The ability to adapt to changing contexts is one of the fundamental aspects of human cognition. In particular, behavioral flexibility allows us to quickly learn the consequences of our actions and to select behaviors according to our goals (also known as goal-directed learning). Recent results suggest that goal-directed behaviors rely on associative and sensorimotor circuits of fronto-striatal loops. However, the nature of the interactions between these cerebral networks and their dynamics during learning remains unknown. To address this issue, we will study the large-scale neuronal interactions using magnetoencephalographic (MEG) data collected from healthy participants parforming a learning task. During the internship, the student will be able to study the neuronal bases of learning and adaptive behaviors, and learn tools for the analysis of behavioral neurophysiology data (BrainVISA and MNE) which allows the study the functional connectivity. Experience of python language is desired.
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