Language Evolution and Computation Bibliography

Can a child who is not exposed to a model for language nevertheless construct a communication system characterized by combinatorial structure? We know that deaf children whose hearing losses prevent them from acquiring spoken language, and whose hearing parents have not exposed them to sign language, use gestures, called homesigns, to communicate. In this study, we call upon a new formal analysis that characterizes the statistical profile of grammatical rules and, when applied to child language data, finds that young children's language is consistent with a productive grammar rather than rote memorization of specific word combinations in caregiver speech. We apply this formal analysis to homesign, and find that homesign can also be characterized as having productive grammar. Our findings thus provide evidence that a child can create a combinatorial linguistic system without external linguistic input, and offer unique insight into how the capacity of language evolved as part of human biology.

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.

A hard look in the mirror, as they say, is good for fitness and vitality. The time seems ripe, then, fifty years after the birth of modern linguistics, to reexamine its foundations. Or rather, the rubble, as the editors of Probabilistic linguistics suggest: corpus statistics, Markov ...

Recent demonstrations of statistical learning in infants have reinvigorated the innateness versus learning debate in language acquisition. This article addresses these issues from both computational and developmental perspectives. First, I argue that statistical learning using transitional probabilities cannot reliably segment words when scaled to a realistic setting (e.g. childdirected English). To be successful, it must be constrained by knowledge of phonological structure. Then, turning to the bona fide theory of innateness - the Principles and Parameters framework - I argue that a full explanation of children's grammar development must abandon the domain-specific learning model of triggering, in favor of probabilistic learning mechanisms that might be domain-general but nevertheless operate in the domain-specific space of syntactic parameters.

It is a simple observation that children make mistakes when they learn a language. Yet, to the trained eye, these mistakes are far from random; in fact, they closely resemble perfectly grammatical utterances by adults--who speak other languages. This type of error analysis suggests a novel view of language learning: children are born with a fixed set of hypotheses about language--Chomsky's Universal Grammar--and these hypotheses compete to match the child's ambient language in a Darwinian fashion. The book presents evidence for this perspective from the study of children's words and grammar, and how language changes over time.

If every productive form of linguistic expression can be described by some idealized human grammar, an individuals's variable linguistic behavior (Weinreich, Labov, & Herzog, 1968) can be modeled as a statistical distribution of multiple idealized grammars. The distribution of grammars is determined by the interaction between the biological constraints on human grammar and the properties of linguistic data in the environment during the course of language acquisition. Such interaction can be formalized precisely and quantitatively in a mathematical model of language learning. Consequently, we model language change as the change in grammar distribution over time, which can be related to the statistical properties of historical linguistic data. As an empirical test, we apply the proposed model to explain the loss of the verb-second phenomenon in Old French and Old English based on corpus studies of historical texts.

This paper argues that developmental patterns in child language be taken seriously in computational models of language acquisition, and proposes a formal theory that meets this criterion. We first present developmental facts that are problematic for statistical learning approaches which assume no prior knowledge of grammar, and for traditional learnability models which assume the learner moves from one UG-defined grammar to another. In contrast, we view language acquisition as a population of grammars associated with 'weights', that compete in a Darwinian selectionist process. Selection is made possible by the variational properties of individual grammars; specifically, their differential compatibility with the primary linguistic data in the environment. In addition to a convergence proof, we present empirical evidence in child language development, that a learner is best modeled as multiple grammars in co-existence and competition.