Investigating the origins of cross-cultural variation in kinship terminology with artificial language learning

每个社会都有一套谈论家庭的词汇。这组亲属术语通常比它所指的家庭关系或亲属类型的数量要少：同一个术语可以表示多种类型，比如祖母既可以表示你父亲的母亲，也可以表示你母亲的母亲。亲属系统-亲属术语如何映射到亲属类型-在不同的语言中各不相同，并影响我们如何理解家庭成员被分组在一起或彼此区分。例如，在印度尼西亚语中，兄弟姐妹是根据相对年龄来区分的，而英语使用者则根据性别来区分他们的兄弟姐妹。 从理论上讲，有超过100亿种可能的方式将亲属术语映射到亲属类型。然而，只有少数这样的映射出现在世界上的语言中：例如，没有一种语言使用一个单词来表示“母亲”，“父亲”和“兄弟姐妹”。这种受约束的变异模式表明，一些亲属系统更有可能在社会和语言进化中幸存下来，这可能是由于它们在可学习性或交际功能方面的优势。 语言通过学习和使用的循环持续存在，对语言提出了相反的要求，即通过用不同的单词编码概念之间的区别来充分表达，但也要足够简单，通过减少整体上不同的单词数量来学习。肯普和Regier（2012）发现，世界各地语言的亲属系统最佳地权衡了这些要求：尽管每个系统在简单性和表达性方面有所不同，但它们在表达性方面最简单，在简单性方面最有表现力。此外，与有关家庭结构的文化习俗密切相关，亲属术语受到社会压力和语言压力的影响。通过结合实验语言进化和语言人类学的见解，我的研究将探讨这些语言压力如何塑造亲属术语，以及社会结构在多大程度上可以影响亲属术语的限制，这些压力，从而产生我们观察跨语言的亲属术语的类型学。 我们可以使用人工语言学习等实验方法来研究类型学共性，人工语言学习复制了语言进化的学习和使用过程。这些研究表明，这些过程的要求可以影响哪些语言特性倾向于出现和持续，因此可以揭示导致亲属系统中限制性变化的压力。例如，Smith等人（2020）已经表明，简单的人工亲属系统比复杂的系统更准确地学习，并且学习中的错误倾向于减少不同亲属术语的数量，这表明学习者可能对亲属系统的简单性有偏见。 拟议的研究将通过更准确地模拟亲属术语发展的条件，包括学习偏见，表达偏见以及围绕家庭角色的社会意识形态的影响，来扩展这种人工语言方法。我将前景现有的跨文化的变化，测试是否参与者的学习或沟通行为不同的证明与非证明组织的亲属系统。我将使用KinBank，一个包含1000多个社会的亲属术语的数据库，来为这些人工亲属系统的设计提供信息。通过一系列实验和计算机模拟语言进化，我将测试代理人是否比未经证明的更准确地学习证明亲属系统，以及他们是否在证明与未经证明的系统的沟通任务中更成功。如果我们看到经过验证的系统有更高的成功率，这将表明语言进化有利于亲属系统，这些系统旨在便于学习和有效沟通。

Every society has a set of words for talking about family. This set of kin terminology is often smaller than the number of family relationships, or kin types, that it refers to: the same term can denote multiple types, like grandmother denoting both your father's mother and your mother's mother. Kinship systems - how kin terminology maps onto kin types - vary across languages and affect how we understand family members to be grouped together or distinguished from one another. For instance, in Indonesian, siblings are distinguished terminologically by relative age, while English speakers distinguish their siblings by gender. Theoretically, there are over 10 billion possible ways to map kin terms to kin types. However, only a handful of these mappings occur in the world's languages: there is no language that uses one single word for 'mother', 'father', and 'sibling', for instance. This pattern of constrained variation suggests that some kinship systems are more likely to survive social and linguistic evolution, perhaps due to advantages in their learnability or communicative function. Language persists through a cycle of learning and use, placing contrasting requirements on language to be sufficiently expressive, by encoding distinctions between concepts with distinct words, but also simple enough to be learned, by reducing the number of distinct words overall. Kemp and Regier (2012) found that kinship systems across the world's languages optimally trade-off these requirements: though each system varies in simplicity and expressivity, they are maximally simple given their expressivity and maximally expressive given their simplicity. Additionally, being closely linked to cultural conventions about family structures, kin terminology is shaped by social pressures as well as linguistic ones. By combining insights from experimental language evolution and linguistic anthropology, my research will investigate how these linguistic pressures shape kinship terminology, and to what extent social structures can influence kinship terms within the constraints of those pressures, giving rise to the typology of kinship terms we observe cross-linguistically. We can study typological universals using experimental methods like artificial language learning, which replicate the processes of learning and use by which language evolves. Such studies have shown that the demands of these processes can affect which linguistic properties tend to arise and persist, and can therefore shed light on the pressures which caused constrained variation in kinship systems. For instance, Smith et al (2020) have shown that simpler artificial kinship systems are learned more accurately than complex ones, and that errors in learning tended to reduce the number of distinct kin terms, indicating that learners may have a bias for simplicity in kinship systems. The proposed research will expand upon this artificial language approach by more accurately simulating the conditions under which kin terms develop, including learning biases, expressivity biases, and the effect of social ideology surrounding family roles. I will foreground the existing cross-cultural variation, testing whether participants' learning or communication behaviour differs for attested versus non-attested organisations of kinship systems. I will use KinBank, a database of kinship terminology for over 1000 societies, to inform the design of these artificial kinship systems. Across a series of experiments and computer simulations of language evolution, I will test whether agents learn attested kinship systems more accurately than unattested ones, and whether they are more successful in communicative tasks with attested versus unattested systems. If we see a higher success rate with attested systems, this would suggest that language evolution favours kinship systems designed for ease of learning and efficient communication.