Constraints on language acquisition and how they change (or don't) with age

语言习得的限制以及它们如何随着年龄而改变（或不改变）

• 批准号: 408041-2011
• 负责人: Hudson, Carla
• 金额: $ 1.75万
• 依托单位: University of British Columbia
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2011
• 资助国家: 加拿大
• 起止时间: 2011-01-01 至 2012-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=488914
• 关键词: Constraints; language; acquisition; how; they

Language is a quintessential human ability - it plays a role in almost every human activity. Yet despite its importance, we know relatively little about how language is actually acquired. That is the goal of this research, to better understand the processes involved. Using miniature artificial languages, I examine what learners acquire from different kinds of input to discover what is learnable and why, and how this may differ for child vs. adult learners. The research focuses on what learners extract from noisy input data. In real life, language learners take the input they hear or see and from it extract regularities about the language. However, the input often provides only probabilistic support for the patterns acquired. E.g., to indicate past tense in English you add -ed to the verb, that is, unless the verb is irregular. Despite the presence of irregulars, when a speaker is asked to use a novel verb in the past tense, they will usually add -ed, indicating that they have extracted the overall pattern. Speech errors also introduce noise, yet children do not acquire a rule like '98% of the time you have a singular subject use -s on the end of the verb if the event is in the present'. Even more extreme examples are seen in children learning from non-native speakers, who usually produce many more errors than native speakers. However, even these children end up with a language that is regular and deterministic, very much unlike their input. Learners seem to be very good at extracting patterns from data that does not perfectly fit the pattern. Sometimes, however, the pattern to be learned is actually variable, meaning that children have to learn the variation, rather than filter it out. I ask how learners do this, how they learn the patterns despite the variation, as well as how they learn that the pattern is the variation, with the goal of better understanding the constraints on human language learning. This research will contribute to our understanding of human language learning, and may inform language therapy and improve learning outcomes for adult language learners.

语言是一种典型的人类能力——它在几乎所有人类活动中都发挥着作用。然而，尽管语言很重要，但我们对语言实际上是如何习得的知之甚少。这是本研究的目标，更好地理解所涉及的过程。使用微型人工语言，我检查学习者从不同类型的输入中获得什么，以发现什么是可学习的以及为什么，以及这对于儿童与成人学习者有何不同。该研究的重点是学习者从嘈杂的输入数据中提取什么。在现实生活中，语言学习者获取他们听到或看到的输入，并从中提取语言的规律。然而，输入通常只为所获取的模式提供概率支持。例如，要表示英语中的过去时，您可以在动词后添加 -ed，也就是说，除非动词不规则。尽管存在不规则的情况，当要求说话者在过去时态中使用新动词时，他们通常会添加 -ed，表明他们已经提取了整体模式。言语错误也会引入噪音，但孩子们并没有掌握这样的规则：“98% 的情况下，如果事件发生在现在，你有一个单数主语，则在动词末尾使用 -s”。更极端的例子是向非母语人士学习的孩子，他们通常会比母语人士产生更多的错误。然而，即使这些孩子最终也会得到一种规则且确定性的语言，与他们的输入非常不同。学习者似乎非常擅长从不完全符合模式的数据中提取模式。然而，有时，要学习的模式实际上是可变的，这意味着孩子们必须学习变化，而不是过滤掉它。我问学习者如何做到这一点，尽管有变化，他们如何学习模式，以及他们如何知道模式就是变化，目的是更好地理解人类语言学习的限制。这项研究将有助于我们对人类语言学习的理解，并可能为语言治疗提供信息并改善成人语言学习者的学习成果。