Stephen R. Bested
Papers
2
Total Citations
14
H-Index
2
About
Stephen R. Bested is a motor learning researcher whose work explores how robotic guidance and practice conditions shape human skill acquisition and error processing. His key research areas include motor learning, haptic guidance, and the neural mechanisms underlying error detection and correction. Bested’s major contributions center on understanding when and how robotic assistance benefits—or hinders—learning. His 2019 study on “The influence of robotic guidance on error detection and correction mechanisms” (9 citations) demonstrated that while robotic guidance can acutely improve movement smoothness, it does not necessarily influence endpoint accuracy, revealing a dissociation between performance and learning. In related work, “Combining Unassisted and Robot-Guided Practice Benefits Motor Learning for a Golf Putting Task” (5 citations), he showed that mixing guided and unguided practice optimizes learning outcomes, challenging the assumption that full assistance is always beneficial. These findings have implications for rehabilitation and skill training, suggesting that strategic, partial guidance may be more effective than continuous support. Bested’s research bridges robotics, neuroscience, and sport science, offering practical insights for designing training protocols that enhance long-term motor learning.
Research Focus
Key Achievements
Top Papers
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