Rule generalization from impoverished input

从贫乏输入中泛化规则

• 批准号: RGPIN-2022-05030
• 负责人: Shi, Rushen
• 金额: $ 4.01万
• 依托单位: Université du Québec à Montréal
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=763742
• 关键词: Rule; generalization; impoverished; input

My NSERC program aims at determining the mechanisms underlying children's amazing ability to learn language. My past NSERC projects studied how infants learn & use functional items (functors) for early acquisition. Functors, which are highly frequent words & affixes (e.g., articles, tense endings), have crucial grammatical functions. They are, however, absent in children's early speech. Thus, the classic view is that children have no grammatical knowledge in the first 2 years of life. I challenged this view, by testing the prosody-functor bootstrapping model. Using subtle, sensitive measures of perception & comprehension, we discovered that infants have knowledge of functors from the first year of life, long before they can talk, and that functors help them break into the grammar, starting around the first birthday. Our findings changed the established view about the early state of linguistic knowledge. Our new research will focus on rule generalization involving functors. Specifically, we will test how infants acquire abstract grammatical rules from inconsistent input. Infants do not receive instruction on rules from parents; they rely only on hearing rule output to derive the rules & to generalize to novel instances. But children's input is known to be impoverished, with partial rule output & exceptions. These observations motivated Chomsky to posit that the grammar cannot be induced from the input, and the child is guided by her innate linguistic knowledge. However, many others believe that input is rich in distributional cues and that the child is capable of inducing a grammar from the cues. My new NSERC studies will be the first to test these important theoretical positions. We will first test the hypothesis that the language-universal frequency property of functors (i.e., a tiny set but highly frequent relative to a massive set of infrequent lexical items such as nouns/verbs) is crucial for grammatical rules to be learnable. Infants will be trained & tested in artificial languages, and the hypothesized learnable & unlearnable input groups will be compared. The training will be fully rule-consistent. Next, we will use artificial grammars with the learnable property of functors, but will expose infant groups to varying impoverished training input (e.g., including exceptions). The goal is to simulate the natural environment and test the power/limit of induction & possible effects of innate knowledge. Finally, we will use a natural language, testing the hypothesis that innate structural knowledge guides infants to attend to certain relations between functors & lexical items, but not to others, even though the input supports both equally. These new studies address the question of nature versus nurture, one of the most fundamental debates in psychology, biology, neural science & linguistics, one that impacts many areas of research (e.g., speech sci., language disorder/therapy, childhood education, language policy, etc) and our future society.

我的NSERC项目旨在确定儿童惊人的语言学习能力的内在机制。我过去的NSERC项目研究了婴儿如何学习和使用功能项目（函子）进行早期习得。函子，是高频率的单词和词缀（例如，文章，时态结尾），具有重要的语法功能。然而，它们在儿童早期的语言中是不存在的。因此，经典的观点是，儿童在生命的前2年没有语法知识。我通过测试韵律-函子自举模型对这种观点提出了质疑。通过对感知和理解的微妙而敏感的测量，我们发现婴儿在出生后的第一年就有了关于函子的知识，远在他们能说话之前，函子帮助他们从一岁左右开始进入语法。我们的发现改变了关于语言知识早期状态的既定观点。我们的新研究将集中在涉及函子的规则推广。具体来说，我们将测试婴儿如何从不一致的输入中获得抽象的语法规则。婴儿没有从父母那里得到关于规则的指导;他们只依靠听到的规则输出来推导规则并概括到新的实例。但是孩子们的输入是贫乏的，只有部分规则输出和例外。这些观察促使乔姆斯基认为语法不能从输入中归纳出来，儿童是由她天生的语言知识引导的。然而，许多其他人认为，输入是丰富的分布线索，儿童能够从线索中归纳出语法。我的新NSERC研究将是第一个测试这些重要的理论立场。我们将首先检验函子的语言通用频率属性（即，相对于诸如名词/动词之类的大量不频繁词汇项的集合而言，小集合但高度频繁）对于语法规则的可学习性是至关重要的。婴儿将接受人工语言的训练和测试，并将比较假设的可学习和不可学习的输入组。培训将完全符合规则。接下来，我们将使用具有函子可学习属性的人工语法，但将使婴儿群体暴露于不同的贫乏训练输入（例如，包括例外）。目标是模拟自然环境，测试归纳的能力/极限以及先天知识的可能影响。最后，我们将使用一种自然语言，测试先天结构知识引导婴儿注意函子和词汇项之间的某些关系，而不是其他关系的假设，即使输入同样支持两者。这些新的研究解决了先天与后天的问题，这是心理学，生物学，神经科学和语言学中最基本的争论之一，影响了许多研究领域（例如，语言科学，语言障碍/治疗，儿童教育，语言政策等）和我们的未来社会。