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Testing the Limits of Long-Distance Learning: Learning Beyond a Three-Segment Window

机译：测试远程学习的局限性：超越三段式窗口的学习

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Traditional flat-structured bigram and trigram models of phonotactics are useful because they capture a large number of facts about phonological processes. Additionally, these models predict that local interactions should be easier to learn than long-distance ones because long-distance dependencies are difficult to capture with these models. Long-distance phonotactic patterns have been observed by linguists in many languages, who have proposed different kinds of models, including feature-based bigram and trigram models, as well as precedence modeis. Contrary to flat-structured bigram and trigram models, these alternatives capture unbounded dependencies because at an abstract level of representation, the relevant elements are locally dependent, even if they are not adjacent at the observable level. Using an artificial grammar learning paradigm, we provide additional support for these alternative models of phonotactics. Participants in two experiments were exposed to a long-distance consonant-harmony pattern in which the first consonant of a five-syllable word was [s] or [J] ("sh") and triggered a suffix that was either [-su] or [-Ju] depending on the sibilant quality of this first consonant. Participants learned this pattern, despite the large distance between the trigger and the target, suggesting that when participants learn long-distance phonological patterns, that pattern is learned without specific reference to distance.
机译：传统的平面结构音韵学的双字母组和三字母组模型很有用，因为它们捕获了大量有关语音过程的事实。另外，这些模型预测本地交互应该比长距离交互更容易学习，因为长距离依赖关系很难用这些模型捕获。语言学家已经用许多种语言观察到了长距离的音律学模式，他们提出了不同种类的模型，包括基于特征的双字母组和三字母组模型以及优先模式。与平面结构的bigram和trigram模型相反，这些替代方案捕获了无限制的依赖性，因为在抽象表示级别上，即使相关元素在可观察级别上不相邻，它们在本地也是相关的。使用人工语法学习范式，我们为这些其他的音韵学模型提供了额外的支持。两个实验的参与者都处于长距离辅音和声模式，其中五个音节单词的第一个辅音为[s]或[J]（“ sh”），并触发后缀为[-su]或[-Ju]取决于此第一个辅音的轻松质量。尽管触发器和目标之间的距离很大，但参与者还是学会了这种模式，这表明当参与者学习长距离语音模式时，无需特别参考距离就可以学习该模式。

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