Achieving compositional language in a population of iterated learners

Lewys Brace, Seth Bullock, Jason Noble

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

4 Citations (Scopus)


Iterated learning takes place when the input into a particular individual's learning process is itself the output of another individual's learning process. This is an important feature to capture when investigating human language change, or the dynamics of culturally learned behaviours in general. Over the last fifteen years, the Iterated Learning Model (ILM) has been used to shed light on how the population-level characteristics of learned communication arise. However, until now each iteration of the model has tended to feature a single immature language user learning from their interactions with a single mature language user. Here, the ILM is extended to include a population of immature and mature language users. We demonstrate that the structure and make-up of this population influences the dynamics of language change that occur over generational time. In particular, we show that, by increasing the number of trainers from which an agent learns, the agent in question learns a fully compositional language at a much faster rate, and with less training data. It is also shown that, so long as the number of mature agents is large enough, this finding holds even if a learner's trainers include other agents that do not yet posses full linguistic competence.
Original languageUndefined/Unknown
Title of host publicationAdvances in Artificial Life: Proceedings of the Thirteenth European Conference on Artificial Life (ECAL 2015)
EditorsPaul Andrews, Leo Caves, Rene Doursat, Simon Hickinbotham, Fiona Polack, Susan Stepney, Tim Taylor, Jon Timmis
PublisherMassachusetts Institute of Technology (MIT) Press
Number of pages8
Publication statusPublished - 2015

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