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Non-linear stimulus-response behavior of the human stance control system is predicted by optimization of a system with sensory and motor noise.

van der Kooij H, Peterka RJ - J Comput Neurosci (2010)

Bottom Line: Different combinations of internal sensory and/or motor noise sources were added to the model to identify the properties of noise sources that were able to account for the experimental remnant sway characteristics.Robust findings were that remnant sway characteristics were best predicted by models that included both sensory and motor noise, the graviceptive noise magnitude was about ten times larger than the proprioceptive noise, and noise sources with signal-dependent properties provided better explanations of remnant sway.Overall results indicate that humans dynamically weight sensory system contributions to stance control and tune their corrective responses to minimize the energetic effects of sensory noise and external stimuli.

View Article: PubMed Central - PubMed

Affiliation: Department of Biomechanical Engineering, University of Twente, 7500 AE, Enschede, The Netherlands. H.vanderKooij@utwente.nl

ABSTRACT
We developed a theory of human stance control that predicted (1) how subjects re-weight their utilization of proprioceptive and graviceptive orientation information in experiments where eyes closed stance was perturbed by surface-tilt stimuli with different amplitudes, (2) the experimentally observed increase in body sway variability (i.e. the "remnant" body sway that could not be attributed to the stimulus) with increasing surface-tilt amplitude, (3) neural controller feedback gains that determine the amount of corrective torque generated in relation to sensory cues signaling body orientation, and (4) the magnitude and structure of spontaneous body sway. Responses to surface-tilt perturbations with different amplitudes were interpreted using a feedback control model to determine control parameters and changes in these parameters with stimulus amplitude. Different combinations of internal sensory and/or motor noise sources were added to the model to identify the properties of noise sources that were able to account for the experimental remnant sway characteristics. Various behavioral criteria were investigated to determine if optimization of these criteria could predict the identified model parameters and amplitude-dependent parameter changes. Robust findings were that remnant sway characteristics were best predicted by models that included both sensory and motor noise, the graviceptive noise magnitude was about ten times larger than the proprioceptive noise, and noise sources with signal-dependent properties provided better explanations of remnant sway. Overall results indicate that humans dynamically weight sensory system contributions to stance control and tune their corrective responses to minimize the energetic effects of sensory noise and external stimuli.

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Model of human stance control in which sensory information from proprioceptive and graviceptive systems is weighted (wp and wg) to provide an estimate of body-in-space sway angle, bsest. However, bsest is potentially biased from true body-in-space orientation due to sustained perturbations such as stance on a tilted surface. The model provides a mechanism based on feedback from a low-pass filtered torque signal, t, to drive body motion in a direction that reduces the corrective torque necessary to maintain stance. This torque feedback mechanism can be thought of as providing a slowly time-varying internal reference signal, bsref, for comparison with bsest. The difference between bsref and bsest is fed through a time delay, neural controller, and second order activation dynamics to produce a corrective torque, tc, that stabilizes human stance. The different neural feedback pathways are affected by sensory noise (vg, vt, and vp). In addition motor noise, vm, adds variability to the corrective torque. Besides the neural feedback pathways, intrinsic muscle/tendon dynamics contribute to the stabilizing corrective torque. Muscle/tendon dynamics are represented by a spring (ki) and damper (bi). Transfer function equations for this model and model components are given in Appendix B
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Fig2: Model of human stance control in which sensory information from proprioceptive and graviceptive systems is weighted (wp and wg) to provide an estimate of body-in-space sway angle, bsest. However, bsest is potentially biased from true body-in-space orientation due to sustained perturbations such as stance on a tilted surface. The model provides a mechanism based on feedback from a low-pass filtered torque signal, t, to drive body motion in a direction that reduces the corrective torque necessary to maintain stance. This torque feedback mechanism can be thought of as providing a slowly time-varying internal reference signal, bsref, for comparison with bsest. The difference between bsref and bsest is fed through a time delay, neural controller, and second order activation dynamics to produce a corrective torque, tc, that stabilizes human stance. The different neural feedback pathways are affected by sensory noise (vg, vt, and vp). In addition motor noise, vm, adds variability to the corrective torque. Besides the neural feedback pathways, intrinsic muscle/tendon dynamics contribute to the stabilizing corrective torque. Muscle/tendon dynamics are represented by a spring (ki) and damper (bi). Transfer function equations for this model and model components are given in Appendix B

Mentions: We used a multi-stage approach to investigate the hypothesis that a model described below (Fig. 2) and associated optimizations based on this model can account for the experimentally observed sensory re-weighting, the properties of the neural controller, and the patterns of remnant sway. The analysis stages include (Stage 1) estimation of stance control system parameters and sensory weights, (Stage 2) exploration of alternative internal noise sources (sensory only, motor only, and combinations of sensory and motor sources) and various functional forms of these noise sources to determine which noise source best accounts for the experimentally observed remnant sway, (Stage 3) given a particular noise source, identification of a behavioral criterion that predicts the observed sensory re-weighting, (Stage 4) given a particular noise source, identification of a behavioral criterion that predicts the observed neural controller parameters, and (Stage 5) demonstration that the identified noise source accurately predicts spontaneous sway behavior.Fig. 2


Non-linear stimulus-response behavior of the human stance control system is predicted by optimization of a system with sensory and motor noise.

van der Kooij H, Peterka RJ - J Comput Neurosci (2010)

Model of human stance control in which sensory information from proprioceptive and graviceptive systems is weighted (wp and wg) to provide an estimate of body-in-space sway angle, bsest. However, bsest is potentially biased from true body-in-space orientation due to sustained perturbations such as stance on a tilted surface. The model provides a mechanism based on feedback from a low-pass filtered torque signal, t, to drive body motion in a direction that reduces the corrective torque necessary to maintain stance. This torque feedback mechanism can be thought of as providing a slowly time-varying internal reference signal, bsref, for comparison with bsest. The difference between bsref and bsest is fed through a time delay, neural controller, and second order activation dynamics to produce a corrective torque, tc, that stabilizes human stance. The different neural feedback pathways are affected by sensory noise (vg, vt, and vp). In addition motor noise, vm, adds variability to the corrective torque. Besides the neural feedback pathways, intrinsic muscle/tendon dynamics contribute to the stabilizing corrective torque. Muscle/tendon dynamics are represented by a spring (ki) and damper (bi). Transfer function equations for this model and model components are given in Appendix B
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC3108015&req=5

Fig2: Model of human stance control in which sensory information from proprioceptive and graviceptive systems is weighted (wp and wg) to provide an estimate of body-in-space sway angle, bsest. However, bsest is potentially biased from true body-in-space orientation due to sustained perturbations such as stance on a tilted surface. The model provides a mechanism based on feedback from a low-pass filtered torque signal, t, to drive body motion in a direction that reduces the corrective torque necessary to maintain stance. This torque feedback mechanism can be thought of as providing a slowly time-varying internal reference signal, bsref, for comparison with bsest. The difference between bsref and bsest is fed through a time delay, neural controller, and second order activation dynamics to produce a corrective torque, tc, that stabilizes human stance. The different neural feedback pathways are affected by sensory noise (vg, vt, and vp). In addition motor noise, vm, adds variability to the corrective torque. Besides the neural feedback pathways, intrinsic muscle/tendon dynamics contribute to the stabilizing corrective torque. Muscle/tendon dynamics are represented by a spring (ki) and damper (bi). Transfer function equations for this model and model components are given in Appendix B
Mentions: We used a multi-stage approach to investigate the hypothesis that a model described below (Fig. 2) and associated optimizations based on this model can account for the experimentally observed sensory re-weighting, the properties of the neural controller, and the patterns of remnant sway. The analysis stages include (Stage 1) estimation of stance control system parameters and sensory weights, (Stage 2) exploration of alternative internal noise sources (sensory only, motor only, and combinations of sensory and motor sources) and various functional forms of these noise sources to determine which noise source best accounts for the experimentally observed remnant sway, (Stage 3) given a particular noise source, identification of a behavioral criterion that predicts the observed sensory re-weighting, (Stage 4) given a particular noise source, identification of a behavioral criterion that predicts the observed neural controller parameters, and (Stage 5) demonstration that the identified noise source accurately predicts spontaneous sway behavior.Fig. 2

Bottom Line: Different combinations of internal sensory and/or motor noise sources were added to the model to identify the properties of noise sources that were able to account for the experimental remnant sway characteristics.Robust findings were that remnant sway characteristics were best predicted by models that included both sensory and motor noise, the graviceptive noise magnitude was about ten times larger than the proprioceptive noise, and noise sources with signal-dependent properties provided better explanations of remnant sway.Overall results indicate that humans dynamically weight sensory system contributions to stance control and tune their corrective responses to minimize the energetic effects of sensory noise and external stimuli.

View Article: PubMed Central - PubMed

Affiliation: Department of Biomechanical Engineering, University of Twente, 7500 AE, Enschede, The Netherlands. H.vanderKooij@utwente.nl

ABSTRACT
We developed a theory of human stance control that predicted (1) how subjects re-weight their utilization of proprioceptive and graviceptive orientation information in experiments where eyes closed stance was perturbed by surface-tilt stimuli with different amplitudes, (2) the experimentally observed increase in body sway variability (i.e. the "remnant" body sway that could not be attributed to the stimulus) with increasing surface-tilt amplitude, (3) neural controller feedback gains that determine the amount of corrective torque generated in relation to sensory cues signaling body orientation, and (4) the magnitude and structure of spontaneous body sway. Responses to surface-tilt perturbations with different amplitudes were interpreted using a feedback control model to determine control parameters and changes in these parameters with stimulus amplitude. Different combinations of internal sensory and/or motor noise sources were added to the model to identify the properties of noise sources that were able to account for the experimental remnant sway characteristics. Various behavioral criteria were investigated to determine if optimization of these criteria could predict the identified model parameters and amplitude-dependent parameter changes. Robust findings were that remnant sway characteristics were best predicted by models that included both sensory and motor noise, the graviceptive noise magnitude was about ten times larger than the proprioceptive noise, and noise sources with signal-dependent properties provided better explanations of remnant sway. Overall results indicate that humans dynamically weight sensory system contributions to stance control and tune their corrective responses to minimize the energetic effects of sensory noise and external stimuli.

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