Silva Susana, Folia Vasiliki, Hagoort Peter, Petersson Karl Magnus
Cognitive Neuroscience Research Group, Centre for Biomedical Research (CBMR), University of Algarve.
Neurocognition and Language Group, Center for Psychology at the University of Porto.
Cogn Sci. 2017 Jan;41(1):137-157. doi: 10.1111/cogs.12343. Epub 2016 Feb 23.
The suitability of the artificial grammar learning (AGL) paradigm to capture relevant aspects of the acquisition of linguistic structures has been empirically tested in a number of EEG studies. Some have shown a syntax-related P600 component, but it has not been ruled out that the AGL P600 effect is a response to surface features (e.g., subsequence familiarity) rather than the underlying syntax structure. Therefore, in this study, we controlled for the surface characteristics of the test sequences (associative chunk strength) and recorded the EEG before (baseline preference classification) and after (preference and grammaticality classification) exposure to a grammar. After exposure, a typical, centroparietal P600 effect was elicited by grammatical violations and not by unfamiliar subsequences, suggesting that the AGL P600 effect signals a response to structural irregularities. Moreover, preference and grammaticality classification showed a qualitatively similar ERP profile, strengthening the idea that the implicit structural mere-exposure paradigm in combination with preference classification is a suitable alternative to the traditional grammaticality classification test.
人工语法学习（AGL）范式在捕捉语言结构习得相关方面的适用性已在多项脑电图（EEG）研究中得到实证检验。一些研究显示了与句法相关的P600成分，但并未排除AGL的P600效应是对表面特征（如子序列熟悉度）而非潜在句法结构的反应。因此，在本研究中，我们控制了测试序列的表面特征（联想组块强度），并在接触语法之前（基线偏好分类）和之后（偏好和语法性分类）记录脑电图。接触后，语法违规引发了典型的中央顶叶P600效应，而不熟悉的子序列则未引发该效应，这表明AGL的P600效应表明对结构不规则性有反应。此外，偏好和语法性分类显示出定性相似的事件相关电位（ERP）图谱，强化了这样一种观点，即隐式结构单纯暴露范式与偏好分类相结合是传统语法性分类测试的合适替代方法。
