The effect of haptic guidance, aging, and initial skill level on motor learning of a steering task.
In a previous study, we found that haptic guidance from a robotic steering wheel can improve short-term learning of steering of a simulated vehicle, in contrast to several studies of other tasks that had found that the guidance either impairs or does not aid motor learning. In this study, we examined whether haptic guidance-as-needed can improve long-term retention (across 1 week) of the steering task, with age and initial skill level as independent variables. Training with guidance-as-needed allowed all participants to learn to steer without experiencing large errors. For young participants (age 18-30), training with guidance-as-needed produced better long-term retention of driving skill than did training without guidance. For older participants (age 65-92), training with guidance-as-needed improved long-term retention in tracking error, but not significantly. However, for a subset of less skilled, older subjects, training with guidance-as-needed significantly improved long-term retention. The benefits of guidance-based training were most evident as an improved ability to straighten the vehicle direction when coming out of turns. In general, older participants not only systematically performed worse at the task than younger subjects (errors approximately 3 times greater), but also apparently learned more slowly, forgetting a greater percentage of the learned task during the 1 week layoffs between the experimental sessions. This study demonstrates that training with haptic guidance can benefit long-term retention of a driving skill for young and for some old drivers. Training with haptic guidance is more useful for people with less initial skill.
Marchal-Crespo
Laura
L
Department of Mechanical and Aerospace Engineering, Henry Samueli School of Engineering, 4200 Engineering Gateway Building, Irvine, CA 92697-3975, USA. lmarchal@uci.edu
McHughen
Stephanie
S
Cramer
Steven C
SC
Reinkensmeyer
David J
DJ
D000328:Adult; D000368:Aged; D000375:Aging / Q000523:psychology*; D000465:Algorithms; D003196:Computer Graphics; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D030141:Feedback, Psychological / Q000502:physiology; D005260:Female; D006801:Humans; D007858:Learning / Q000502:physiology*; D008297:Male; D009048:Motor Skills / Q000502:physiology*; D011597:Psychomotor Performance / Q000502:physiology*; D014910:Wheelchairs; D055815:Young Adult