Optimality: From neural networks to universal grammar
Optimality: From neural networks to universal grammar
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DOI：
10.1126/science.275.5306.1604
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发表时间：
1997-03-14
期刊：
影响因子：
56.9
通讯作者：
Smolensky, P
中科院分区：
文献类型：
--
作者：
Prince, A;Smolensky, P
神经计算理论的概念能否对心智的形式理论有所贡献？近期的研究探索了神经计算的一个原则——优化——对语法理论的影响。对符号语言结构的优化提供了一种新的语法架构——优选论的核心。语法性等同于优选性这一主张阐明了广泛的现象，从儿童语言中产出和理解之间的巨大差异，到语言的可习得性，再到语言理论的基本问题：所有语言的语法有哪些共同之处，又可能有哪些不同？
Can concepts from the theory of neural computation contribute to formal theories of the mind? Recent research has explored the implications of one principle of neural computation, optimization, for the theory of grammar. Optimization over symbolic linguistic structures provides the core of a new grammatical architecture, optimality theory. The proposition that grammaticality equals optimality sheds light on a wide range of phenomena, from the gulf between production and comprehension in child language, to language learnability, to the fundamental questions of linguistic theory: What is it that the grammars of all languages share, and how may they differ?