Do languages and genes share cultural evolutionary history?

Languages and genes tell stories about the past but statistical analysis reveals that these are not always the same

Blowing in the wind: using ‘North Wind and the Sun’ texts to sample phoneme inventories

Language documentation faces a persistent and pervasive problem: How much material is enough to represent a language fully? How much text would we need to sample the full phoneme inventory of a language? In the phonetic/phonemic domain, what proportion of the phoneme inventory can we expect to sample in a text of a given length? Answering these questions in a quantifiable way is tricky, but asking them is necessary. The cumulative collection of Illustrative Texts published in the Illustration series in this journal over more than four decades (mostly renditions of the ‘North Wind and the Sun’) gives us an ideal dataset for pursuing these questions. Here we investigate a tractable subset of the above questions, namely: What proportion of a language’s phoneme inventory do these texts enable us to recover, in the minimal sense of having at least one allophone of each phoneme? We find that, even with this low bar, only three languages (Modern Greek, Shipibo and the Treger dialect of Breton) attest all phonemes in these texts. Unsurprisingly, these languages sit at the low end of phoneme inventory sizes (respectively 23, 24 and 36 phonemes). We then estimate the rate at which phonemes are sampled in the Illustrative Texts and extrapolate to see how much text it might take to display a language’s full inventory. Finally, we discuss the implications of these findings for linguistics in its quest to represent the world’s phonetic diversity, and for JIPA in its design requirements for Illustrations and in particular whether supplementary panphonic texts should be included.1 Introduction 2 Language sample 3 Data coding 3.1 Lengh/gemination 3.2 Diphthongs vs. vowel sequences 3.3 Tonal contrasts 3.4 Illustration of an Illustration: Shilluk 4 Overview of the JIPA Illustration text corpus 5 Transcript coverage 6 The nature of phoneme frequency distributions 7 Recovering the full phoneme inventory 8 Evaluating the methods against a larger corpus 9 Returning to the cross-linguistic data 10 Estimating the amount of audio needed 11 Effect on recovery of cross-linguistic frequency/rarity 12 Discussion 12.1 Recommendations for JIPA 12.2 How many data are needed to fully capture a language’s phoneme inventory? 13 Conclusio

A lexicostatistical study of the Khasian languages: Khasi, Pnar, Lyngngam, and War

This paper presents the results of lexicostatistical, glottochronological, and Bayesian phylogenetic analyses of a 200 word data set for Standard Khasi, Lyngngam, Pnar and War. Very few works have appeared on the subject of the internal classification of the Khasian branch of Austroasiatic, leaving the existing reference literature disappointingly incomplete. The present analysis supports both the strong identity of Khasian as a unitary branch, with an internally nested branching structure that fits neatly with known historical, geographical and linguistic facts. Additionally, lexically based dating methods suggest that the internal diversification of Khasian began roughly between 1500 and 2000 years ago.Copyright Information: Copyright for this paper vested in the authors. Released under Creative Commons Attribution Licens

Games and enculturation: A cross-cultural analysis of games and values in Austronesia

While most animals play, only humans play games. As animal play serves to teach offspring important life-skills in a safe scenario, human games might, in similar ways, teach important culturally relevant skills. Humans in all cultures play games; however, it is not clear whether variation in the characteristics of games across cultural groups is related to group-level attributes. Here we investigate specifically whether the cooperativeness of games covaries with socio-ecological differences across cultural groups. We hypothesize that cultural groups that engage in frequent inter-group conflict, cooperative sustenance acquisition, or that have less stratified social structures, might more frequently play cooperative games as compared to groups that do not share these characteristics. To test these hypotheses, we gathered data from the ethnographic record on 25 ethnolinguistic groups in the Austronesian language family. We show that cultural groups with higher levels of inter-group conflict and cooperative land-based hunting play cooperative games more frequently than other groups. Additionally, cultural groups with higher levels of intra-group conflict play competitive games more frequently than other groups. These findings indicate that games are not randomly distributed among cultures, but rather relate to the socio-ecological settings of the cultural groups that practice them. We argue that games serve as training grounds for group-specific norms and values and thereby have an important function in enculturation during childhood. Moreover, games might server an important role in the maintenance of cultural diversity.Introduction Children’s play Games Possible drivers of cooperative goal structures - Interdependence in foraging. - Intra- and inter-group conflict. Lack of social stratification Methods - Games - Cultural covariate data - Statistical analyses Results - Descriptive statistics - Cultural variables and goal structures Discussion Conclusio

Bayesian phylogenetic analysis of linguistic data using BEAST

Bayesian phylogenetic methods provide a set of tools to efficiently evaluate large linguistic datasets by reconstructing phylogenies—family trees—that represent the history of language families. These methods provide a powerful way to test hypotheses about prehistory, regarding the subgrouping, origins, expansion, and timing of the languages and their speakers. Through phylogenetics, we gain insights into the process of language evolution in general and into how fast individual features change in particular. This article introduces Bayesian phylogenetics as applied to languages. We describe substitution models for cognate evolution, molecular clock models for the evolutionary rate along the branches of a tree, and tree generating processes suitable for linguistic data. We explain how to find the best-suited model using path sampling or nested sampling. The theoretical background of these models is supplemented by a practical tutorial describing how to set up a Bayesian phylogenetic analysis using the software tool BEAST2.1. Introduction 2. Bayesian phylogenetics 3. Models of evolution 4. Rate variation and calibration 5. Tree priors 6. Choosing the best analysis 7. Exploring the space of trees using BEAST2 8. Hypothesis testing with trees 9. Conclusio