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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 2 analysis. Examples of different noise model predictions of remnant power spectra for models with sensory only, motor only, or combinations of sensory and motor noise. The model predictions were derived from noise model fits to the remnant power spectra across the 5 stimulus amplitudes using control model parameters derived from the Stage 1 analysis. The fit error and the ratio between graviceptive and proprioceptive noise are shown for each noise model
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Fig5: Results of the Stage 2 analysis. Examples of different noise model predictions of remnant power spectra for models with sensory only, motor only, or combinations of sensory and motor noise. The model predictions were derived from noise model fits to the remnant power spectra across the 5 stimulus amplitudes using control model parameters derived from the Stage 1 analysis. The fit error and the ratio between graviceptive and proprioceptive noise are shown for each noise model

Mentions: The results of four representative fits of noise models to the remnant PSDs are shown in Fig. 5. One includes only motor noise (M3 top left), one includes only sensory noise (S1 bottom left), and two include combinations of motor and sensory noise (S2M1, S1M3 right column). All of the noise models predicted remnant PSDs that increased in magnitude with increasing stimulus amplitude. However, there were large differences between noise models in the extent to which the various models could account for the stimulus-amplitude dependency, the variation across the full frequency range, and the detailed saw-tooth structure of the experimental remnant PSDs. The remnant PSDs predicted from the motor-only noise models were similar to the experimental PSDs only in the mid-frequency range of about 0.1 to 0.3 Hz, and even in this range the prediction for the highest stimulus amplitude was uniformly lower than the experimental data. At both lower and higher frequencies, the motor-only models predicted much lower PSD values across all stimulus amplitudes than experimentally measured.Fig. 5


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 2 analysis. Examples of different noise model predictions of remnant power spectra for models with sensory only, motor only, or combinations of sensory and motor noise. The model predictions were derived from noise model fits to the remnant power spectra across the 5 stimulus amplitudes using control model parameters derived from the Stage 1 analysis. The fit error and the ratio between graviceptive and proprioceptive noise are shown for each noise model
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC3108015&req=5

Fig5: Results of the Stage 2 analysis. Examples of different noise model predictions of remnant power spectra for models with sensory only, motor only, or combinations of sensory and motor noise. The model predictions were derived from noise model fits to the remnant power spectra across the 5 stimulus amplitudes using control model parameters derived from the Stage 1 analysis. The fit error and the ratio between graviceptive and proprioceptive noise are shown for each noise model
Mentions: The results of four representative fits of noise models to the remnant PSDs are shown in Fig. 5. One includes only motor noise (M3 top left), one includes only sensory noise (S1 bottom left), and two include combinations of motor and sensory noise (S2M1, S1M3 right column). All of the noise models predicted remnant PSDs that increased in magnitude with increasing stimulus amplitude. However, there were large differences between noise models in the extent to which the various models could account for the stimulus-amplitude dependency, the variation across the full frequency range, and the detailed saw-tooth structure of the experimental remnant PSDs. The remnant PSDs predicted from the motor-only noise models were similar to the experimental PSDs only in the mid-frequency range of about 0.1 to 0.3 Hz, and even in this range the prediction for the highest stimulus amplitude was uniformly lower than the experimental data. At both lower and higher frequencies, the motor-only models predicted much lower PSD values across all stimulus amplitudes than experimentally measured.Fig. 5

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