Language Evolution and Computation Bibliography

The complexity, variation, and change of languages make evident the importance of representation and learning in the acquisition and evolution of language. For example, analytic studies of simple language in unstructured populations have shown complex dynamics, depending on the fidelity of language transmission. In this study we extend these analysis of evolutionary dynamics to include grammars inspired by the principles and parameters paradigm. In particular, the space of languages is structured so that some pairs of languages are more similar than others, and mutations tend to change languages to nearby variants. We found that coherence emerges with lower learning fidelity than predicted by earlier work with an unstructured language space.

This paper describes a framework for studies of the adaptive acquisition and evolution of language, with the following components: language learning begins by associating words with cognitively salient representations (``grounding''); the sentences of each language are determined by properties of lexical items, and so only these need to be transmitted by learning; the learnable languages allow multiple agreements, multiple crossing agreements, and reduplication, as mildly context sensitive and human languages do; infinitely many different languages are learnable; many of the learnable languages include infinitely many sentences; in each language, inferential processes can be defined over succinct representations of the derivations themselves; the languages can be extended by innovative responses to communicative demands. Preliminary analytic results and a robotic implementation are described.