Endress Ansgar D, Mehler Jacques
Harvard University, Cambridge, MA 02138, USA.
Q J Exp Psychol (Hove). 2009 Nov;62(11):2187-209. doi: 10.1080/17470210902783646. Epub 2009 May 2.
Previous research suggests that artificial-language learners exposed to quasi-continuous speech can learn that the first and the last syllables of words have to belong to distinct classes (e.g., Endress & Bonatti, 2007; Pena, Bonatti, Nespor, & Mehler, 2002). The mechanisms of these generalizations, however, are debated. Here we show that participants learn such generalizations only when the crucial syllables are in edge positions (i.e., the first and the last), but not when they are in medial positions (i.e., the second and the fourth in pentasyllabic items). In contrast to the generalizations, participants readily perform statistical analyses also in word middles. In analogy to sequential memory, we suggest that participants extract the generalizations using a simple but specific mechanism that encodes the positions of syllables that occur in edges. Simultaneously, they use another mechanism to track the syllable distribution in the speech streams. In contrast to previous accounts, this model explains why the generalizations are faster than the statistical computations, require additional cues, and break down under different conditions, and why they can be performed at all. We also show that that similar edge-based mechanisms may explain many results in artificial-grammar learning and also various linguistic observations.
先前的研究表明，接触准连续语音的人工语言学习者能够学会单词的首音节和末音节必须属于不同的类别（例如，恩德雷斯和博纳蒂，2007年；佩纳、博纳蒂、内斯波尔和梅勒，2002年）。然而，这些归纳的机制存在争议。在这里，我们表明，只有当关键音节处于边缘位置（即首音节和末音节）时，参与者才会学习到此类归纳，而当它们处于中间位置（即五音节项目中的第二个和第四个音节）时则不会。与这些归纳不同的是，参与者在单词中间部分也能轻松地进行统计分析。与序列记忆类似，我们认为参与者使用一种简单但特定的机制来提取归纳，该机制对出现在边缘位置的音节的位置进行编码。同时，他们使用另一种机制来跟踪语音流中的音节分布。与之前的解释不同，该模型解释了为什么归纳比统计计算更快、需要额外的线索、在不同条件下会失效，以及为什么它们能够被执行。我们还表明，类似的基于边缘的机制可能解释人工语法学习中的许多结果以及各种语言观察结果。
