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Language, more than anything else, is what makes us human. It appears that no communication system of equivalent power exists elsewhere in the animal kingdom. Any normal human child will learn a language based on rather sparse data in the surrounding world, while even the brightest chimpanzee, exposed to the same environment, will not. Why not? How, and why, did language evolve in our species and not in others? Since Darwin's theory of evolution, questions about the origin of language have generated a rapidly-growing scientific literature, stretched across a number of disciplines, much of it directed at specialist audiences. The diversity of perspectives - from linguistics, anthropology, speech science, genetics, neuroscience and evolutionary biology - can be bewildering. Tecumseh Fitch cuts through this vast literature, bringing together its most important insights to explore one of the biggest unsolved puzzles of human history.

One of the great unknowns in language evolution is the transition from unstructured sign combination to grammatical structure. This paper investigates the central — while hitherto overlooked — role of functor–argument metaphor. This type of metaphor pervades modern language, but is absent in animal communication. It arises from the semantic clash between the default meanings of terms. Functor–argument metaphor became logically possible in protolanguage once sufficient vocabulary and basic compositionality arose, allowing for novel combinations of terms. For example, the verb to hide, a functor, could be combined not only with a concrete, spatial entity like food as its argument, but also with an abstract, non-spatial one like anger. Through this clash, to hide is reinterpreted as a metaphorical action. Functor–argument metaphor requires the possibility of term combinability and the existence of compositionality. At the same time, it transcends compositionality, forcing a non-literal interpretation. We argue that functor–argument metaphor led the development of protolanguage into fully-fledged language in multiple ways. Not only did it expand expressiveness, but it drove the development of syntax including the conventionalization and fixation of word order, and the development of demonstratives. Thus, functor–argument metaphor fills in multiple gaps in the trajectory from a protolanguage, with only some terms and simple term combinations, to the elaborate grammatical structures of fully-fledged human languages.

this article proposes a number of models to examine through which mechanisms a population of autonomous agents could arrive at a repertoire of perceptually grounded categories that is sufficiently shared to allow successful communication. the models are inspired by the main approaches to human categorisation being discussed in the literature: nativism, empiricism, and culturalism. colour is taken as a case study. although we take no stance on which position is to be accepted as final truth with respect to human categorisation and naming, we do point to theoretical constraints that make each position more or less likely and we make clear suggestions on what the best engineering solution would be. specifically, we argue that the collective choice of a shared repertoire must integrate multiple constraints, including constraints coming from communication.

Human infant vocalization is viewed as a critical foundation for vocal learning and language. All apes share distress sounds (shrieks and cries) and laughter. Another vocal type, speech-like sounds, common in human infants, is rare but not absent in other apes. These three vocal types form a basis for especially informative cross-species comparisons. To make such comparisons possiblewe need empirical research documenting the frequency of occurrence of all three. The present work provides a comprehensive portrayal of these three vocal types in the human infant from longitudinal research in various circumstances of recording. Recently, the predominant vocalizations of the human infant have been shown to be speech-like sounds, or 'protophones', including both canonical and non-canonical babbling. The research shows that protophones outnumber cries by a factor of at least five based on data from random-sampling of all-day recordings across the first year. The present work expands on the prior reports, showing the protophones vastly outnumber both cry and laughter in both all-day and laboratory recordings in various circumstances. The data provide new evidence of the predominance of protophones in the infant vocal landscape and illuminate their role in human vocal learning and the origin of language. This article is part of the theme issue 'Vocal learning in animals and humans'.

The study of language origin and divergence is important for understanding the history of human populations and their cultures. The Sino-Tibetan language family is the second largest in the world after Indo-European, and there is a long-running debate about its phylogeny and the time depth of its original divergence 1 . Here we perform a Bayesian phylogenetic analysis to examine two competing hypotheses of the origin of the Sino-Tibetan language family: the ‘northern-origin hypothesis’ and the ‘southwestern-origin hypothesis’. The northern-origin hypothesis states that the initial expansion of Sino-Tibetan languages occurred approximately 4,000–6,000 years before present ( bp ; taken as ad 1950) in the Yellow River basin of northern China 2 – 4 , and that this expansion is associated with the development of the Yangshao and/or Majiayao Neolithic cultures. The southwestern-origin hypothesis states that an early expansion of Sino-Tibetan languages occurred before 9,000 years bp from a region in southwest Sichuan province in China 5 or in northeast India 6 , where a high diversity of Tibeto-Burman languages exists today. Consistent with the northern-origin hypothesis, our Bayesian phylogenetic analysis of 109 languages with 949 lexical root-meanings produced an estimated time depth for the divergence of Sino-Tibetan languages of approximately 4,200–7,800 years bp , with an average value of approximately 5,900 years bp . In addition, the phylogeny supported a dichotomy between Sinitic and Tibeto-Burman languages. Our results are compatible with the archaeological records, and with the farming and language dispersal hypothesis 7 of agricultural expansion in China. Our findings provide a linguistic foothold for further interdisciplinary studies of prehistoric human activity in East Asia. Divergence estimates from phylogenetic analyses of 109 languages of the Sino-Tibetan family support a model in which this family originates in the Yellow River basin of northern China.

Analyses of digital corpora of annotated texts reveal the influence of stochastic drift versus selection in grammatical shifts in English and provide a general method for quantitatively testing theories of language change. Gauging language evolution Languages and genes are transmitted between generations, both being subject to change with each step through random fluctuation as well as natural selection. Joshua Plotkin and colleagues assess the contribution of these two evolutionary mechanisms in a large body of texts ranging from the 12th to the 21st centuries. Some of the findings, for example that rare words are more prone to random drift than common ones, are perhaps not surprising. However, selection for the irregular forms of some verbs tends to buck the usual assumption of regularization over time, and might be related to changing frequencies of rhyming patterns. The findings suggest a more important role for random processes in language evolution than previously considered. Both language and genes evolve by transmission over generations with opportunity for differential replication of forms 1 . The understanding that gene frequencies change at random by genetic drift, even in the absence of natural selection, was a seminal advance in evolutionary biology 2 . Stochastic drift must also occur in language as a result of randomness in how linguistic forms are copied between speakers 3 , 4 . Here we quantify the strength of selection relative to stochastic drift in language evolution. We use time series derived from large corpora of annotated texts dating from the 12th to 21st centuries to analyse three well-known grammatical changes in English: the regularization of past-tense verbs 5 , 6 , 7 , 8 , 9 , the introduction of the periphrastic ‘do’ 10 , and variation in verbal negation 11 . We reject stochastic drift in favour of selection in some cases but not in others. In particular, we infer selection towards the irregular forms of some past-tense verbs, which is likely driven by changing frequencies of rhyming patterns over time. We show that stochastic drift is stronger for rare words, which may explain why rare forms are more prone to replacement than common ones 6 , 9 , 12 . This work provides a method for testing selective theories of language change against a null model and reveals an underappreciated role for stochasticity in language evolution.

As linguists theorize about language endangerment and loss (LEL), we must understand the big picture: the coexistence of languages in particular polities and how the competition that sometimes arises is resolved. Many concerns have been voiced about LEL since the early 1990s, but theoretical developments regarding language vitality lag far behind linguists’ current investment in language advocacy. While discussing issues such as the failure to connect the subject matter to language evolution in general, the framing of LEL as deleterious almost exclusively to ‘indigenous peoples’, a lack of historical time depth, and the omission of the ecological factors in typical approaches to LEL, I argue that linguistics should theorize about language vitality more adequately than has been the case to date.

Computer-mediated communication is driving fundamental changes in the nature of written language. We investigate these changes by statistical analysis of a dataset comprising 107 million Twitter messages (authored by 2.7 million unique user accounts). Using a latent vector autoregressive model to aggregate across thousands of words, we identify high-level patterns in diffusion of linguistic change over the United States. Our model is robust to unpredictable changes in Twitter's sampling rate, and provides a probabilistic characterization of the relationship of macro-scale linguistic influence to a set of demographic and geographic predictors. The results of this analysis offer support for prior arguments that focus on geographical proximity and population size. However, demographic similarity - especially with regard to race - plays an even more central role, as cities with similar racial demographics are far more likely to share linguistic influence. Rather than moving towards a single unified \"netspeak\" dialect, language evolution in computer-mediated communication reproduces existing fault lines in spoken American English.

The introduction of a mathematical and computational framework within which to analyze the interplay between language learning and language evolution. The nature of the interplay between language learning and the evolution of a language over generational time is subtle. We can observe the learning of language by children and marvel at the phenomenon of language acquisition; the evolution of a language, however, is not so directly experienced. Language learning by children is robust and reliable, but it cannot be perfect or languages would never change—and English, for example, would not have evolved from the language of the Anglo-Saxon Chronicles. In this book Partha Niyogi introduces a framework for analyzing the precise nature of the relationship between learning by the individual and evolution of the population. Learning is the mechanism by which language is transferred from old speakers to new. Niyogi shows that the evolution of language over time will depend upon the learning procedure—that different learning algorithms may have different evolutionary consequences. He finds that the dynamics of language evolution are typically nonlinear, with bifurcations that can be seen as the natural explanatory construct for the dramatic patterns of change observed in historical linguistics. Niyogi investigates the roles of natural selection, communicative efficiency, and learning in the origin and evolution of language—in particular, whether natural selection is necessary for the emergence of shared languages. Over the years, historical linguists have postulated several accounts of documented language change. Additionally, biologists have postulated accounts of the evolution of communication systems in the animal world. This book creates a mathematical and computational framework within which to embed those accounts, offering a research tool to aid analysis in an area in which data is often sparse and speculation often plentiful.

Human language is not the same as human speech. We use gestures and signs to communicate alongside, or instead of, speaking. Yet gestures and speech are processed in the same areas of the human brain, and the study of how both have evolved is central to research on the origins of human communication. Written by one of the pioneers of the field, this is the first book to explain how speech and gesture evolved together into a system that all humans possess. Nearly all theorizing about the origins of language either ignores gesture, views it as an add-on or supposes that language began in gesture and was later replaced by speech. David McNeill challenges the popular 'gesture-first' theory that language first emerged in a gesture-only form and proposes a groundbreaking theory of the evolution of language which explains how speech and gesture became unified.