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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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Results of the Stage 1 analysis. (a) Gain (upper graph) and phase (lower) of the mean experimental frequency response functions, FRFs, (points connected by dotted lines) and model fitted transfer functions (solid lines) of surface tilt to body sway for the five different stimulus amplitudes. The proprioceptive weight parameters, wp, were allowed to vary over experimental conditions. Graviceptive weights, wg, also varied but were linked to wp values such that wg = 1-wp. The model-fitted parameters that were constant over the five stimulus amplitudes were joint stiffness (ki = 40.5 Nm/rad) and damping (bi = 68.8 Nms/rad), the neural controller proportional (kp = 943.9 Nm/rad) and derivative (kd = 313.5 Nms/rad) gains, the lumped neural time delay (τd = 0.097 s), and the gain (kt = 0.0018 rad/Nm) and time constant (τt = 17.4 s) of the low-pass filter of the torque feedback loop. (b) The model-fitted proprioceptive weights decreased with increasing stimulus amplitude and accounted for the systematic decrease in gain with increasing stimulus amplitude
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Fig3: Results of the Stage 1 analysis. (a) Gain (upper graph) and phase (lower) of the mean experimental frequency response functions, FRFs, (points connected by dotted lines) and model fitted transfer functions (solid lines) of surface tilt to body sway for the five different stimulus amplitudes. The proprioceptive weight parameters, wp, were allowed to vary over experimental conditions. Graviceptive weights, wg, also varied but were linked to wp values such that wg = 1-wp. The model-fitted parameters that were constant over the five stimulus amplitudes were joint stiffness (ki = 40.5 Nm/rad) and damping (bi = 68.8 Nms/rad), the neural controller proportional (kp = 943.9 Nm/rad) and derivative (kd = 313.5 Nms/rad) gains, the lumped neural time delay (τd = 0.097 s), and the gain (kt = 0.0018 rad/Nm) and time constant (τt = 17.4 s) of the low-pass filter of the torque feedback loop. (b) The model-fitted proprioceptive weights decreased with increasing stimulus amplitude and accounted for the systematic decrease in gain with increasing stimulus amplitude

Mentions: The experimentally determined gain and phase values of the mean FRFs showed systematic changes as a function of the amplitude of the surface tilt stimulus (Fig 3(a), points connected by dotted lines). The FRF gain values at each individual frequency generally decreased with increasing stimulus amplitude such that the sets of gain values corresponding to the different stimulus amplitudes maintained very similar shapes across the frequency range. The FRF phase data from different stimulus amplitudes had similar values at frequencies of about 0.1 Hz and below but showed some divergence at higher stimulus frequencies with the largest phase lag for the lowest stimulus amplitude and the least phase lag for the highest stimulus amplitude.Fig. 3


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)

Results of the Stage 1 analysis. (a) Gain (upper graph) and phase (lower) of the mean experimental frequency response functions, FRFs, (points connected by dotted lines) and model fitted transfer functions (solid lines) of surface tilt to body sway for the five different stimulus amplitudes. The proprioceptive weight parameters, wp, were allowed to vary over experimental conditions. Graviceptive weights, wg, also varied but were linked to wp values such that wg = 1-wp. The model-fitted parameters that were constant over the five stimulus amplitudes were joint stiffness (ki = 40.5 Nm/rad) and damping (bi = 68.8 Nms/rad), the neural controller proportional (kp = 943.9 Nm/rad) and derivative (kd = 313.5 Nms/rad) gains, the lumped neural time delay (τd = 0.097 s), and the gain (kt = 0.0018 rad/Nm) and time constant (τt = 17.4 s) of the low-pass filter of the torque feedback loop. (b) The model-fitted proprioceptive weights decreased with increasing stimulus amplitude and accounted for the systematic decrease in gain with increasing stimulus amplitude
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC3108015&req=5

Fig3: Results of the Stage 1 analysis. (a) Gain (upper graph) and phase (lower) of the mean experimental frequency response functions, FRFs, (points connected by dotted lines) and model fitted transfer functions (solid lines) of surface tilt to body sway for the five different stimulus amplitudes. The proprioceptive weight parameters, wp, were allowed to vary over experimental conditions. Graviceptive weights, wg, also varied but were linked to wp values such that wg = 1-wp. The model-fitted parameters that were constant over the five stimulus amplitudes were joint stiffness (ki = 40.5 Nm/rad) and damping (bi = 68.8 Nms/rad), the neural controller proportional (kp = 943.9 Nm/rad) and derivative (kd = 313.5 Nms/rad) gains, the lumped neural time delay (τd = 0.097 s), and the gain (kt = 0.0018 rad/Nm) and time constant (τt = 17.4 s) of the low-pass filter of the torque feedback loop. (b) The model-fitted proprioceptive weights decreased with increasing stimulus amplitude and accounted for the systematic decrease in gain with increasing stimulus amplitude
Mentions: The experimentally determined gain and phase values of the mean FRFs showed systematic changes as a function of the amplitude of the surface tilt stimulus (Fig 3(a), points connected by dotted lines). The FRF gain values at each individual frequency generally decreased with increasing stimulus amplitude such that the sets of gain values corresponding to the different stimulus amplitudes maintained very similar shapes across the frequency range. The FRF phase data from different stimulus amplitudes had similar values at frequencies of about 0.1 Hz and below but showed some divergence at higher stimulus frequencies with the largest phase lag for the lowest stimulus amplitude and the least phase lag for the highest stimulus amplitude.Fig. 3

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.

Show MeSH
Related in: MedlinePlus