Language Evolution and Computation Bibliography

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

What cognitive mechanisms support the emergence of linguistic conventions from repeated interaction? We present results from a large-scale, multi-player replication of the classic tangrams task, focusing on three foundational properties of conventions: arbitrariness, stability, and reduction of utterance length over time. These results motivate a theory of convention-formation where agents, though initially uncertain about word meanings in context, assume others are using language with such knowledge. Thus, agents may learn about meanings by reasoning about a knowledgeable, informative partner; if all agents engage in such a process, they successfully coordinate their beliefs, giving rise to a conventional communication system. We formalize this theory in a computational model of language understanding as social inference and demonstrate that it produces all three properties in a simplified domain.

A number of recent works have proposed techniques for end-to-end learning of communication protocols among cooperative multi-agent populations, and have simultaneously found the emergence of grounded human-interpretable language in the protocols developed by the agents, learned without any human supervision! In this paper, using a Task & Talk reference game between two agents as a testbed, we present a sequence of ‘negative’ results culminating in a ‘positive’ one – showing that while most agent-invented languages are effective (i.e. achieve near-perfect task rewards), they are decidedly not interpretable or compositional. In essence, we find that natural language does not emerge ‘naturally’, despite the semblance of ease of natural-language-emergence that one may gather from recent literature. We discuss how it is possible to coax the invented languages to become more and more human-like and compositional by increasing restrictions on how two agents may communicate.

Recurrent neural networks have recently shown significant potential in different language applications, ranging from natural language processing to language modelling. This paper introduces a research effort to use such networks to develop and evaluate natural language acquisition on a humanoid robot. Here, the problem is twofold. First, the focus will be put on using the gesture-word combination stage observed in infants to transition from single to multi-word utterances. Secondly, research will be carried out in the domain of connecting action learning with language learning. In the former, the long-short term memory architecture will be implemented, whilst in the latter multiple time-scale recurrent neural networks will be used. This will allow for comparison between the two architectures, whilst highlighting the strengths and shortcomings of both with respect to the language learning problem. Here, the main research efforts, challenges and expected outcomes are described.

Learning to communicate through interaction, rather than relying on explicit supervision, is often considered a prerequisite for developing a general AI. We study a setting where two agents engage in playing a referential game and, from scratch, develop a communication protocol necessary to succeed in this game. Unlike previous work, we require that messages they exchange, both at train and test time, are in the form of a language (i.e. sequences of discrete symbols). We compare a reinforcement learning approach and one using a differentiable relaxation (straightthrough Gumbel-softmax estimator (Jang et al., 2017)) and observe that the latter is much faster to converge and it results in more effective protocols. Interestingly, we also observe that the protocol we induce by optimizing the communication success exhibits a degree of compositionality and variability (i.e. the same information can be phrased in different ways), both properties characteristic of natural languages. As the ultimate goal is to ensure that communication is accomplished in natural language, we also perform experiments where we inject prior information about natural language into our model and study properties of the resulting protocol.

Both language and genes evolve by transmission over generations with opportunity for differential replication of forms. 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. Stochastic drift must also occur in language as a result of randomness in how linguistic forms are copied between speakers. 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, the introduction of the periphrastic ‘do’, and variation in verbal negation. 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. 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.

In the past few decades, scholars from several disciplines have pursued the curious parallel noted by Darwin between the genetic evolution of species and the cultural evolution of beliefs, skills, knowledge, languages, institutions, and other forms of socially transmitted information. Here, I review current progress in the pursuit of an evolutionary science of culture that is grounded in both biological and evolutionary theory, but also treats culture as more than a proximate mechanism that is directly controlled by genes. Both genetic and cultural evolution can be described as systems of inherited variation that change over time in response to processes such as selection, migration, and drift. Appropriate differences between genetic and cultural change are taken seriously, such as the possibility in the latter of nonrandomly guided variation or transformation, blending inheritance, and one-to-many transmission. The foundation of cultural evolution was laid in the late 20th century with population-genetic style models of cultural microevolution, and the use of phylogenetic methods to reconstruct cultural macroevolution. Since then, there have been major efforts to understand the sociocognitive mechanisms underlying cumulative cultural evolution, the consequences of demography on cultural evolution, the empirical validity of assumed social learning biases, the relative role of transformative and selective processes, and the use of quantitative phylogenetic and multilevel selection models to understand past and present dynamics of society-level change. I conclude by highlighting the interdisciplinary challenges of studying cultural evolution, including its relation to the traditional social sciences and humanities.

It is widely recognized that language plays a key role in the transmission of human culture, but relatively little is known about the mechanisms by which language simultaneously encourages both cultural stability and cultural innovation. This paper examines this issue by focusing on the use of language to transmit categories, focusing on two universal devices: labels (e.g., shark, woman) and generics (e.g., "sharks attack swimmers"; "women are nurturing"). We propose that labels and generics each assume two key principles: norms and essentialism. The normative assumption permits transmission of category information with great fidelity, whereas essentialism invites innovation by means of an open-ended, placeholder structure. Additionally, we sketch out how labels and generics aid in conceptual alignment and the progressive "looping" between categories and cultural practices. In this way, human language is a technology that enhances and expands the categorization capacities that we share with other animals.

Evolutionary theory describes the dynamics of population change in settings affected by reproduction, selection, mutation, and drift. In the context of human cognition, evolutionary theory is most often invoked to explain the origins of capacities such as language, metacognition, and spatial reasoning, framing them as functional adaptations to an ancestral environment. However, evolutionary theory is useful for understanding the mind in a second way: as a mathematical framework for describing evolving populations of thoughts, ideas, and memories within a single mind. In fact, deep correspondences exist between the mathematics of evolution and of learning, with perhaps the deepest being an equivalence between certain evolutionary dynamics and Bayesian inference. This equivalence permits reinterpretation of evolutionary processes as algorithms for Bayesian inference and has relevance for understanding diverse cognitive capacities, including memory and creativity.

Most languages of the world are taken to result from a combination of a vertical transmission process from older to younger generations of speakers or signers and (mostly) gradual changes that accumulate over time. In contrast, creole languages emerge within a few generations out of highly multilingual societies in situations where no common first language is available for communication (as, for instance, in plantations related to the Atlantic slave trade). Strikingly, creoles share a number of linguistic features (the ‘creole profile’), which is at odds with the striking linguistic diversity displayed by non-creole languages 1–4 . These common features have been explained as reflecting a hardwired default state of the possible grammars that can be learned by humans 1 , as straightforward solutions to cope with the pressure for efficient and successful communication 5 or as the byproduct of an impoverished transmission process 6 . Despite their differences, these proposals agree that creoles emerge from a very limited and basic communication system (a pidgin) that only later in time develops the characteristics of a natural language, potentially by innovating linguistic structure. Here we analyse 48 creole languages and 111 non-creole languages from all continents and conclude that the similarities (and differences) between creoles can be explained by genealogical and contact processes 7,8 , as with non-creole languages, with the difference that creoles have more than one language in their ancestry. While a creole profile can be detected statistically, this stems from an over-representation of Western European and West African languages in their context of emergence. Our findings call into question the existence of a pidgin stage in creole development and of creole-specific innovations. In general, given their extreme conditions of emergence, they lend support to the idea that language learning and transmission are remarkably resilient processes. There are striking similarities among creole languages. Blasi et al. show that these similarities can in fact be explained by the same processes as for non-creole languages, the difference being that creoles have more than one language in their ancestry.

An evolutionary model for emergence of diversity in language is developed. We investigated the effects of two real life observations, namely, people prefer people that they communicate with well, and people interact with people that are physically close to each other. Clearly these groups are relatively small compared to the entire population. We restrict selection of the teachers from such small groups, called imitation sets, around parents. Then the child learns language from a teacher selected within the imitation set of her parent. As a result, there are subcommunities with their own languages developed. Within subcommunity comprehension is found to be high. The number of languages is related to the relative size of imitation set by a power law.

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

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

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

At the onset of vocal development, both songbirds and humans produce variable vocal babbling with broadly distributed acoustic features. Over development, these vocalizations differentiate into the well-defined, categorical signals that characterize adult vocal behaviour. A broadly distributed signal is ideal for vocal exploration, that is, for matching vocal production to the statistics of the sensory input. The developmental transition to categorical signals is a gradual process during which the vocal output becomes differentiated and stable. But does it require categorical input? We trained juvenile zebra finches with playbacks of their own developing song, produced just a few moments earlier, updated continuously over development. Although the vocalizations of these self-tutored (ST) birds were initially broadly distributed, birds quickly developed categorical signals, as fast as birds that were trained with a categorical, adult song template. By contrast, siblings of those birds that received no training (isolates) developed phonological categories much more slowly and never reached the same level of category differentiation as their ST brothers. Therefore, instead of simply mirroring the statistical properties of their sensory input, songbirds actively transform it into distinct categories. We suggest that the early self-generation of phonological categories facilitates the establishment of vocal culture by making the song easier to transmit at the micro level, while promoting stability of shared vocabulary at the group level over generations.This article is part of the themed issue 'New frontiers for statistical learning in the cognitive sciences'.

Linguistic universals arise from the interaction between the processes of language learning and language use. A test case for the relationship between these factors is linguistic variation, which tends to be conditioned on linguistic or sociolinguistic criteria. How can we explain the scarcity of unpredictable variation in natural language, and to what extent is this property of language a straightforward reflection of biases in statistical learning? We review three strands of experimental work exploring these questions, and introduce a Bayesian model of the learning and transmission of linguistic variation along with a closely matched artificial language learning experiment with adult participants. Our results show that while the biases of language learners can potentially play a role in shaping linguistic systems, the relationship between biases of learners and the structure of languages is not straightforward. Weak biases can have strong effects on language structure as they accumulate over repeated transmission. But the opposite can also be true: strong biases can have weak or no effects. Furthermore, the use of language during interaction can reshape linguistic systems. Combining data and insights from studies of learning, transmission and use is therefore essential if we are to understand how biases in statistical learning interact with language transmission and language use to shape the structural properties of language.This article is part of the themed issue 'New frontiers for statistical learning in the cognitive sciences'.

We offer a new solution to the unsolved problem of how infants break into word learning based on the visual statistics of everyday infant-perspective scenes. Images from head camera video captured by 8 1/2 to 10 1/2 month-old infants at 147 at-home mealtime events were analysed for the objects in view. The images were found to be highly cluttered with many different objects in view. However, the frequency distribution of object categories was extremely right skewed such that a very small set of objects was pervasively present-a fact that may substantially reduce the problem of referential ambiguity. The statistical structure of objects in these infant egocentric scenes differs markedly from that in the training sets used in computational models and in experiments on statistical word-referent learning. Therefore, the results also indicate a need to re-examine current explanations of how infants break into word learning.This article is part of the themed issue 'New frontiers for statistical learning in the cognitive sciences'.

The novel entitled A Story of the Stone provides us precise details of life and social structure of the 18th century China. Its writing lasted a long duration of about 10 years, in which the author's habit may change significantly. It had been published anonymously up to the beginning of the 20th century, which left a mystery of the author's attribution. In the present work we focus our attention on scaling behavior embedded in the sentence series from this novel, hope to find how the ideas are organized from single sentences to the whole text. Especially we are interested in the evolution of scale invariance to monitor the changes of the author's language habit and to find some clues on the author's attribution. The sentence series are separated into a total of 69 non-overlapping segments with a length of 500 sentences each. The correlation dependent balanced estimation of diffusion entropy (cBEDE) is employed to evaluate the scaling behaviors embedded in the short segments. It is found that the total, the part attributed currently to Xueqin Cao (X-part), and the other part attributed to E Gao (E-part), display scale invariance in a large scale up to 103 sentences, while their scaling exponents are almost identical. All the segments behave scale invariant in considerable wide scales, most of which reach one third of the length. In the curve of scaling exponent versus segment number, the X-part has rich patterns with averagely larger values, while the E-part has a U-shape with a significant low bottom. This finding is a new clue to support the attribution of the E-part to E Gao.

Human language is composed of sequences of reusable elements. The origins of the sequential structure of language is a hotly debated topic in evolutionary linguistics. In this paper, we show that sets of sequences with language-like statistical properties can emerge from a process of cultural evolution under pressure from chunk-based memory constraints. We employ a novel experimental task that is non-linguistic and non-communicative in nature, in which participants are trained on and later asked to recall a set of sequences one-by-one. Recalled sequences from one participant become training data for the next participant. In this way, we simulate cultural evolution in the laboratory. Our results show a cumulative increase in structure, and by comparing this structure to data from existing linguistic corpora, we demonstrate a close parallel between the sets of sequences that emerge in our experiment and those seen in natural language.

Traditionally, the formation of vocabularies has been studied by agent-based models (primarily, the naming game) in which random pairs of agents negotiate word-meaning associations at each discrete time step. This article proposes a first approximation to a novel question: To what extent is the negotiation of word-meaning associations influenced by the order in which agents interact? Automata networks provide the adequate mathematical framework to explore this question. Computer simulations suggest that on two-dimensional lattices the typical features of the formation of word-meaning associations are recovered under random schemes that update small fractions of the population at the same time; by contrast, if larger subsets of the population are updated, a periodic behavior may appear.

In this article, I argue that a comparative approach focusing on the cognitive capacities and behavioral mechanisms that underlie vocal learning in songbirds and humans can provide valuable insights into the evolutionary origins of language. The experimental approaches I discuss use abnormal song and atypical linguistic input to study the processes of individual learning, social interaction, and cultural transmission. Atypical input places increased learning and communicative pressure on learners, so exploring how they respond to this type of input provides a particularly clear picture of the biases and constraints at work during learning and use. Furthermore, simulating the cultural transmission of these unnatural communication systems in the laboratory informs us about how learning and social biases influence the structure of communication systems in the long run. Findings based on these methods suggest fundamental similarities in the basic social-cognitive mechanisms underlying vocal learning in birds and humans, and continuing research promises insights into the uniquely human mechanisms and into how human cognition and social behavior interact, and ultimately impact on the evolution of language.

Human languages evolve by a process of descent with modification in which parent languages give rise to daughter languages over time and in a manner that mimics the evolution of biological species. Descent with modification is just one of many parallels between biological and linguistic evolution that, taken together, offer up a Darwinian perspective on how languages evolve. Combined with statistical methods borrowed from evolutionary biology, this Darwinian perspective has brought new opportunities to the study of the evolution of human languages. These include the statistical inference of phylogenetic trees of languages, the study of how linguistic traits evolve over thousands of years of language change, the reconstruction of ancestral or proto-languages, and using language change to date historical events.

Language is systematically structured at all levels of description, arguably setting it apart from all other instances of communication in nature. In this article, I survey work over the last 20 years that emphasises the contributions of individual learning, cultural transmission, and biological evolution to explaining the structural design features of language. These 3 complex adaptive systems exist in a network of interactions: individual learning biases shape the dynamics of cultural evolution; universal features of linguistic structure arise from this cultural process and form the ultimate linguistic phenotype; the nature of this phenotype affects the fitness landscape for the biological evolution of the language faculty; and in turn this determines individuals' learning bias. Using a combination of computational simulation, laboratory experiments, and comparison with real-world cases of language emergence, I show that linguistic structure emerges as a natural outcome of cultural evolution once certain minimal biological requirements are in place.

It is well accepted that languages change rapidly in a process of cultural evolution. But some animal communication systems, in particular bird song, also exhibit cultural change. So where exactly is the difference? This article argues that the main selectionist pressure on human languages is not biological-that is, related to survival and fecundity-but instead is linked to producing enough expressive power for the needs of the community, maximizing communicative success, and reducing cognitive effort. The key question to be answered by an "evolutionary linguistics" approach to language is, What are the causal mechanisms sustaining an evolutionary dynamic based on these selection criteria? In other words, what cognitive mechanisms and social interaction patterns are needed, and how do they allow a language to emerge and remain shared, despite profound variation and never-ending change?

This study offers evidence for an environmental effect on languages while relying on continuous linguistic and continuous ecological variables. Evidence is presented for a positive association between the typical ambient humidity of a language's native locale and that language's degree of reliance on vowels. The vowel-usage rates of over 4000 language varieties were obtained, and several methods were employed to test whether these usage rates are associated with ambient humidity. The results of these methods are generally consistent with the notion that reduced ambient humidity eventually yields a reduced reliance of languages on vowels, when compared to consonants. The analysis controls simultaneously for linguistic phylogeny and contact between languages. The results dovetail with previous work, based on binned data, suggesting that consonantal phonemes are more common in some ecologies. In addition to being based on continuous data and a larger data sample, however, these findings are tied to experimental research suggesting that dry air affects the behavior of the larynx by yielding increased phonatory effort. The results of this study are also consistent with previous work suggesting an interaction of aridity and tonality. The data presented here suggest that languages may evolve, like the communication systems of other species, in ways that are influenced subtly by ecological factors. It is stressed that more work is required, however, to explore this association and to establish a causal relationship between ambient air characteristics and the development of languages.

Nativist theories have argued that language involves syntactic principles which are unlearnable from the input children receive. A paradigm case of these innate principles is the structure dependence of auxiliary inversion in complex polar questions (Chomsky, 1968, 1975, 1980). Computational approaches have focused on the properties of the input in explaining how children acquire these questions. In contrast, we argue that messages are structured in a way that supports structure dependence in syntax. We demonstrate this approach within a connectionist model of sentence production (Chang, 2009) which learned to generate a range of complex polar questions from a structured message without positive exemplars in the input. The model also generated different types of error in development that were similar in magnitude to those in children (e.g., auxiliary doubling, Ambridge, Rowland, & Pine, 2008; Crain & Nakayama, 1987). Through model comparisons we trace how meaning constraints and linguistic experience interact during the acquisition of auxiliary inversion. Our results suggest that auxiliary inversion rules in English can be acquired without innate syntactic principles, as long as it is assumed that speakers who ask complex questions express messages that are structured into multiple propositions.

Human language is unique among all forms of animal communication. It is unlikely that any other species, including our close genetic cousins the Neanderthals, ever had language, and so-called sign ‘language’ in Great Apes is nothing like human language. Language evolution shares many features with biological evolution, and this has made it useful for tracing recent human history and for studying how culture evolves among groups of people with related languages. A case can be made that language has played a more important role in our species’ recent (circa last 200,000 years) evolution than have our genes.

Most theories of learning would predict a gradual acquisition and refinement of skills as learning progresses, and while some highlight exponential growth, this fails to explain why natural cognitive development typically progresses in stages. Models that do span multiple developmental stages typically have parameters to "switch" between stages. We argue that by taking an embodied view, the interaction between learning mechanisms, the resulting behavior of the agent, and the opportunities for learning that the environment provides can account for the stage-wise development of cognitive abilities. We summarize work relevant to this hypothesis and suggest two simple mechanisms that account for some developmental transitions: neural readiness focuses on changes in the neural substrate resulting from ongoing learning, and perceptual readiness focuses on the perceptual requirements for learning new tasks. Previous work has demonstrated these mechanisms in replications of a wide variety of infant language experiments, spanning multiple developmental stages. Here we piece this work together as a single model of ongoing learning with no parameter changes at all. The model, an instance of the Epigenetic Robotics Architecture (Morse et al 2010) embodied on the iCub humanoid robot, exhibits ongoing multi-stage development while learning pre-linguistic and then basic language skills.

The emergence of signaling systems has been observed in numerous experimental and real-world contexts, but there is no consensus on which (if any) shared mechanisms underlie such phenomena. A number of explanatory mechanisms have been proposed within several disciplines, all of which have been instantiated as credible working models. However, they are usually framed as being mutually incompatible. Using an exemplar-based framework, we replicate these models in a minimal configuration which allows us to directly compare them. This reveals that the development of optimal signaling is driven by similar mechanisms in each model, which leads us to propose three requirements for the emergence of conventional signaling. These are the creation and transmission of referential information, a systemic bias against ambiguity, and finally some form of information loss. Considering this, we then discuss some implications for theoretical and experimental approaches to the emergence of learned communication.

Language existed before human populations became separated (all descendant populations have language) but language did not emerge until long after these population divergences occurred (behavioral modernity only showed then). Distinguishing capacity for language from externalized language resolves the apparent paradox, eliminates the need of proto-language, and rules out monogenesis. Speech emerged only after the capacity for language became (sufficiently) fixated in the species. This accords well with a fundamental property of human language. Rules mapping to meaning rely on structural properties only, while rules mapping to sound are (also) sensitive to linear order, reflecting properties of sensorimotor modalities. The asymmetry suggests (i) primacy of internal language over speech/sign, and (ii) evolution of capacity of language preceding externalized language. Click phonemes with their unique geneological, genetic and geographical distribution may be relevant here. All biologically Khoisan groups speak click languages, which are spoken by biologically Khoisan groups only. Separation followed possession of internal language but preceded externalized language. Clicks were recruited for externalization in San populations only after deepest separation.

Can a child who is not exposed to a model for language nevertheless construct a communication system characterized by combinatorial structure? We know that deaf children whose hearing losses prevent them from acquiring spoken language, and whose hearing parents have not exposed them to sign language, use gestures, called homesigns, to communicate. In this study, we call upon a new formal analysis that characterizes the statistical profile of grammatical rules and, when applied to child language data, finds that young children's language is consistent with a productive grammar rather than rote memorization of specific word combinations in caregiver speech. We apply this formal analysis to homesign, and find that homesign can also be characterized as having productive grammar. Our findings thus provide evidence that a child can create a combinatorial linguistic system without external linguistic input, and offer unique insight into how the capacity of language evolved as part of human biology.

The digital age is changing our children’s lives and childhood dramatically. New technologies transform the way people interact with each other, the way stories are shared and distributed, and the way reality is presented and perceived. Parents experience that toddlers can handle tablets and apps with a level of sophistication the children’s grandparents can only envy. In Great Britain, a recent survey of preschoolers shows that a rising number of toddlers are now put to bed with a tablet instead of a bedtime story. In the USA, a telephone survey of 1,009 parents of children aged 2–24 months (Zimmerman et al., 2007a) documents that by 3 months of age, about 40% of children regularly watched television, DVDs or videos, while by 24 months the proportion rose to 90%. Moreover, with the advance and exponential use of social media, children see their parents constantly interacting with mobile devices, instead of with people around them. Still, research in the US indicates that assistive social robots seem to have a favorable effect on children’s language development (Westlund et al.). Existing theories of language acquisition emphasize the role of language input and the child’s interaction with the environment as crucial to language development. From this perspective, we need to ask: What are the consequences of this new digital reality for children’s acquisition of the most fundamental of all human skills: language and communication? Are new theories needed that can help us understand how children acquire language? Do the new digital environment and the new ways of interaction change the way languages are learned, or the quality of language acquisition? Is the use of new media beneficial or harmful to children’s language and cognitive development? Can new technologies be tailored to support child growth and, most importantly, can they be designed to enhance language learning in vulnerable children? These questions and issues can only be addressed bymeans of an interdisciplinary approach that aims at developing new methods of data collection and analysis in a longitudinal perspective. This type of research is however not yet documented.

The diverse and well-studied order Primates serves as an excellent model for understanding the evolution of acoustic communication among mammals. Over the past 60 million years, primates have evolved into more than 300 extant species that range from nocturnal to diurnal, arboreal to terrestrial, and solitary to groups of thousands, and they range in body mass from the 30-g pygmy mouse lemur (Microcebus myoxinus) to the 175-kg eastern lowland gorilla (Gorilla beringei graueri). Nonhuman primates vary in their auditory sensitivity and perceptual capabilities and emit a wide range of often complex vocalizations. Some aspects of primate audition and vocalizations have been related to each other and/or phylogeny, anatomy, and ecology, but many aspects have yet to be fully understood. The integration of anatomical and behavioral data on acoustic communication, and the correlates thereof, have significant potential for reconstructing behavior in the fossil record, including that of humans. This volume presents a comprehensive review of nonhuman primate audition and vocal communication to bridge these closely related topics that are often addressed separately. The first section of the book is a discussion of primate sound production, reception, and perception, as well as habitat acoustics in the environmental settings occupied by primates in the wild. The second section focuses on vocal communication in extant primates, including consideration of spectral analyses of primate calls and the evolutionary relationships among hearing, vocal communication, and human language. The goal for this comprehensive approach is to provide new insights into these related topics.

Inspired by previous work on emergent language in referential games, we propose a novel multi-modal, multi-step referential game, where the sender and receiver have access to distinct modalities of an object, and their information exchange is bidirectional and of arbitrary duration. The multi-modal multi-step setting allows agents to develop an internal language significantly closer to natural language, in that they share a single set of messages, and that the length of the conversation may vary according to the difficulty of the task. We examine these properties empirically using a dataset consisting of images and textual descriptions of mammals, where the agents are tasked with identifying the correct object. Our experiments indicate that a robust and efficient communication protocol emerges, where gradual information exchange informs better predictions and higher communication bandwidth improves generalization.

Human beings are talkative. What advantage did their ancestors find in communicating so much? Numerous authors consider this advantage to be “obvious” and “enormous”. If so, the problem of the evolutionary emergence of language amounts to explaining why none of the other primate species evolved anything even remotely similar to language. What I propose here is to reverse the picture. On closer examination, language resembles a losing strategy. Competing for providing other individuals with information, sometimes striving to be heard, makes apparently no sense within a Darwinian framework. At face value, language as we can observe it should never have existed or should have been counter-selected. In other words, the selection pressure that led to language is still missing. The solution I propose consists in regarding language as a social signaling device that developed in a context of generalized insecurity that is unique to our species. By talking, individuals advertise their alertness and their ability to get informed. This hypothesis is shown to be compatible with many characteristics of language that otherwise are left unexplained.