John de Grosbois
Papers
1
Total Citations
9
H-Index
1
About
John de Grosbois is a researcher whose work lies at the intersection of motor learning, human-robot interaction, and sensorimotor control. His research explores how robotic guidance shapes the brain’s error detection and correction mechanisms, with implications for rehabilitation and skill acquisition. In his highly cited 2019 paper, "The influence of robotic guidance on error detection and correction mechanisms," de Grosbois demonstrated that while robotic assistance can improve performance during training, it may impair the learner’s ability to detect and correct errors independently—a critical insight for designing effective robotic training protocols. With 9 citations, this work has influenced discussions on the trade-offs between guidance and autonomy in motor learning. de Grosbois’s contributions are particularly relevant for researchers in neurorehabilitation and human-robot collaboration, where understanding how feedback and assistance alter neural processes is key. His findings challenge assumptions about the benefits of robotic guidance and underscore the importance of preserving error-driven learning. For students and researchers, de Grosbois’s work offers a nuanced perspective on how technology can both aid and hinder human skill development.
Research Focus
Key Achievements
Top Papers
- 1