ArticlePLoS computational biology2019
The dynamics of motor learning through the formation of internal models.
Article in PLoS computational biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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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.
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Who cites it
10 citing papers in PubMed.
- A modular architecture for trial-by-trial learning of redundant muscle activity patterns in novel sensorimotor tasks.PLoS computational biology · 2026Article
- Sense of agency for a new motor skill emerges via the formation of a structural internal model.Communications psychology · 2025Article
- Sensorimotor environment but not task rule reconfigures population dynamics in rhesus monkey posterior parietal cortex.Nature communications · 2025Article
- Human motor learning dynamics in high-dimensional tasks.PLoS computational biology · 2024Article
- Corticostriatal activity related to performance during continuous de novo motor learning.Scientific reports · 2024Article
- Article
- A precise and adaptive neural mechanism for predictive temporal processing in the frontal cortex.Neuron · 2021Article
- Article
- A Framework for Optimizing Co-adaptation in Body-Machine Interfaces.Frontiers in neurorobotics · 2021Article
- Distinct neural signatures in a sensorimotor synchronization-continuation task.Imaging neuroscience (Cambridge, Mass.)Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
A medical student learning to perform a laparoscopic procedure or a recently paralyzed user of a powered wheelchair must learn to operate machinery via interfaces that translate their actions into commands for an external device. Since the user's actions are selected from a number of alternatives that would result in the same effect in the control space of the external device, learning to use such interfaces involves dealing with redundancy. Subjects need to learn an externally chosen many-to-one map that transforms their actions into device commands. Mathematically, we describe this type of learning as a deterministic dynamical process, whose state is the evolving forward and inverse internal models of the interface. The forward model predicts the outcomes of actions, while the inverse model generates actions designed to attain desired outcomes. Both the mathematical analysis of the proposed model of learning dynamics and the learning performance observed in a group of subjects demonstrate a first-order exponential convergence of the learning process toward a particular state that depends only on the initial state of the inverse and forward models and on the sequence of targets supplied to the users. Noise is not only present but necessary for the convergence of learning through the minimization of the difference between actual and predicted outcomes.
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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.