The Categorisation Game or Naming Game looks at how agents in a population converge on a shared system for referring to continuous stimuli (Steels, 2005; Nowak & Krakauer, 1999). Agents play games with each other, one referring to an object with a word and the other trying to guess what object the first agent was referring to. Through experience with the world and feedback from other agents, agents update their words. Eventually, agents are able to communicate effectively. The model is usually couched in terms of agents trying to agree on labels for colours (a continuous meaning space). In this post I’ll show that the algorithms used have implicit mutual exclusivity biases, which favour monolingual viewpoints. I’ll also show that this bias is not necessary and obscures some interesting insights into evolutionary dynamics of langauge.
The way children learn language sets the adaptive landscape on which languages evolve. This is acknowledged by many, but there are few connections between models of language acquisition and models of language Evolution (some exceptions include Yang (2002), Yu & Smith (2007) and Chater & Christiansen (2009)).
However, the chasm between the two fields may be getting smaller, as theories are defined as models which are both more interpretable to the more technically-minded Language Evolutionists and extendible into populations and generations.
Also, strangely, models of word learning have been getting simpler over time. This may reflect a move from attributing language acquisition to specific mechanisms towards a more general cognitive explanation. I review some older models here, and a recent publication by Fazly et al.