cannot be attributed to the knowledge of a single sentence. A simple recurrent neural network model called coupled dynamical recognizers is proposed to simulate the situation where two agents alternately make a model to imitate the conversation, predict the next utterance of the other speaker, and speak to each other. In speech processes a “smoothness principle” that demands preservation of a context flow should be obeyed in the present simulations. The experimental results show that there are some characteristic correlations between the agent’s inner model and the utterance pattern. We discuss, from the dynamical systems view, how the existence of two different levels of representation being constructed by each agent causes divergent utterance patterns and open-endedness of the conversation. An agent, namely a dynamical recognizer, has temporal geometrical patterns in his/her inner state space on which his/her next utterance depends. When a cloud-like representation is formed as a model of conversation, it provides the speaker with indeterminacy in choosing words, which leads to variety of discourse.
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تاریخ انتشار 2000