Language Evolution and Computation Bibliography

Linguistic forms are shaped by forces operating on vastly different time scales. Some of these forces operate directly at the moment of speaking, whereas others accumulate over time in personal and social memory. Our challenge is to understand how forces with very different time scales mesh together in the current moment to determine the emergence of linguistic form.

In this paper we present a self-organizing neural network model of early lexical development called DevLex. The network consists of two self-organizing maps (a growing semantic map and a growing phonological map) that are connected via associative links trained by Hebbian learning. The model captures a number of important phenomena that occur in early lexical acquisition by children, as it allows for the representation of a dynamically changing linguistic environment in language learning. In our simulations, DevLex develops topographically organized representations for linguistic categories over time, models lexical confusion as a function of word density and semantic similarity, and shows age-of-acquisition effects in the course of learning a growing lexicon. These results match up with patterns from empirical research on lexical development, and have significant implications for models of language acquisition based on self-organizing neural networks.

Recent work in language acquisition has shown how linguistic form emerges from the operation of self-organizing systems. The emergentist framework emphasizes ways in which the formal structures of language emerge from the interaction of social patterns, patterns implicit in the input, and pressures arising from general aspects of the cognitive system. Emergentist models have been developed to study the acquisition of auditory and articulatory patterns during infancy and the ways in which the learning of the first words emerges from the linkage of auditory, articulatory, and conceptual systems. Neural network models have also been used to study the learning of inflectional markings and basic syntactic patterns. Using both neural network modeling and concepts from the study of dynamic systems, it is possible to analyze language learning as the integration of emergent dynamic systems.