Language Evolution and Computation Bibliography

Human language shows combinatoriality in its phonology (both in speech and in sign language) and its grammar, while both types appear to be absent in the communication systems of our closest evolutionary relatives. In this article, we observe that productive combinatoriality is difficult to evolve, because it requires multiple components to be put in place simultaneously for it to function. To understand how it nevertheless evolved in human language, we focus on combinatoriality in phonology, for which most evidence is available. We discuss findings and theories from three domains: linguistics (descriptive, experimental and corpus linguistics), comparative biology (including some fossil indicators) and (computer) models. We tentatively conclude that many of the biological prerequisites for combinatorial phonology and compositional semantics are shared with other animals, but that a uniquely human pressure for large vocabularies and uniquely human processes of cultural evolution are key in understanding the origins of combinatoriality in language

Abstract A fundamental characteristic of human speech is that it uses a limited set of basic building blocks (phonemes, syllables), that are put to use in many different combinations to mark differences in meaning. This article investigates the evolution of such combinatorial ...

What are the ``design features'' of human language that need to be explained? Starting from R. Jackendoff's scenario for the evolution of language, we argue that it is the transitions between stages that pose the crucial challenges for accounts of the evolution of language. We review a number of formalisms for conceptualizations, sound, and the mapping between them, and describe and evaluate the differences between each of Jackendoff's stages in terms of these formalisms. We conclude from this discussion that the transitions to combinatorial phonology, compositional semantics and hierarchical phrase structure can be formally characterized. Modeling these transitions is a major challenge for language evolution research.

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.

Abstract According to a controversial hypothesis, a characteristic unique to human language is recursion. Contradicting this hypothesis, it has been claimed that the starling, one of the two animal species tested for this ability to date, is able to distinguish acoustic stimuli ...

A fundamental, universal property of human language is that its phonology is combinatorial. That is, one can identify a set of basic, distinct units (phonemes, syllables) that can be productively combined in many different ways. In this paper, we develop a methodological framework based on evolutionary game theory for studying the evolutionary transition from holistic to combinatorial signal systems, and use it to evaluate a number of existing models and theories. We find that in all problematic linguistic assumptions are made or crucial components of evolutionary explanations are omitted. We present a novel model to investigate the hypothesis that combinatorial phonology results from optimizing signal systems for perceptual distinctiveness. Our model differs from previous models in three important respects. First, signals are modeled as trajectories through acoustic space; hence, both holistic and combinatorial signals have a temporal structure. Second, acoustic distinctiveness is defined in terms of the probability of confusion. Third, we show a path of ever increasing fitness from unstructured, holistic signals to structured signals that can be analyzed as combinatorial. On this path, every innovation represents an advantage even if no-one else in a population has yet obtained it.

The origins of human language, with its extraordinarily complex structure and multitude of functions, remains among the most challenging problems for evolutionary biology and the cognitive sciences. Although many will agree progress on this issue would have important consequences for linguistic theory, many remain sceptical about whether the topic is amenable to rigorous, scientific research at all. Complementing recent developments toward better empirical validation, this thesis explores how formal models from both linguistics and evolutionary biology can help to constrain the many theories and scenarios in this field.

I first review a number of foundational mathematical models from three branches of evolutionary biology -- population genetics, evolutionary game theory and social evolution theory -- and discuss the relation between them. This discussion yields a list of ten requirements on evolutionary scenarios for language, and highlights the assumptions implicit in the various formalisms. I then look in more details at one specific step-by-step scenario, proposed by Ray Jackendoff, and consider the linguistic formalisms that could be used to characterise the evolutionary transitions from one stage to the next. I conclude from this review that the main challenges in evolutionary linguistics are to explain how three major linguistic innovations -- combinatorial phonology, compositional semantics and hierarchical phrase-structure -- could have spread through a population where they are initially rare.

In the second part of the thesis, I critically evaluate some existing formal models of each of these major transitions and present three novel alternatives. In an abstract model of the evolution of speech sounds (viewed as trajectories through an acoustic space), I show that combinatorial phonology is a solution for robustness against noise and the only evolutionary stable strategy (ESS). In a model of the evolution of simple lexicons in a noisy environment, I show that the optimal lexicon uses a structured mapping from meanings to sounds, providing a rudimentary compositional semantics. Lexicons with this property are also ESS's. Finally, in a model of the evolution and acquisition of context-free grammars, I evaluate the conditions under which hierarchical phrase-structure will be favoured by natural selection, or will be the outcome of a process of cultural evolution.

In the last chapter of the thesis, I discuss the implications of these models for the debates in linguistics on innateness and learnability, and on the nature of language universals. A mainly negative point to make is that formal learnability results cannot be used as evidence for an innate, language-specific specialisation for language. A positive point is that with the evolutionary models of language, we can begin to under- stand how universal properties and tendencies in natural languages can result from the intricate interaction between innate learning biases and a process of cultural evolution over many generations.

Abstract Language acquisition is a special kind of learning problem because the outcome of learning of one generation is the input for the next. That makes it possible for languages to adapt to the particularities of the learner. In this paper, I show that this type of language ...

Compositionality is one of the fundamental properties of natural language. Explaining its evolution remains a challenging problem because most existing explanations require a structured language to be already present in the population before compositionality can successfully spread in a population. In this paper, I study whether a communication system can evolve that shows the preservation of topology between meaning-space and signal-space, without assuming that individuals have any prior processing mechanism for compositionality. I present a formalism to describe a communication system where there is noise in signaling and variation in the values of meanings. In contrast to previous models, both the noise and values depend on the topology of the signal- and meaning spaces. I study a model of a population of agents that each try to optimize their communicative success under these circumstances. The results show that the preservation of topology between follows naturally from the assumptions on noise, values and individual-based optimization.

Research in language evolution is concerned with the question of how complex linguistic structures can emerge from the interactions between many communicating individuals. Thus it complements psycholinguistics, which investigates the processes involved in individual adult language processing, and child language development studies, which investigate how children learn a given (fixed) language. We focus on the framework of language games and argue that they offer a fresh and formal perspective on many current debates in cognitive science, including those on the synchronic-versus-diachronic perspective on language, the embodiment and situatedness of language and cognition, and the self-organization of linguistic patterns. We present a measure for the quality of a lexicon in a population, and derive four characteristics of the optimal lexicon: specificity, coherence, distinctiveness, and regularity. We present a model of lexical dynamics that shows the spontaneous emergence of these characteristics in a distributed population of individuals that incorporate embodiment constraints. Finally, we discuss how research in cognitive science could contribute to improving existing language game models.

Human, syntactic language is one of the most intriguing behaviors and receives increasing attention from researchers in numerous fields. Here we present a model that goes an important step further than previous work because it explicitly connects low-level perception and categorization, hierarchical meaning construction and syntactic language. The model thus shows a solution to the `symbol grounding problem' (Harnad, 1990): the meaning of the symbolic system - logical symbols and syntactic rules - is grounded in its relation with a simplified but realistic world. We discuss the different components of this collaborative effort: (i) a realistic simulation of Newtonian dynamics of objects in a 2D plane; (ii) schemabased event-perception and categorization; (iii) a semantics based on predicate logic; and (iv) a categorial grammar for the production and interpretation of language. The integration of the different components poses on the one hand novel and important constraints; on the other hand, it allows for experiments that help to identify the relations between the different levels. We note some important similarities and differences with SHRDLU (Winograd, 1976) and the Talking Heads experiment (Steels et al., 2002), and give an agenda for future experiments.

In this paper we explore the similarities between a mathematical model of language evolution and several A-life simulations. We argue that the mathematical model makes some problematic simplifications, but that a combination with computational models can help to adapt and extend existing language evolution scenario's.