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Cited by 225Motor Control and AdaptationEmbodied and Extended CognitionMemory and Neural Mechanisms

Internal models in biological control

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McNamee, Daniel, Wolpert, Daniel M.·2019·Annual Review of Control, Robotics, and Autonomous Systems
DOI 10.1146/annurev-control-060117-105206PMC PMC6520231
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Abstract

Rationality principles such as optimal feedback control and Bayesian inference underpin a probabilistic framework that has accounted for a range of empirical phenomena in biological sensorimotor control. To facilitate the optimization of flexible and robust behaviors consistent with these theories, the ability to construct internal models of the motor system and environmental dynamics can be crucial. In the context of this theoretic formalism, we review the computational roles played by such internal models and the neural and behavioral evidence for their implementation in the brain.

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  • Forward models can help embodied agents to estimate and adjust the positions of their effectors in the course of action, by providing a representation of the expected position at each moment.

    “In particular, forward models can help embodied agents to estimate and adjust the positions of their effectors in the course of action, by providing a representation of the expected position at each moment.”

    From Consciousness in Artificial Intelligence: Insights from the Science of Consciousness (2023) · cites this paper as McNamee & Wolpert 2019

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Authors
McNamee, D., & Wolpert, D. M.
Affiliations
University of Cambridge (GB)
Corresponding author
  • McNamee, Daniel
  • Wolpert, Daniel M.
ORCID
Venue
Annual Review of Control, Robotics, and Autonomous Systems
Publisher
Annual Reviews
Published
2019-05-03
Volume
2
Issue
1
Pages
339-364
ISSN
2573-5144 (ISSN-L)
DOI
10.1146/annurev-control-060117-105206
PMC
PMC6520231
OpenAlex
W2906340543
Cited by
225
References
140
Citations by year
2026: 23 · 2025: 34 · 2024: 45 · 2023: 40 · 2022: 42 · 2021: 30 · 2020: 10 · 2019: 1
FWCI
2.63
Type
journal article
Language
en
Open access
Locations
  • Annual Review of Control Robotics and Autonomous Systems (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
  • Cambridge University Engineering Department Publications Database (submittedVersion)
Funders
Wellcome Trust (097803, 110257/A/15/Z, 097803/Z/11/Z)
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Motor Control and Adaptation
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Embodied and Extended Cognition
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
Keywords
internal models, biological control, optimal feedback control, Bayesian inference, sensorimotor control, motor system, environmental dynamics, neural implementation, computational neuroscience

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