Learning biases in opaque interactions
Learning biases in opaque interactions
复制标题
不透明交互中的学习偏差
DOI：
10.1017/s0952675719000320
复制
发表时间：
2019
期刊：
影响因子：
1.3
通讯作者：
B. Prickett
中科院分区：
文献类型：
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作者：
B. Prickett
这项研究使用人工语言学习实验和计算模型来检验Kiparsky关于语音习得中的最大利用率和透明度偏差的主张。最大利用率偏向倾向于最大限度地利用所有规则的音系模式，而透明度偏向倾向于偏向于不不透明的模式。实验结果表明，这些偏见影响语言特定部分的可学性，最大限度地利用影响个人规则的获得，透明度影响规则顺序的获得。两个模型被用来模拟实验：期望驱动的调和串行化学习器和序列到序列的神经网络。这些模拟的结果表明，这两个模型的学习都受到这些偏差的影响，这表明偏差来自学习过程，而不是模型中的任何显式结构。
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.