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Cited by 212Motor Control and AdaptationEmbodied and Extended CognitionBayesian Modeling and Causal Inference

Probabilistic models in human sensorimotor control

Open access
Wolpert, Daniel M.·2007·Human Movement Science
DOI 10.1016/j.humov.2007.05.005PMC PMC2637437
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Abstract

Sensory and motor uncertainty form a fundamental constraint on human sensorimotor control. Bayesian decision theory (BDT) has emerged as a unifying framework to understand how the central nervous system performs optimal estimation and control in the face of such uncertainty. BDT has two components: Bayesian statistics and decision theory. Here we review Bayesian statistics and show how it applies to estimating the state of the world and our own body. Recent results suggest that when learning novel tasks we are able to learn the statistical properties of both the world and our own sensory apparatus so as to perform estimation using Bayesian statistics. We review studies which suggest that humans can combine multiple sources of information to form maximum likelihood estimates, can incorporate prior beliefs about possible states of the world so as to generate maximum a posteriori estimates and can use Kalman filter-based processes to estimate time-varying states. Finally, we review Bayesian decision theory in motor control and how the central nervous system processes errors to determine loss functions and select optimal actions. We review results that suggest we plan movements based on statistics of our actions that result from signal-dependent noise on our motor outputs. Taken together these studies provide a statistical framework for how the motor system performs in the presence of uncertainty.

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  • With the notable exception of signal-dependent noise, how neural activity in biological control networks shapes variability in motor output remains largely unexplored.

    “However, with the notable exception of signal-dependent noise 54, how neural activity in biological control networks shapes variability in motor output remains largely unexplored (though see 55).”

    From A virtual rodent predicts the structure of neural activity across behaviours (2024) · cites this paper as [55]

  • According to recent statistical models of perception, the interpretation of multisensory signals could be sped up and made more precise by incorporating memory information ('priors') in the decision process.

    “According to recent statistical models of perception, the interpretation of multisensory signals could be sped up and made more precise by incorporating memory information (‘priors’) in the decision process [44], [45], [46].”

    From If I were you: Perceptual illusion of body swapping (2008) · cites this paper as [46]

  • Bayesian models of cognition have primarily flourished as explanations of the complex dependency of behavior on the environment in domains including intuitive physics, causal learning, perception, motor control, and language.

    “Bayesian models of cognition have primarily flourished as explanations of the complex dependency of behavior on the environment in domains including intuitive physics, causal learning, perception, motor control, and language [1-10]”

    From Bayesian Brains without Probabilities (2016) · cites this paper as [9]

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Authors
Wolpert, D. M.
Affiliations
University of Cambridge (GB)
Corresponding author
Wolpert, Daniel M.
ORCID
Wolpert, Daniel M.
Venue
Human Movement Science
Publisher
Elsevier BV
Published
2007-08
Volume
26
Issue
4
Pages
511-524
ISSN
0167-9457 (ISSN-L)
DOI
10.1016/j.humov.2007.05.005
PMC
PMC2637437
OpenAlex
W2164603955
Cited by
212
References
66
Citations by year
2026: 6 · 2025: 10 · 2024: 8 · 2023: 7 · 2022: 10 · 2021: 6 · 2020: 13 · 2019: 9 · 2018: 14 · 2017: 14 · 2016: 11 · 2015: 18 · 2014: 11 · 2013: 23 · 2012: 19
FWCI
4.24
Type
journal article
Language
en
Open access
Licence
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations
  • Human Movement Science (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
  • Cambridge University Engineering Department Publications Database (submittedVersion)
Funders
Wellcome Trust (077730)
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Motor Control and Adaptation
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Embodied and Extended Cognition
  • Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
Keywords
Bayesian decision theory, sensorimotor control, Bayesian analysis, maximum likelihood estimation, maximum a posteriori estimation, Kalman filter, sensory uncertainty, prior beliefs, error processing, loss functions, motor planning, signal-dependent noise, optimal control, state estimation
MeSH terms
Bayes Theorem, Central Nervous System, Culture, Decision Theory, Humans, Kinesthesis, Movement, Orientation, Perception, Proprioception, Psychomotor Performance, Social Environment, Models, Statistical, Likelihood Functions, Uncertainty

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