ReviewFrontiers in human neuroscience2021
Mechanisms of Human Motor Learning Do Not Function Independently.
Review in Frontiers in human neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it, 23 citations in OpenAlex.
- Motor Learning in Older Adults with Mild Cognitive Impairment: A Systematic Review.Neuropsychology review · 2026Pooled it
- Successful reinforcement history suppresses explicit and implicit error corrections.PLoS computational biology · 2026Article
- Hypothesis testing governs strategic motor learning.NPJ science of learning · 2026Article
- Human-inspired sensorimotor controller for dynamic motion adaptation: a study in robotic arms.Frontiers in neurorobotics · 2026Article
- A structural causal model for robot-assisted upper-limb neurorehabilitation.Frontiers in rehabilitation sciences · 2026Article
- Involvement of Perimovement Neural Beta-Oscillations in Strategic Aiming for Motor Adaptation.The European journal of neuroscience · 2025Article
- Review
- Differential Aging Effects on Implicit and Explicit Sensorimotor Learning.bioRxiv : the preprint server for biology · 2024Article
- Imprecise perception of hand position during early motor adaptation.Journal of neurophysiology · 2024Article
- The effect of reward on motor learning: different stage, different effect.Frontiers in human neuroscience · 2024Review
- Cerebellar Degeneration Impairs Strategy Discovery but Not Strategy Recall.Cerebellum (London, England) · 2023Article
- Neural Mechanisms of Neuro-Rehabilitation Using Transcranial Direct Current Stimulation (tDCS) over the Front-Polar Area.Brain sciences · 2023Review
- Exploring the role of task success in implicit motor adaptation.Journal of neurophysiology · 2023Article
- Implicit reward-based motor learning.bioRxiv : the preprint server for biology · 2023Article
- Exploring the role of task success in implicit motor adaptation.bioRxiv : the preprint server for biology · 2023Article
- Author Response to Macpherson et al.Physical therapy · 2022Article
- Implicit sensorimotor adaptation is preserved in Parkinson's disease.Brain communications · 2022Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
Human motor learning is governed by a suite of interacting mechanisms each one of which modifies behavior in distinct ways and rely on different neural circuits. In recent years, much attention has been given to one type of motor learning, called motor adaptation. Here, the field has generally focused on the interactions of three mechanisms: sensory prediction error SPE-driven, explicit (strategy-based), and reinforcement learning. Studies of these mechanisms have largely treated them as modular, aiming to model how the outputs of each are combined in the production of overt behavior. However, when examined closely the results of some studies also suggest the existence of additional interactions between the sub-components of each learning mechanism. In this perspective, we propose that these sub-component interactions represent a critical means through which different motor learning mechanisms are combined to produce movement; understanding such interactions is critical to advancing our knowledge of how humans learn new behaviors. We review current literature studying interactions between SPE-driven, explicit, and reinforcement mechanisms of motor learning. We then present evidence of sub-component interactions between SPE-driven and reinforcement learning as well as between SPE-driven and explicit learning from studies of people with cerebellar degeneration. Finally, we discuss the implications of interactions between learning mechanism sub-components for future research in human motor learning.
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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.