It Pays to Go Off-Track: Practicing with Error-Augmenting Haptic Feedback Facilitates Learning of a Curve-Tracing Task

Abstract

Researchers in the domain of haptic training are now entering the long-standing debate regarding whether or not it is best to learn a skill by experiencing errors. Haptic training paradigms provide fertile ground for exploring how various theories about feedback, errors and physical guidance intersect during motor learning. Our objective was to determine how error minimizing, error augmenting and no haptic feedback while learning a self-paced curve-tracing task impact performance on delayed (1 day) retention and transfer tests, which indicate learning. We assessed performance using movement time and tracing error to calculate a measure of overall performance – the speed accuracy cost function. Our results showed that despite exhibiting the worst performance during skill acquisition, the error augmentation group had significantly better accuracy (but not overall performance) than the error minimization group on delayed retention and transfer tests. The control group’s performance fell between that of the two experimental groups but was not significantly different from either on the delayed retention test. We propose that the nature of the task (requiring online feedback to guide performance) coupled with the error augmentation group’s frequent off-target experience and rich experience of error-correction promoted information processing related to error-detection and error-correction that are essential for motor learning.

Self-Control of Haptic Assistance for Motor Learning: Influences of Frequency and Opinion of Utility

“…The apparatus consisted of a tabletop haptic device (SensAble Phantom Omni, currently Geomagic Touch; Rock Hill, SC, United States) and standard computer monitor operated via a custom software program as previously reported ( Williams et al, 2016 ), with a few noted exceptions, and we refer interested readers to that article for details omitted here. The computer monitor used in the present study was a Dell UltraSharp TM 1703FP and the resolution resulted in a visual gain of 1.38 between displacements on the monitor and movements of the haptic device in space.…”

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

“…One could have predicted that the fixed guidance provided by the robot should have yielded "only temporary boosts to performance" (Schmidt, Lee, Winstein, Wulf, & Zelaznik, 2019;pp. 338) and suggest that other forms of guidance are preferable (partial guidance: e.g., Marchal Crespo & Reinkensmeyer, 2008b, or error-augmenting guidance: e.g., Williams, Tremblay, & Carnahan, 2016). However,…”

“…Specifically, at least two types of guidance have emerged in the literature. Studies have employed both error reduction (i.e., guiding the limb towards or with a correct reference: Kümmel et al, 2014;Manson et al, 2014;Marchal-Crespo, McHughen, Cramer, & Reinkensmeyer, 2010;Marchal-Crespo & Reinkensmeyer, 2008a) and error amplification guidance (i.e., increasing error, moving the limb further away from the correct reference: Marchal-Crespo, Schneider, Jaeger, & Riener, 2014;Williams, Tremblay, & Carnahan, 2016). Both the error reduction and error amplification approaches have been shown to be effective for enhancing performance and learning outcomes.…”

“…In contrast, error amplification during robotic guidance has yielded results that seem to be relatively permanent (e.g., Marchal-Crespo et al, 2014;Williams et al, 2016). For example, Williams et al (2016) showed that participants exposed to error amplification guidance when learning a tracing task had significantly better performance in both delayed retention and transfer tests compared to the error minimization group. The enhanced learning showed by the error augmentation group has been attributed to the increase in control processes used during acquisition.…”

“…Although error amplification guidance resulted in enhanced learning, it is worth mentioning that participants still did not outperform acquisition without robotic guidance (i.e., no guidance). Because error amplification guidance does not yield better motor learning outcomes than no guidance, which can both be explained by the development of error detection and correction mechanisms (e.g., Williams et al, 2016), it remains unclear why error reduction guidance has an immediate impact on performance. Therefore, the current study aimed to test if error reduction guidance on trajectory accuracy combined with the motor learning benefits of unassisted practice (i.e., errorful performance) can further optimize the performance and learning of a novel golf putting task.…”