Language Evolution and Computation Bibliography

This article examines a popular trend of postulating that gestures have played a crucial role in the emergence of human language. Language evolution is frequently understood as a transition from a system, in which signals (whether vocal or manual) have fixed meanings and are used asymmetrically by senders and receivers, through specific cognitive and neurological changes, to a system, in which signals are (1) flexibly referential, i.e., can stand for a variety of ideas and (2) intersubjective, i.e., can be used equally in production and comprehension with any member of the community. The function assigned to gestures in gesture-first theories is to provide a first version of the more advanced open-ended communication in the form of spontaneous pantomimes that initiates a subsequent expansion of this system, its conventionalization and eventually a switch to the vocal modality. In the present article, I examine a particular theory that claims that pantomime was enabled by changes within the system of complex action recognition, and imitation. I argue that while the theory is promising, the notion of a pantomime it employs, presupposes two sophisticated abilities that themselves are left unexplained: symbolization and intentional communication. I point out two ways to remedy the situation, namely, constructing a leaner understanding of pantomime or supplementing the theory with an explanation for the emergence of these abilities. In this article I pursue a third option: identifying an alternative mechanism that can lead to a suitably complex language precursor while avoiding pantomime and its problematic cognitive bases altogether. This mechanism is ontogenetic ritualization, a well-known process responsible for the development of gestures in non-human primates. I outline the possibility that when placed in appropriate sociocultural circumstances, in which complementary actions around objects are required, this process can lead to signals that are modestly referential and intersubjective.

Unlike a standard online experiment, a gaming app lets participants interact freely with a vast number of partners, as many times as they wish. The gain is not merely one of statistical power. Cultural evolutionists can use gaming apps to allow large numbers of participants to communicate synchronously; to build realistic transmission chains that avoid the losses of information that occurs in linear chains; and to study the effects of partner choice as well as partner control in social interactions. We are releasing an app designed to take advantage of these opportunities and generate realistic language evolution dynamics.

The increasing availability of large digital corpora of cross-linguistic data is revolutionizing many branches of linguistics. Overall, it has triggered a shift of attention from detailed questions about individual features to more global patterns amenable to rigorous, but statistical, analyses. This engenders an approach based on successive approximations where models with simplified assumptions result in frameworks that can then be systematically refined, always keeping explicit the methodological commitments and the assumed prior knowledge. Therefore, they can resolve disputes between competing frameworks quantitatively by separating the support provided by the data from the underlying assumptions. These methods, though, often appear as a ‘black box’ to traditional practitioners. In fact, the switch to a statistical view complicates comparison of the results from these newer methods with traditional understanding, sometimes leading to misinterpretation and overly broad claims. We describe here this evolving methodological shift, attributed to the advent of big, but often incomplete and poorly curated data, emphasizing the underlying similarity of the newer quantitative to the traditional comparative methods and discussing when and to what extent the former have advantages over the latter. In this review, we cover briefly both randomization tests for detecting patterns in a largely model-independent fashion and phylolinguistic methods for a more model-based analysis of these patterns. We foresee a fruitful division of labor between the ability to computationally process large volumes of data and the trained linguistic insight identifying worthy prior commitments and interesting hypotheses in need of comparison.

With increasing amounts of digitally available data from all over the world, manual annotation of cognates in multi-lingual word lists becomes more and more time-consuming in historical linguistics. Using available software packages to pre-process the data prior to manual analysis can drastically speed-up the process of cognate detection. Furthermore, it allows us to get a quick overview on data which have not yet been intensively studied by experts. LingPy is a Python library which provides a large arsenal of routines for sequence comparison in historical linguistics. With LingPy, linguists can not only automatically search for cognates in lexical data, but they can also align the automatically identified words, and output them in various forms, which aim at facilitating manual inspection. In this tutorial, we will briefly introduce the basic concepts behind the algorithms employed by LingPy and then illustrate in concrete workflows how automatic sequence comparison can be applied to multi-lingual word lists. The goal is to provide the readers with all information they need to (1) carry out cognate detection and alignment analyses in LingPy, (2) select the appropriate algorithms for the appropriate task, (3) evaluate how well automatic cognate detection algorithms perform compared to experts, and (4) export their data into various formats useful for additional analyses or data sharing. While basic knowledge of the Python language is useful for all analyses, our tutorial is structured in such a way that scholars with basic knowledge of computing can follow through all steps as well.

The historical connection between the Transeurasian languages, i.e. the Japonic, Koreanic, Tungusic, Mongolic, and Turkic languages, is among the most disputed issues of historical linguistics. Here, we will combine the power of classical historical-comparative linguistics and computational Bayesian phylogenetic methods to infer a phylogeny of the Transeurasian languages. To this end, we will use lexical etymologies supporting the reconstruction of proto-Transeurasian forms with meanings that belong to the Leipzig-Jakarta 200 basic vocabulary list. Our application of Bayesian phylogenetic inference to the classification of the Transeurasian languages is unprecedented. In addition to the methodological implications for Bayesian inference applied to proposed language phyla at relatively deep time depths and with relatively sparse sets of surviving daughter languages, our research has also factual implications for the existing theories of Transeurasian relationships. Our results move the field forward in that they provide a quantitative basis to test various competing hypotheses with regard to the internal structure of the Transeurasian family.

It has been observed by several researchers that the Khoisan palate tends to lack a prominent alveolar ridge. A biomechanical model of click production was created to examine if these sounds might be subject to an anatomical bias associated with alveolar ridge size. Results suggest the bias is plausible, taking the form of decreased articulatory effort and improved volume change characteristics; however, further modeling and experimental research is required to solidify the claim.

Human communication is unparalleled in the animal kingdom. The key distinctive feature of our language is productivity : we are able to express an infinite number of ideas using a limited set of words. Traditionally, it has been argued or assumed that productivity emerged as a consequence of very specific, innate grammatical systems. Here we formally develop an alternative hypothesis: productivity may have rather solely arisen as a consequence of increasing the number of signals (e.g. sentences) in a communication system, under the additional assumption that the processing mechanisms are algorithmically unconstrained. Using tools from algorithmic information theory, we examine the consequences of two intuitive constraints on the probability that a language will be infinitely productive. We prove that under maximum entropy assumptions, increasing the complexity of a language will not strongly pressure it to be finite or infinite. In contrast, increasing the number of signals in a language increases the probability of languages that have—in fact—infinite cardinality. Thus, across evolutionary time, the productivity of human language could have arisen solely from algorithmic randomness combined with a communicative pressure for a large number of signals.

Speakers constantly learn language from the environment by sampling their linguistic input and adjusting their representations accordingly. Logically, people should attend more to the environment and adjust their behavior in accordance with it more the lower their success in the environment is. We test whether the learning of linguistic input follows this general principle in two studies: a corpus analysis of a TV game show, Jeopardy, and a laboratory task modeled after Go Fish. We show that lower (non-linguistic) success in the task modulates learning of and reliance on linguistic patterns in the environment. In Study 1, we find that poorer performance increases conformity with linguistic norms, as reflected by increased preference for frequent grammatical structures. In Study 2, which consists of a more interactive setting, poorer performance increases learning from the immediate social environment, as reflected by greater repetition of others’ grammatical structures. We propose that these results have implications for models of language production and language learning and for the propagation of language change. In particular, they suggest that linguistic changes might spread more quickly in times of crisis, or when the gap between more and less successful people is larger. The results might also suggest that innovations stem from successful individuals while their propagation would depend on relatively less successful individuals. We provide a few historical examples that are in line with the first suggested implication, namely, that the spread of linguistic changes is accelerated during difficult times, such as war time and an economic downturn.

Interest in the origins and evolution of language has been around for as long as language has been around. However, only recently has the empirical study of language come of age. We argue that the field has sufficiently advanced that it now needs its own journal—the Journal of Language Evolution.

We make the case that, contra standard assumption in linguistic theory, the sound systems of human languages are adapted to their environment. While not conclusive, this plausible case rests on several points discussed in this work: First, human behavior is generally adaptive and the assumption that this characteristic does not extend to linguistic structure is empirically unsubstantiated. Second, animal communication systems are well known to be adaptive within species across a variety of phyla and taxa. Third, research in laryngology demonstrates clearly that ambient desiccation impacts the performance of the human vocal cords. The latter point motivates a clear, testable hypothesis with respect to the synchronic global distribution of language types. Fourth, this hypothesis is supported in our own previous work, and here we discuss new approaches being developed to further explore the hypothesis. We conclude by suggesting that the time has come to more substantively examine the possibility that linguistic sound systems are adapted to their physical ecology.