Acquisition of Novel and Complex Motor Skills: Stable Solutions Where Intrinsic Noise Matters Less

Sternad, Dagmar; Huber, Meghan E.; Kuznetsov, Nikita А.

doi:10.1007/978-1-4939-1338-1_8

Cited by 51 publications

(62 citation statements)

References 46 publications

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“…The variability of the instantaneous frequency within each cycle was also not essential to the achievement of the task. These observations are consistent with findings in complex tasks that have redundancy, where variability is found in those dimensions that "do not matter" for task achievement (Cohen and Sternad 2009;Scholz and Schöner 1999;Sternad et al 2014;Todorov and Jordan 2002).…”

Section: Hypothesis 1: Self-guided Learning With and Without Metronomesupporting

confidence: 91%

“…Even the absolute frequencies persisted in each individual. This scenario sharply contrasts with observations in adaptation studies, highlighting that de novo skill learning constitutes a very different paradigm (Shmuelof et al 2012;Sternad et al 2014). The robustness must be ascribed to the extensive practice, and probably the self-guided nature of practice, where subjects largely rely on intrinsic proprioceptive information.…”

Section: Hypothesis 2: Robust Retention Of Spatiotemporal Patternscontrasting

confidence: 62%

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Robust retention of individual sensorimotor skill after self-guided practice

Park

Sternad

2015

Journal of Neurophysiology

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Long-term retention of a motor skill has received relatively little systematic study, even though lasting neuroplasticity is the holy grail of any clinical intervention. This study examined the acquisition and retention of a novel bimanual polyrhythmic skill, practiced with sparse explicit feedback mimicking real-life scenarios. Self-paced and metronome-paced practice conditions were compared in their effect on long-term retention. Two groups of subjects first underwent extensive practice of 20 practice sessions over 2 mo, then followed up with three retention sessions after 3 mo. Results showed that subjects developed robust spatiotemporal patterns, despite the lack of reward and little quantitative error feedback about their performance (Hypothesis 1). These movement patterns were reproduced after a 3-mo interval, frequently even in the first trial, with no intermediate practice (Hypothesis 2). Self-paced training of movement patterns led to slightly less variability in the retention test (Hypothesis 3). These results document the specificity and stability of kinematic patterns and their underlying neuroplastic changes and underscore the effectiveness of self-guided practice. The findings are discussed in the context of current neuroimaging results and their clinical implications.

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Section: Hypothesis 1: Self-guided Learning With and Without Metronomesupporting

confidence: 91%

Section: Hypothesis 2: Robust Retention Of Spatiotemporal Patternscontrasting

confidence: 62%

Robust retention of individual sensorimotor skill after self-guided practice

Park

Sternad

2015

Journal of Neurophysiology

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“…Investigations into the development of an accurate and robust internal model which underlies expertise are made difficult, because typically years of practice are required. Consequently, current research has focused on a variety of voluntary eye -hand coordination tasks in which certain individuals are able to rapidly acquire exceptional skill [9,10]. As expertise develops, the accuracy and uniformity of task performance increases, but muscular activations [11] and overall brain activation decrease, except in those brain regions most essential for task performance [12,13].…”

Section: Introductionmentioning

confidence: 99%

Control at stability's edge minimizes energetic costs: expert stick balancing

Milton

Meyer

Zhvanetsky

et al. 2016

J. R. Soc. Interface.

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Stick balancing on the fingertip is a complex voluntary motor task that requires the stabilization of an unstable system. For seated expert stick balancers, the time delay is 0.23 s, the shortest stick that can be balanced for 240 s is 0.32 m and there is a % 0:88 dead zone for the estimation of the vertical displacement angle in the saggital plane. These observations motivate a switching-type, pendulum -cart model for balance control which uses an internal model to compensate for the time delay by predicting the sensory consequences of the stick's movements. Numerical simulations using the semi-discretization method suggest that the feedback gains are tuned near the edge of stability. For these choices of the feedback gains, the cost function which takes into account the position of the fingertip and the corrective forces is minimized. Thus, expert stick balancers optimize control with a combination of quick manoeuvrability and minimum energy expenditures.

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“…Their existence potentially explains how the central nervous system is able to perform tasks accurately despite the redundant and noisy sensorimotor system [1]- [4]. They also appear at task level: grasping and manipulation require coordination between objects in the environment and the degrees of freedom of the hands.…”

Section: Introductionmentioning

confidence: 99%

Learning task-space synergies using Riemannian geometry

Zeestraten

Havoutis

Calinon

et al. 2017

2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Abstract-In the context of robotic control, synergies can form elementary units of behavior. By specifying taskdependent coordination behaviors at a low control level, one can achieve task-specific disturbance rejection. In this work we present an approach to learn the parameters of such lowlevel controllers by demonstration. We identify a synergy by extracting covariance information from demonstration data. The extracted synergy is used to derive a time-invariant state feedback controller through optimal control. To cope with the non-Euclidean nature of robot poses, we utilize Riemannian geometry, where both estimation of the covariance and the associated controller take into account the geometry of the pose manifold. We demonstrate the efficacy of the approach experimentally in a bimanual manipulation task.

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Acquisition of Novel and Complex Motor Skills: Stable Solutions Where Intrinsic Noise Matters Less

Cited by 51 publications

References 46 publications

Robust retention of individual sensorimotor skill after self-guided practice

Robust retention of individual sensorimotor skill after self-guided practice

Control at stability's edge minimizes energetic costs: expert stick balancing

Learning task-space synergies using Riemannian geometry

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