Abstract
Previous research by Kirby et al. has found that strikingly compositional language systems can be developed in the laboratory via iterated learning of an artificial language. However, our reanalysis of the data indicates that while iterated learning prompts an increase in language compositionality, the increase is followed by an apparent decrease. This decrease in compositionality is inexplicable, and seems to arise from chance events in a small dataset (four transmission chains). The current study thus investigates the iterated emergence of language structure on a larger scale using Amazon Mechanical Turk, encompassing twenty-four independent chains of learners over ten generations. This richer dataset provides further evidence that iterated learning causes languages to become more compositional, although the trend levels off before the 10th generation. Moreover, analysis of the data (and reanalysis of Kirby et al.) reveals that systematic units arise along some meaning dimensions before others, giving insight into the biases of learners.
Highlights
This article reports on a large-scale implementation of an iterated artificial language learning task
Our reanalysis of the data indicates that while iterated learning prompts an increase in language compositionality, the increase is followed by an apparent decrease
Developed as a computational, agentbased model of cultural transmission, iterated learning has been extended into a series of artificial language studies in the laboratory, in which a human participant learns an artificial language, completes a test round; the results of the test determine the language taught to the generation
Summary
This article reports on a large-scale implementation of an iterated artificial language learning task. It makes two substantial contributions to the literature It replicates previously reported results using a statistically much more robust dataset. Learners of our artificial languages show a significant tendency to increase compositionality It examines the different dimensions of meaning for which compositionality could emerge, showing that some aspects of meaning develop compositionality in advance of others. The core feature of the paradigm is that an agent attempts to learn a language, after which that agent’s output is passed on to a new learner, complete with errors or innovations.
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