Description du sujet de thèse

This project aims to uncover neural mechanisms of motor adaptation. The tools will include advanced machine learning, mathematical modeling (dynamical systems) on existing data and conducting experiments on healthy participants performing motor tasks with simultaneous EEG recordings.

Contexte de travail

Context (Why studying this makes sense): Motor adaptation is a crucial process in human life, enabling us to adjust our movements based on sensory feedback. For instance, imagine being asked to play your favorite game of petanque with a set of balls that are heavier than usual. You would need several trials to adapt your movements to achieve your baseline performance. This adaptation relies on the sensory prediction error—the difference between the intended and actual outcomes on each trial. Understanding motor adaptation has broad applications, from rehabilitation in patients after brain or body injuries to optimizing motor skills in sports, work, and robotics (motor adaptation is essential for advanced dexterity).

Despite the ubiquity and importance of motor adaptation, it is still not fully understood, especially at the neural level.