Language Evolution and Computation Bibliography

How does the behavior of individuals in a society influence whether knowledge accumulates over generations? We explore this question using a simple model of cultural evolution as a process of ``iterated learning,'' where each agent in a sequence learns and passes on a piece of information. Using both mathematical analyses involving rational Bayesian agents and laboratory experiments with human participants, we vary whether agents observe data from the environment and what kind of information they receive from the previous agent. Our mathematical and empirical results both suggest that merely observing the behavior of other learners is not sufficient to produce cumulative cultural evolution, but that knowledge can be accumulated over generations when agents are able to communicate the plausibility of different hypotheses.

Imitation alone cannot explain language evolution. Two additional ingredients have been proposed that may help explain the evolution of language systems: learning biases and social collaboration. An experimental method was developed that isolated the roles of collaboration and learning biases in the development of novel communication systems. Participants played a Pictionary-like task to develop ad hoc graphical communication systems in one of two conditions: one in which they interacted with a partner (Interaction condition), and one in which they received the same images from a apseudo-partnera but did not interact (Pseudo-Interaction condition). Comparison of the resultant communication systems showed that the Interaction condition yielded higher identification accuracy, greater refinement of graphical signs and more alignment on a set of shared graphical signs (in fact, graphical alignment did not occur at all in the Pseudo-Interaction condition). Thus, collaboration plays a crucial role in the evolution of human communication systems.

Existing accounts of the origin of human communication assume a pre-existing behavioral system shared among members of a social group. This paper is concerned with the origin of that system; specifically, it explores its characteristics and functionality as well as the circumstances under which it could have appeared. A number of agent-based computer simulations test whether the capacities for arbitrary imitation and pattern completion can lead to a behavioral system that could be co-opted for communication. The results show that arbitrary imitation and pattern completion may indeed generate a population-wide shared behavioral system whose structure reflects the structure of the environment, and therefore could easily have been co-opted for communication. This system may have paved the way for other biological capacities widely believed to be necessary for communication, such as shared intentionality and symbolicity, to co-evolve.

Human languages include vast numbers of learned, arbitrary signal-meaning mappings but also many complex signal-meaning mappings that are systematically related to each other (i.e. not arbitrary). Although arbitrariness and systematicity are clearly related, the development of the two in communication systems has been explored independently. We present an experiment in which participants invent signs from scratch to refer to a set of real concepts that share semantic features. Through interaction, the systematic re-use of arbitrary elements emerges.

Similarities between human languages are often taken as evidence of constraints on language learning. However, such similarities could also be the result of descent from a common ancestor. In the framework of iterated learning, language evolution converges to an equilibrium that is independent of its starting point, with the effect of shared ancestry decaying over time. Therefore, the central question is the rate of this convergence, which we formally analyze here. We show that convergence occurs in a number of generations that is O(n log n) for Bayesian learning of the ranking of n constraints or the values of n binary parameters. We also present simulations confirming this result and indicating how convergence is affected by the entropy of the prior distribution over languages.

We develop a Bayesian Iterated Learning Model (BILM) that models the cultural evolution of language as it is transmitted over generations of learners. We study the outcome of iterated learning in relation to the behavior of individual agents (their biases) and the social structure through which they transmit their behavior. BILM makes individual learning biases explicit and offers a direct comparison of how individual biases relate to the outcome of iterated learning. Most earlier BILMs use simple one parent to one child (monadic) chains of homogeneous learners to study the outcome of iterated learning in terms of bias manipulations. Here, we develop a BILM to study two novel manipulations in social parameters: population size and population heterogeneity, to determine more precisely what the transmission process itself can add to the outcome of iterated learning. Our monadic model replicates the existing BILM results, however our manipulations show that the outcome of iterated learning is sensitive to more factors than are explicitly encoded in the prior. This calls into question the appropriateness of assuming strong Bayesian inference in the iterated learning framework and has important implications for the study of language evolution in general.

Cognitive scientists tend to focus on the behavior of single individuals thinking and perceiving on their own. This is natural because our own introspection provides us with unique insight into this level. However, interacting groups of people also create emergent structures that are not intentionally produced by any individual. People participate in collective behavior patterns that they may not even be able to perceive, let alone understand. Social phenomena such as rumors, linguistic conventions, the emergence of a standard currency, transportation systems, the World Wide Web, resource harvesting, crowding, and scientific establishments arise because of individualsa beliefs and goals, but the eventual form that these phenomena take is rarely the goal of any individual.

In his book Grooming, Gossip and the Evolution of Language, biologist Robin Dunbar (1997) proposes a new way of looking at the evolution of language. According to this view, language evolved to provide a new social bonding mechanism: Gossiping. This allows humans to live in larger groups than other primates, which increasing predation risks forced our ancestors to do. We use a computational multi-agent model to test the internal workings of this hypothesis, with interesting results. Our work provides a fundamentally new kind of evidence for Dunbaras theory, by experimentally demonstrating that greater group sizes can stimulate the evolution of language as a tool for social cohesion.

The past couple of decades have seen an explosion of research on language evolution, initially fueled by Pinker and Bloomas (1990) groundbreaking article arguing for the natural selection of biological structures dedicated to language. The new millennium has seen a shift toward explaining language evolution in terms of cultural evolution rather than biological adaptation. Crucially, this research has many important implications for cognitive science, not only in terms of the nature of the biases to consider in language acquisition but also for cognition, more generally. In this symposium, we therefore take stock of current work on the cultural evolution of language, highlighting key implications of this work for cognitive scientists from different perspectives, ranging from philosophical considerations (Chater) and Bayesian analyses (Griffiths) to evolutionary psycholinguistics (Kirby) and molecular genetics (Christiansen).