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The P600 in Implicit Artificial Grammar Learning
Author(s) -
Silva Susana,
Folia Vasiliki,
Hagoort Peter,
Petersson Karl Magnus
Publication year - 2017
Publication title -
cognitive science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.498
H-Index - 114
eISSN - 1551-6709
pISSN - 0364-0213
DOI - 10.1111/cogs.12343
Subject(s) - grammaticality , p600 , syntax , grammar , preference , psychology , computer science , natural language processing , artificial intelligence , linguistics , cognitive psychology , mathematics , electroencephalography , event related potential , neuroscience , philosophy , statistics , n400
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.