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. 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.