Bahlmann Jörg, Schubotz Ricarda I, Mueller Jutta L, Koester Dirk, Friederici Angela D
Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
Brain Res. 2009 Nov 17;1298:161-70. doi: 10.1016/j.brainres.2009.08.017. Epub 2009 Aug 15.
Sequence processing has been investigated in a number of studies using serial reaction time tasks or simple artificial grammar tasks. Little, however, is known about higher-order sequence processing entailing the hierarchical organization of events. Here, we manipulated the regularities within sequentially occurring, non-linguistic visual symbols by applying two types of prediction rules. In one rule (the adjacent dependency rule), the sequences consisted of alternating items from two different categories. In the second rule (the hierarchical dependency rule), a hierarchical structure was generated using the same set of item types. Thus, predictions about non-adjacent elements were required for the latter rule. Functional Magnetic Resonance Imaging (fMRI) was used to investigate the neural correlates of the application of the two prediction rules. We found that the hierarchical dependency rule correlated with activity in the pre-supplementary motor area, and the head of the caudate nucleus. In addition, in a hypothesis-driven ROI analysis in Broca's area (BA 44), we found a significantly higher hemodynamic response to the hierarchical dependency rule than to the adjacent dependency rule. These results suggest that this neural network supports hierarchical sequencing, possibly contributing to the integration of sequential elements into higher-order structural events. Importantly, the findings suggest that Broca's area is also engaged in hierarchical sequencing in domains other than language.
在一些研究中，已经使用序列反应时任务或简单人工语法任务对序列处理进行了探究。然而，对于涉及事件层次组织的高阶序列处理却知之甚少。在此，我们通过应用两种类型的预测规则，操纵了顺序出现的非语言视觉符号中的规律。在一种规则（相邻依赖规则）中，序列由来自两个不同类别的交替项目组成。在第二种规则（层次依赖规则）中，使用同一组项目类型生成了一种层次结构。因此，后一种规则需要对非相邻元素进行预测。功能磁共振成像（fMRI）被用于探究这两种预测规则应用的神经关联。我们发现层次依赖规则与补充运动前区以及尾状核头部的活动相关。此外，在一项针对布洛卡区（BA 44）的假设驱动的感兴趣区域分析中，我们发现相比于相邻依赖规则，层次依赖规则引发了显著更高的血液动力学反应。这些结果表明，这个神经网络支持层次序列，可能有助于将序列元素整合到高阶结构事件中。重要的是，这些发现表明布洛卡区在语言以外的领域也参与层次序列处理。
