<p>The equilibrium point hypothesis (EPH) proposes that movement control arises from shifting referent configurations (<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\lambda \)</EquationSource> </InlineEquation>) that set muscle activation thresholds, with behavior emerging from neuromechanical interactions. Here, we review theoretical, neurophysiological, and computational evidence indicating that, for realistic multi-joint behavior, EPH is under-specified: it does not resolve inverse kinematics/dynamics, impedance regulation, or temporal coordination, and it conflicts with established force–length and force–velocity muscle properties and task-dependent reflex modulation. Perturbation, load, and obstacle-avoidance studies reveal flexible, goal-dependent corrections inconsistent with passive convergence to fixed referent states. Neural data show continuous preparatory and online control of kinematics and dynamics, contradicting EPH’s central premise. We conclude that, despite its historical influence and contributions, EPH lacks the mechanistic adequacy required of a contemporary theory of motor control and should be succeeded by biologically grounded frameworks that integrate neural processes, biomechanics, and task-level function across basic and applied domains.</p>

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The equilibrium point hypothesis revisited: why threshold control does not explain human movement

  • Madhur Mangalam,
  • Nick Stergiou

摘要

The equilibrium point hypothesis (EPH) proposes that movement control arises from shifting referent configurations ( \(\lambda \) ) that set muscle activation thresholds, with behavior emerging from neuromechanical interactions. Here, we review theoretical, neurophysiological, and computational evidence indicating that, for realistic multi-joint behavior, EPH is under-specified: it does not resolve inverse kinematics/dynamics, impedance regulation, or temporal coordination, and it conflicts with established force–length and force–velocity muscle properties and task-dependent reflex modulation. Perturbation, load, and obstacle-avoidance studies reveal flexible, goal-dependent corrections inconsistent with passive convergence to fixed referent states. Neural data show continuous preparatory and online control of kinematics and dynamics, contradicting EPH’s central premise. We conclude that, despite its historical influence and contributions, EPH lacks the mechanistic adequacy required of a contemporary theory of motor control and should be succeeded by biologically grounded frameworks that integrate neural processes, biomechanics, and task-level function across basic and applied domains.