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The article analyzes the neural and functional grounding of language skills as well as their emergence in hominid evolution, hypothesizing stages leading from abilities known to exist in monkeys and apes and presumed to exist in our hominid ancestors right through to modern spoken and signed languages. The starting point is the observation that both premotor area F5 in monkeys and Broca's area in humans contain a "mirror system" active for both execution and observation of manual actions, and that F5 and Broca's area are homologous brain regions. This grounded the mirror system hypothesis of Rizzolatti and Arbib (1998) which offers the mirror system for grasping as a key neural "missing link" between the abilities of our nonhuman ancestors of 20 million years ago and modern human language, with manual gestures rather than a system for vocal communication providing the initial seed for this evolutionary process. The present article, however, goes "beyond the mirror" to offer hypotheses on evolutionary changes within and outside the mirror systems which may have occurred to equip Homo sapiens with a language-ready brain. Crucial to the early stages of this progression is the mirror system for grasping and its extension to permit imitation. Imitation is seen as evolving via a so-called simple system such as that found in chimpanzees (which allows imitation of complex "object-oriented" sequences but only as the result of extensive practice) to a so-called complex system found in humans (which allows rapid imitation even of complex sequences, under appropriate conditions) which supports pantomime. This is hypothesized to have provided the substrate for the development of protosign, a combinatorially open repertoire of manual gestures, which then provides the scaffolding for the emergence of protospeech (which thus owes little to nonhuman vocalizations), with protosign and protospeech then developing in an expanding spiral. It is argued that these stages involve biological evolution of both brain and body. By contrast, it is argued that the progression from protosign and protospeech to languages with full-blown syntax and compositional semantics was a historical phenomenon in the development of Homo sapiens, involving few if any further biological changes.

Psycholinguistic research has shown that the influence of abstract syntactic knowledge on performance is shaped by particular sentences that have been experienced. To explore this idea, the authors applied a connectionist model of sentence production to the development and use of abstract syntax. The model makes use of (a) error-based learning to acquire and adapt sequencing mechanisms and (b) meaning–form mappings to derive syntactic representations. The model is able to account for most of what is known about structural priming in adult speakers, as well as key findings in preferential looking and elicited production studies of language acquisition. The model suggests how abstract knowledge and concrete experience are balanced in the development and use of syntax.

Abstract. One of the key properties of (natural) languages is that they are hierarchical. Phrases combine into larger phrases eventually covering complete sentences. The semantics of each phrase combine to form the complex meaning of the whole. A key question in explaining the origins and evolution of language is therefore how such hierarchical structures may originate and spread in a population. This paper reports on breakthrough experiments in which unbounded hierarchical structure can emerge as a side-effect of repair strategies in a population of agents. 1

Research into the evolution of grammar requires that we employ formalisms and processing mechanisms that are powerful enough to handle features found in human natural languages. But the formalism needs to have some additional properties compared to those used in other linguistics research that are specifically relevant for handling the emergence and progressive co-ordination of grammars in a population of agents. This document introduces Fluid Construction Grammar, a formalism with associated parsing, production, and learning processes designed for language evolution research. The present paper focuses on a formal definition of the unification and merging algorithms used in Fluid Construction Grammar. The complexity and soundness of the algorithms and their relation to unification in logic programming and other unification-based grammar formalisms are discussed.

We describe a pattern acquisition algorithm that learns, in an unsupervised fashion, a streamlined representation of linguistic structures from a plain natural-language corpus. This paper addresses the issues of learning structured knowledge from a large-scale natural language data set, and of generalization to unseen text. The implemented algorithm represents sentences as paths on a graph whose vertices are words (or parts of words). Significant patterns, determined by recursive context-sensitive statistical inference, form new vertices. Linguistic constructions are represented by trees composed of significant patterns and their associated equivalence classes. An input module allows the algorithm to be subjected to a standard test of English as a Second Language (ESL) proficiency. The results

in parsing

We describe a linguistic pattern acquisition algorithm that learns, in an unsupervised fashion, a streamlined representation of corpus data. This is achieved by compactly coding recursively structured constituent patterns, and by placing strings that have an identical backbone and similar context structure into the same equivalence class. The resulting representations constitute an efficient encoding of linguistic knowledge and support systematic generalization to unseen sentences.

The recruitment theory of language origins argues that language users recruit and try out different strategies for solving the task of communication and retain those that maximise communicative success and cognitive economy. Each strategy requires specific cognitive neural mechanisms, which in themselves serve a wide range of purposes and therefore may have evolved or could be learned independently of language. The application of a strategy has an impact on the properties of the emergent language and this fixates the use of the strategy in the population. Although neurological evidence can be used to show that certain cognitive neural mechanisms are common to linguistic and non-linguistic tasks, this only shows that recruitment has happened, not why. To show the latter, we need models demonstrating that the recruitment of a particular strategy and hence the mechanisms to carry out this strategy lead to a better communication system. This paper gives concrete examples how such models can be built and shows the kinds of results that can be expected from them.

If first language is rational in the sense that acquisition produces an end-state model of language that is a proper reflection of input and that optimally prepares speakers for comprehension and production, second language is usually not. This paper considers the apparent irrationalities of L2 acquisition, that is the shortcomings where input fails to become intake. It describes how ‘learned attention’, a key concept in contemporary associative and connectionist theories of animal and human learning, explains these effects. The fragile features of L2 acquisition are those which, however available as a result of frequency, recency, or context, fall short of intake because of one of the factors of contingency, cue competition, salience, interference, overshadowing, blocking, or perceptual learning, which are all shaped by the L1. Each phenomenon is explained within associative learning theory and exemplified in language learning. Paradoxically, the successes of L1 acquisition and the limitations of L2 acquisition both derive from the same basic learning principles.