Evidence map›Paper›PMID 33704439›Full record

ArticleBrain : a journal of neurology2021

The computational neurology of movement under active inference.

Thomas Parr, Jakub Limanowski, Vishal Rawji, Karl Friston

Open access · hybridAbstract read
In one paragraph

Article in Brain : a journal of neurology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.

0numbers the graph read from it
0cells of the map it votes in
33citing papers in PubMed
5.3field-weighted citation impact, top 3% of its field
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

33 citing papers in PubMed, 61 citations in OpenAlex.

  1. Article
  2. Review
  3. Review
  4. Review
  5. Active inference and speech motor control.Psychonomic bulletin & review · 2026
    Review
  6. Article
  7. SPM-30 years and beyond.Cerebral cortex (New York, N.Y. : 1991) · 2025
    Review
  8. Priors and proprioceptive predictions.Current opinion in behavioral sciences · 2025
    Article
  9. Embodied decisions as active inference.PLoS computational biology · 2025
    Article
  10. Article
  11. Article
  12. Article
  13. An active inference account of stuttering behavior.Frontiers in human neuroscience · 2025
    Article
  14. Review
  15. The Many Roles of Precision in Action.Entropy (Basel, Switzerland) · 2024
    Review
  16. Collective behavior from surprise minimization.Proceedings of the National Academy of Sciences of the United States of America · 2024
    Article
  17. Article
  18. Deep kinematic inference affords efficient and scalable control of bodily movements.Proceedings of the National Academy of Sciences of the United States of America · 2023
    Article
  19. Article
  20. Article
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

4 authors at 3 institutions in 2 countries.

Thomas ParrWellcome Centre for Human Neuroimaging, Queen Square Institute of Neurology, University College London, London WC1N 3BG, UK.ORCID 0000-0001-5108-5743
Jakub LimanowskiFaculty of Psychology and Center for Tactile Internet with Human-in-the-Loop, Technische Universität Dresden, Dresden, Germany.ORCID 0000-0001-9673-5500
Vishal RawjiDepartment of Clinical and Movement Neurosciences, Queen Square Institute of Neurology, University College London, London WC1N 3BG, UK.ORCID 0000-0001-7291-0204
Karl FristonWellcome Centre for Human Neuroimaging, Queen Square Institute of Neurology, University College London, London WC1N 3BG, UK.ORCID 0000-0001-7984-8909
Wellcome Centre for Human Neuroimaging · GBNational Hospital for Neurology and Neurosurgery · GBTechnische Universität Dresden · DE

Funding

Wellcome Principal Research Fellow 088130/Z/09/ZWellcome Trust
6 · The paper itself

Abstract

We propose a computational neurology of movement based on the convergence of theoretical neurobiology and clinical neurology. A significant development in the former is the idea that we can frame brain function as a process of (active) inference, in which the nervous system makes predictions about its sensory data. These predictions depend upon an implicit predictive (generative) model used by the brain. This means neural dynamics can be framed as generating actions to ensure sensations are consistent with these predictions-and adjusting predictions when they are not. We illustrate the significance of this formulation for clinical neurology by simulating a clinical examination of the motor system using an upper limb coordination task. Specifically, we show how tendon reflexes emerge naturally under the right kind of generative model. Through simulated perturbations, pertaining to prior probabilities of this model's variables, we illustrate the emergence of hyperreflexia and pendular reflexes, reminiscent of neurological lesions in the corticospinal tract and cerebellum. We then turn to the computational lesions causing hypokinesia and deficits of coordination. This in silico lesion-deficit analysis provides an opportunity to revisit classic neurological dichotomies (e.g. pyramidal versus extrapyramidal systems) from the perspective of modern approaches to theoretical neurobiology-and our understanding of the neurocomputational architecture of movement control based on first principles.

Indexed as

Computer SimulationModels, NeurologicalBrainHumansMovementNeurologyactive inferenceBayesianmovementneurologyplanning

Identifiers

PMID33704439
PMCPMC8320263
OpenAlexW3134192892

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.