Studies of artificial language learning provide insight into how learning biases and iterated learning may shape natural languages. Prior work has looked at how learners deal with unpredictable variation and how a language changes across multiple generations of learners. The present study combines these features, exploring how word order variation is preserved or regularized over generations. We investigate how these processes are affected by (1) learning biases, (2) the size of the language community, and (3) the amount of input provided. Our results show that when the input comes from a single speaker, adult learners frequency match, reproducing the variability in the input across three generations. However, when the same amount of input is distributed across multiple speakers, frequency matching breaks down. When regularization occurs, there is a strong bias for SOV word order (relative to OSV and VSO). Finally, when the amount of input provided by multiple speakers is increased, learners are able to frequency match. These results demonstrate that both population size and the amount of input per speaker each play a role in language convergence.

中文翻译：

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需要一个村庄：社区规模在语言规范化中的作用

人工语言学习的研究提供了对学习偏差和迭代学习如何塑造自然语言的见解。之前的工作着眼于学习者如何处理不可预测的变化以及语言如何在多代学习者之间发生变化。本研究结合了这些特征，探索了词序变化如何在几代人中保持或规范化。我们调查了这些过程如何受到 (1) 学习偏差、(2) 语言社区的规模和 (3) 提供的输入量的影响。我们的结果表明，当输入来自单个说话者时，成年学习者的频率匹配，重现了三代人输入的可变性。然而，当相同数量的输入分布在多个扬声器上时，频率匹配就会失效。当正则化发生时，SOV 词序存在强烈偏差（相对于 OSV 和 VSO）。最后，当多个说话者提供的输入量增加时，学习者能够进行频率匹配。这些结果表明，人口规模和每个说话者的输入量都在语言收敛中发挥作用。