Previous research by Kirby, Cornish & Smith (2008) 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 (4 transmission chains). The current study thus investigates the iterated emergence of language structure on a larger scale using Amazon Mechanical Turk, encompassing 24 independent chains of learners over 10 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, Cornish & Smith, 2008) reveals that systematic units arise along some meaning dimensions before others, giving insight into the biases of learners.

中文翻译：

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在线迭代学习任务中语言结构的出现

Kirby, Cornish & Smith (2008) 之前的研究发现，通过人工语言的迭代学习，可以在实验室中开发出惊人的组合语言系统。然而，我们对数据的重新分析表明，虽然迭代学习促使语言组合性增加，但增加后明显减少。这种组合性的下降是无法解释的，似乎是由小数据集（4 个传输链）中的偶然事件引起的。因此，当前的研究使用 Amazon Mechanical Turk 在更大范围内调查了语言结构的迭代出现，包括 10 代以上的 24 个独立学习者链。这个更丰富的数据集提供了进一步的证据，证明迭代学习会导致语言变得更加组合，尽管趋势在第 10 代之前趋于平稳。此外，数据分析（以及对 Kirby、Cornish 和 Smith，2008 年的重新分析）表明，系统单元在某些意义维度上先于其他维度出现，从而洞察学习者的偏见。