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Achieving compositional language in a population of iterated learners

Achieving compositional language in a population of iterated learners
Achieving compositional language in a population of iterated learners
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.
349-356
MIT Press
Brace, Lewys
6dbbb475-1b48-4ed0-aa1f-29721a1f63f1
Bullock, Seth
2ad576e4-56b8-4f31-84e0-51bd0b7a1cd3
Noble, Jason
440f07ba-dbb8-4d66-b969-36cde4e3b764
Andrews, Paul
Caves, Leo
Doursat, Rene
Hickinbotham, Simon
Polack, Fiona
Stepney, Susan
Taylor, Tim
Timmis, Jon
Brace, Lewys
6dbbb475-1b48-4ed0-aa1f-29721a1f63f1
Bullock, Seth
2ad576e4-56b8-4f31-84e0-51bd0b7a1cd3
Noble, Jason
440f07ba-dbb8-4d66-b969-36cde4e3b764
Andrews, Paul
Caves, Leo
Doursat, Rene
Hickinbotham, Simon
Polack, Fiona
Stepney, Susan
Taylor, Tim
Timmis, Jon

Brace, Lewys, Bullock, Seth and Noble, Jason (2015) Achieving compositional language in a population of iterated learners. Andrews, Paul, Caves, Leo, Doursat, Rene, Hickinbotham, Simon, Polack, Fiona, Stepney, Susan, Taylor, Tim and Timmis, Jon (eds.) In Advances in Artificial Life, ECAL 2015. MIT Press. pp. 349-356 .

Record type: Conference or Workshop Item (Paper)

Abstract

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.

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Brace_ECAL_2015.pdf - Accepted Manuscript
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Published date: 2015
Venue - Dates: conference; 2015-01-01, 2015-01-01
Organisations: Agents, Interactions & Complexity

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Local EPrints ID: 376655
URI: https://eprints.soton.ac.uk/id/eprint/376655
PURE UUID: 4b0fabe5-0dee-47a1-a607-e86e4e8cc448

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Date deposited: 18 May 2015 15:56
Last modified: 17 Jul 2017 21:07

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