The ordering of subject, verb, and object is one of the fundamental components of the syntax of natural languages. The distribution of basic word orders across the world's languages is highly nonuniform, with the majority of languages being either subjectobject-verb (SOV) or subject-verb-object (SVO). Explaining this fact using psychological accounts of language acquisition or processing requires understanding how the present distribution has resulted from ancestral distributions and the rates of change between orders. We show that Bayesian phylogenetics can provide quantitative answers to three important questions: how word orders are likely to change over time, which word orders were dominant historically, and whether strong inferences about the origins of syntax can be drawn from modern languages. We find that SOV to SVO change is more common than the reverse and VSO to SVO change is more common than VSO to SOV, and that if the seven language families we consider share a common ancestor then that common ancestor likely had SOV word order, but also that there are limits on how confidently we can make inferences about ancestral word order based on modern-day observations. These results shed new light on old questions from historical linguistics and provide clear targets for psychological explanations of word-order distributions. language evolution | computational historical linguistics | psycholinguistics T he sentence "dog bites man" is easily understood by a speaker of English, but switching the order of the words either renders it incomprehensible ("dog man bites") or changes its meaning ("man bites dog"). Only one of the six possible orders of these three words-the subject (S), verb (V), and object (O)-is commonly used in English sentences. However, the ordering of these three elements is a fundamental component of syntax, and varies significantly across languages. English is an SVO language, but among the world's languages, the six possible basic word orders are, from most to least common: SOV (48%), SVO (41%), VSO (8%), VOS (2%), OVS (1%), and OSV (0%) (1, 2).Several attempts have been made to explain the crosslinguistic word-order distribution in psychological terms (3-6). All of these efforts have proceeded under the assumption that SOV is somehow optimal (e.g., requiring minimum working memory for parsing), since it is the most common word order. This is a "functionalist" approach, where some word orders are claimed to better facilitate the transmission of information. However, several linguists have noted that change from SOV to SVO appears to have been more common historically than change from SVO to SOV (7-10). If we suppose that, on the whole, languages tend to change from less to more functional word orders, then we should seek theories by which SVO is psychologically superior to SOV, not the other way around. Under this view, SOV is predominant not due to greater functionality but as a vestige of even greater dominance in the past.Traditional functionalist explanations also assume that VSO is les...

Short-term memory is implicated in a range of cognitive abilities and is critical for understanding primate cognitive evolution. To investigate the effects of phylogeny, ecology and sociality on short-term memory, we tested the largest and most diverse primate sample to date (421 non-human primates across 41 species) in an experimental delayed-response task. Our results confirm previous findings that longer delays decrease memory performance across species and taxa. Our analyses demonstrate a considerable contribution of phylogeny over ecological and social factors on the distribution of short-term memory performance in primates; closely related species had more similar short-term memory abilities. Overall, individuals in the branch of Hominoidea performed better compared to Cercopithecoidea, who in turn performed above Platyrrhini and Strepsirrhini. Interdependencies between phylogeny and socioecology of a given species presented an obstacle to disentangling the effects of each of these factors on the evolution of short-term memory capacity. However, this study offers an important step forward in understanding the interspecies and individual variation in short-term memory ability by providing the first phylogenetic reconstruction of this trait’s evolutionary history. The dataset constitutes a unique resource for studying the evolution of primate cognition and the role of short-term memory in other cognitive abilities.

While global patterns of human genetic diversity are increasingly well characterized, the diversity of human languages remains less systematically described. Here, we outline the Grambank database. With over 400,000 data points and 2400 languages, Grambank is the largest comparative grammatical database available. The comprehensiveness of Grambank allows us to quantify the relative effects of genealogical inheritance and geographic proximity on the structural diversity of the world’s languages, evaluate constraints on linguistic diversity, and identify the world’s most unusual languages. An analysis of the consequences of language loss reveals that the reduction in diversity will be strikingly uneven across the major linguistic regions of the world. Without sustained efforts to document and revitalize endangered languages, our linguistic window into human history, cognition, and culture will be seriously fragmented.

The use of computational methods to assign absolute datings to language divergence is receiving renewed interest, as modern approaches based on Bayesian statistics offer alternatives to the discredited techniques of glottochronology. The datings provided by these new analyses depend crucially on the use of calibration, but the methodological issues surrounding calibration have received comparatively little attention. Especially, underappreciated is the extent to which traditional historical linguistic scholarship can contribute to the calibration process via loanword analysis. Aiming at a wide audience, we provide a detailed discussion of calibration theory and practice, evaluate previously used calibrations, recommend best practices for justifying calibrations, and provide a concrete example of these practices via a detailed derivation of calibrations for the Uralic language family. This article aims to inspire a higher quality of scholarship surrounding all statistical approaches to language dating, and especially closer engagement between practitioners of statistical methods and traditional historical linguists, with the former thinking more carefully about the arguments underlying their calibrations and the latter more clearly identifying results of their work which are relevant to calibration, or even suggesting calibrations directly.

We present a new open source software tool called BEASTling, designed to simplify the preparation of Bayesian phylogenetic analyses of linguistic data using the BEAST 2 platform. BEASTling transforms comparatively short and human-readable configuration files into the XML files used by BEAST to specify analyses. By taking advantage of Creative Commons-licensed data from the Glottolog language catalog, BEASTling allows the user to conveniently filter datasets using names for recognised language families, to impose monophyly constraints so that inferred language trees are backward compatible with Glottolog classifications, or to assign geographic location data to languages for phylogeographic analyses. Support for the emerging cross-linguistic linked data format (CLDF) permits easy incorporation of data published in cross-linguistic linked databases into analyses. BEASTling is intended to make the power of Bayesian analysis more accessible to historical linguists without strong programming backgrounds, in the hopes of encouraging communication and collaboration between those developing computational models of language evolution (who are typically not linguists) and relevant domain experts.