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A Connectionist Study on the Interplay of Nouns and Pronouns in Personal Pronoun Acquisition

机译:人称代词习得中名词与代词相互作用的连接主义研究

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Cascade-correlation learning is used to model pronoun acquisition in children. The cascade-correlation algorithm is a feed-forward neural network that builds its own topology from input and output units. Personal pronoun acquisition is an interesting non-linear problem in psychology. A mother will refer to her son as you and herself as me, but the son must infer for himself that when he speaks to his mother, she becomes you and he becomes me. Learning the shifting reference of these pronouns is a difficult task that most children master. We show that learning of two different noun-and-pronoun addressee patterns is consistent with naturalistic studies. We observe a surprising factor in pronoun reversal: increasing the amount of exposure to noun patterns can decrease or eliminate reversal errors in children.
机译:级联相关学习用于模拟儿童代词习得。级联相关算法是一种前馈神经网络,可以根据输入和输出单元构建自己的拓扑。人称代词习得是心理学中一个有趣的非线性问题。母亲会称呼儿子为您,而她称呼自己为我,但儿子必须为自己推断,当他与母亲说话时,她成为您,他成为我。学习这些代词的变化参考是大多数孩子们掌握的一项艰巨任务。我们表明,学习两种不同的名词和代词收件人模式与自然主义研究是一致的。我们发现代词反转的一个令人惊讶的因素:增加名词模式的暴露量可以减少或消除儿童的反转错误。

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