Language Evolution and Computation Bibliography

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Proceedings :: Proceedings of the 27th Annual Conference of the Cognitive Science Society
2005
Does Language Shape the Way We Conceptualize the World?PDF
Proceedings of the 27th Annual Conference of the Cognitive Science Society, 2005
In this paper it is argued that the way the world is conceptualized for language is language dependent and the result of negotiation between language users. This is investigated in a computer experiment in which a population of artificial agents constructs a shared language to ...MORE ⇓
In this paper it is argued that the way the world is conceptualized for language is language dependent and the result of negotiation between language users. This is investigated in a computer experiment in which a population of artificial agents constructs a shared language to talk about a world that can be conceptualized in multiple and possibly conflicting ways. It is argued that the establishment of a successful communication system requires that feedback about the communicative success is propagated to the ontological level, and thus that language shapes the way we conceptualize the world for communication.
A Bayesian view of language evolution by iterated learningPDF
Proceedings of the 27th Annual Conference of the Cognitive Science Society, 2005
Models of language evolution have demonstrated how aspects of human language, such as compositionality, can arise in populations of interacting agents. This paper analyzes how languages change as the result of a particular form of interaction: agents learning from one another. We ...MORE ⇓
Models of language evolution have demonstrated how aspects of human language, such as compositionality, can arise in populations of interacting agents. This paper analyzes how languages change as the result of a particular form of interaction: agents learning from one another. We show that, when the learners are rational Bayesian agents, this process of iterated learning converges to the prior distribution over languages assumed by those learners. The rate of convergence is set by the amount of information conveyed by the data seen by each generation; the less informative the data, the faster the process converges to the prior.