Language Evolution and Computation Bibliography

The evolution of the faculty of language largely remains an enigma. In this essay, we ask why. Language's evolutionary analysis is complicated because it has no equivalent in any nonhuman species. There is also no consensus regarding the essential nature of the language "phenotype." According to the "Strong Minimalist Thesis," the key distinguishing feature of language (and what evolutionary theory must explain) is hierarchical syntactic structure. The faculty of language is likely to have emerged quite recently in evolutionary terms, some 70,000-100,000 years ago, and does not seem to have undergone modification since then, though individual languages do of course change over time, operating within this basic framework. The recent emergence of language and its stability are both consistent with the Strong Minimalist Thesis, which has at its core a single repeatable operation that takes exactly two syntactic elements a and b and assembles them to form the set {a, b}.

Understanding the evolution of language requires evidence regarding origins and processes that led to change. In the last 40 years, there has been an explosion of research on this problem as well as a sense that considerable progress has been made. We argue instead that the richness of ideas is accompanied by a poverty of evidence, with essentially no explanation of how and why our linguistic computations and representations evolved. We show that, to date, (1) studies of nonhuman animals provide virtually no relevant parallels to human linguistic communication, and none to the underlying biological capacity; (2) the fossil and archaeological evidence does not inform our understanding of the computations and representations of our earliest ancestors, leaving details of origins and selective pressure unresolved; (3) our understanding of the genetics of language is so impoverished that there is little hope of connecting genes to linguistic processes any time soon; (4) all modeling attempts have made unfounded assumptions, and have provided no empirical tests, thus leaving any insights into language's origins unverifiable. Based on the current state of evidence, we submit that the most fundamental questions about the origins and evolution of our linguistic capacity remain as mysterious as ever, with considerable uncertainty about the discovery of either relevant or conclusive evidence that can adjudicate among the many open hypotheses. We conclude by presenting some suggestions about possible paths forward.

Language serves as a cornerstone for human cognition, yet much about its evolution remains puzzling. Recent research on this question parallels Darwin's attempt to explain both the unity of all species and their diversity. What has emerged from this research is that the unified nature of human language arises from a shared, species-specific computational ability. This ability has identifiable correlates in the brain and has remained fixed since the origin of language approximately 100 thousand years ago. Although songbirds share with humans a vocal imitation learning ability, with a similar underlying neural organization, language is uniquely human.

We propose a novel account for the emergence of human language syntax. Like many evolutionary innovations, language arose from the adventitious combination of two pre-existing, simpler systems that had been evolved for other functional tasks. The first system, Type E(xpression), is found in birdsong, where the same song marks territory, mating availability, and similar “expressive” functions. The second system, Type L(exical), has been suggestively found in non-human primate calls and in honeybee waggle dances, where it demarcates predicates with one or more “arguments,” such as combinations of calls in monkeys or compass headings set to sun position in honeybees. We show that human language syntax is composed of two layers that parallel these two independently evolved systems: an “E” layer resembling the Type E system of birdsong and an “L” layer providing words. The existence of the “E” and “L” layers can be confirmed using standard linguistic methodology. Each layer, E and L, when considered separately, is characterizable as a finite state system, as observed in several non-human species. When the two systems are put together they interact, yielding the unbounded, non-finite state, hierarchical structure that serves as the hallmark of full-fledged human language syntax. In this way, we account for the appearance of a novel function, language, within a conventional Darwinian framework, along with its apparently unique emergence in a single species.

Language serves as a cornerstone for human cognition, yet much about its evolution remains puzzling. Recent research on this question parallels Darwin's attempt to explain both the unity of all species and their diversity. What has emerged from this research is that the unified nature of human language arises from a shared, species-specific computational ability. This ability has identifiable correlates in the brain and has remained fixed since the origin of language approximately 100 thousand years ago. Although songbirds share with humans a vocal imitation learning ability, with a similar underlying neural organization, language is uniquely human.

Among the many puzzling questions about language, two are salient: First, why are there any languages at all, evidently unique to the human lineage. Second, why are there so many languages? These are in fact the basic questions of origin and variation that so occupied Darwin and other evolutionary thinkers and comprise modern biologys explanatory core: why do we observe this particular array of living forms in the world and not others -- the key problem of reconciling the underlying unity of organisms with their apparent diversity, invariance and variation. Here we examine these two questions from the viewpoint of modern linguistics, biology, and dynamical system theory.

Language acquisition maps linguistic experience, primary linguistic data (PLD), onto linguistic knowledge, a grammar. Classically, computational models of language acquisition assume a single target grammar and one PLD source, the central question being whether the target grammar can be acquired from the PLD. However, real-world learners confront populations with variation, i.e., multiple target grammars and PLDs. Removing this idealization has inspired a new class of population-based language acquisition models. This paper contrasts 2 such models. In the first, iterated learning (IL), each learner receives PLD from one target grammar but different learners can have different targets. In the second, social learning (SL), each learner receives PLD from possibly multiple targets, e. g., from 2 parents. We demonstrate that these 2 models have radically different evolutionary consequences. The IL model is dynamically deficient in 2 key respects. First, the IL model admits only linear dynamics and so cannot describe phase transitions, attested rapid changes in languages over time. Second, the IL model cannot properly describe the stability of languages over time. In contrast, the SL model leads to nonlinear dynamics, bifurcations, and possibly multiple equilibria and so suffices to model both the case of stable language populations, mixtures of more than 1 language, as well as rapid language change. The 2 models also make distinct, empirically testable predictions about language change. Using historical data, we show that the SL model more faithfully replicates the dynamics of the evolution of Middle English.

In this article we present new results of a novel computational approach to the interaction of two important cognitive-linguistic phenomena: (1) language learning; and (2) language change over time (diachronic linguistics). We exploit the insight that while language learning takes place at the individual level, language change is more properly regarded as an ensemble property that takes place at the level of populations of language learners. We show by analytical and computer simulation methods that language learning can be regarded as the driving force behind a dynamical systems account of language change. We apply this model to the specific case of historical change from Classical Portuguese to European Portuguese, demonstrating how a particular language learning model coupled with data on the differences between Classical and European Portuguese leads to specific predictions for possible language-change envelopes. The main investigative message of this paper is to show how this methodology can be applied to a specific case, that of Portuguese. The main moral underscores the individual/population difference; we show that simply because an individual will choose a particular grammar does not mean that all other grammars will be eliminated.

Linguists' intuitions about language change can be captured by a dynamical systems model derived from the dynamics of language acquisition. Rather than having to posit a separate model for diachronic change, as has sometimes been done by drawing on assumptions from population biology (cf. Cavalli-Sforza and Feldman, 1973; 1981; Kroch, 1990), this new model dispenses with these independent assumptions by showing how the behavior of individual language learners leads to emergent, global population characteristics of linguistic communities over several generations. As the simplest case, we formalize the example of two grammars and show that even this situation leads directly to a nonlinear (quadratic) dynamical system. We study this one parameter model in a variety of situations for different kinds of acquisition algorithms and maturational times, showing how different learning theories can have very different evolutionary consequences. This allows us to formulate an evolutionary criterion for the adequacy of grammatical and learning theories. An application of the computational model to the historical loss of Verb Second from Old French to Modern French is described showing how otherwise adequate grammatical theories might fail the evolutionary criterion.

This paper shows how to formally characterize language learning in a finite parameter space, for instance, in the principles-and-parameters approach to language, as a Markov structure. New language learning results follow directly; we can explicitly calculate how many positive examples on average (``sample complexity'') it will take for a learner to correctly identify a target language with high probability. We show how sample complexity varies with input distributions and learning regimes. In particular we find that the average time to converge under reasonable language input distributions for a simple three-parameter system first described by Gibson and Wexler (1994) is psychologically plausible, in the range of 100-150 positive examples. We further find that a simple random step algorithm - that is, simply jumping from one language hypothesis to another rather than changing one parameter at a time - works faster and always converges to the right target language, in contrast to the single-step, local parameter setting method advocated in some recent work.