We present a study investigating the neural correlates of artificial grammar learning – a process of implicit processing of regularities in the environment. Participants observed visual stimuli that were created using a set of complex rules and then classified items from a new stimulus set as either consistent with these rules or not. Unlike previous event-related potentials (ERP) studies in this area, we used a short-term learning procedure normally used in behavioral experiments. With this short-term learning paradigm, we were able to detect ERP-components related to two different types of implicit knowledge. We found component (P600) related to the violation of the learned abstract grammatical structure. We also found early ERP-components (N200) related to the violation of learned combinations of elements in stimuli (frequency structure). It was possible to observe these distinct results because of the specific design of the study in which frequency structure and abstract grammaticality were independently varied. The results show neural correlates of classical artificial grammar learning and speak in favor of two distinct mechanisms of implicit learning: one responsible for abstract rules learning and another – for the learning of frequency structure of the environment.
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Ivanchei, I.I., Absatova, K.A. & Kurgansky, A.V. ERP Correlates of the Short-term Implicit Artificial Grammar Learning. Hum Physiol 45, 604–613 (2019). https://doi.org/10.1134/S0362119719060045
- implicit learning
- artificial grammar learning
- associative chunk strength