ArticleJournal of neurophysiology2016
Persistence of reduced neuromotor noise in long-term motor skill learning.
Article in Journal of neurophysiology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
12 citing papers in PubMed.
- The effect of transcranial random noise stimulation on the movement time and components of noise, co-variation, and tolerance in a perceptual-motor task.Scientific reports · 2025Trial
- Bridging the divide in motor learning research.Nature human behaviour · 2026Review
- Emergent motor timing enhances time perception.iScience · 2025Article
- Simulated operant reflex conditioning environment reveals effects of feedback parameters.PloS one · 2024Article
- Skilled motor control of an inverted pendulum implies low entropy of states but high entropy of actions.PLoS computational biology · 2023Article
- Back to reality: differences in learning strategy in a simplified virtual and a real throwing task.Journal of neurophysiology · 2021Article
- Multiple processes independently predict motor learning.Journal of neuroengineering and rehabilitation · 2020Article
- Learning and transfer of complex motor skills in virtual reality: a perspective review.Journal of neuroengineering and rehabilitation · 2019Review
- Differential control of task and null space variability in response to changes in task difficulty when learning a bimanual steering task.Experimental brain research · 2019Article
- Gender Differences in Throwing Revisited: Sensorimotor Coordination in a Virtual Ball Aiming Task.Frontiers in human neuroscience · 2019Article
- It's Not (Only) the Mean that Matters: Variability, Noise and Exploration in Skill Learning.Current opinion in behavioral sciences · 2018Article
- Exploiting the geometry of the solution space to reduce sensitivity to neuromotor noise.PLoS computational biology · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
It is well documented that variability in motor performance decreases with practice, yet the neural and computational mechanisms that underlie this decline, particularly during long-term practice, are little understood. Decreasing variability is frequently examined in terms of error corrections from one trial to the next. However, the ubiquitous noise from all levels of the sensorimotor system is also a significant contributor to overt variability. While neuromotor noise is typically assumed and modeled as immune to practice, the present study challenged this notion. We investigated the long-term practice of a novel motor skill to test whether neuromotor noise can be attenuated, specifically when aided by reward. Results showed that both reward and self-guided practice over 11 days improved behavior by decreasing noise rather than effective error corrections. When the challenge for obtaining reward increased, subjects reduced noise even further. Importantly, when task demands were relaxed again, this reduced level of noise persisted for 5 days. A stochastic learning model replicated both the attenuation and persistence of noise by scaling the noise amplitude as a function of reward. More insight into variability and intrinsic noise and its malleability has implications for training and rehabilitation interventions.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.