Modeling language contact with the Iterated Learning Model
This addresses the problem of understanding language stability in contact scenarios for linguists, but it is incremental as it applies an existing model type to a new context.
The study used an iterated learning model to investigate why languages resist change during contact, finding that the same dynamics that make languages expressive and compositional also help them maintain core traits after mixing with another language.
Contact between languages has the potential to transmit vocabulary and other language features; however, this does not always happen. Here, an iterated learning model is used to examine, in a simple way, the resistance of languages to change during language contact. Iterated learning models are agent-based models of language change, they demonstrate that languages that are expressive and compositional arise spontaneously as a consequence of a language transmission bottleneck. A recently introduced type of iterated learning model, the Semi-Supervised ILM is used to simulate language contact. These simulations do not include many of the complex factors involved in language contact and do not model a population of speakers; nonetheless the model demonstrates that the dynamics which lead languages in the model to spontaneously become expressive and compositional, also cause a language to maintain its core traits even after mixing with another language.