Projects per year
Abstract
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.
Original language | English |
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Title of host publication | ALIFE 2024 |
Subtitle of host publication | Proceedings of the 2024 Artificial Life Conference |
Publisher | Massachusetts Institute of Technology (MIT) Press |
Pages | 54-61 |
Number of pages | 8 |
Volume | 54 |
DOIs | |
Publication status | Published - 22 Jul 2024 |
Event | ALIFE 2024: 2024 Artificial Life Conference - Denmark, Copenhagen, Denmark Duration: 22 Jul 2024 → 26 Sept 2024 https://2024.alife.org/ |
Conference
Conference | ALIFE 2024 |
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Abbreviated title | ALIFE |
Country/Territory | Denmark |
City | Copenhagen |
Period | 22/07/24 → 26/09/24 |
Internet address |
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8459 AI For Collective Intelligence
Bullock, S. (Principal Investigator)
1/02/24 → 31/01/29
Project: Research, Parent