This study uses an artificial language learning experiment and computational modelling to test Kiparsky's claims about Maximal Utilisation and Transparency biases in phonological acquisition. A Maximal Utilisation bias would prefer phonological patterns in which all rules are maximally utilised, and a Transparency bias would prefer patterns that are not opaque. Results from the experiment suggest that these biases affect the learnability of specific parts of a language, with Maximal Utilisation affecting the acquisition of individual rules, and Transparency affecting the acquisition of rule orderings. Two models were used to simulate the experiment: an expectation-driven Harmonic Serialism learner and a sequence-to-sequence neural network. The results from these simulations show that both models’ learning is affected by these biases, suggesting that the biases emerge from the learning process rather than any explicit structure built into the model.

中文翻译：

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不透明交互中的学习偏差

本研究使用人工语言学习实验和计算模型来测试 Kiparsky 关于语音习得中的最大利用和透明度偏差的主张。最大利用偏差更喜欢所有规则都被最大利用的语音模式，而透明度偏差更喜欢不透明的模式。实验结果表明，这些偏差会影响语言特定部分的可学习性，最大利用率会影响单个规则的获得，而透明度会影响规则顺序的获得。使用两个模型来模拟实验：期望驱动的 Harmonic Serialism 学习器和序列到序列的神经网络。这些模拟的结果表明，两个模型的学习都受到这些偏差的影响，