Language Evolution and Computation Bibliography

The amount of available digital data for the languages of the world is constantly increasing. Unfortunately, most of the digital data are provided in a large variety of formats and therefore not amenable for comparison and re-use. The Cross-Linguistic Data Formats initiative proposes new standards for two basic types of data in historical and typological language comparison (word lists, structural datasets) and a framework to incorporate more data types (e.g. parallel texts, and dictionaries). The new specification for cross-linguistic data formats comes along with a software package for validation and manipulation, a basic ontology which links to more general frameworks, and usage examples of best practices.

ColorSims 2.0 is an extension to the existing python package ColorSims [14] which includes notable updates and additions to the original package. ColorSims/ColorSims 2.0 is a python package for simulating the cultural evolution of linguistic color naming conventions. The package can be initialized with random agents or population data (i.e. World Color Survey). The components of the package are modular, allowing the user to vary them independently. Implemented parameters include: dimensions of the color space, population size, social network structure, and agent learning mechanisms (i.e. reinforcement learning, updating) within the evolutionary dynamics in addition to on-board utilities for storing data and visualizing simulation results.

Can we communicate across the barrier of languages, with images instead of sounds? The scientists behind the color game will document the evolution of a new kind of language, a language beyond words. They will explore the way that new symbols emerge, acquire a meaning, or change their meaning, over time and across space. Will the color game give birth to different dialects, languages that only some people can understand but not others? Will the images of the color game evolve in the same way that words for colour evolved through human history? These are some of the questions that the creators of the Color Game hope to answer.

By capturing statistical patterns in large corpora, machine learning has enabled significant advances in natural language processing, including in machine translation, question answering, and sentiment analysis. However, for agents to intelligently interact with humans, simply capturing the statistical patterns is insufficient. In this paper we investigate if, and how, grounded compositional language can emerge as a means to achieve goals in multi-agent populations. Towards this end, we propose a multi-agent learning environment and learning methods that bring about emergence of a basic compositional language. This language is represented as streams of abstract discrete symbols uttered by agents over time, but nonetheless has a coherent structure that possesses a defined vocabulary and syntax. We also observe emergence of non-verbal communication such as pointing and guiding when language communication is unavailable.

Theoretical models of critical mass have shown how minority groups can initiate social change dynamics in the emergence of new social conventions. Here, we study an artificial system of social conventions in which human subjects interact to establish a new coordination equilibrium. The findings provide direct empirical demonstration of the existence of a tipping point in the dynamics of changing social conventions. When minority groups reached the critical mass—that is, the critical group size for initiating social change—they were consistently able to overturn the established behavior. The size of the required critical mass is expected to vary based on theoretically identifiable features of a social setting. Our results show that the theoretically predicted dynamics of critical mass do in fact emerge as expected within an empirical system of social coordination.

It's a Saturday morning in February, and Chloe, a curious 3-year-old in a striped shirt and leggings, is exploring the possibilities of a new toy. Her father, Gary Marcus, a developmental cognitive scientist at New York University (NYU) in New York City, has brought home some strips of tape designed to adhere Lego bricks to surfaces. Chloe, well-versed in Lego, is intrigued. But she has always built upward. Could she use the tape to build sideways or upside down? Marcus suggests building out from the side of a table. Ten minutes later, Chloe starts sticking the tape to the wall. "We better do it before Mama comes back," Marcus says in a singsong voice. "She won't be happy." (Spoiler: The wall paint suffers.)

Do the mechanisms underlying language in fact serve general-purpose functions that preexist this uniquely human capacity? To address this contentious and empirically challenging issue, we systematically tested the predictions of a well-studied neurocognitive theory of language motivated by evolutionary principles. Multiple metaanalyses were performed to examine predicted links between language and two general-purpose learning systems, declarative and procedural memory. The results tied lexical abilities to learning only in declarative memory, while grammar was linked to learning in both systems in both child first language and adult second language, in specific ways. In second language learners, grammar was associated with only declarative memory at lower language experience, but with only procedural memory at higher experience. The findings yielded large effect sizes and held consistently across languages, language families, linguistic structures, and tasks, underscoring their reliability and validity. The results, which met the predicted pattern, provide comprehensive evidence that language is tied to general-purpose systems both in children acquiring their native language and adults learning an additional language. Crucially, if language learning relies on these systems, then our extensive knowledge of the systems from animal and human studies may also apply to this domain, leading to predictions that might be unwarranted in the more circumscribed study of language. Thus, by demonstrating a role for these systems in language, the findings simultaneously lay a foundation for potentially important advances in the study of this critical domain.

Vocalizations are a pervasive feature of nonhuman primate social life, yet we know surprisingly little about their function. We review studies supporting the hypothesis that many primate vocalizations function to facilitate social interactions by reducing uncertainty about the signaler's intentions and likely behavior. Such interactions help to establish and maintain the social bonds that increase reproductive success. Compared with humans, songbirds, and a few other mammals, primates have small vocal repertoires that show little acoustic modification during development. However, their ability to modify call usage is extensive and tuned to variation in the social context, including the historical relationship between caller and listener and the caller's assessment of how a listener is likely to respond. We suggest parallels between the decision to vocalize and neurophysiological studies of other, nonvocal social decisions between interacting monkeys. The selective factors driving the early stages of language evolution may have come from the need to make decisions about when and how to call within the context of social challenges.

What happens when a new social convention replaces an old one? While the possible forces favoring norm change-such as institutions or committed activists-have been identified for a long time, little is known about how a population adopts a new convention, due to the difficulties of finding representative data. Here, we address this issue by looking at changes that occurred to 2,541 orthographic and lexical norms in English and Spanish through the analysis of a large corpora of books published between the years 1800 and 2008. We detect three markedly distinct patterns in the data, depending on whether the behavioral change results from the action of a formal institution, an informal authority, or a spontaneous process of unregulated evolution. We propose a simple evolutionary model able to capture all of the observed behaviors, and we show that it reproduces quantitatively the empirical data. This work identifies general mechanisms of norm change, and we anticipate that it will be of interest to researchers investigating the cultural evolution of language and, more broadly, human collective behavior.

FOXP2, initially identified for its role in human speech, contains two nonsynonymous substitutions derived in the human lineage. Evidence for a recent selective sweep in Homo sapiens, however, is at odds with the presence of these substitutions in archaic hominins. Here, we comprehensively reanalyze FOXP2 in hundreds of globally distributed genomes to test for recent selection. We do not find evidence of recent positive or balancing selection at FOXP2. Instead, the original signal appears to have been due to sample composition. Our tests do identify an intronic region that is enriched for highly conserved sites that are polymorphic among humans, compatible with a loss of function in humans. This region is lowly expressed in relevant tissue types that were tested via RNA-seq in human prefrontal cortex and RT-PCR in immortalized human brain cells. Our results represent a substantial revision to the adaptive history of FOXP2, a gene regarded as vital to human evolution.

It remains a mystery how Pama–Nyungan, the world’s largest hunter-gatherer language family, came to dominate the Australian continent. Some argue that social or technological advantages allowed rapid language replacement from the Gulf Plains region during the mid-Holocene. Others have proposed expansions from refugia linked to climatic changes after the last ice age or, more controversially, during the initial colonization of Australia. Here, we combine basic vocabulary data from 306 Pama–Nyungan languages with Bayesian phylogeographic methods to explicitly model the expansion of the family across Australia and test between these origin scenarios. We find strong and robust support for a Pama–Nyungan origin in the Gulf Plains region during the mid-Holocene, implying rapid replacement of non-Pama–Nyungan languages. Concomitant changes in the archaeological record, together with a lack of strong genetic evidence for Holocene population expansion, suggests that Pama–Nyungan languages were carried as part of an expanding package of cultural innovations that probably facilitated the absorption and assimilation of existing hunter-gatherer groups. A Bayesian phylogeographic analysis of vocabulary from 306 Pama–Nyungan languages suggests that the language family rose to dominance across Australia in a process of rapid replacement following an origin in the Gulf Plains region during the mid-Holocene.

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.

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.

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.

The innovation of iconic gestures is essential to establishing the vocabularies of signed languages, but might iconicity also play a role in the origin of spoken words? Can people create novel vocalizations that are comprehensible to naïve listeners without prior convention? We launched a contest in which participants submitted non-linguistic vocalizations for 30 meanings spanning actions, humans, animals, inanimate objects, properties, quantifiers and demonstratives. The winner was determined by the ability of naïve listeners to infer the meanings of the vocalizations. We report a series of experiments and analyses that evaluated the vocalizations for: (1) comprehensibility to naïve listeners; (2) the degree to which they were iconic; (3) agreement between producers and listeners in iconicity; and (4) whether iconicity helps listeners learn the vocalizations as category labels. The results show contestants were able to create successful iconic vocalizations for most of the meanings, which were largely comprehensible to naïve listeners, and easier to learn as category labels. These findings demonstrate how iconic vocalizations can enable interlocutors to establish understanding in the absence of conventions. They suggest that, prior to the advent of full-blown spoken languages, people could have used iconic vocalizations to ground a spoken vocabulary with considerable semantic breadth.

For the complex human brain that enables us to communicate in natural language, we gathered good understandings of principles underlying language acquisition and processing, knowledge about sociocultural conditions, and insights into activity patterns in the brain. However, we were not yet able to understand the behavioural and mechanistic characteristics for natural language and how mechanisms in the brain allow to acquire and process language. In bridging the insights from behavioural psychology and neuroscience, the goal of this paper is to contribute a computational understanding of appropriate characteristics that favour language acquisition. Accordingly, we provide concepts and refinements in cognitive modelling regarding principles and mechanisms in the brain and propose a neurocognitively plausible model for embodied language acquisition from real-world interaction of a humanoid robot with its environment. In particular, the architecture consists of a continuous time recurrent neural network, where parts have different leakage characteristics and thus operate on multiple timescales for every modality and the association of the higher level nodes of all modalities into cell assemblies. The model is capable of learning language production grounded in both, temporal dynamic somatosensation and vision, and features hierarchical concept abstraction, concept decomposition, multi-modal integration, and self-organisation of latent representations. ARTICLE HISTORY Received 25 June 2016 Accepted 1 February 2017

Language, which allows complex ideas to be communicated through symbolic sequences, is a characteristic feature of our species and manifested in a multitude of forms. Using large written corpora for many different languages and scripts, we show that the occurrence probability distributions of signs at the left and right ends of words have a distinct heterogeneous nature. Characterizing this asymmetry using quantitative inequality measures, viz. information entropy and the Gini index, we show that the beginning of a word is less restrictive in sign usage than the end. This property is not simply attributable to the use of common affixes as it is seen even when only word roots are considered. We use the existence of this asymmetry to infer the direction of writing in undeciphered inscriptions that agrees with the archaeological evidence. Unlike traditional investigations of phonotactic constraints which focus on language-specific patterns, our study reveals a property valid across languages and writing systems. As both language and writing are unique aspects of our species, this universal signature may reflect an innate feature of the human cognitive phenomenon.

Early modern humans developed mental capabilities that were immeasurably greater than those of non-human primates. We see this in the rapid innovation in tool making, the development of complex language, and the creation of sophisticated art forms, none of which we find in our closest relatives. While we can readily observe the results of this high-order cognitive capacity, it is difficult to see how it could have developed. We take up the topic of cave art and archeoacoustics, particularly the discovery that cave art is often closely connected to the acoustic properties of the cave chambers in which it is found. Apparently, early modern humans were able to detect the way sound reverberated in these chambers, and they painted artwork on surfaces that were acoustic "hot spots," i.e., suitable for generating echoes. We argue that cave art is a form of cross-modality information transfer, in which acoustic signals are transformed into symbolic visual representations. This form of information transfer across modalities is an instance of how the symbolic mind of early modern humans was taking shape into concrete, externalized language. We also suggest that the earliest rock art found in Africa may constitute one of the first fossilized proxies for the expression of full-fledged human linguistic behavior.

Cross-situational learning and social pragmatic theories are prominent mechanisms for learning word meanings (i.e., word-object pairs). In this paper, the role of reinforcement is investigated for early word-learning by an artificial agent. When exposed to a group of speakers, the agent comes to understand an initial set of vocabulary items belonging to the language used by the group. Both cross-situational learning and social pragmatic theory are taken into account. As social cues, joint attention and prosodic cues in caregiver's speech are considered. During agent-caregiver interaction, the agent selects a word from the caregiver's utterance and learns the relations between that word and the objects in its visual environment. The "novel words to novel objects" language-specific constraint is assumed for computing rewards. The models are learned by maximizing the expected reward using reinforcement learning algorithms [i.e., table-based algorithms: Q-learning, SARSA, SARSA-λ, and neural network-based algorithms: Q-learning for neural network (Q-NN), neural-fitted Q-network (NFQ), and deep Q-network (DQN)]. Neural network-based reinforcement learning models are chosen over table-based models for better generalization and quicker convergence. Simulations are carried out using mother-infant interaction CHILDES dataset for learning word-object pairings. Reinforcement is modeled in two cross-situational learning cases: (1) with joint attention (Attentional models), and (2) with joint attention and prosodic cues (Attentional-prosodic models). Attentional-prosodic models manifest superior performance to Attentional ones for the task of word-learning. The Attentional-prosodic DQN outperforms existing word-learning models for the same task.

What role does speaker population size play in shaping rates of language evolution? There has been little consensus on the expected relationship between rates and patterns of language change and speaker population size, with some predicting faster rates of change in smaller populations, and others expecting greater change in larger populations. The growth of comparative databases has allowed population size effects to be investigated across a wide range of language groups, with mixed results. One recent study of a group of Polynesian languages revealed greater rates of word gain in larger populations and greater rates of word loss in smaller populations. However, that test was restricted to 20 closely related languages from small Oceanic islands. Here, we test if this pattern is a general feature of language evolution across a larger and more diverse sample of languages from both continental and island populations. We analyzed comparative language data for 153 pairs of closely-related sister languages from three of the world's largest language families: Austronesian, Indo-European, and Niger-Congo. We find some evidence that rates of word loss are significantly greater in smaller languages for the Indo-European comparisons, but we find no significant patterns in the other two language families. These results suggest either that the influence of population size on rates and patterns of language evolution is not universal, or that it is sufficiently weak that it may be overwhelmed by other influences in some cases. Further investigation, for a greater number of language comparisons and a wider range of language features, may determine which of these explanations holds true.

In searching for the roots of human language, comparative researchers investigate whether precursors to language are already present in our closest relatives, the non-human primates. As the majority of studies into primates' communication use a unimodal approach with focus on one signal type only, researchers investigate very different aspects depending on whether they are interested in vocal, gestural, or facial communication. Here, we focus on two signal types and discuss how meaning is created in the gestural (visual, tactile/auditory) as compared to the vocal modality in non-human primates, to highlight the different research foci across these modalities. First, we briefly describe the defining features of meaning in human language and introduce some debates concerning meaning in non-human communication. Second, with focus on these features, we summarize the current evidence for meaningful communication in gestural as compared to vocal communication and demonstrate that meaning is operationalized very differently by researchers in these two fields. As a result, it is currently not possible to generalize findings across these modalities. Rather than arguing for or against the occurrence of semantic communication in non-human primates, we aim at pointing to gaps of knowledge in studying meaning in our closest relatives, and these gaps might be closed.

This paper discusses the maximum robustness approach for studying cases of adaptation in language. We live in an age where we have more data on more languages than ever before, and more data to link it with from other domains. This should make it easier to test hypotheses involving adaptation, and also to spot new patterns that might be explained by adaptation. However, there is not much discussion of the overall approach to research in this area. There are outstanding questions about how to formalize theories, what the criteria are for directing research and how to integrate results from different methods into a clear assessment of a hypothesis. This paper addresses some of those issues by suggesting an approach which is causal, incremental and robust. It illustrates the approach with reference to a recent claim that dry environments select against the use of precise contrasts in pitch. Study 1 replicates a previous analysis of the link between humidity and lexical tone with an alternative dataset and finds that it is not robust. Study 2 performs an analysis with a continuous measure of tone and finds no significant correlation. Study 3 addresses a more recent analysis of the link between humidity and vowel use and finds that it is robust, though the effect size is small and the robustness of the measurement of vowel use is low. Methodological robustness of the general theory is addressed by suggesting additional approaches including iterated learning, a historical case study, corpus studies, and studying individual speech.

Traditionally, diachronic language change has been attributed to intra-linguistic factors, which, in analogy to genetic drift, result in diversification of languages as a consequence of the social and geographical separation of linguistic communities (Lupyan and Dale, 2016). More recently, extra-linguistic factors have been implicated in language change as languages adapt to ecological niches formed by geographic, demographic, and cultural characteristics of social environments (Dale and Lupyan, 2012; Reali et al., 2018). One way of conceptualizing these extra-linguistic factors is to distinguish linguistic communities along a continuum of variation in population size, geographical spread, and amount of contact with other languages: Inward-facing, esoteric communities have small populations with shared knowledge and little language contact whereas outward-facing, exoteric communities have large populations, assembled into diverse social networks with substantial amounts of non-shared knowledge and contact with other languages (Thurston, 1987; Wray and Grace, 2007).

In this article we evaluate claims that language structure adapts to sociolinguistic environment. We present the results of two typological case studies examining the effects of the number of native (=L1) speakers and the proportion of adult second language (=L2) learners on language structure. Data from more than 300 languages suggest that testing the effect of population size and proportion of adult L2 learners on features of verbal and nominal complexity produces conflicting results on different grammatical features. The results show that verbal inflectional synthesis adapts to the sociolinguistic environment but the number of genders does not. The results also suggest that modeling population size together with proportion of L2 improves model fit compared to modeling them independently of one another. We thus argue that surveying population size alone may be insufficient to detect possible adaptation of linguistic structure to the sociolinguistic environment. Rather, other features, such as proportion of L2 speakers, prestige and social network density, should be studied, and if demographic numeric data are used, they should not be used in isolation but rather in competition with other sociolinguistic features. We also suggest that not all types of language structures within a given grammatical domain are equally sensitive to the effect of sociolinguistic variables, and that more exploratory studies are needed before we can arrive at a reliable set of grammatical features that may be potentially most (and least) adaptive to social structures.

Our uniquely human ability to learn and use languages (aka language-readiness) has been hypothesized to result from species-specific changes in brain development and wiring that habilitated a new neural workspace supporting cross-modular thinking, among other abilities (Boeckx and Benítez-Burraco, 2014; see Arbib, 2012, 2017 for a similar view). Strikingly, behavioral modernity did not emerge on a par with cognitive modernity. On the contrary, it is only well after our split from Neanderthals and Denisovans that modern behavior becomes evident around the world (see Mellars et al., 2007; but also Hoffmann et al., 2018; for tentative evidence of behavioral modernity in Neanderthals). This emergence of modern behavior has been linked to the rise of modern languages, i.e., exhibiting features such as elaborate syntax including extensive use of recursion. The potential of these languages to convey sophisticated meanings and know-how in ways that allows sharing of knowledge with others is assumed to have arisen in a reciprocal relationship with complex cultural practices (Sinha, 2015a,b; Tattersall, 2017). Thus, even if not its main trigger, complex language is at the very least a by-product and facilitator of modern behavior.

Spectacular progress in the information processing sciences (machine learning, wearable sensors) promises to revolutionize the study of cognitive development. Here, we analyse the conditions under which 'reverse engineering' language development, i.e., building an effective system that mimics infant's achievements, can contribute to our scientific understanding of early language development. We argue that, on the computational side, it is important to move from toy problems to the full complexity of the learning situation, and take as input as faithful reconstructions of the sensory signals available to infants as possible. On the data side, accessible but privacy-preserving repositories of home data have to be setup. On the psycholinguistic side, specific tests have to be constructed to benchmark humans and machines at different linguistic levels. We discuss the feasibility of this approach and present an overview of current results.

Language acquisition and change are thought to be causally connected. We demonstrate a method for quantifying the strength of this connection in terms of the 'basic reproductive ratio' of linguistic constituents. It represents a standardized measure of reproductive success, which can be derived both from diachronic and from acquisition data. By analyzing phonotactic English data, we show that the results of both types of derivation correlate, so that phonotactic acquisition indeed predicts phonotactic change, and vice versa. After drawing that general conclusion, we discuss the role of utterance frequency and show that the latter exhibits destabilizing effects only on late acquired items, which belong to phonotactic periphery. We conclude that - at least in the evolution of English phonotactics - acquisition serves conservation, while innovation is more likely to occur in adult speech and affects items that are less entrenched but comparably frequent.

Neurocognitive and neurolinguistics theories make explicit statements relating specialized cognitive and linguistic processes to specific brain loci. These linking hypotheses are in need of neurobiological justification and explanation. Recent mathematical models of human language mechanisms constrained by fundamental neuroscience principles and established knowledge about comparative neuroanatomy offer explanations for where, when and how language is processed in the human brain. In these models, network structure and connectivity along with action- and perception-induced correlation of neuronal activity co-determine neurocognitive mechanisms. Language learning leads to the formation of action perception circuits (APCs) with specific distributions across cortical areas. Cognitive and linguistic processes such as speech production, comprehension, verbal working memory and prediction are modelled by activity dynamics in these APCs, and combinatorial and communicative-interactive knowledge is organized in the dynamics within, and connections between APCs. The network models and, in particular, the concept of distributionally-specific circuits, can account for some previously not well understood facts about the cortical 'hubs' for semantic processing and the motor system's role in language understanding and speech sound recognition. A review of experimental data evaluates predictions of the APC model and alternative theories, also providing detailed discussion of some seemingly contradictory findings. Throughout, recent disputes about the role of mirror neurons and grounded cognition in language and communication are assessed critically.

Language transmission, the passing on of language features such as words between people, is the process of inheritance that underlies linguistic evolution. To understand how language transmission works, we need a mechanistic understanding based on empirical evidence of lasting change of language usage. Here, we analysed 200 million online conversations to investigate transmission between individuals. We find that the frequency of word usage is inherited over conversations, rather than only the binary presence or absence of a word in a person's lexicon. We propose a mechanism for transmission whereby for each word someone encounters there is a chance they will use it more often. Using this mechanism, we measure that, for one word in around every hundred a person encounters, they will use that word more frequently. As more commonly used words are encountered more often, this means that it is the frequencies of words which are copied. Beyond this, our measurements indicate that this per-encounter mechanism is neutral and applies without any further distinction as to whether a word encountered in a conversation is commonly used or not. An important consequence of this is that frequencies of many words can be used in concert to observe and measure language transmission, and our results confirm this. These results indicate that our mechanism for transmission can be used to study language patterns and evolution within populations.

In this review article, I propose a continuous evolution from the auditory-vocal apparatus and its mechanisms of neural control in non-human primates, to the peripheral organs and the neural control of human speech. Although there is an overall conservatism both in peripheral systems and in central neural circuits, a few changes were critical for the expansion of vocal plasticity and the elaboration of proto-speech in early humans. Two of the most relevant changes were the acquisition of direct cortical control of the vocal fold musculature and the consolidation of an auditory-vocal articulatory circuit, encompassing auditory areas in the temporoparietal junction and prefrontal and motor areas in the frontal cortex. This articulatory loop, also referred to as the phonological loop, enhanced vocal working memory capacity, enabling early humans to learn increasingly complex utterances. The auditory-vocal circuit became progressively coupled to multimodal systems conveying information about objects and events, which gradually led to the acquisition of modern speech. Gestural communication accompanies the development of vocal communication since very early in human evolution, and although both systems co-evolved tightly in the beginning, at some point speech became the main channel of communication.

Language and music share many commonalities, both as natural phenomena and as subjects of intellectual inquiry. Rather than exhaustively reviewing these connections, we focus on potential cross-pollination of methodological inquiries and attitudes. We highlight areas in which scholarship on the evolution of language may inform the evolution of music. We focus on the value of coupled empirical and formal methodologies, and on the futility of mysterianism, the declining view that the nature, origins and evolution of language cannot be addressed empirically. We identify key areas in which the evolution of language as a discipline has flourished historically, and suggest ways in which these advances can be integrated into the study of the evolution of music.

Language has evolved over centuries and was gradually enriched and improved. The question, how people find assignment between meanings and referents, remains unanswered. There are many of computational models based on the statistical co-occurrence of meaning-reference pairs. Unfortunately, these mapping strategies show poor performance in an environment with a higher number of objects or noise. Therefore, we propose a more robust noise-resistant algorithm. We tested the performance of this novel algorithm with simulated and physical iCub robots. We developed a testing scenario consisting of objects with varying visual properties presented to the robot accompanied by utterances describing the given object. The results suggest that the proposed mapping procedure is robust, resistant against noise and shows better performance than one-step mapping for all levels of noise in the linguistic input, as well as slower performance degradation with increasing noise. Furthermore, the proposed procedure increases the clustering accuracy of both modalities.

The neurobiology of language has to specify the cognitive architecture of complex language functions such as speaking and comprehending language, and, in addition, how these functions are mapped onto the underlying anatomical and physiological building blocks of the brain (the neural architecture). Here it is argued that the constraints provided by the classical anatomical measures (cytoarchitectonics and myeloarchitectonics) are in our current understanding only very loose constraints for detailed specifications of cognitive functions, including language learning and language processing. However, measures of the computational features of brain tissue might provide stronger constraints. For understanding cognitive specialization, for the time being we thus have to put our cards on measures of functional instead of structural neuroanatomy. The implication for an evolutionary stance on the neurobiology of language is that in a crossspecies comparative perspective one needs to identify the factors that gave rise to the properties of the canonical microcircuits in the neocortex, and to the large scale network organization that created the language-readiness of the human brain.

Although humans are unmatched in their capacity to produce speech and learn language, comparative approaches in diverse animalmodelsareabletoshedlightonthebiologicalunderpinnings of language-relevant traits. In the study of vocal learning, a trait crucial for spoken language, passerine birds have been the dominant models, driving invaluable progress in understanding the neurobiology and genetics of vocal learning despite being only distantly related to humans. To date, there is sparse evidence that our closest relatives, nonhuman primates have the capability to learn new vocalisations. However, a number of other mammals have shown the capacity for vocal learning, such as some cetaceans, pinnipeds, elephants, and bats, and we anticipate that with further study more species will gain membership to this (currently) select club. A broad, cross-species comparison of vocal learning, coupled with careful consideration of the components underlying this trait, is crucial to determine how human speech and spoken language is biologically encoded and how it evolved. We emphasise the need to draw on the pool of promising species that havethusfarbeenunderstudiedorneglected.This isbynomeansa call for fewer studies in songbirds, or an unfocused treasure-hunt, but rather an appeal for structured comparisons across a range of species, considering phylogenetic relationships, ecological and morphological constrains, developmental and social factors, and neurogenetic underpinnings. Herein, we promote a comparative approachhighlightingthe importanceofstudyingvocal learning ina broad range of model species, and describe a common framework for targeted cross-taxon studies to shed light on the biology and evolution of vocal learning.

The search for the anatomical basis of language has traditionally been a search for specializations. More recently such research has focused both on aspects of brain organization that are unique to humans and aspects shared with other primates. This work has mostly concentrated on the architecture of connections between brain areas. However, as specializations can take many guises, comparison of anatomical organization across species is often complicated. We demonstrate how viewing different types of specializations within a common framework allows one to better appreciate both shared and unique aspects of brain organization. We illustrate this point by discussing recent insights into the anatomy of the dorsal language pathway to the frontal cortex and areas for laryngeal control in the motor cortex.

Predicting the occurrence of future events from prior ones is vital for animal perception and cognition. Although how such sequence learning (a form of relational knowledge) relates to particular operations in language remains controversial, recent evidence shows that sequence learning is disrupted in frontal lobe damage associated with aphasia. Also, neural sequencing predictions at different temporal scales resemble those involved in language operations occurring at similar scales. Furthermore, comparative work in humans and monkeys highlights evolutionarily conserved frontal substrates and predictive oscillatory signatures in the temporal lobe processing learned sequences of speech signals. Altogether this evidence supports a relational knowledge hypothesis of language evolution, proposing that language processes in humans are functionally integrated with an ancestral neural system for predictive sequence learning.

The production of vocalizations by monkeys and apes is often described as highly constrained and fundamentally different from human speech. We review recent field studies of baboons and bonobos that suggest greater flexibility. Calls function to reduce the uncertainty inherent in social interactions. Vocal production, like individuals’ responses to calls, is subtly tuned to variation in the social context, including a caller’s assessment of how a listener is likely to respond. We suggest parallels between the decision to vocalize and laboratory, neurophysiological tests of social decisions. We also discuss implications for theories of language evolution.

While a long history of neuropsychological research places language function within a primarily left-lateralized frontotemporal system, recent neuroimaging work has extended this language network to include a number of regions traditionally thought of as 'domain-general'. These include dorsal frontal, parietal, and medial temporal lobe regions known to underpin cognitive functions such as attention and memory. In this paper, we argue that these domain-general systems are not required for language processing and are instead an artefact of the tasks typically used to study language. Recent work from our lab shows that when syntactic processing - arguably the only domain-specific language function - is measured in a task-free, naturalistic manner, only the left-lateralized frontotemporal syntax system and auditory network are activated. When syntax is measured within the context of a task, several other domain-general networks come online and are functionally connected to the frontotemporal system. While we have long argued that syntactic processing does not occur in isolation but is processed in parallel with semantics and pragmatics - functions of the wider language system - our recent work makes a strong case for the domain-specificity of the frontotemporal syntax system and its autonomy from domain-general networks.

Progress in linking between the disparate levels of cognitive description and neural implementation requires explicit, testable, computationally based hypotheses. One such hypothesis is the dendrophilia hypothesis, which suggests that human syntactic abilities rely on our supra-regular computational abilities, implemented via an auxiliary memory store (a ‘stack’) centred on Broca’s region via its connections with other cortical areas. Because linguistic phonology requires less powerful computational abilities than this, at the finitestate level, I suggest that there may be continuity between animal rule learning and human phonology, and that the circuits underlying this provided the precursors of our unusual syntactic abilities.

Human language shows combinatoriality in its phonology (both in speech and in sign language) and its grammar, while both types appear to be absent in the communication systems of our closest evolutionary relatives. In this article, we observe that productive combinatoriality is difficult to evolve, because it requires multiple components to be put in place simultaneously for it to function. To understand how it nevertheless evolved in human language, we focus on combinatoriality in phonology, for which most evidence is available. We discuss findings and theories from three domains: linguistics (descriptive, experimental and corpus linguistics), comparative biology (including some fossil indicators) and (computer) models. We tentatively conclude that many of the biological prerequisites for combinatorial phonology and compositional semantics are shared with other animals, but that a uniquely human pressure for large vocabularies and uniquely human processes of cultural evolution are key in understanding the origins of combinatoriality in language

Structured sequence processing tasks inform us about statistical learning abilities that are relevant to many areas of cognition, including language. Despite the ubiquity of these abilities across different tasks and cognitive domains, recent research in humans has demonstrated that these cognitive capacities do not represent a single, domain-general system, but are subject to modality-specific and stimulusspecific constraints. Sequence processing studies in nonhuman primates have provided initial insights into the evolution of these abilities. However, few studies have examined similarities and/or differences in sequence learning across sensory modalities. We review how behavioural and neuroimaging experiments assess sequence processing abilities across sensory modalities, and how these tasks could be implemented in nonhuman primates to better understand the evolution of these cognitive systems.

Contemporary disputes about the origins and evolution of language are reviewed. The main issues involved are: how many mutations gave rise to the Language faculty, whether a new cognitive domain was thereby created, how powerful Language was from the beginning, whether the initial function of Language was private thought or public communication, and whether natural selection influenced its rise.

The well-established framework of evolutionary dynamics can be applied to the fascinating open problems how human brains are able to acquire and adapt language and how languages change in a population. Schemas for handling grammatical constructions are the replicating unit. They emerge and multiply with variation in the brains of individuals and undergo selection based on their contribution to needed expressive power, communicative success and the reduction of cognitive effort. Adopting this perspective has two major benefits. (i) It makes a bridge to neurobiological models of the brain that have also adopted an evolutionary dynamics point of view, thus opening a new horizon for studying how human brains achieve the remarkably complex competence for language. And (ii) it suggests a new foundation for studying cultural language change as an evolutionary dynamics process. The paper sketches this novel perspective, provides references to empirical data and computational experiments, and points to open problems.

We provide a unified mathematical explanation of two classical forms of spatial linguistic spread. The wave model describes the radiation of linguistic change outwards from a central focus. Changes can also jump between population centres in a process known as hierarchical diffusion. It has recently been proposed that the spatial evolution of dialects can be understood using surface tension at linguistic boundaries. Here we show that the inclusion of long-range interactions in the surface tension model generates both wave-like spread, and hierarchical diffusion, and that it is surface tension that is the dominant effect in deciding the stable distribution of dialect patterns. We generalize the model to allow population mixing which can induce shrinkage of linguistic domains, or destroy dialect regions from within.

The origin of population-scale coordination has puzzled philosophers and scientists for centuries. Recently, game theory, evolutionary approaches and complex systems science have provided quantitative insights on the mechanisms of social consensus. However, the literature is vast and widely scattered across fields, making it hard for the single researcher to navigate it. This short review aims to provide a compact overview of the main dimensions over which the debate has unfolded and to discuss some representative examples. It focuses on those situations in which consensus emerges 'spontaneously' in the absence of centralized institutions and covers topics that include the macroscopic consequences of the different microscopic rules of behavioural contagion, the role of social networks and the mechanisms that prevent the formation of a consensus or alter it after it has emerged. Special attention is devoted to the recent wave of experiments on the emergence of consensus in social systems.

Humans and nonhuman primates can learn about the organization of stimuli in the environment using implicit sequential pattern learning capabilities. However, most previous artificial grammar learning studies with nonhuman primates have involved relatively simple grammars and short input sequences. The goal in the current experiments was to assess the learning capabilities of monkeys on an artificial grammar-learning task that was more complex than most others previously used with nonhumans. Three experiments were conducted using a joystick-based, symmetrical-response serial reaction time task in which two monkeys were exposed to grammar-generated sequences at sequence lengths of four in Experiment 1, six in Experiment 2, and eight in Experiment 3. Over time, the monkeys came to respond faster to the sequences generated from the artificial grammar compared to random versions. In a subsequent generalization phase, subjects generalized their knowledge to novel sequences, responding significantly faster to novel instances of sequences produced using the familiar grammar compared to those constructed using an unfamiliar grammar. These results reveal that rhesus monkeys can learn and generalize the statistical structure inherent in an artificial grammar that is as complex as some used with humans, for sequences up to eight items long. These findings are discussed in relation to whether or not rhesus macaques and other primate species possess implicit sequence learning abilities that are similar to those that humans draw upon to learn natural language grammar.

We study sequential language games in which two players, each with private information, communicate to achieve a common goal. In such games, a successful player must (i) infer the partner’s private information from the partner’s messages, (ii) generate messages that are most likely to help with the goal, and (iii) reason pragmatically about the partner’s strategy. We propose a model that captures all three characteristics and demonstrate their importance in capturing human behavior on a new goal-oriented dataset we collected using crowdsourcing.

Languages with many speakers tend to be structurally simple while small communities sometimes develop languages with great structural complexity. Paradoxically, the opposite pattern appears to be observed for non-structural properties of language such as vocabulary size. These apparently opposite patterns pose a challenge for theories of language change and evolution. We use computational simulations to show that this inverse pattern can depend on a single factor: ease of diffusion through the population. A population of interacting agents was arranged on a network, passing linguistic conventions to one another along network links. Agents can invent new conventions, or replicate conventions that they have previously generated themselves or learned from other agents. Linguistic conventions are either Easy or Hard to diffuse, depending on how many times an agent needs to encounter a convention to learn it. In large groups, only linguistic conventions that are easy to learn, such as words, tend to proliferate, whereas small groups where everyone talks to everyone else allow for more complex conventions, like grammatical regularities, to be maintained. Our simulations thus suggest that language, and possibly other aspects of culture, may become simpler at the structural level as our world becomes increasingly interconnected.

Human languages exhibit both striking diversity and abstract commonalities. Whether these commonalities are shaped by potentially universal principles of human information processing has been of central interest in the language and psychological sciences. Research has identified one such abstract property in the domain of word order: Although sentence word-order preferences vary across languages, the superficially different orders result in short grammatical dependencies between words. Because dependencies are easier to process when they are short rather than long, these findings raise the possibility that languages are shaped by biases of human information processing. In the current study, we directly tested the hypothesized causal link. We found that learners exposed to novel miniature artificial languages that had unnecessarily long dependencies did not follow the surface preference of their native language but rather systematically restructured the input to reduce dependency lengths. These results provide direct evidence for a causal link between processing preferences in individual speakers and patterns in linguistic diversity.

We investigate the emergence of iconicity, specifically a bouba-kiki effect in miniature artificial languages under different functional constraints: when the languages are reproduced and when they are used communicatively. We ran transmission chains of (a) participant dyads who played an interactive communicative game and (b) individual participants who played a matched learning game. An analysis of the languages over six generations in an iterated learning experiment revealed that in the Communication condition, but not in the Reproduction condition, words for spiky shapes tend to be rated by naive judges as more spiky than the words for round shapes. This suggests that iconicity may not only be the outcome of innovations introduced by individuals, but, crucially, the result of interlocutor negotiation of new communicative conventions. We interpret our results as an illustration of cultural evolution by random mutation and selection (as opposed to by guided variation).

We set out an account of how self-domestication plays a crucial role in the evolution of language. In doing so, we focus on the growing body of work that treats language structure as emerging from the process of cultural transmission. We argue that a full recognition of the importance of cultural transmission fundamentally changes the kind of questions we should be asking regarding the biological basis of language structure. If we think of language structure as reflecting an accumulated set of changes in our genome, then we might ask something like, "What are the genetic bases of language structure and why were they selected?" However, if cultural evolution can account for language structure, then this question no longer applies. Instead, we face the task of accounting for the origin of the traits that enabled that process of structure-creating cultural evolution to get started in the first place. In light of work on cultural evolution, then, the new question for biological evolution becomes, "How did those precursor traits evolve?" We identify two key precursor traits: (1) the transmission of the communication system through learning; and (2) the ability to infer the communicative intent associated with a signal or action. We then describe two comparative case studies-the Bengalese finch and the domestic dog-in which parallel traits can be seen emerging following domestication. Finally, we turn to the role of domestication in human evolution. We argue that the cultural evolution of language structure has its origin in an earlier process of self-domestication.

Language flexibly supports the human ability to communicate using different sensory modalities, such as writing and reading in the visual modality and speaking and listening in the auditory domain. Although it has been argued that nonhuman primate communication abilities are inherently multisensory, direct behavioural comparisons between human and nonhuman primates are scant. Artificial grammar learning (AGL) tasks and statistical learning experiments can be used to emulate ordering relationships between words in a sentence. However, previous comparative work using such paradigms has primarily investigated sequence learning within a single sensory modality. We used an AGL paradigm to evaluate how humans and macaque monkeys learn and respond to identically structured sequences of either auditory or visual stimuli. In the auditory and visual experiments, we found that both species were sensitive to the ordering relationships between elements in the sequences. Moreover, the humans and monkeys produced largely similar response patterns to the visual and auditory sequences, indicating that the sequences are processed in comparable ways across the sensory modalities. These results provide evidence that human sequence processing abilities stem from an evolutionarily conserved capacity that appears to operate comparably across the sensory modalities in both human and nonhuman primates. The findings set the stage for future neurobiological studies to investigate the multisensory nature of these sequencing operations in nonhuman primates and how they compare to related processes in humans.

Recent advances in behavioural and computational neuroscience, cognitive robotics, and in the hardware implementation of large-scale neural networks, provide the opportunity for an accelerated understanding of brain functions and for the design of interactive robotic systems based on brain-inspired control systems. This is especially the case in the domain of action and language learning, given the significant scientific and technological developments in this field. In this work we describe how a neuroanatomically grounded spiking neural network for visual attention has been extended with a word learning capability and integrated with the iCub humanoid robot to demonstrate attention-led object naming. Experiments were carried out with both a simulated and a real iCub robot platform with successful results. The iCub robot is capable of associating a label to an object with a ‘preferred’ orientation when visual and word stimuli are presented concurrently in the scene, as well as attending to said object, thus naming it. After learning is complete, the name of the object can be recalled successfully when only the visual input is present, even when the object has been moved from its original position or when other objects are present as distractors.

Machines that learn like children provide deep insights into how the mind and body act together to bootstrap knowledge and skills. Deon, a fictional engineer in the 2015 sci-fi film Chappie, wants to create a machine that can think and feel. To this end, he writes an artificial-intelligence program that can learn like a child. Deon's test subject, Chappie, starts off with a relatively blank mental slate. By simply observing and experimenting with his surroundings, he acquires general knowledge, language and complex skills—a task that eludes even the most advanced AI systems we have today.

Training agents to communicate with one another given taskbased supervision only has attracted considerable attention recently, due to the growing interest in developing models for human-agent interaction. Prior work on the topic focused on simple environments, where training using policy gradient was feasible despite the non-stationarity of the agents during training. In this paper, we present a more challenging environment for testing the emergence of communication from raw pixels, where training using policy gradient fails. We propose a new model and training algorithm, that utilizes the structure of a learned representation space to produce more consistent speakers at the initial phases of training, which stabilizes learning. We empirically show that our algorithm substantially improves performance compared to policy gradient. We also propose a new alignment-based metric for measuring context-independence in emerged communication and find our method increases context-independence compared to policy gradient and other competitive baselines.

Automatic phylogenetic inference plays an increasingly important role in computational historical linguistics. Most pertinent work is currently based on expert cognate judgments. This limits the scope of this approach to a small number of well-studied language families. We used machine learning techniques to compile data suitable for phylogenetic inference from the ASJP database, a collection of almost 7,000 phonetically transcribed word lists over 40 concepts, covering two third of the extant world-wide linguistic diversity. First, we estimated Pointwise Mutual Information scores between sound classes using weighted sequence alignment and general-purpose optimization. From this we computed a dissimilarity matrix over all ASJP word lists. This matrix is suitable for distance-based phylogenetic inference. Second, we applied cognate clustering to the ASJP data, using supervised training of an SVM classifier on expert cognacy judgments. Third, we defined two types of binary characters, based on automatically inferred cognate classes and on sound-class occurrences. Several tests are reported demonstrating the suitability of these characters for character-based phylogenetic inference. Background & Summary The cultural transmission of natural languages with its patterns of near-faithful replication from generation to generation, and the diversification resulting from population splits, are known to display striking similarities to biological evolution [1, 2]. The mathematical tools to recover evolutionary history developed in computational biology — phylogenetic inference — play an increasingly important role in the study of the diversity and history of human languages. [3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14] The main bottleneck for this research program is the so far still limited availability of suitable data. Most extant studies rely on manually curated ar X iv :1 80 2. 06 07 9v 1 [ cs .C L ] 1 7 Fe b 20 18 collections of expert judgments pertaining to the cognacy of core vocabulary items or the grammatical classification of languages. Collecting such data is highly labor intensive. Therefore sizeable collections currently exist only for a relatively small number of well-studied language families. [8, 11, 15, 16, 17, 18] Basing phylogenetic inference on expert judgments, especially judgments regarding the cognacy between words, also raises methodological concerns. The experts making those judgments are necessarily historical linguists with some prior information about the genetic relationships between the languages involved. In fact, it is virtually impossible to pass a judgment about cognacy without forming a hypothesis about such relations. In this way, data are enriched with prior assumptions of human experts in a way that is hard to control or to precisely replicate. Modern machine learning techniques provide a way to greatly expand the empirical base of phylogenetic linguistics while avoiding the above-mentioned methodological problem. The Automated Similarity Judgment Program (ASJP) [19] database contains 40-item core vocabulary lists from more than 7,000 languages and dialects across the globe, covering about 75% of the extant linguistic diversity. All data are in phonetic transcription with little additional annotations.1 It is, at the current time, the most comprehensive collection of word lists available. Phylogenetic inference techniques comes in two flavors, distance-based and character-based methods. Distance-based methods require as input a matrix of pairwise distances between taxa. Character-based methods operate on a character matrix, i.e. a classification of the taxa under consideration according to a list of discrete, finite-valued characters. While some distance-based methods are computationally highly efficient, character-based methods usually provide more precise results and afford more fine-grained analyses. The literature contains proposals to extract both pairwise distance matrices and character data from phonetically transcribed word lists. [20, 21, 22] In this paper we apply those methods to the ASJP data and make both a distance matrix and a character matrix for 6,892 languages and dialects2 derived this way available to the community. Also, we demonstrate the suitability of the results for phylogenetic inference. While both the raw data and the algorithmic methods used in this study are freely publicly available, the computational effort required was considerable (about ten days computing time on a 160-cores parallel server). Therefore the resulting resource is worth publishing in its own right. 1The only expert judgments contained in the ASJP data are rather unsystematic manual identifications of loan words. This information is ignored in the present study. 2These are all languages in ASJP v. 17 except reconstructed, artificial, pidgin and creole languages.

Different structural features of human language change at different rates and thus exhibit different temporal stabilities. Existing methods of linguistic stability estimation depend upon the prior genealogical classification of the world’s languages into language families; these methods result in unreliable stability estimates for features which are sensitive to horizontal transfer between families and whenever data are aggregated from families of divergent time depths. To overcome these problems, we describe a method of stability estimation without family classifications, based on mathematical modelling and the analysis of contemporary geospatial distributions of linguistic features. Regressing the estimates produced by our model against those of a genealogical method, we report broad agreement but also important differences. In particular, we show that our approach is not liable to some of the false positives and false negatives incurred by the genealogical method. Our results suggest that the historical evolution of a linguistic feature leaves a footprint in its global geospatial distribution, and that rates of evolution can be recovered from these distributions by treating language dynamics as a spatially extended stochastic process.

The availability of large diachronic corpora has provided the impetus for a growing body of quantitative research on language evolution and meaning change. The central quantities in this research are token frequencies of linguistic elements in the texts, with changes in frequency taken to reflect the popularity or selective fitness of an element. However, corpus frequencies may change for a wide variety of reasons, including purely random sampling effects, or because corpora are composed of contemporary media and fiction texts within which the underlying topics ebb and flow with cultural and socio-political trends. In this work, we introduce a computationally simple model for controlling for topical fluctuations in corpora—the topical-cultural advection model—and demonstrate how it provides a robust baseline of variability in word frequency changes over time. We validate the model on a diachronic corpus spanning two centuries, and a carefully-controlled artificial language change scenario, and then use it to correct for topical fluctuations in historical time series. Finally, we show that the model can be used to show that emergence of new words typically corresponds with the rise of a trending topic. This suggests that some lexical innovations occur due to growing communicative need in a subspace of the lexicon, and that the topical-cultural advection model can be used to quantify this.

Han Chinese experienced substantial population migrations and admixture in history, yet little is known about the evolutionary process of Chinese dialects. Here, we used phylogenetic approaches and admixture inference to explicitly decompose the underlying structure of the diversity of Chinese dialects, based on the total phoneme inventories of 140 dialect samples from seven traditional dialect groups: Mandarin, Wu, Xiang, Gan, Hakka, Min and Yue. We found a north-south gradient of phonemic differences in Chinese dialects induced from historical population migrations. We also quantified extensive horizontal language transfers among these dialects, corresponding to the complicated socio-genetic history in China. We finally identified that the middle latitude dialects of Xiang, Gan and Hakka were formed by admixture with other four dialects. Accordingly, the middle-latitude areas in China were a linguistic melting pot of northern and southern Han populations. Our study provides a detailed phylogenetic and historical context against family-tree model in China.

There is growing interest in the language developed by agents interacting in emergentcommunication settings. Earlier studies have focused on the agents’ symbol usage, rather than on their representation of visual input. In this paper, we consider the referential games of Lazaridou et al. (2017), and investigate the representations the agents develop during their evolving interaction. We find that the agents establish successful communication by inducing visual representations that almost perfectly align with each other, but, surprisingly, do not capture the conceptual properties of the objects depicted in the input images. We conclude that, if we are interested in developing language-like communication systems, we must pay more attention to the visual semantics agents associate to the symbols they use.

One of the distinguishing aspects of human language is its compositionality, which allows us to describe complex environments with limited vocabulary. Previously, it has been shown that neural network agents can learn to communicate in a highly structured, possibly compositional language based on disentangled input (e.g. handengineered features). Humans, however, do not learn to communicate based on well-summarized features. In this work, we train neural agents to simultaneously develop visual perception from raw image pixels, and learn to communicate with a sequence of discrete symbols. The agents play an image description game where the image contains factors such as colors and shapes. We train the agents using the obverter technique where an agent introspects to generate messages that maximize its own understanding. Through qualitative analysis, visualization and a zero-shot test, we show that the agents can develop, out of raw image pixels, a language with compositional properties, given a proper pressure from the environment.

Multi-agent reinforcement learning offers a way to study how communication could emerge in communities of agents needing to solve specific problems. In this paper, we study the emergence of communication in the negotiation environment, a semi-cooperative model of agent interaction. We introduce two communication protocols – one grounded in the semantics of the game, and one which is a priori ungrounded and is a form of cheap talk. We show that self-interested agents can use the pre-grounded communication channel to negotiate fairly, but are unable to effectively use the ungrounded channel. However, prosocial agents do learn to use cheap talk to find an optimal negotiating strategy, suggesting that cooperation is necessary for language to emerge. We also study communication behaviour in a setting where one agent interacts with agents in a community with different levels of prosociality and show how agent identifiability can aid negotiation.

The ability of algorithms to evolve or learn (compositional) communication protocols has traditionally been studied in the language evolution literature through the use of emergent communication tasks. Here we scale up this research by using contemporary deep learning methods and by training reinforcement-learning neural network agents on referential communication games. We extend previous work, in which agents were trained in symbolic environments, by developing agents which are able to learn from raw pixel data, a more challenging and realistic input representation. We find that the degree of structure found in the input data affects the nature of the emerged protocols, and thereby corroborate the hypothesis that structured compositional language is most likely to emerge when agents perceive the world as being structured.

We study the stability of two coexisting languages (Catalan and Spanish) in Catalonia (North-Eastern Spain), a key European region in political and economic terms. Our analysis relies on recent, abundant empirical data that is studied within an analytic model of population dynamics. This model contemplates the possibilities of long-term language coexistence or extinction. We establish that the most likely scenario is a sustained coexistence. The data needs to be interpreted under different circumstances, some of them leading to the asymptotic extinction of one of the languages involved. We delimit the cases in which this can happen. Asymptotic behavior is often unrealistic as a predictor for complex social systems, hence we make an attempt at forecasting trends of speakers towards 2030. These also suggest sustained coexistence between both tongues, but some counterintuitive dynamics are unveiled for extreme cases in which Catalan would be likely to lose an important fraction of speakers. As an intermediate step, model parameters are obtained that convey relevant information about the prestige and interlinguistic similarity of the tongues as perceived by the population. This is the first time that these parameters are quantified rigorously for this couple of languages. Remarkably, Spanish is found to have a larger prestige specially in areas which historically had larger communities of Catalan monolingual speakers. Limited, spatially-segregated data allows us to examine more fine grained dynamics, thus better addressing the likely coexistence or extinction. Variation of the model parameters across regions are informative about how the two languages are perceived in more urban or rural environments.

The English language has evolved dramatically throughout its lifespan, to the extent that a modern speaker of Old English would be incomprehensible without translation. One concrete indicator of this process is the movement from irregular to regular (-ed) forms for the past tense of verbs. In this study we quantify the extent of verb regularization using two vastly disparate datasets: (1) Six years of published books scanned by Google (2003–2008), and (2) A decade of social media messages posted to Twitter (2008–2017). We find that the extent of verb regularization is greater on Twitter, taken as a whole, than in English Fiction books. Regularization is also greater for tweets geotagged in the United States relative to American English books, but the opposite is true for tweets geotagged in the United Kingdom relative to British English books. We also find interesting regional variations in regularization across counties in the United States. However, once differences in population are accounted for, we do not identify strong correlations with socio-demographic variables such as education or income.

Building intelligent agents that can communicate with and learn from humans in natural language is of great value. Supervised language learning is limited by the ability of capturing mainly the statistics of training data, and is hardly adaptive to new scenarios or flexible for acquiring new knowledge without inefficient retraining or catastrophic forgetting. We highlight the perspective that conversational interaction serves as a natural interface both for language learning and for novel knowledge acquisition and propose a joint imitation and reinforcement approach for grounded language learning through an interactive conversational game. The agent trained with this approach is able to actively acquire information by asking questions about novel objects and use the justlearned knowledge in subsequent conversations in a one-shot fashion. Results compared with other methods verified the effectiveness of the proposed approach.

The topics dealt with in this thesis are all part of the general problem of social consensus, namely how a convention flourish and decay and what motivates people to conform to it. Examples range from driving on the right side of the street, to language, rules of courtesy or moral judgments. Some conventions arise directly from the need to coordinate or conform, such as fashion or speaking the same language, others, instead, apply to situations where there is a tension between individual and collective interest, such as cooperation, reciprocity, etc. This thesis is developed around three main questions still open in the research field of collective human behavior: how coexistence of concurrent conventions is possible, why cooperation in real systems is more common than predicted and how a population undergoes collective behavioral change, namely how an initially minority norm can supplant a majority ones. In the first work, we study the impact of concurrent social pressures in consensus processes. We propose a model of opinion competition where individuals participate in different social networks and receive conflicting social influences. The dynamics take place in two distinct domains, which we model as layers of a multiplex network. The novelty of our study lies to the fact that individuals can have different options in the different layers. This naturally reflects a common situation where an individual can possess some different opinions in different social contexts as a result of consensus with other individuals in the one context but not in the other. Our analysis shows that the latter property enriches the system’s dynamics and allows not only for consensus into a single state for both layers, but also for active dynamical states of coexistence of both options. In the second model, we analyze the influence of opinion dynamic in competitive strategical games. Cooperation between humans is quite common and stable behavior even in situations where both game theory and experiments predict defection prevalence. One of the reasons could be just the fact that individuals engaging in strategic interactions are also exposed to social influence and, consequently, to the spread of opinions. We present a new evolutionary game model where game and opinions dynamics take place in different layers of a multiplex network. We show that the coupling between the two dynamical processes can lead to cooperation in scenarios where the pure game dynamics predicts defection and, in some particular setting, gives rise to a metastable state in which nodes that adopt the same strategy self-organize into local groups. In the last work, we present the first extensive quantitative analysis of the phenomenon of norm change by looking at 2,365 orthographic and lexical norms shifts occurred in English and Spanish over the last two centuries as recorded by millions of digitized books. We are able to identify three distinct patterns in the data depending on the nature of the norm shift. Furthermore, we propose a simple evolutionary model that captures all the identified mechanisms and reproduces quantitatively the transitions between norms. This work advances the current understanding of norm shifts in language change, most often limited to qualitative illustrations (e.g., the observation that adoption curve of the new norm follows an ‘S-shaped’ behavior.