The P600 in Implicit Artificial Grammar Learning

Silva, S., Folia, V., Hagoort, P., & Petersson, K. M. (2017). The P600 in Implicit Artificial Grammar Learning. Cognitive Science, 41(1), 137-157. doi:10.1111/cogs.12343.
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. A typical, centroparietal P600 effect was elicited by grammatical violations after exposure, 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.
Publication type
Journal article
Publication date
2017

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