Influence of error-augmentation on the dynamics of visuomotor skill acquisition: insights from proxy-process models.

Our study addresses the critical question of how learners acquire skills without the constant crutch of feedback, using a specialized training approach with intermittent feedback. Despite recognized benefits in skill retention, the underlying mechanisms of intermittent feedback in motor control neuroscience remain elusive. Leveraging a previously published dataset from visuomotor learning experiments with intermittent feedback, we tested a wide range of proxy-process models that posit the presence of an inferred error signal even when an explicit sensory performance is not present. The model structures encompassed a spectrum from first-order to higher-order variants, incorporating both constant and error-dependent rates of change in error. Furthermore, these proxy-process models investigated the impact of error-augmentation (EA) training on visuomotor learning dynamics. Rigorous cross-validation consistently identified a second-order proxy-process model structure accurately predicting motor learning across subjects and learning tasks. Model parameters elucidated the varying influences of EA settings on the rates of change in error, inter-trial variability, and steady-state performance. We then introduced a dynamic-Proxy support Multi-Rate Motor Learning (dPxMRML) model, which shed light on EA's effects on the fast and slow learning dynamics. The dPxMRML model accurately predicted subjects' performance during and beyond training phases, highlighting EA settings conducive to long-term retention. This research yields crucial insights for personalized training program design, applicable in neuro-rehabilitation, sports, and performance training.NEW & NOTEWORTHY Breaking new ground in motor learning, our research unveils the intricacies of skill acquisition without continuous feedback. By using a specialized training approach with intermittent feedback, our study reveals the previously elusive mechanisms behind this process. The introduction of innovative proxy-process models, particularly the dynamic-Proxy support Multi-Rate Motor Learning (dPxMRML) model, brings a fresh perspective to understanding the impact of error-augmentation (EA) training on learning and retention of motor skills.

Evidence of multiple coordinate representations during generalization of motor learning.

Several studies have suggested that the motor system takes advantage of a coordinate system when learning a novel sensorimotor environment. Such investigations, however, have not distinguished between initial preferences of a coordinate system versus possible changes due to learning. Here, we present experimental methods that specifically entertain the possibility of multiple coordinate systems during generalization. Subjects trained with their right arm on a viscous force field. We evaluated their performances for both arms in an untrained workspace before and after training using three fields, each representing extrapolation with a candidate coordinate system. Surprisingly, our results showed evidence of improvement (pre to post) in all fields for both limbs. These findings are consistent with the hypothesis of multiple, simultaneous coordinate systems involved in generalization. We also investigated how feedback might affect the results and found in several cases that performance was better for visual displays that were aligned with the limb (in first person) versus non-aligned.

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.

Models of Motor Learning Generalization.

This study used evidence from trial-by-trial errors to understand how humans can generalize what they learn across different movement directions while reaching. We trained 15 healthy subjects to reach in six directions in the presence of challenging visuomotor distortions. We then tested a number of candidate models suggested by the literature of how the brain might use error to improve performance. Our cross-validated results point to a discrete affine model whose generalization, or influence of practice in one direction to neighboring directions, is reduced nearly to zero by 60 degrees away, and the subjects learned 6.25 times more from the error that was observed at a movement direction than neighboring directions.

Simultaneous coordinate representations are influenced by visual feedback in a motor learning task.

It has been widely accepted that the CNS develops a representation (model) of the environment, but what is not entirely clear is the coordinate reference frame used. We explored how visual feedback influenced the coordinate frame in which the CNS stores and recalls these memories of learned skills in a reaching-generalization task. Four groups of subjects trained to perform reaching movements in a perturbing force field, two with aligned (first person) visual feedback and two with non-aligned (vertical screen). After 170 trials of practice, we asked subjects to extrapolate (generalize) what they learned to a new part of the workspace in novel force environments (endpoint-based versus joint-based extrapolated force fields). Regardless of the test condition, all subjects improved their ability to generalize skills to the new workspace. There was evidence that the extrapolation of their learned skills was based on both object-centered and joint-based coordinates. Consistent with previous studies, subjects performed significantly better in joint-extrapolated force field, but only if the visual feedback was vertical. Subjects performed equivalently in both force fields with aligned (first person) feedback. These findings suggest that the type of visual feedback biases the way subjects perform, and that learning results can be significantly influenced by feedback.