Language Evolution and Computation Bibliography

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.

We share our thoughts with other minds, but we do not understand how. Having a common language certainly helps, but infants' and tourists' communicative success clearly illustrates that sharing thoughts does not require signals with a pre-assigned meaning. In fact, human communicators jointly build a fleeting conceptual space in which signals are a means to seek and provide evidence for mutual understanding. Recent work has started to capture the neural mechanisms supporting those fleeting conceptual alignments. The evidence suggests that communicators and addressees achieve mutual understanding by using the same computational procedures, implemented in the same neuronal substrate, and operating over temporal scales independent from the signals' occurrences.

Why are there different languages? A common explanation is that different languages arise from the gradual accumulation of random changes. Here, we argue that, beyond these random factors, linguistic differences, from sounds to grammars, may also reflect adaptations to different environments in which the languages are learned and used. The aspects of the environment that could shape language include the social, the physical, and the technological.

Imitation and innovation work in tandem to support cultural learning in children and facilitate our capacity for cumulative culture. Here we propose an integrated theoretical account of how the unique demands of acquiring instrumental skills and cultural conventions provide insight into when children imitate, when they innovate, and to what degree. For instrumental learning, with an increase in experience, high fidelity imitation decreases and innovation increases. By contrast, for conventional learning, imitative fidelity stays high, regardless of experience, and innovation stays low. We synthesize cutting edge research on the development of imitative flexibility and innovation to provide insight into the social learning mechanisms underpinning the uniquely human mind.

A full account of human speech evolution must consider its multisensory, rhythmic, and cooperative characteristics. Humans, apes, and monkeys recognize the correspondence between vocalizations and their associated facial postures, and gain behavioral benefits from them. Some monkey vocalizations even have a speech-like acoustic rhythmicity but lack the concomitant rhythmic facial motion that speech exhibits. We review data showing that rhythmic facial expressions such as lip-smacking may have been linked to vocal output to produce an ancestral form of rhythmic audiovisual speech. Finally, we argue that human vocal cooperation (turn-taking) may have arisen through a combination of volubility and prosociality, and provide comparative evidence from one species to support this hypothesis.

Language serves as a cornerstone for human cognition, yet much about its evolution remains puzzling. Recent research on this question parallels Darwin's attempt to explain both the unity of all species and their diversity. What has emerged from this research is that the unified nature of human language arises from a shared, species-specific computational ability. This ability has identifiable correlates in the brain and has remained fixed since the origin of language approximately 100 thousand years ago. Although songbirds share with humans a vocal imitation learning ability, with a similar underlying neural organization, language is uniquely human.

Networks of interconnected nodes have long played a key role in Cognitive Science, from artificial neural networks to spreading activation models of semantic memory. Recently, however, a new Network Science has been developed, providing insights into the emergence of global, system-scale properties in contexts as diverse as the Internet, metabolic reactions, and collaborations among scientists. Today, the inclusion of network theory into Cognitive Sciences, and the expansion of complex-systems science, promises to significantly change the way in which the organization and dynamics of cognitive and behavioral processes are understood. In this paper, we review recent contributions of network theory at different levels and domains within the Cognitive Sciences.

Language serves as a cornerstone for human cognition, yet much about its evolution remains puzzling. Recent research on this question parallels Darwin's attempt to explain both the unity of all species and their diversity. What has emerged from this research is that the unified nature of human language arises from a shared, species-specific computational ability. This ability has identifiable correlates in the brain and has remained fixed since the origin of language approximately 100 thousand years ago. Although songbirds share with humans a vocal imitation learning ability, with a similar underlying neural organization, language is uniquely human.

Cognition materializes in an interpersonal space. The emergence of complex behaviors requires the coordination of actions among individuals according to a shared set of rules. Despite the central role of other individuals in shaping one's mind, most cognitive studies focus on processes that occur within a single individual. We call for a shift from a single-brain to a multi-brain frame of reference. We argue that in many cases the neural processes in one brain are coupled to the neural processes in another brain via the transmission of a signal through the environment. Brain-to-brain coupling constrains and shapes the actions of each individual in a social network, leading to complex joint behaviors that could not have emerged in isolation.

The historical origins of natural language cannot be observed directly. We can, however, study systems that support language and we can also develop models that explore the plausibility of different hypotheses about how language emerged. More recently, evolutionary linguists have begun to conduct language evolution experiments in the laboratory, where the emergence of new languages used by human participants can be observed directly. This enables researchers to study both the cognitive capacities necessary for language and the ways in which languages themselves emerge. One theme that runs through this work is how individual-level behaviours result in population-level linguistic phenomena. A central challenge for the future will be to explore how different forms of information transmission affect this process.

Scaling laws are ubiquitous in nature, and they pervade neural, behavioral and linguistic activities. A scaling law suggests the existence of processes or patterns that are repeated across scales of analysis. Although the variables that express a scaling law can vary from one type of activity to the next, the recurrence of scaling laws across so many different systems has prompted a search for unifying principles. In biological systems, scaling laws can reflect adaptive processes of various types and are often linked to complex systems poised near critical points. The same is true for perception, memory, language and other cognitive phenomena. Findings of scaling laws in cognitive science are indicative of scaling invariance in cognitive mechanisms and multiplicative interactions among interdependent components of cognition.

Studies of language change have begun to contribute to answering several pressing questions in cognitive sciences, including the origins of human language capacity, the social construction of cognition and the mechanisms underlying culture change in general. Here, we describe recent advances within a new emerging framework for the study of language change, one that models such change as an evolutionary process among competing linguistic variants. We argue that a crucial and unifying element of this framework is the use of probabilistic, data-driven models both to infer change and to compare competing claims about social and cognitive influences on language change.

Children learn language from their parents and then use the acquired system throughout the rest of their life with little change. At least that is commonly assumed. But a recent paper by Galantucci adds to the growing evidence that adults (and children) are able to create and negotiate complex communication systems from scratch and relatively quickly, without a prior model. This raises questions of what cognitive mechanisms are implied in this joint construction of communication systems, and what the implications are for the origins of human language.

What roles do syntax and semantics have in the grammar of a language? What are the consequences of these roles for syntactic structure, and why does it matter? We sketch the Simpler Syntax Hypothesis, which holds that much of the explanatory role attributed to syntax in contemporary linguistics is properly the responsibility of semantics. This rebalancing permits broader coverage of empirical linguistic phenomena and promises a tighter integration of linguistic theory into the cognitive scientific enterprise. We suggest that the general perspective of the Simpler Syntax Hypothesis is well suited to approaching language processing and language evolution, and to computational applications that draw upon linguistic insights.

Several important recent advances in various sciences (particularly biology and physics) are based on complex network analysis, which provides tools for characterizing statistical properties of networks and explaining how they may arise. This article examines the relevance of this trend for the study of human languages. We review some early efforts to build up language networks, characterize their properties, and show in which direction models are being developed to explain them. These insights are relevant, both for studying fundamental unsolved puzzles in cognitive science, in particular the origins and evolution of language, but also for recent data-driven statistical approaches to natural language.

Understanding developmental and evolutionary aspects of the language faculty requires comparing adult languages users' abilities with those of non-verbal subjects, such as babies and non-human animals. Classically, comparative work in this area has relied on the rich theoretical frameworks developed by linguists in the generative grammar tradition. However, the great variety of generative theories and the fact that they are models of language specifically makes it difficult to know what to test in animals and children lacking the expressive abilities of normal, mature adults. We suggest that this problem can be mitigated by tapping equally rich, but more formal mathematical approaches to language.

We use words to communicate about things and kinds of things, their properties, relations and actions. Researchers are now creating robotic and simulated systems that ground language in machine perception and action, mirroring human abilities. A new kind of computational model is emerging from this work that bridges the symbolic realm of language with the physical realm of real-world referents. It explains aspects of context-dependent shifts of word meaning that cannot easily be explained by purely symbolic models. An exciting implication for cognitive modeling is the use of grounded systems to `step into the shoes' of humans by directly processing first-person-perspective sensory data, providing a new methodology for testing various hypotheses of situated communication and learning.

In a recent article Mitchener and Nowak present a model of the evolutionary dynamics of language. The model exhibits regular and chaotic oscillations in changes to the proportions of grammars spoken in a population over the course of evolution. These oscillations are within the purview of evolutionary game theory, but they suggest the lack of an evolutionarily stable strategy. Implications for self-organization across scales of adaptation are discussed.

Recent demonstrations of statistical learning in infants have reinvigorated the innateness versus learning debate in language acquisition. This article addresses these issues from both computational and developmental perspectives. First, I argue that statistical learning using transitional probabilities cannot reliably segment words when scaled to a realistic setting (e.g. childdirected English). To be successful, it must be constrained by knowledge of phonological structure. Then, turning to the bona fide theory of innateness - the Principles and Parameters framework - I argue that a full explanation of children's grammar development must abandon the domain-specific learning model of triggering, in favor of probabilistic learning mechanisms that might be domain-general but nevertheless operate in the domain-specific space of syntactic parameters.

A new theoretical approach to language has emerged in the past 10-15 years that allows linguistic observations about form-meaning pairings, known as 'constructions', to be stated directly. Constructionist approaches aim to account for the full range of facts about language, without assuming that a particular subset of the data is part of a privileged 'core'. Researchers in this field argue that unusual constructions shed light on more general issues, and can illuminate what is required for a complete account of language.

Why is language the way it is? How did language come to be this way? And why is our species alone in having complex language? These are old unsolved questions that have seen a renaissance in the dramatic recent growth in research being published on the origins and evolution of human language. This review provides a broad overview of some of the important current work in this area. We highlight new methodologies (such as computational modeling), emerging points of consensus (such as the importance of pre-adaptation), and the major remaining controversies (such as gestural origins of language). We also discuss why language evolution is such a difficult problem, and suggest probable directions research may take in the near future.

The computational and robotic synthesis of language evolution is emerging as a new exciting field of research. The objective is to come up with precise operational models of how communities of agents, equipped with a cognitive apparatus, a sensori-motor system, and a body, can arrive at shared grounded communication systems. Such systems may have similar characteristics to animal communication or human language. Apart from its technological interest in building novel applications in the domain of human?robot or robot?robot interaction, this research is of interest to the many disciplines concerned with the origins and evolution of language and communication.

A small number of discrete choices (`parameters') embedded within a system of otherwise universal principles create the extensive superficial differences between unrelated languages like English, Japanese, and Mohawk. Most current thinking about the evolution of language ignores or denies the existence of these parameters because it can see no rationale for them. That the human language faculty is organized in this way makes more sense if language is compared to a cipher or code. As such, it would have a purpose of concealing information from some at the same time as it communicates information to others.

Language is a biological trait that radically changed the performance of one species and the appearance of the planet. Understanding how human language came about is one of the most interesting tasks for evolutionary biology. Here we discuss how natural selection can guide the emergence of some basic features of human language, including arbitrary signs, words, syntactic communication and grammar. We show how natural selection can lead to the duality of patterning of human language: sequences of phonemes form words; sequences of words form sentences. Finally, we present a framework for the population dynamics of grammar acquisition, which allows us to study the cultural evolution of grammar and the biological evolution of universal grammar.

Language is an apparent miracle. Children master it with exceptional ease, while simultaneously struggling to walk, hold a fork, and recognize that others have thoughts and emotions that differ from their own. They perform, with near perfection, mental computation and generalizations about language which are virtually impossible for state of the art computers. They grasp the tree-like phrase structure of language even though their parents have never taught them, and most probably couldn't even if they wanted to (such properties of language are not the stuff of school education). And children babble on about the present, past, and future, creating imaginary worlds that no one but they can see.

Sequential learning plays a role in a variety of common tasks, such as human language processing, animal communication, and the learning of action sequences. In this article, we investigate sequential learning in non-human primates from a comparative perspective, focusing on three areas: the learning of arbitrary, fixed sequences; statistical learning; and the learning of hierarchical structure. Although primates exhibit many similarities to humans in their performance on sequence learning tasks, there are also important differences. Crucially, non-human primates appear to be limited in their ability to learn and represent the hierarchical structure of sequences. We consider the evolutionary implications of these differences and suggest that limitations in sequential learning may help explain why non-human primates lack human-like language.

The evolution of speech can be studied independently of the evolution of language, with the advantage that most aspects of speech acoustics, physiology and neural control are shared with animals, and thus open to empirical investigation. At least two changes were necessary prerequisites for modern human speech abilities: (1) modification of vocal tract morphology, and (2) development of vocal imitative ability. Despite an extensive literature, attempts to pinpoint the timing of these changes using fossil data have proven inconclusive. However, recent comparative data from nonhuman primates have shed light on the ancestral use of formants (a crucial cue in human speech) to identify individuals and gauge body size. Second, comparative analysis of the diverse vertebrates that have evolved vocal imitation (humans, cetaceans, seals and birds) provides several distinct, testable hypotheses about the adaptive function of vocal mimicry. These developments suggest that, for understanding the evolution of speech, comparative analysis of living species provides a viable alternative to fossil data. However, the neural basis for vocal mimicry and for mimesis in general remains unknown.

Much current discussion of the evolution of language has concerned the emergence of a stage in which single vocal or gestural signals were used symbolically. Assuming the existence of such a stage, the present review decomposes the emergence of modern language into nine partially ordered steps, each of which contributes to precision and variety of expression. Bickerton's proposed `protolanguage' falls somewhere in the middle of this succession. In addition to the by-now accepted evidence from language learning, language disorders, and ape language experiments, modern languages provide evidence of these stages of evolution through the presence of detectable `fossils' in vocabulary and grammar.