Mueller Jutta L, Bahlmann Joerg, Friederici Angela D
Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
J Cogn Neurosci. 2008 May;20(5):892-905. doi: 10.1162/jocn.2008.20511.
Humans can derive sequential dependencies from unfamiliar artificial speech within several of minutes of exposure. However, there is an ongoing debate about the nature of the underlying learning processes. In a widely discussed study Peña et al. [Peña, M., Bonatti, L. L., Nespor, M., & Mehler, J. Signal-driven computations in speech processing. Science, 298, 604-607, 2002] argued for the importance of subtle acoustic cues in the signal, such as pauses, in order to switch between two computational mechanisms, which are conceptualized as rule-based versus statistical. The present study was aimed to approach this problem by recording event-related potentials in response to correct and incorrect phrases consisting of bisyllabics after short exposure to either rule-based or random artificial speech streams. Rule-based streams contained dependencies of the form AXC, whereby A elements reliably predicted the C elements and X elements were variable. Participants were exposed to four input and test phases. Two of the input streams were rule-based and contained either only probabilistic information related to the distribution of the AXC stimuli or an additional acoustic cue indicating the boundaries of relevant units. The other two streams were random variations of the rule-based streams. During the test phase in the condition with pause cues, an early negativity and a later positivity emerged for correct and incorrect items in comparison to their acoustically identical counterparts, which were presented after the random control condition. In the noncued condition, only negativities were seen. The timing and the scalp distribution of the negativities were different for correct and incorrect sequences in both the cued and the noncued conditions. The results are interpreted in support of a view of grammatical learning in which both distributional and acoustic cues may contribute to different aspects of syntactic learning.
人类在接触不熟悉的人工语音几分钟内就能从其中提取序列依赖性。然而，对于潜在学习过程的本质，目前仍存在争议。在一项被广泛讨论的研究中，佩尼亚等人[佩尼亚，M.，博纳蒂，L. L.，内斯波尔，M.，& 梅勒，J. 语音处理中的信号驱动计算。《科学》，298，604 - 607，2002]认为信号中微妙的声学线索（如停顿）很重要，以便在两种计算机制之间切换，这两种机制被概念化为基于规则的与基于统计的。本研究旨在通过记录在短时间接触基于规则或随机人工语音流后，对由双音节词组成的正确和错误短语的事件相关电位来解决这个问题。基于规则的语音流包含AXC形式的依赖性，其中A元素可靠地预测C元素，而X元素是可变的。参与者经历了四个输入和测试阶段。其中两个输入语音流是基于规则的，要么只包含与AXC刺激分布相关的概率信息，要么包含一个额外的声学线索来指示相关单元的边界。另外两个语音流是基于规则的语音流的随机变体。在有停顿线索的条件下的测试阶段，与在随机控制条件后呈现的声学相同的对应项相比，正确和错误项目出现了早期负波和后期正波。在无线索条件下，只观察到负波。在有线索和无线索条件下，正确和错误序列的负波的时间和头皮分布都不同。这些结果被解释为支持一种语法学习观点，即分布性线索和声学线索都可能对句法学习的不同方面做出贡献。
