Language Evolution and Computation Bibliography

Although it is widely agreed that learning the syntax of natural languages involves acquiring structure-dependent rules, recent work on acquisition has nevertheless attempted to characterize the outcome of learning primarily in terms of statistical generalizations about surface distributional information. In this paper we investigate whether surface statistical knowledge or structural knowledge of English is used to infer properties of a novel language under conditions of impoverished input. We expose learners to artificial-language patterns that are equally consistent with two possible underlying grammars--one more similar to English in terms of the linear ordering of words, the other more similar on abstract structural grounds. We show that learners' grammatical inferences overwhelmingly favor structural similarity over preservation of superficial order. Importantly, the relevant shared structure can be characterized in terms of a universal preference for isomorphism in the mapping from meanings to utterances. Whereas previous empirical support for this universal has been based entirely on data from cross-linguistic language samples, our results suggest it may reflect a deep property of the human cognitive system--a property that, together with other structure-sensitive principles, constrains the acquisition of linguistic knowledge.

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.

The debate on language origin and evolution has benefited from a largely interdisciplinary effort, involving linguists, anthropologists, sociologist as well as physicists, mathematicians and computer scientists. A fundamental question is whether a shared communication system can emerge from repeated interactions among individuals, not relying on any a priori or innate language-specific structure. Modeling, and in particular language games, proved to be a powerful tool to gain insight on this beautiful mystery. In particular, fruitful investigations has been done concerning the possibility for a population of individuals to exploit local communication acts to build up a shared vocabulary [1] or a system of linguistic categories reproducing the universality and the hierarchies observed in anthropological data [2–4]. A particular effort has been also devoted to the origin of the complex organization of syntax in hierarchical structures, one of the core design features of human language. As Gong and coauthors highlighted in this review [5], a combinatorial and compositional structure can emerge out of a holistic language due to communication purposes, and explaining how this could possibly happen still represents an intriguing challenge [6–11]. It is important to remark how theoretical investigations should be, and are more and more, paralleled by a growing attention to a careful comparison with data on language formation. Different kind of data can be exploited to shed light on different questions. Diachronic and historical data related to migration patterns have been used for instance to study the timescales of language evolution. Anthropological studies on pre-industrialized populations [12,13] have been crucial in the understanding language universals. At the same times, experiments in cognitive science helped in shading light on the mechanisms emerging when individuals are called to perform communicative tasks [14]. It is worth mentioning in this perspective how advances in information and communication technologies allow nowadays the realization of focused experiments also in the framework of the emergence of linguistic structures exploiting the huge basin of web users. In particular, a general trend is emerging for the adoption of web-games as a very interesting laboratory to run experiments in the social-sciences and whenever the contribution of human beings is crucially required for research purposes. This is opening tremendous opportunities to monitor the emergence of specific linguistic features and their co-evolution with the structure of our conceptual spaces.

We survey recent computer modelling research of language evolution, focusing on a rule-based model simulating the lexicon-syntax coevolution and an equation-based model quantifying the language competition dynamics. We discuss four predictions of these models: (a) correlation between domain-general abilities (e.g. sequential learning) and language-specific mechanisms (e.g. word order processing); (b) coevolution of language and relevant competences (e.g. joint attention); (c) effects of cultural transmission and social structure on linguistic understandability; and (d) commonalities between linguistic, biological, and physical phenomena. All these contribute significantly to our understanding of the evolutions of language structures, individual learning mechanisms, and relevant biological and socio-cultural factors. We conclude the survey by highlighting three future directions of modelling studies of language evolution: (a) adopting experimental approaches for model evaluation; (b) consolidating empirical foundations of models; and (c) multi-disciplinary collaboration among modelling, linguistics, and other relevant disciplines.

Iconicity, a resemblance between properties of linguistic form (both in spoken and signed languages) and meaning, has traditionally been considered to be a marginal, irrelevant phenomenon for our understanding of language processing, development and evolution. Rather, the arbitrary and symbolic nature of language has long been taken as a design feature of the human linguistic system. In this paper, we propose an alternative framework in which iconicity in face-to-face communication (spoken and signed) is a powerful vehicle for bridging between language and human sensori-motor experience, and, as such, iconicity provides a key to understanding language evolution, development and processing. In language evolution, iconicity might have played a key role in establishing displacement (the ability of language to refer beyond what is immediately present), which is core to what language does; in ontogenesis, iconicity might play a critical role in supporting referentiality (learning to map linguistic labels to objects, events, etc., in the world), which is core to vocabulary development. Finally, in language processing, iconicity could provide a mechanism to account for how language comes to be embodied (grounded in our sensory and motor systems), which is core to meaningful communication.

Human languages are rule governed, but almost invariably these rules have exceptions in the form of irregularities. Since rules in language are efficient and productive, the persistence of irregularity is an anomaly. How does irregularity linger in the face of internal (endogenous) and external (exogenous) pressures to conform to a rule? Here we address this problem by taking a detailed look at simple past tense verbs in the Corpus of Historical American English. The data show that the language is open, with many new verbs entering. At the same time, existing verbs might tend to regularize or irregularize as a consequence of internal dynamics, but overall, the amount of irregularity sustained by the language stays roughly constant over time. Despite continuous vocabulary growth, and presumably, an attendant increase in expressive power, there is no corresponding growth in irregularity. We analyze the set of irregulars, showing they may adhere to a set of minority rules, allowing for increased stability of irregularity over time. These findings contribute to the debate on how language systems become rule governed, and how and why they sustain exceptions to rules, providing insight into the interplay between the emergence and maintenance of rules and exceptions in language.

I describe the Utrecht Machine (UM), a discrete artificial regulatory network designed for studying how evolution discovers biochemical computation mechanisms. The corresponding binary genome format is compatible with gene deletion, duplication, and recombination. In the simulation presented here, an agent consisting of two UMs, a sender and a receiver, must encode, transmit, and decode a binary word over time using the narrow communication channel between them. This communication problem has chicken-and-egg structure in that a sending mechanism is useless without a corresponding receiving mechanism. An in-depth case study reveals that a coincidence creates a minimal partial solution, from which a sequence of partial sending and receiving mechanisms evolve. Gene duplications contribute by enlarging the regulatory network. Analysis of 60,000 sample runs under a variety of parameter settings confirms that crossover accelerates evolution, that stronger selection tends to find clumsier solutions and finds them more slowly, and that there is implicit selection for robust mechanisms and genomes at the codon level. Typical solutions associate each input bit with an activation speed and combine them almost additively. The parents of breakthrough organisms sometimes have lower fitness scores than others in the population, indicating that populations can cross valleys in the fitness landscape via outlying members. The simulation exhibits back mutations and population-level memory effects not accounted for in traditional population genetics models. All together, these phenomena suggest that new evolutionary models are needed that incorporate regulatory network structure.

The evolution of the faculty of language largely remains an enigma. In this essay, we ask why. Language's evolutionary analysis is complicated because it has no equivalent in any nonhuman species. There is also no consensus regarding the essential nature of the language "phenotype." According to the "Strong Minimalist Thesis," the key distinguishing feature of language (and what evolutionary theory must explain) is hierarchical syntactic structure. The faculty of language is likely to have emerged quite recently in evolutionary terms, some 70,000-100,000 years ago, and does not seem to have undergone modification since then, though individual languages do of course change over time, operating within this basic framework. The recent emergence of language and its stability are both consistent with the Strong Minimalist Thesis, which has at its core a single repeatable operation that takes exactly two syntactic elements a and b and assembles them to form the set {a, b}.

The evolution of languages shares certain characteristics with that of genes, such as the predominantly vertical line of transmission and the retention of traces of past events such as contact. Thus, studies of language phylogenies and their correlations with genetic phylogenies can enrich our understanding of human prehistory, while insights gained from genetic studies of past population contact can help shed light on the processes underlying language contact and change. As demonstrated by recent research, these evolutionary processes are more complex than simple models of gene-language coevolution predict, with linguistic boundaries only occasionally functioning as barriers to gene flow. More frequently, admixture takes place irrespective of linguistic differences, but with a detectable impact of contact-induced changes in the languages concerned.

Understanding the evolution of language requires evidence regarding origins and processes that led to change. In the last 40 years, there has been an explosion of research on this problem as well as a sense that considerable progress has been made. We argue instead that the richness of ideas is accompanied by a poverty of evidence, with essentially no explanation of how and why our linguistic computations and representations evolved. We show that, to date, (1) studies of nonhuman animals provide virtually no relevant parallels to human linguistic communication, and none to the underlying biological capacity; (2) the fossil and archaeological evidence does not inform our understanding of the computations and representations of our earliest ancestors, leaving details of origins and selective pressure unresolved; (3) our understanding of the genetics of language is so impoverished that there is little hope of connecting genes to linguistic processes any time soon; (4) all modeling attempts have made unfounded assumptions, and have provided no empirical tests, thus leaving any insights into language's origins unverifiable. Based on the current state of evidence, we submit that the most fundamental questions about the origins and evolution of our linguistic capacity remain as mysterious as ever, with considerable uncertainty about the discovery of either relevant or conclusive evidence that can adjudicate among the many open hypotheses. We conclude by presenting some suggestions about possible paths forward.

Like speech and language, the songs of many songbirds consist of learned, rapidly produced, structured sequences of distinct vocal units, originating from an interplay between experience and learning biases. Songs are species specific, but also show considerable within species variation in elements or element sequencing. This variation implies that birds possess mechanisms to identify, categorize and combine sounds. I review the abilities for speech sound perception and categorization, as well as for grammatical rule learning by birds. Speech sound perception in birds is in many ways comparable to human speech perception. Birds can also detect and generalize patterns underlying artificially arranged strings of vocal elements. However, there is a need for more comparative studies to examine the limits of their rule learning abilities and how they relate to those of humans.

Iterated learning describes the process whereby an individual learns their behaviour by exposure to another individual's behaviour, who themselves learnt it in the same way. It can be seen as a key mechanism of cultural evolution. We review various methods for understanding how behaviour is shaped by the iterated learning process: computational agent-based simulations; mathematical modelling; and laboratory experiments in humans and non-human animals. We show how this framework has been used to explain the origins of structure in language, and argue that cultural evolution must be considered alongside biological evolution in explanations of language origins.

Human languages vary in many ways but also show striking cross-linguistic universals. Why do these universals exist? Recent theoretical results demonstrate that Bayesian learners transmitting language to each other through iterated learning will converge on a distribution of languages that depends only on their prior biases about language and the quantity of data transmitted at each point; the structure of the world being communicated about plays no role (Griffiths & Kalish, 2005, 2007). We revisit these findings and show that when certain assumptions about the relationship between language and the world are abandoned, learners will converge to languages that depend on the structure of the world as well as their prior biases. These theoretical results are supported with a series of experiments showing that when human learners acquire language through iterated learning, the ultimate structure of those languages is shaped by the structure of the meanings to be communicated.

The topic is characterized by a highly interdisciplinary approach to the issue of action and language integration. Such an approach, combining computational models and cognitive robotics experiments with neuroscience, psychology, philosophy, and linguistic approaches, can be a powerful means that can help researchers disentangle ambiguous issues, provide better and clearer definitions, and formulate clearer predictions on the links between action and language. In the introduction we briefly describe the papers and discuss the challenges they pose to future research. We identify four important phenomena the papers address and discuss in light of empirical and computational evidence: (a) the role played not only by sensorimotor and emotional information but also of natural language in conceptual representation; (b) the contextual dependency and high flexibility of the interaction between action, concepts, and language; (c) the involvement of the mirror neuron system in action and language processing; (d) the way in which the integration between action and language can be addressed by developmental robotics and Human-Robot Interaction.

My previous two books were long and academic in tone. This book is shorter (under 60,000 words) and more likely to be read by busy people (I hope). The Origins of Language: A Slim Guide offers a concise and accessible overview of what is known about the evolution of the human capacity for language. Non-human animals communicate in simple ways: they may be able to form simple concepts, to feel some limited empathy for others, to cooperate to some extent, and to engage in mind-reading. Human language, however, is characterized by its ability to efficiently express a wide range of subtle and complex meanings. After the first simple beginnings, human language underwent an explosion of complexity, leading to the very complicated systems of grammar and pronunciation found in modern languages. Professor Hurford looks at the very varied aspects of this evolution, covering human prehistory; the relation between instinct and learning; biology and culture; trust, altruism, and cooperation; animal thought; human and non-human vocal anatomy; the meanings and forms of the first words; and the growth of complex systems of grammar and pronunciation. Written by an internationally recognized expert in the field, it draws on a number of disciplines besides linguistics, including philosophy, neuroscience, genetics, and animal behaviour, and will appeal to a wide range of readers interested in language origins and evolution.

How did social communication evolve in primates? In this volume, primatologists, linguists, anthropologists, cognitive scientists and philosophers of science systematically analyze how their specific disciplines demarcate the research questions and methodologies involved in the study of the evolutionary origins of social communication in primates in general, and in humans in particular. In the first part of the book, historians and philosophers of science address how the epistemological frameworks associated with primate communication and language evolution studies have changed over time, and how these conceptual changes affect our current studies on the subject matter. In the second part, scholars provide cutting-edge insights into the various means through which primates communicate socially in both natural and experimental settings. They examine the behavioral building blocks by which primates communicate, and they analyze what the cognitive requirements are for displaying communicative acts. Chapters highlight cross-fostering and language experiments with primates, primate mother-infant communication, the display of emotions and expressions, manual gestures and vocal signals, joint attention, intentionality and theory of mind. The primary focus of the third part is on how these various types of communicative behavior possibly evolved, and how they can be understood as evolutionary precursors to human language. Leading scholars analyze how both manual and vocal gestures gave way to mimetic and imitational protolanguage, and how the latter possibly transitioned into human language. In the final part, we turn to the hominin lineage, and anthropologists, archeologists and linguists investigate what the necessary neurocognitive, anatomical and behavioral features are in order for human language to evolve, and how language differs from other forms of primate communication.

“The meaning of a word is its use in the language”. In the first half of the 20th century Ludwig Wittgenstein introduced this idea into philosophy and especially in the last few decades, related disciplines such as psychology and linguistics started embracing the view that that natural language is a dynamic system of arbitrary and culturally learnt conventions. From the end of the nineties on, researchers around Luc Steels transferred this notion of communication to the field of artificial intelligence by letting software agents and later robots play so-called language games in order to self-organize communication systems without requiring prior linguistic or conceptual knowledge. Continuing and advancing that research, the work presented in this thesis investigates lexicon formation in humanoid robots, i.e. the emergence of shared lexical knowledge in populations of robotic agents. Central to this is the concept of referential uncertainty, which is the difficulty of guessing a previously unknown word from the context. First in a simulated environments and later with physical robots, this work starts from very simple lexicon formation models and then systematically analyzes how an increasing complexity in communicative interactions leads to an increasing complexity of representations and learning mechanisms. We evaluate lexicon formation models with respect to their robustness, scaling and their applicability to robotic interaction scenarios and one result of this work is that the predominating approaches in the literature do not scale well and are not able to cope with the challenges stemming from grounding words in the real-world perceptions of physical robots. In order to overcome these limitations, we present an alternative lexicon formation model and evaluate its performance.