A central question in language acquisition is how children master sentence types that they have seldom, if ever, heard. Here we report the findings of a pre-registered, randomised, single-blind intervention study designed to test the prediction that, for one such sentence type, complex questions (e.g., Is the crocodile who’s hot eating?), children could combine schemas learned, on the basis of the input, for complex noun phrases (the [THING] who’s [PROPERTY]) and simple questions (Is [THING] [ACTION]ing?) to yield a complex-question schema (Is [the [THING] who’s [PROPERTY]] ACTIONing?). Children aged 4;2 to 6;8 (M = 5;6, SD = 7.7 months) were trained on simple questions (e.g., Is the bird cleaning?) and either (Experimental group, N = 61) complex noun phrases (e.g., the bird who’s sad) or (Control group, N = 61) matched simple noun phrases (e.g., the sad bird). In general, the two groups did not differ on their ability to produce novel complex questions at test. However, the Experimental group did show (a) some evidence of generalising a particular complex NP schema (the [THING] who’s [PROPERTY] as opposed to the [THING] that’s [PROPERTY]) from training to test, (b) a lower rate of auxiliary-doubling errors (e.g., *Is the crocodile who’s hot is eating?), and (c) a greater ability to produce complex questions on the first test trial. We end by suggesting some different methods – specifically artificial language learning and syntactic priming – that could potentially be used to better test the present account.

中文翻译：

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教授不可学习的东西：复杂是/否问题的训练研究

语言习得的一个核心问题是儿童如何掌握他们很少听到的句子类型。在这里，我们报告了一项预先登记的、随机的、单盲干预研究的结果，该研究旨在测试对于这样一种句子类型的复杂问题（例如，鳄鱼是热吃的吗？)，孩子们可以根据输入结合学习到的图式，用于复杂的名词短语 ([THING] 谁是 [财产]) 和简单的问题 ([事物] [行动] 在吗？) 产生一个复杂的问题模式 ([[THING] 谁是 [PROPERTY]] 正在行动吗？）。4;2 至 6;8 岁儿童（米= 5;6,标清= 7.7 个月）接受了简单问题的培训（例如，鸟清洁了吗？) 和任一（实验组，ñ= 61) 复杂的名词短语（例如，悲伤的鸟) 或 (对照组,ñ= 61) 匹配的简单名词短语（例如，悲伤的鸟）。总的来说，两组在测试中提出新颖复杂问题的能力没有差异。然而，实验组确实展示了（a）一些推广特定复杂 NP 模式的证据（[THING] 谁是 [财产]与即 [财产] 的 [事物]) 从训练到测试，(b) 辅助倍增错误率较低（例如，*热的鳄鱼在吃东西吗？)，和 (c) 在第一次测试试验中产生复杂问题的能力更强。最后，我们提出了一些不同的方法——特别是人工语言学习和句法启动——它们可能被用来更好地测试当前的描述。