Learning and development in neural networks: The importance of starting small

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Learning and development in neural networks: The importance of starting small (1993)

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by Jeffrey L. Elman

Venue:	Cognition
Citations:	531 - 17 self

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@ARTICLE{Elman93learningand,
    author = {Jeffrey L. Elman},
    title = {Learning and development in neural networks: The importance of starting small},
    journal = {Cognition},
    year = {1993},
    volume = {48},
    pages = {71--99}
}

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Abstract

It is a striking fact that in humans the greatest learnmg occurs precisely at that point in time- childhood- when the most dramatic maturational changes also occur. This report describes possible synergistic interactions between maturational change and the ability to learn a complex domain (language), as investigated in con-nectionist networks. The networks are trained to process complex sentences involving relative clauses, number agreement, and several types of verb argument structure. Training fails in the case of networks which are fully formed and ‘adultlike ’ in their capacity. Training succeeds only when networks begin with limited working memory and gradually ‘mature ’ to the adult state. This result suggests that rather than being a limitation, developmental restrictions on resources may constitute a necessary prerequisite for mastering certain complex domains. Specifically, successful learning may depend on starting small.

Keyphrases

neural network maturational change con-nectionist network possible synergistic interaction number agreement relative clause certain complex domain complex sentence adult state complex domain several type verb argument structure time childhood striking fact necessary prerequisite dramatic maturational change developmental restriction successful learning

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