Language Evolution and Computation Bibliography

Infants' own activities create and actively select their learning experiences. Here we review recent models of embodied information seeking and curiosity-driven learning and show that these mechanisms have deep implications for development and evolution. We discuss how these mechanisms yield self-organized epigenesis with emergent ordered behavioral and cognitive developmental stages. We describe a robotic experiment that explored the hypothesis that progress in learning, in and for itself, generates intrinsic rewards: The robot learners probabilistically selected experiences according to their potential for reducing uncertainty. In these experiments, curiosity-driven learning led the robot learner to successively discover object affordances and vocal interaction with its peers. We explain how a learning curriculum adapted to the current constraints of the learning system automatically formed, constraining learning and shaping the developmental trajectory. The observed trajectories in the robot experiment share many properties with those in infant development, including a mixture of regularities and diversities in the developmental patterns. Finally, we argue that such emergent developmental structures can guide and constrain evolution, in particular with regard to the origins of language.

Human vocalization systems are characterized by complex structural properties. They are combinatorial, based on the systematic reuse of phonemes, and the set of repertoires in human languages is characterized by both strong statistical regularities—universals—and a great diversity. Besides, they are conventional codes culturally shared in each community of speakers. What are the origins of the forms of speech? What are the mechanisms that permitted their evolution in the course of phylogenesis and cultural evolution? How can a shared speech code be formed in a community of individuals? This chapter focuses on the way the concept of self-organization, and its interaction with natural selection, can throw light on these three questions. In particular, a computational model is presented which shows that a basic neural equipment for adaptive holistic vocal imitation, coupling directly motor and perceptual representations in the brain, can generate spontaneously shared combinatorial systems of vocalizations in a society of babbling individuals. Furthermore, we show how morphological and physiological innate constraints can interact with these self-organized mechanisms to account for both the formation of statistical regularities and diversity in vocalization systems.

This paper presents computational experiments that illustrate how one can precisely conceptualize language evolution as a Darwinian process. We show that there is potentially a wide diversity of replicating units and replication mechanisms involved in language evolution. Computational experiments allow us to study systemic properties coming out of populations of linguistic replicators: linguistic replicators can adapt to specific external environments; they evolve under the pressure of the cognitive constraints of their hosts, as well as under the functional pressure of communication for which they are used; one can observe neutral drift; coalitions of replicators may appear, forming higher level groups which can themselves become subject to competition and selection.

Speech is the principal supporting medium of language. In this book Pierre-Yves Oudeyer considers how spoken language first emerged. He presents an original and integrated view of the interactions between self-organization and natural selection, reformulates questions about the origins of speech, and puts forward what at first sight appears to be a startling proposal - that speech can be spontaneously generated by the coupling of evolutionarily simple neural structures connecting perception and production. He explores this hypothesis by constructing a computational system to model the effects of linking auditory and vocal motor neural nets. He shows that a population of agents which used holistic and unarticulated vocalizations at the outset are inexorably led to a state in which their vocalizations have become discrete, combinatorial, and categorized in the same way by all group members. Furthermore, the simple syntactic rules that have emerged to regulate the combinations of sounds exhibit the fundamental properties of modern human speech systems.

Table of Contents
1. The Self-Organization Revolution in Science
2. The Human Speech Code
3. Self-Organization and Evolution
4. Existing Theories
5. Artificial Systems as Research Tools for Natural Sciences
6. The Artificial System
7. Learning Perceptuo-Motor Correspondences
8. Strong Combinatoriality and Phonotactics
9. New Scenarios
10. Constructing for Understanding

What kind of motivation drives child language development? This article presents a computational model and a robotic experiment to articulate the hypothesis that children discover communication as a result of exploring and playing with their environment. The considered robotic agent is intrinsically motivated towards situations in which it optimally progresses in learning. To experience optimal learning progress, it must avoid situations already familiar but also situations where nothing can be learnt. The robot is placed in an environment in which both communicating and non-communicating objects are present. As a consequence of its intrinsic motivation, the robot explores this environment in an organized manner focusing first on non-communicative activities and then discovering the learning potential of certain types of interactive behaviour. In this experiment, the agent ends up being interested by communication through vocal interactions without having a specific drive for communication.

This paper shows how a society of agents can self-organize a shared vocalization system that is discrete, combinatorial and has a form of primitive phonotactics, starting from holistic inarticulate vocalizations. The originality of the system is that: (1) it does not include any explicit pressure for communication; (2) agents do not possess capabilities of coordinated interactions, in particular they do not play language games; (3) agents possess no specific linguistic capacities; and (4) initially there exists no convention that agents can use. As a consequence, the system shows how a primitive speech code may bootstrap in the absence of a communication system between agents, i.e. before the appearance of language.

In this chapter, we show that from a minimal neural kit for vocal replication, a shared combinatorial speech code with structural regularities and diversity spontaneously self-organizes in a population of agents. This allows to understand that the evolutionary step from vocal replication systems to modern human speech systems might have been rather small.

Sound is a medium used by humans to carry information. The existence of this kind of medium is a pre-requisite for language. It is organized into a code, called speech, which provides a repertoire of forms that is shared in each language community. This code is necessary to support the linguistic interactions that allow humans to communicate. How then may a speech code be formed prior to the existence of linguistic interactions? Moreover, the human speech code is characterized by several properties: speech is digital and compositional (vocalizations are made of units re-used systematically in other syllables); phoneme inventories have precise regularities as well as great diversity in human languages; all the speakers of a language community categorize sounds in the same manner, but each language has its own system of categorization, possibly very different from every other. How can a speech code with these properties form? These are the questions we will approach in the paper. We will study them using the method of the artificial. We will build a society of artificial agents, and study what mechanisms may provide answers. This will not prove directly what mechanisms were used for humans, but rather give ideas about what kind of mechanism may have been used. This allows us to shape the search space of possible answers, in particular by showing what is sufficient and what is not necessary. The mechanism we present is based on a low-level model of sensorymotor interactions. We show that the integration of certain very simple and non language-specific neural devices allows a population of agents to build a speech code that has the properties mentioned above. The originality is that it pre-supposes neither a functional pressure for communication, nor the ability to have coordinated social interactions (they do not play language or imitation games). It relies on the self-organizing properties of a generic coupling between perception and production both within agents, and on the interactions between agents.

The speech code is a vehicle of language: it defines a set of forms used by a community to carry information. Such a code is necessary to support the linguistic interactions that allow humans to communicate. How then may a speech code be formed prior to the existence of linguistic interactions? Moreover, the human speech code is discrete and compositional, shared by all the individuals of a community but different across communities, and phoneme inventories are characterized by statistical regularities. How can a speech code with these properties form? We try to approach these questions in the paper, using the `methodology of the artificial'. We build a society of artificial agents, and detail a mechanism that shows the formation of a discrete speech code without pre-supposing the existence of linguistic capacities or of coordinated interactions. The mechanism is based on a low-level model of sensory-motor interactions. We show that the integration of certain very simple and non language-specific neural devices leads to the formation of a speech code that has properties similar to the human speech code. This result relies on the self-organizing properties of a generic coupling between perception and production within agents, and on the interactions between agents. The artificial system helps us to develop better intuitions on how speech might have appeared, by showing how self-organization might have helped natural selection to find speech.

This paper shows how phonological structures can be culturally selected so as to become learnable and adapted to the ecological niche formed by the brains and bodies of speakers. A computational model of the cultural formation of syllable systems illustrates how general learning and physical biases can influence the evolution of the structure of vocalization systems. We use the artificial life methodology of building a society of artificial agents, equipped with motor, perceptual and cognitive systems that are generic and have a realistic complexity. We demonstrate that agents, playing the ''imitation game,'' build shared syllable systems and show how these syllable systems relate to existing human syllable systems. Detailed experiments study the learnability of the self-organized syllable systems. In particular, we reproduce the critical period effect and the artificial language learning effect without the need for innate biases which specify explicitly in advance the form of possible phonological structures. The ability of children agents to learn syllable systems is explained by the cultural evolutionary history of these syllable systems, which were selected for learnability.

This paper presents algorithms that allow a robot to express its emotions by modulating the intonation of its voice. They are very simple and efficiently provide life-like speech thanks to the use of concatenative speech synthesis. We describe a technique which allows to continuously control both the age of a synthetic voice and the quantity of emotions that are expressed. Also, we present the first large-scale data mining experiment about the automatic recognition of basic emotions in informal everyday short utterances. We focus on the speaker-dependent problem. We compare a large set of machine learning algorithms, ranging from neural networks, Support Vector Machines or decision trees, together with 200 features, using a large database of several thousands examples. We show that the difference of performance among learning schemes can be substantial, and that some features which were previously unexplored are of crucial importance. An optimal feature set is derived through the use of a genetic algorithm. Finally, we explain how this study can be applied to real world situations in which very few examples are available. Furthermore, we describe a game to play with a personal robot which facilitates teaching of examples of emotional utterances in a natural and rather unconstrained manner.

A unified connectionist model of the perceptual magnet effect (the perceptual warping of vowels) is proposed, and relies on the concept of population coding in neural maps. Unlike what has been often stated, we claim that the imprecision of the classical sum of vectors coding/decoding scheme is not a drawback and can account for psychological observations. Furthermore, we show that coupling these neural maps allows the formation of vowel systems, which are shared symbolic systems, from initially continuous and uniform perception and production. This has important consequences for existing theories of phonetics.

Many models, computational or not, exist that describe the acquisition of speech: they all rely on the pre-existence of some sort of linguistic structure in the input, i.e. speech itself. Very few address the question of how this coherence and structure appeared. We try here to give a solution concerning syllable systems. We propose an operational model that shows how a society of robotic of agents, endowed with a set of non-linguistically specific motor, perceptual, cognitive and social constraints (some of them are obstacles whereas others are opportunities), can collectively build a coherent and structured syllable system from scratch. As opposed to many existing abstract models of the origins of language, as few shortcuts as possible were taken in the way the constraints are implemented. The structural properties of the produced sound systems are extensively studied under the light of phonetics and phonology and more broadly language theory. The model brings more plausibility in favor of theories of language that defend the idea that there needs no innate linguistic specific abilities to explain observed regularities in world languages.

This paper presents a model of the origins of syllable systems that brings plausibility to the theory which claims that language learning, and in particular phonological acquisition, needs not innate linguistically specific information, as believed by many researchers of the Chomskyan school, but is rather made possible by the interaction between general motor, perceptual, cognitive and social constraints through a self-organizing process. The strategy is to replace the question of acquisition in a larger and evolutionary (cultural) framework: the model addresses the question of the origins of syllable systems (syllables are the major phonological units in speech). It is based on the artificial life methodology of building a society of agents, endowed with motor, perceptual and cognitive apparati that are generic and realistic. We show that agents effectively build sound systems and how these sound systems relate to existing human sound systems. Results concerning the learnability of the produced sound systems by fresh/baby agents are detailed: the critical period effect and the artificial language effect can effectively be predicted by our model. The ability of children to learn sound systems is explained by the evolutionary history of these sound systems, which were precisely shaped so as to fit the ecological niche formed by the brains and bodies of these children, and not the other way around (as advocated by Chomskyan approaches to language).

A computational model of the origins of syllables systems is presented : a society of robotic agents endowed with realistic motor, perceptual and cognitive apparati is shown to build from scratch shared syllable systems in a decentralized manner. Furthermo re, these systems share many structural properties with those of human languages.

The naming game is a formal mechanism that describes the development of a lexicon in a society of culturally interacting agents. We will here use a cellular automaton version of this game to study the influence of an extra-linguistic structure over the evolution of the lexicon ...