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Learning biases predict a word order universal

机译:学习偏见预言一个普遍的词序

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How recurrent typological patterns, or universals, emerge from the extensive diversity found across the world's languages constitutes a central question for linguistics and cognitive science. Recent challenges to a fundamental assumption of generative linguistics-that universal properties of the human language acquisition faculty constrain the types of grammatical systems which can occur-suggest the need for new types of empirical evidence connecting typology to biases of learners. Using an artificial language learning paradigm in which adult subjects are exposed to a mix of grammatical systems (similar to a period of linguistic change), we show that learners' biases mirror a word-order universal, first proposed by Joseph Greenberg, which constrains typological patterns of adjective, numeral, and noun ordering. We briefly summarize the results of a probabilistic model of the hypothesized biases and their effect on learning, and discuss the broader implications of the results for current theories of the origins of cross-linguistic word-order preferences.
机译:世界各地语言的广泛多样性如何导致反复出现的类型学模式或普遍性,构成了语言学和认知科学的中心问题。生成语言学的基本假设的最新挑战是人类语言习得教师的普遍性质限制了可能出现的语法系统的类型,这表明需要新型的经验证据将类型学与学习者的偏见联系起来。我们使用一种人工语言学习范例,在该范例中,成年受试者暴露于多种语法系统中(类似于一段时间的语言变化),我们显示出学习者的偏见反映了词序普遍性,这是约瑟夫·格林伯格首先提出的,它限制了类型学形容词,数字和名词顺序的模式。我们简要总结了假设偏差及其对学习的影响的概率模型的结果,并讨论了该结果对跨语言单词顺序首选项起源的当前理论的更广泛含义。

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