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Optimality and Modularity in Human Movement: From Optimal Control to Muscle Synergies

  • Bastien BerretEmail author
  • Ioannis Delis
  • Jérémie Gaveau
  • Frédéric Jean
Chapter
Part of the Springer Tracts in Advanced Robotics book series (STAR, volume 124)

Abstract

In this chapter, we review recent work related to the optimal and modular control hypotheses for human movement. Optimal control theory is often thought to imply that the brain continuously computes global optima for each motor task it faces. Modular control theory typically assumes that the brain explicitly stores genuine synergies in specific neural circuits whose combined recruitment yields task-effective motor inputs to muscles. Put this way, these two influential motor control theories are pushed to extreme positions. A more nuanced view, framed within Marr’s tri-level taxonomy of a computational theory of movement neuroscience, is discussed here. We argue that optimal control is best viewed as helping to understand “why” certain movements are preferred over others but does not say much about how the brain would practically trigger optimal strategies. We also argue that dimensionality reduction found in muscle activities may be a by-product of optimality and cannot be attributed to neurally hardwired synergies stricto sensu, in particular when the synergies are extracted from simple factorization algorithms applied to electromyographic data; their putative nature is indeed strongly dictated by the methodology itself. Hence, more modeling work is required to critically test the modularity hypothesis and assess its potential neural origins. We propose that an adequate mathematical formulation of hierarchical motor control could help to bridge the gap between optimality and modularity, thereby accounting for the most appealing aspects of the human motor controller that robotic controllers would like to mimic: rapidity, efficiency, and robustness.

Notes

Acknowledgements

This work is supported by a public grant overseen by the French National research Agency (ANR) as part of the Investissement d Avenir program, through the iCODE project funded by the IDEX Paris-Saclay, ANR-11-IDEX-0003-02.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  • Bastien Berret
    • 1
    • 2
    • 3
    Email author
  • Ioannis Delis
    • 4
    • 5
  • Jérémie Gaveau
    • 6
  • Frédéric Jean
    • 7
  1. 1.CIAMSUniversité Paris-Sud, Université Paris-SaclayOrsayFrance
  2. 2.CIAMSUniversité d’OrléansOrl éansFrance
  3. 3.Institut Universitaire de France (IUF)ParisFrance
  4. 4.Department of Biomedical EngineeringColumbia UniversityNew YorkUSA
  5. 5.School of Biomedical SciencesUniversity of LeedsLeedsUK
  6. 6.INSERM U1093-CAPSUniversité Bourgogne Franche-ComtéDijonFrance
  7. 7.Unité de Mathématiques AppliquéesENSTA ParisTech, Université Paris-SaclayPalaiseauFrance

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