An artificial language learning study of the relationship between stress position and quantity sensitivity in stress patterning

重音和重音是许多人类语言的语音模式的重要韵律特征，尽管它们的习得对于掌握第二语言至关重要，但非母语韵律模式可能很难学习。几十年的语言学研究已经产生了现有重音模式的类型学，并为估计这些类型在人类语言中的流行程度奠定了基础。语言学的一个关键目标是理解语言中的自然模式，出现频率被认为是自然性的一个指标。这个研究项目背后的前提是，识别自然模式的另一种方法是研究学习的容易程度：更自然的模式应该比不太自然的模式更容易获得。然而，虽然心理语言学家研究了听者感知压力的能力，但很少有研究调查人类学习压力模式特征的难易程度以及影响学习的潜在偏见。本研究旨在探讨使用三种自然语言的成年人如何在一系列人工语言学习实验中学习非母语重音模式。研究计划的跨语言性质是至关重要的，因为对人类学习的理解需要研究具有不同文化和语言经验的演讲者。该研究项目补充了有关压力类型学的现有知识，为人类行为和认知的跨学科研究提供了新的信息，一系列人工语言学习研究调查了边缘对齐之间的关系，（重音位于单词的开头还是结尾）和数量敏感性（是否音节与更多的内容是首选的位置为词重音）使用贫困的刺激设计。在这种方法中，参与者接触到的语言形式，足以让他们学习一个复杂的压力模式的组成部分，在一个构建的迷你语言。然而，获得完整概括所需的形式在初始训练阶段被保留。然后在后续测试中引入缺失的形式，沿着引入与模式的学习方面一致或不一致的新形式。参与者决定测试中出现的哪些新形式与迷你语言兼容。当参与者的推断与新数据一起呈现时，提供了关于未被教导的偏好的信息，这些偏好被解释为影响学习的认知偏见的证据。从方法上讲，控制被用来梳理“内置”的偏好，从偏见可能与模式的母语。该项目包括计划在三个项目年内与三种不同语言的母语者进行四项相互关联的研究，共进行十二项实验。参与者将在德克萨斯大学奥斯汀分校和校外网站进行测试。这项研究提供了新的信息，关于压力模式的学习容易性，以及人类在最初接触贫困数据集时使用所学信息完成语言模式的概括。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

Stress and accent are important prosodic characteristics of the sound patterns of many human languages, and although their acquisition is crucial for the mastery of a second language, non-native prosodic patterns can be difficult to learn. Decades of research in linguistics has produced a typology of existing stress patterns and a basis for estimating the prevalence of these types in human languages. A key goal of linguistics is to understand natural patterns in language, and frequency of occurrence has been taken as one indicator of naturalness. The premise behind this research program is that another way of identifying natural patterns is to study ease of learning: more natural patterns should be more easily acquired than less natural patterns. However, while psycholinguists have studied listeners’ ability to perceive stress, little research has investigated the ease with which humans learn characteristics of stress patterns and the latent biases that influence learning. This project investigates how adult speakers of three natural human languages learn non-native stress patterns in a series of artificial language learning experiments. The cross-linguistic nature of the research program is critical, given that an understanding of human learning requires studying speakers with diverse cultural and linguistic experiences. The research program complements received knowledge about stress typology, contributes new information to cross-disciplinary studies of human behavior and cognition, and supports education.A series of artificial language learning studies investigates relationships between edge alignment (whether stress is positioned at the beginning or end of a word) and quantity sensitivity (whether syllables with more content are preferred locations for word stress) using a poverty of the stimulus design. In this approach, participants are exposed to linguistic forms that are sufficient for them to learn components of a complex stress pattern in a constructed mini-language. However, forms necessary to acquire a complete generalization are withheld during an initial training phase. The missing forms are then introduced in a subsequent test, along with new forms that either are or are not consistent with the learned aspects of the pattern. Participants decide which new forms presented in the test are compatible with the mini-language. The participants’ inferences, when presented with new data, provides information about untaught preferences that are interpreted as evidence for cognitive biases that influence learning. Methodologically, controls are used to tease apart “built-in” preferences from biases which might be related to patterns in the native language. The project includes plans for four interrelated studies with native speakers of three distinct languages in the three project years, for a total of twelve experiments. Participants will be tested at the University of Texas at Austin and at off-campus sites. This research contributes new information about ease of learning in regard to stress patterning and about humans’ use of learned information to complete generalizations about linguistic patterns when they are initially exposed to an impoverished data set.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.