Models of Motor Learning Generalization

Manual dexterity in school-age children measured by the Grooved Pegboard test: Evaluation of training effect and performance in dual-task

Background

Manual dexterity is the ability to manipulate objects using the hands and fingers for a specific task. Although manual dexterity is widely investigated in the general and special population at all ages, numerous aspects still remain to be explored in children. The aim of this study was to assess the presence of the training effect of the execution of the Grooved Pegboard test (GPT) and to measure the performance of the GPT in dual-task (DT), i.e., during a motor task and a cognitive task.

Methods

In this observational, cross-sectional study manual dexterity was assessed in children aged between 6 and 8. The procedure consisted of two phases: (1) the execution of five consecutive trials of the GPT to evaluate the training effect; (2) the execution of one trial of the GPT associated with a motor task (finger tapping test, GPT-FTT), and one trial of the GPT associated with a cognitive task (counting test, GPT-CT) to evaluate the performance in DT.

Results

As for the training effect, a significant difference (p < 0.001) between the five trials of the GPT (i.e., GPT1, GPT2, GPT3, GPT4, GPT5) was detected. In particular, we found a significant difference between GPT1 and GPT3 (p < 0.05), GPT1 and GPT4 (p < 0.001), and GPT1 and GPT5 (p < 0.001), as well as between GPT2 and GPT4 (p < 0.001), and GPT2 and GPT5 (p < 0.001).As for the performance in DT, no differences between the best trial of the GPT (i.e., GPT5) and both the GPT-FTT and GPT-CT was found.

Conclusion

Our findings suggest that the execution of the GPT in children has a training effect up to the third consecutive trial. Furthermore, the administration of the GPT in DT does not affect GPT performance.

Direction-Specific Iterative Tuning of Motor Commands With Local Generalization During Randomized Reaching Practice Across Movement Directions

During motor learning, people often practice reaching in variety of movement directions in a randomized sequence. Such training has been shown to enhance retention and transfer capability of the acquired skill compared to the blocked repetition of the same movement direction. The learning system must accommodate such randomized order either by having a memory for each movement direction, or by being able to generalize what was learned in one movement direction to the controls of nearby directions. While our preliminary study used a comprehensive dataset from visuomotor learning experiments and evaluated the first-order model candidates that considered the memory of error and generalization across movement directions, here we expanded our list of candidate models that considered the higher-order effects and error-dependent learning rates. We also employed cross-validation to select the leading models. We found that the first-order model with a constant learning rate was the best at predicting learning curves. This model revealed an interaction between the learning and forgetting processes using the direction-specific memory of error. As expected, learning effects were observed at the practiced movement direction on a given trial. Forgetting effects (error increasing) were observed at the unpracticed movement directions with learning effects from generalization from the practiced movement direction. Our study provides insights that guide optimal training using the machine-learning algorithms in areas such as sports coaching, neurorehabilitation, and human-machine interactions.

Sparse Identification Of Motor Learning Using Proxy Process Models

Enhanced neurorehabilitation using robotic and virtual-reality technologies requires a computational framework that can readily assess the time course of motor learning in order to recommend optimal training conditions. Error-feedback plays an important role in the acquisition of motor skills for goal-directed movements by facilitating the learning of internal models. In this study, we investigated changes in movement errors during sparse and intermittent "catch" (no-vision) trials, which served as a "proxy" of the underlying process of internal model formations. We trained 15 healthy subjects to reach for visual targets under eight distinct visuomotor distortions, and we removed visual feedback (novision) intermittently. We tested their learning data from novision trials against our so-called proxy process models, which assumed linear, affine, and second-order model structures. In order to handle sparse (no-vision) observations, we allowed the proxy process models to either update trial-to-trial, predicting across voids of sparse samples or update sample-to-sample, disregarding the trial gaps. We exhaustively cross-validated our models across subjects and across learning tasks. The results revealed that the second-order model with trial-to-trial update best predicted the proxy process of visuomotor learning.