Department of Psychology, Harvard University, United States.
Department of Psychology, Harvard University, United States.
Cogn Psychol. 2019 Nov;114:101227. doi: 10.1016/j.cogpsych.2019.101227. Epub 2019 Jul 17.
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.
人工语言学习的研究为我们了解学习偏差和迭代学习如何塑造自然语言提供了线索。先前的工作研究了学习者如何应对不可预测的变化，以及语言如何在多代学习者中发生变化。本研究结合了这些特征，探讨了词序变化如何在代际之间得到保留或规范。我们研究了这些过程如何受到以下因素的影响：（1）学习偏差；（2）语言社区的大小；（3）提供的输入量。我们的结果表明，当输入来自单个说话者时，成年学习者会根据频率进行匹配，从而在三代人的输入中再现可变性。然而，当相同数量的输入分布在多个说话者中时，频率匹配就会失效。当出现规范化时，会强烈偏向 SOV 词序（相对于 OSV 和 VSO）。最后，当多个说话者提供的输入量增加时，学习者能够根据频率进行匹配。这些结果表明，人口规模和每个说话者提供的输入量都在语言趋同中发挥作用。
