Language Evolution and Computation Bibliography

While most work on the evolution of language has been centered on the evolution of syntax, my focus in this paper is instead on more basic features that separate human communication from the systems of communication used by other animals. In particular, I argue that human language is the only existing system of learned arbitrary reference. While innate communication systems are, by definition, directly transmitted genetically, the transmission of a learned systems must be indirect. Learners must acquire the system by being exposed its the use in the community. Although it is reasonable that a learner has access to the utterances that are produced, it is less clear how accessible the meaning is that the utterance is intended to convey. This particularly problematic if the system of communication is symbolic -- where form and meaning are linked in a purely conventional way. Given this, I propose that the ability to transmit a learned symbolic system of communication from one generation to the next represents a key milestone in the evolution of language.

Human language is a unique ability. It sits apart from other systems of communication in two striking ways: it is syntactic, and it is learned. While most approaches to the evolution of language have focused on the evolution of syntax, this paper explores the computational issues that arise in shifting from a simple innate communication system to an equally simple one that is learned. Associative network learning within an observational learning paradigm is used to explore the computational difficulties involved in establishing and maintaining a simple learned communication system. Because Hebbian learning is found to be sufficient for this task, it is proposed that the basic computational demands of learning are unlikely to account for the rarity of even simple learned communication systems. Instead, it is the problem of *observing* that is likely to be central -- in particular the problem of determining what meaning a signal is intended to convey.

This dissertation identifies the conditions necessary to establish a system of communication in a population of individuals, whether through evolution or learning. A definition of communication is proposed that encompasses the behavior of species ranging from flowers to human beings, and a formal framework for modeling such behavior is presented. Through the use of computational simulations, it is shown that systems of communication evolve in cases where such behavior conveys a selective advantage to both sender and receiver. It is also demonstrated that factors such as kin selection and reciprocal altruism can result in the establishment of communication even when there is no direct pressure on the transmission of signals. In the case of learned communication, it is argued that observational learning is the appropriate learning model. Learning strategies that simply imitate the behavior of others, however, are not suitable. Instead, a learning mechanism must optimize its behavior so as best to communicate with the population it is observing. A Bayesian learning procedure designed to maximize the probability of communicative success is shown to be capable not only of learning an existing communication system, but also constructing such a system from random initial signaling behavior. To examine how animals might actually implement such a procedure, network learning models are considered. It is shown that a simple form of Hebbian learning, well within the grasp of most animals, has the required properties. Given this, it is surprising that learned systems of communication are not more frequent. Evidence from the animal social learning literature suggests that the primary reason for this may be that observational learning is difficult, if not impossible, for non-human animals. Given this, he most basic explanation for why only humans have language may not lie in the ability of learn a complex, syntactic form of communication, but rather in the ability to learn any system of communication at all.

If the members of a population of animals are to enjoy the benefit that might accrue from the exchange of information, their communicative behavior must be coordinated -- most of the time that an animal sends a signal in some type of situation, others respond to the signal in a manner appropriate to the situation that inspired it. We investigate how coordinated communication could emerge among animals capable of producing and responding to simple signals, and how such coordination could be maintained, when new members of a population learn to communicate by observing the other members. We describe a learning procedure that enables an individual to achieve the maximum possible accuracy in communicating with a given population. If all new members of the population use this procedure, or one of the approximations to it we describe, the coordination of the population's communication will steadily increase, ultimately yielding a highly coordinated system. Our results are derived mathematically from a formal model of simple communication systems. We illustrate these results with computational simulations. and discuss their biological plausibility and their relevance to more complex communication systems, including human language.

A Saussurean communication system exists when an entire communicating population uses a single ``language'' that maps states unambiguously onto symbols and then back into the original states. This paper describes a number of simulations performed with a genetic algorithm to investigate the conditions necessary for such communication systems to evolve. The first simulation shows that Saussurean communication evolves in the simple case where direct selective pressure is placed on individuals to be both good transmitters and good receivers. The second simulation demonstrates that, in the more realistic case where selective pressure is only placed on doing well as a receiver, Saussurean communication fails to evolve. Two methods, inspired by research on the Prisoner's Dilemma, are used to attempt to solve this problem. The third simulation shows that, even in the absence of selective pressure on transmission, Saussurean communication can evolve if individuals interact multiple times with the same communication partner and are given the ability to respond differentially based on past interaction. In the fourth simulation, spatially organized populations are used, and it is shown that this allows Saussurean communication to evolve through kin selection.