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Accueil > À noter > Séminaires > Tuesday 19 December 2017. Frederic CREVECOEUR (Université Catholique de Louvain, Belgium). 14h, Salle de Thèses (A104), Faculté des Sciences du Sport, Campus de Luminy (Marseille). Robust and Adaptive Control of Movement in Human : New Twists on an Old Problem

Tuesday 19 December 2017. Frederic CREVECOEUR (Université Catholique de Louvain, Belgium). 14h, Salle de Thèses (A104), Faculté des Sciences du Sport, Campus de Luminy (Marseille). Robust and Adaptive Control of Movement in Human : New Twists on an Old Problem

Mise à jour : 18 octobre

Neural plasticity and flexible representations of reach dynamics allow humans and animals to adapt to a wide range of disturbances applied to the limb such as robotic force fields, Coriolis force fields, and even changes in gravity. This ability has been almost exclusively described across trials, characterizing the gradual improvement of movement performances through learning curves. Although tremendous progress has been achieved, this approach has left completely unexplored the problem of online control during early exposure to novel dynamics, such as when the impact of a force field cannot be anticipated. More precisely, it remains unknown how the brain responds to unexpected disturbances within movements. One possibility is to derive control solutions that aim at rendering the neural controller as insensitive to model errors as possible (robust control). Another possibility is to use motor commands and sensory feedback together to deduce properties of the dynamics, and adapt internal models in real-time (adaptive control). Our recent experimental results show that healthy humans may implement these two strategies. We found that the presence of uncertainty in reach dynamics evoked changes in control consistent with a robust control model. Furthermore, within movement feedback corrections to unexpected force fields evoked online adaptation of the internal models of movement dynamics. These two components of movement execution may provide a powerful framework for linking control over time with learning over trials.