Language Evolution and Computation Bibliography

The last quarter of the 20th century saw a surge in research in the evolution of language, and this activity continues to grow and extend its influence in the present century. This article is a personal review of some conclusions that can be deemed to have been established in that period. Many of these modern conclusions had ancient precursors as speculative hypotheses with little empirical backing. Modern empirical research in a range of fields has driven foundations deeper, and careful theoretical work has begun to weave a more consistent network of ideas across disciplines. Many mysteries remain, but some clear outlines of the evolutionary bases of humans? most distinctive capacity have begun to emerge. Often the clearer outlines have revealed more complex problems than was vaguely suspected earlier. Three propositions have been selected here, and each will be briefly discussed in a separate section. The three propositions are: 'Language' is not a single monolithic behaviour; Animals have rich conceptual systems; Primates are not necessarily the closest to human-like capacities.

Computational models were constructed to investigate how the meanings of basic colour terms were learned, and to determine why these words have prototype properties, and why they partition the colour space. A Bayesian model of acquisition was able to learn colo ur term systems with these properties, but could equally well learn colour term systems which did not partition the colour space or have prototype properties, and so it failed to explain the empirical data concerning these words. Computational evolutionary simulations were then conducted by creating a community of artificial people using multiple copies of the Bayesian model. These artificial people then learned colour words from one-another, and colour term systems were allowed to evolve over a number of generations. The emergent colour terms always partitioned the colour space and had prototype properties. These results demonstrate that the Bayesian model is able to account for the properties of colour term systems only when it is placed in a social contex t and so they provide evidence of the importance of understanding language as a product of both psychology and social interaction.