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Abstract

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.

@article{dowman02modeling,
  author={Mike Dowman},
  title={Modeling Language as a Product of Learning and Social Interactions},
  journal={Cognitive Systems},
  year={2003},
  volume={6},
  number={1},
  url={http://www.isrl.uiuc.edu/~amag/langev/paper/dowman02modeling.html}
}

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