Combining Unassisted and Robot-Guided Practice Benefits Motor Learning for a Golf Putting Task

in Journal of Motor Learning and Development
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  • 1 University of Toronto
  • | 2 University of Notre Dame Australia Sydney
  • | 3 Aix-Marseille University
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Robotic guidance has been employed with limited effectiveness in neurologically intact and patient populations. For example, our lab has effectively used robotic guidance to acutely improve movement smoothness of a discrete trajectory without influencing movement endpoint distributions. The purpose of the current study was to investigate the efficacy of combining robotic guidance and unassisted trials in the learning of a golf putting task. Participants completed a pre-test, an acquisition phase, and an immediate and delayed (24-hour) post-test. During the pre-test, kinematic data from the putter was converted into highly accurate, consistent, and smooth trajectories delivered by a robot arm. During acquisition, three groups performed putts towards three different targets with robotic guidance on either 0%, 50%, or 100% of acquisition trials. Only the 50% guidance group statistically reduced both the ball endpoint distance and variability between the pre-test and the immediate or 24-hr post-test. The results of the 50% guidance group yielded seminal evidence that combining both unassisted and robotic guidance trials (i.e., mixed practice) could facilitate at least short-term motor learning for a golf putting task. Such work is relevant to incorporating robotic guidance in sport skills and other practical areas (e.g., rehabilitation).

The authors are with the Perceptual-Motor Behaviour Laboratory, Centre for Motor Control, Faculty of Kinesiology and Physical Education, University of Toronto, Toronto, Ontario, Canada. Bested is also with the School of Medicine, University of Notre Dame Australia Sydney, Darlinghurst, New South Wales, Australia. Manson is also with the Cognitive Neuroscience Laboratory, Aix-Marseille University, Marseille, France.

Tremblay (luc.tremblay@utoronto.ca) is corresponding author.
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