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

语言是人类的一种典型能力--它几乎在人类的每一项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。这就是这项研究的目标，以更好地了解所涉及的过程。使用微型人工语言，我检查了学习者从不同类型的输入中获得了什么，以发现什么是可学习的，为什么是可学习的，以及儿童学习者和成人学习者的这一点有何不同。研究的重点是学习者从有噪声的输入数据中提取什么。在现实生活中，语言学习者接受他们听到或看到的输入，并从中提取关于语言的规则。然而，输入通常只为所获得的模式提供概率支持。例如，在英语中，为了表示过去时，你在动词后面加上-，也就是说，除非动词是不规则的。尽管有非常规动词的存在，但当说话人被要求使用一个过去时的新动词时，他们通常会加上ed，这表明他们已经提取了整体模式。言语错误也会带来噪音，但孩子们不会养成这样的规则：在98%的情况下，你会使用单数主语--如果事件发生在现在时，动词末尾的S就会出现。更极端的例子出现在向非母语人士学习的孩子身上，他们通常比母语人士犯更多的错误。然而，即使是这些孩子，最终得到的语言也是规则的和确定性的，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，他们如何在变化中学习模式，以及他们如何学习模式就是变化，目的是更好地理解对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参考，并改善成人语言学习者的学习结果。

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.