Metrical Presentation Boosts Implicit Learning of Artificial Grammar

Tatiana Selchenkova (1) , Clément François (2, 3, 4) , Daniele Schön (5, 6) , Alexandra Corneyllie (1) , Fabien Perrin (1) , Barbara Tillmann (1)

1 CRNL - Centre de recherche en neurosciences de Lyon - Lyon Neuroscience Research Center
2 Cognition and Brain Plasticity Group
3 Department of Basic Psychology
4 Attention, Perception and Acquisition of Language Lab
5 INS - Institut de Neurosciences des Systèmes
6 INSERM - Institut National de la Santé et de la Recherche Médicale

The present study investigated whether a temporal hierarchical structure favors implicit learning. An artificial pitch grammar implemented with a set of tones was presented in two different temporal contexts, notably with either a strongly metrical structure or an isochronous structure. According to the Dynamic Attending Theory, external temporal regularities can entrain internal oscillators that guide attention over time, allowing for temporal expectations that influence perception of future events. Based on this framework, it was hypothesized that the metrical structure provides a benefit for artificial grammar learning in comparison to an isochronous presentation. Our study combined behavioral and event-related potential measurements. Behavioral results demonstrated similar learning in both participant groups. By contrast, analyses of event-related potentials showed a larger P300 component and an earlier N2 component for the strongly metrical group during the exposure phase and the test phase, respectively. These findings suggests that the temporal expectations in the strongly metrical condition helped listeners to better process the pitch dimension, leading to improved learning of the artificial grammar.

Mots clés: EVENT SEQUENCES; MUSIC; TIME; PERCEPTION; EXPLICIT; LANGUAGE; ERP; EXPECTATIONS; ENTRAINMENT; POTENTIALS

Domaines: Neurosciences

Tatiana Selchenkova, Clément François, Daniele Schön, Alexandra Corneyllie, Fabien Perrin, et al.. Metrical Presentation Boosts Implicit Learning of Artificial Grammar. PLoS ONE, 2014, 9 (e112233 ), ⟨10.1371/journal.pone.0112233⟩. ⟨hal-01242894⟩