When less is more: Enhanced statistical learning of non-adjacent dependencies after disruption of bilateral DLPFC
Year: 2020
Type: article
Abstract: Brain networks related to human learning can interact in cooperative but also competitive ways to optimize performance. The investigation of learning and memory processes in a competitive framework is still rare. Previous studies have shown that manipulations reducing the engagement of prefrontal cortical areas could lead to improved statistical learning performance. However, no study has investigated how disruption of the dorsolateral prefrontal cortex (DLPFC) affects the acquisition and consolidation of non-adjacent second-order dependencies. The present study aimed to test the role of the DLPFC, more specifically, the Brodmann 9 area in implicit temporal statistical learning of non-adjacent dependencies. We applied 1 Hz inhibitory transcranial magnetic stimulation or sham stimulation over both the left and right DLPFC intermittently during the learning. The DLPFC-stimulated group showed better performance compared to the sham group after a 24-hour consolidation period. This finding suggests that the disruption of DLPFC during learning induces qualitative changes in the consolidation of non-adjacent statistical regularities. A possible mechanism behind this result is that the stimulation of the DLPFC promotes a shift to model-free learning by weakening the access to model-based processes.
Source: Journal of Memory and Language
Institutions: Friedrich Schiller University Jena, University of Szeged, Eötvös Loránd University, Institute of Cognitive Neuroscience and Psychology, University of Greenwich, Hungarian Academy of Sciences, HUN-REN Research Centre for Natural Sciences, Radboud University Nijmegen, Donders Institute for Brain, Cognition and Behaviour, Lyon 1 Université, Centre National de la Recherche Scientifique, Inserm, Centre de Recherche en Neurosciences de Lyon, Centre Hospitalier Le Vinatier
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: hybrid
Estimated APC paid: $2,100
OpenAlex ID: W3033578022
