How the shapes of words adapt to communicative pressures in spoken language

语言是一个复杂的系统，几乎在我们生活的方方面面都起着举足轻重的作用。鉴于它的许多角色--沟通的工具，抽象思维的载体，社会身份的标志--它的演变受到多种不同因素的影响也就不足为奇了。我研究的长期目标是了解口语的声音系统是如何受到它们在口语交流中的作用的制约和塑造的。这项拟议的研究计划聚焦于一个具体的问题：词形如何适应压力，以实现有效的沟通。该领域最近的研究表明，倾向于可预测的单词(如GO)也比倾向于不可预测的单词(如Recover)更短、产生的努力更少。这种用法和形状之间的联系符合人们在信息论优化下的预期。然而，一个关键的问题仍然没有得到回答：单词的形状和用法是如何演变成这样的？我自己的工作已经开始解决这个问题，但到目前为止发现的用法和形状之间的共同进化的梯度模式不太可能是口语词典中词长和信息内容之间强烈关联的来源。目前的提案列出了解决这一问题的三个短期目标。第一个是使用语料库语音方法，评估130多年来语音样本中单词形状的变化，作为强大的形状用法联系的潜在来源。其次，我将使用自然语言处理的方法，评估大量历史文本(例如Google Ngram语料库)中单词使用的变化，作为形状使用关联的潜在来源。第三个目标是使用迭代的人工语言学习实验来复制实验室中形状用法关联的演变，并加强目标1和2中的观察性研究的结果。这项工作将在语音实验室的模式起源(OOPS-Lab)进行，并借助语音科学计算方法的最先进基础设施。研究团队将包括两名研究生HQP和6名本科生HQP，他们将接受计算和语音方法培训以及专业指导。OOPS-Lab致力于创造一个多样化的研究环境，通过以科学为基础的仔细考虑的方法，实现公平、多样性和包容性，支持成员的学术成长，无论他们的背景如何。通过揭示口语中优化交际效率的进化路径，这项工作将对认知科学和语言学产生广泛的影响：它将帮助我们理解人类语言结构的交际和认知来源。

Language is a complex system that plays a pivotal role in nearly all aspects of our lives. Given its many roles - a tool for communication, a vehicle for abstract thought, a marker of social identity - it is no surprise that its evolution is shaped by a multitude of diverse factors. The long-term goal of my research is to understand how the sound systems of spoken languages are constrained and sculpted by their function in spoken communication. The proposed program of research zooms in on a specific question: how word shapes adapt to pressures towards efficient communication. Recent work in the field has shown that words that tend to be predictable (e.g. go) are also shorter and produced with less effort than words that tend to be unpredictable (e.g. recover). This association between usage and shape is in line with what one would expect under information-theoretic optimisation. A key question, however, remains unanswered: how do word shapes and usage evolve to become aligned this way? My own work has started to address this question, but the gradient patterns of coevolution between usage and shape identified so far are unlikely to be the source of the strong association between word length and information content in the lexicons of spoken languages. The current proposal lays out three short-term objectives to address this issue. The first is to assess changes in word shape in speech samples covering over 130 years as a potential source of robust shape-usage associations using corpus phonetic methods. Second, I will assess changes in word usage in large collections of historical texts (e.g. Google Ngram corpus) as a potential source of shape-usage associations using methods from natural language processing. The third objective is to use iterated artificial language learning experiments to replicate the evolution of shape-usage associations in the lab and strengthen the results of the observational studies in Objectives 1 and 2. The work will be conducted at the Origins of Patterns in Speech Lab (OoPS-Lab) with the help of state-of-the-art infrastructure for computational approaches to speech science. The research team will involve two graduate HQP as well as 6 undergraduate HQP, who will receive training in computational and phonetic methods as well as professional mentorship. The OoPS-Lab is committed to creating a diverse research environment that supports members in their academic growth regardless of their background through a carefully considered and science-based approach to equity, diversity and inclusion. By uncovering the evolutionary pathways through which optimisation for communicative efficiency takes place in spoken languages, this work will have broad implications for cognitive science and linguistics: it will help us understand the communicative and cognitive sources of the structure of human languages.