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

每个社会都有一套用来谈论家庭的词汇。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，比如祖母指的是你父亲的母亲和你母亲的母亲。亲属关系系统--亲属术语如何映射到亲属类型--因语言而异，并影响我们如何理解将家庭成员归类在一起或相互区分的方式。例如，在印度尼西亚语中，兄弟姐妹按相对年龄区分，而说英语的人按性别区分兄弟姐妹。从理论上讲，有超过100亿种可能的方式将亲属称谓词映射到亲属类型。然而，在世界上的语言中，这样的映射只出现在少数几种语言中：例如，没有一种语言只使用一个单词来表示“母亲”、“父亲”和“兄弟姐妹”。这种受限变异的模式表明，一些亲属关系系统更有可能在社会和语言进化中幸存下来，这可能是因为它们在可学习性或交际功能方面的优势。语言在学习和使用的循环中持续存在，对语言提出了不同的要求，使其具有足够的表现力，通过编码具有不同词语的概念之间的区别，但也通过减少总体上不同词语的数量来足够简单地学习。Kemp和Regier(2012)发现，世界各地的亲属称谓系统最好地权衡了这些要求：尽管每个系统在简洁性和表现力上各不相同，但它们的表现力和表现力都是最简单的。此外，亲属称谓与有关家庭结构的文化习俗密切相关，既受到社会压力的影响，也受到语言压力的影响。通过结合实验语言进化和语言人类学的见解，我的研究将调查这些语言压力如何塑造亲属称谓，以及社会结构在多大程度上会在这些压力的限制下影响亲属称谓，从而产生我们跨语言观察的亲属称谓的类型。我们可以使用人工语言学习等实验方法来研究类型学的共性，这种方法复制了语言进化过程中的学习和使用过程。这类研究表明，这些过程的需求可以影响哪些语言属性倾向于出现并持续存在，因此可以揭示导致亲属关系系统受限变化的压力。例如，Smith等人(2020)表明，简单的人工亲属称谓系统比复杂的人工亲属称谓系统学习得更准确，学习中的错误往往会减少不同的亲属称谓词的数量，这表明学习者可能倾向于亲属称谓系统的简单性。这项拟议的研究将通过更准确地模拟亲属称谓形成的条件来扩展这一人工语言方法，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。我将展望现有的跨文化差异，测试参与者的学习或沟通行为是否在有证明的亲属制度组织和未经证明的亲属制度组织中有所不同。我将使用KinBank，一个涵盖1000多个社会的亲属称谓术语数据库，为这些人工亲属称谓系统的设计提供信息。通过一系列关于语言进化的实验和计算机模拟，我将测试代理人是否比未经证实的亲属关系系统更准确地学习亲属关系系统，以及他们是否在与有证明的系统比未经证明的系统的交流任务中更成功。如果我们看到被证明的系统有更高的成功率，这将表明语言进化有利于为便于学习和有效沟通而设计的亲属关系系统。

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.