# Spatial community structure impedes language amalgamation in a population-based iterated learning model

arXiv: https://arxiv.org/abs/2305.11962 · HTML: https://mlsanity.com/paper/2305.11962/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 May 2023.

## Built on by (most cited first, 3 of 3)

- [An iterated learning model of language change that mixes supervised and unsupervised learning](https://mlsanity.com/paper/2405.20818/) (2024)
- [Modeling language contact with the Iterated Learning Model](https://mlsanity.com/paper/2406.06878/) (2024)
- [Image, Word and Thought: A More Challenging Language Task for the Iterated Learning Model](https://mlsanity.com/paper/2601.02911/) (2026)

## Abstract (arXiv)

The iterated learning model is an agent-based model of language evolution notable for demonstrating the emergence of compositional language. In its original form, it modelled language evolution along a single chain of teacher-pupil interactions; here we modify the model to allow more complex patterns of communication within a population and use the extended model to quantify the effect of within-community and between-community communication frequency on language development. We find that a small amount of between-community communication can lead to population-wide language convergence but that this global language amalgamation is more difficult to achieve when communities are spatially embedded.