Evidence map›Paper›PMID 28437451›Full record

ArticlePLoS computational biology2017

Predicting explorative motor learning using decision-making and motor noise.

Xiuli Chen, Kieran Mohr, Joseph M Galea

Abstract read
In one paragraph

Article in PLoS computational biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.

0numbers the graph read from it
0cells of the map it votes in
38citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

38 citing papers in PubMed.

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  8. The Trembling Hand Unraveled: Motor Dynamics and Choice Inconsistency.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025
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  18. Common Mechanisms of Learning in Motor and Cognitive Systems.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2023
    Review
  19. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Xiuli ChenSchool of Psychology, University of Birmingham, Birmingham, United Kingdom.ORCID 0000-0002-1549-4352
Kieran MohrSchool of Psychology, University of Birmingham, Birmingham, United Kingdom.ORCID 0000-0002-6165-7810
Joseph M GaleaSchool of Psychology, University of Birmingham, Birmingham, United Kingdom.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A fundamental problem faced by humans is learning to select motor actions based on noisy sensory information and incomplete knowledge of the world. Recently, a number of authors have asked whether this type of motor learning problem might be very similar to a range of higher-level decision-making problems. If so, participant behaviour on a high-level decision-making task could be predictive of their performance during a motor learning task. To investigate this question, we studied performance during an explorative motor learning task and a decision-making task which had a similar underlying structure with the exception that it was not subject to motor (execution) noise. We also collected an independent measurement of each participant's level of motor noise. Our analysis showed that explorative motor learning and decision-making could be modelled as the (approximately) optimal solution to a Partially Observable Markov Decision Process bounded by noisy neural information processing. The model was able to predict participant performance in motor learning by using parameters estimated from the decision-making task and the separate motor noise measurement. This suggests that explorative motor learning can be formalised as a sequential decision-making process that is adjusted for motor noise, and raises interesting questions regarding the neural origin of explorative motor learning.

Indexed as

Models, NeurologicalAdultAlgorithmsComputational BiologyDecision MakingFemaleHumansLearningMaleMotor ActivityPsychomotor PerformanceYoung Adult

Identifiers

PMID28437451
PMCPMC5421818

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Registered trials

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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.