Centre de Recerca Matemàtica, Bellaterra, Barcelona, Spain.
Institut d'Etudes Cognitives, Ecole Normale Supérieure, Paris, France.
PLoS One. 2022 Apr 7;17(4):e0264509. doi: 10.1371/journal.pone.0264509. eCollection 2022.
A central question in behavioral and social sciences is understanding to what extent cultural traits are inherited from previous generations, transmitted from adjacent populations or produced in response to changes in socioeconomic and ecological conditions. As quantitative diachronic databases recording the evolution of cultural artifacts over many generations are becoming more common, there is a need for appropriate data-driven methods to approach this question. Here we present a new Bayesian method to infer the dynamics of cultural traits in a diachronic dataset. Our method called Evoked-Transmitted Cultural model (ETC) relies on fitting a latent-state model where a cultural trait is a latent variable which guides the production of the cultural artifacts observed in the database. The dynamics of this cultural trait may depend on the value of the cultural traits present in previous generations and in adjacent populations (transmitted culture) and/or on ecological factors (evoked culture). We show how ETC models can be fitted to quantitative diachronic or synchronic datasets, using the Expectation-Maximization algorithm, enabling estimating the relative contribution of vertical transmission, horizontal transmission and evoked component in shaping cultural traits. The method also allows to reconstruct the dynamics of cultural traits in different regions. We tested the performance of the method on synthetic data for two variants of the method (for binary or continuous traits). We found that both variants allow reliable estimates of parameters guiding cultural evolution, and that they outperform purely phylogenetic tools that ignore horizontal transmission and ecological factors. Overall, our method opens new possibilities to reconstruct how culture is shaped from quantitative data, with possible application in cultural history, cultural anthropology, archaeology, historical linguistics and behavioral ecology.
行为和社会科学中的一个核心问题是，要了解文化特征在多大程度上是从上一代遗传下来的，是从邻近的群体传播的，还是针对社会经济和生态条件的变化而产生的。随着记录文化人工制品在许多代中演变的定量历时数据库变得越来越普遍，因此需要适当的数据驱动方法来解决这个问题。在这里，我们提出了一种新的贝叶斯方法来推断历时数据集中文化特征的动态。我们的方法称为诱发-传播文化模型（ETC），它依赖于拟合一个潜在状态模型，其中文化特征是一个潜在变量，它指导着在数据库中观察到的文化人工制品的产生。这种文化特征的动态可能取决于前几代和邻近群体中存在的文化特征的价值（传播文化）和/或生态因素（诱发文化）。我们展示了如何使用期望最大化算法将 ETC 模型拟合到定量历时或同步数据集，从而能够估计垂直传播、水平传播和诱发成分在塑造文化特征方面的相对贡献。该方法还允许在不同地区重建文化特征的动态。我们在两种方法（用于二进制或连续特征）的合成数据上测试了该方法的性能。我们发现，这两种变体都可以可靠地估计指导文化进化的参数，并且它们优于忽略水平传播和生态因素的纯系统发育工具。总的来说，我们的方法为从定量数据中重建文化是如何形成的开辟了新的可能性，可能应用于文化史、文化人类学、考古学、历史语言学和行为生态学。
