Language Evolution and Computation Bibliography

Cognitive Robotics can be defined as the study of cognitive phenomena by their modeling in physical artifacts such as robots. This is a very lively and fascinating field which has already given fundamental contributions to our understanding of natural cognition. Nonetheless, robotics has to date addressed mainly very basic, low-level cognitive phenomena like sensory-motor coordination, perception, and navigation, and it is not clear how the current approach might scale up to explain high-level human cognition. In this paper we argue that a promising way to do that is to merge current ideas and methods of embodied cognition with the Russian tradition of theoretical psychology which views language not only as a communication system but also as a cognitive tool, that is by developing a Vygotskyan cognitive robotics. We substantiate this idea by discussing several domains in which language can improve basic cognitive abilities and permit the development of high-level cognition: learning, categorization, abstraction, memory, voluntary control, and mental life.

In this chapter we introduce the area of research that attempts to study the evolution of communication in embodied agents through adaptive techniques, such us artificial evolution. More specifically, we illustrate the theoretical assumptions behind this type of research, we present the methods that can be used to realize embodied and communicating artificial agents, and we discuss the main research challenges and the criteria for evaluating progresses in this field.

The evolution of communication requires the co-evolution of two abilities: the ability to send useful signals and the ability to react appropriately to perceived signals. This fact poses two related but distinct problems, which are often mixed up: (1) the phylogenetic problem regarding how can communication evolve if the two traits that are necessary for its emergence are complementary and seem to require each other for providing reproductive advantages; (2) the adaptive problem regarding how can communication systems that do not advantage both signallers and receivers in the same way emerge, given their altruistic character. Here we clarify the distinction, and provide some insights on how these problems can be solved in both real and artificial systems by reporting experiments on the evolution of artificial agents that have to evolve a simple food-call communication system. Our experiments show that (1) the phylogenetic problem can be solved thanks to the presence of producer biases that make agents spontaneously produce useful signals, an idea that is complementary to the well-known areceiver biasa hypothesis found in the biological literature, and (2) the adaptive problem can be solved by having agents communicate preferentially among kin, as predicted by kin selection theory. We discuss these results with respect to both the scientific understanding of the evolution of communication and the design of embodied and communicating artificial agents.

This field of research examines how embodied and situated agents, such as robots, evolve language and thus communicate with each other. This book is a comprehensive survey of the research in this emerging field.

The contributions explain the theoretical and methodological foundations of the field, and then illustrate the scientific and technological potentials and promising research directions. The book also provides descriptions of research experiments and related open software and hardware tools, allowing the reader to gain a practical knowledge of the topic.

The book will be of interest to scientists and undergraduate and graduate students in the areas of cognition, artificial life, artificial intelligence and linguistics.

In this chapter we summarize the progresses that have recently been made in the study of the emergence of communication in artificial embodied agents along different dimensions, including the understanding of the adaptive roles of communication, the expressive power and organization complexity of signalling systems, the stability, robustness, and evolvability of communication, and the knowledge gain obtained with such models. Finally, we briefly discuss what we think are the most important open challenges for future research in this area.

The standard view of classical cognitive science stated that cognition consists in the manipulation of language-like structures according to formal rules. Since cognition is linguistic in itself, according to this view language is just a complex communication system and does not influence cognitive processes in any substantial way. This view has been criticized from several perspectives and a new framework (Embodied Cognition) has emerged that considers cognitive processes as non-symbolic and heavily dependent on the dynamical interactions between the cognitive system and its environment. But notwithstanding the successes of the embodied cognitive science in explaining low-level cognitive behaviors, it is still not clear whether and how it can scale up for explaining high-level cognition. In this paper we argue that this can be done by considering the role of language as a cognitive tool: i.e. how language transforms basic cognitive functions in the high-level functions that are characteristic of human cognition. In order to do that, we review some computational models that substantiate this view with respect to categorization and memory. Since these models are based on a very rudimentary form of non-syntactic language we argue that the use of language as a cognitive tool might have been an early discovery in hominid evolution, and might have played a substantial role in the evolution of language itself.

Like any other biological trait, communication can be studied from at least four perspectives: mechanistic, ontogenetic, functional, and phylogenetic. In this article, we focus on the following phylogenetic question: how can communication emerge, given that both signal-producing and signal-responding abilities seem to be adaptively neutral until the complementary ability is present in the population? We explore the problem of co-evolution of speakers and hearers with artificial life simulations: a population of artificial neural networks evolving a food call system. The core of the article is devoted to a careful analysis of the complex evolutionary dynamics demonstrated by our simple simulation. Our analyses reveal an important factor, which might solve the phylogenetic problem: the spontaneous production of good (meaningful) signals by speakers because of the need for organisms to categorize their experience in adaptively relevant ways. We discuss our results with respect both to previous simulative work and to the biological literature on the evolution of communication.

Selective pressures for the evolutionary emergence of human language tend to be interpreted as social in nature, i.e., for better social communication and coordination. Using a simple neural network model of language acquisition we demonstrate that even using language for oneself, i.e., as private or inner speech, improves an individual's categorization of the world and, therefore, makes the individual's behavior more adaptive. We conclude that language may have first emerged due to the advantages it confers on individual cognition, and not only for its social advantages.

The emergence of language in populations of primates that initially lacked language can be simulated with artificial organisms controlled by neural networks and living, evolving, and learning in artificial environment. Some simulations have already been done but most are a task for the future. We dis-cuss language evolution under two topics: language is learned from others on the basis of genetically inherited predispositions, and language has important influences on human cognition. We propose an evolutionary sequence accord-ing to which bipedalism and the emergence of the hands represent a selective pressure for developing an ability to predict the consequences of one's actions, this ability is the basis for learning by imitating other individuals, learning by imitating other individuals is applied to learning to imitate their communicative behaviour. The second topic include the consequences of language for various aspects of human cognition, especially when language is used to talk to oneself.

In this paper, we explore various adaptive factors that can influence the emergence of a communication system that benefits the receiver of signals (the hearer) but not the emitter (the speaker). Using computer simulations of a population of interacting agents whose behaviour is determined by a neural network, we show that a stable communication system does not emerge in groups of unrelated individuals because of its altruistic character. None the less, another set of simulations shows that the emergence of a language that confers an advantage only to hearers, not to speakers, is possible under at least three conditions: (1) if the hearer and the speaker tend to share the same genes, as predicted by kin selection theory; (2) if the population is `docile' and the communication system is culturally transmitted together with other adaptive behaviours, as predicted by Simon's docility theory; and (3) if the linguistic system is used not only for social communication, but also for talking to oneself, in particular as an aid to memory.

The paper describes a neural network model of early language acquisition with an emphasis on how language positively influences the categories with which the child categorizes reality. Language begins when the two separate networks that are responsible for nonlinguistic sensory-motor mappings and for recognizing and repeating linguistic sounds become connected together at 1 year of age. Language makes more similar the internal representations of different inputs that must be responded to with the same action and more different the internal representations of inputs that must be responded to with different actions.

Human language serves a number of different functions, one of the most prominent being communicating about relevant features of the environment. From the point of view of the speaker, if the communicated information is advantageous for the hearer but not for the speaker, this is an altruistic use of language, and, as such, it requires an explanation of its evolution. Simon 1990 proposed an explanation of altruism in humans based on the genetically inherited docility of our species. In this paper we present artificial life simulations that apply Simon's ideas to the problem of the emergence of the altruistic use of language described above. From the point of view of evolutionary theory, the present work represents the first attempt to test Simon's ?docility? theory of altruism with agent-based computer simulations. From the point of view of language evolution, our simulations give an original explanation of (the altruistic aspect of) human language based on one of its most peculiar characteristic, namely, the fact that it is culturally transmitted.