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About

Luc Tremblay investigates the neural and behavioral mechanisms underlying human sensorimotor control, with a particular focus on how robotic guidance shapes motor learning and error processing. His work bridges fundamental questions in motor neuroscience with practical applications in rehabilitation and skill acquisition. Tremblay’s most cited study (2014, 12 citations) examines whether robotic guidance primarily influences movement planning or online control, revealing critical distinctions in how assisted practice alters sensorimotor strategies. He further explores how robotic guidance impacts error detection and correction mechanisms (2019, 9 citations), demonstrating that such interventions can acutely improve movement smoothness without necessarily affecting endpoint accuracy. Notably, his research on combining unassisted and robot-guided practice for a golf putting task (2019, 5 citations) shows that mixed practice schedules can enhance motor learning beyond guidance alone. More recently, Tremblay has investigated how vision, proprioception, and efference copy contribute to endpoint error detection during reaching movements (2021, 3 citations), advancing our understanding of multisensory integration in motor control. His work has important implications for designing more effective robotic rehabilitation protocols and training programs that optimize both planning and online correction processes.