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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 3 analysis. Comparison of the experimental proprioceptive weights, wp, (from Stage 1 analysis, triangles connected by dotted lines) and wp values (dots connected by thick lines) predicted by different behavioral criteria based on the minimization of the sum of mean-square value of body sway, sway velocity, sway acceleration, sway jerk, corrective torque, or the torque rate-of-change. The wp predictions were based on the S1M3 remnant noise model. Predictions of wp were uniformly zero for all motor-only noise models (M1, M2, or M3, squares connected by thin lines)
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Fig6: Results of the Stage 3 analysis. Comparison of the experimental proprioceptive weights, wp, (from Stage 1 analysis, triangles connected by dotted lines) and wp values (dots connected by thick lines) predicted by different behavioral criteria based on the minimization of the sum of mean-square value of body sway, sway velocity, sway acceleration, sway jerk, corrective torque, or the torque rate-of-change. The wp predictions were based on the S1M3 remnant noise model. Predictions of wp were uniformly zero for all motor-only noise models (M1, M2, or M3, squares connected by thin lines)

Mentions: The wp Stage 3 predictions overlayed with the wp values from the Stage 1 analysis are shown in Fig. 6 for the six behavioral criteria. The wp predictions were essentially identical for all of the sensory/motor noise models from the Stage 2 analysis that gave similar predictions of the remnant sway PSDs (cost function values of about 100). The particular wp prediction shown in Fig. 6 is from the S1M3 noise model. The wp predictions for the sensory only noise models were also nearly identical to the results shown in Fig. 6 (wg not shown since wg= 1 - wp).Fig. 6


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 3 analysis. Comparison of the experimental proprioceptive weights, wp, (from Stage 1 analysis, triangles connected by dotted lines) and wp values (dots connected by thick lines) predicted by different behavioral criteria based on the minimization of the sum of mean-square value of body sway, sway velocity, sway acceleration, sway jerk, corrective torque, or the torque rate-of-change. The wp predictions were based on the S1M3 remnant noise model. Predictions of wp were uniformly zero for all motor-only noise models (M1, M2, or M3, squares connected by thin lines)
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC3108015&req=5

Fig6: Results of the Stage 3 analysis. Comparison of the experimental proprioceptive weights, wp, (from Stage 1 analysis, triangles connected by dotted lines) and wp values (dots connected by thick lines) predicted by different behavioral criteria based on the minimization of the sum of mean-square value of body sway, sway velocity, sway acceleration, sway jerk, corrective torque, or the torque rate-of-change. The wp predictions were based on the S1M3 remnant noise model. Predictions of wp were uniformly zero for all motor-only noise models (M1, M2, or M3, squares connected by thin lines)
Mentions: The wp Stage 3 predictions overlayed with the wp values from the Stage 1 analysis are shown in Fig. 6 for the six behavioral criteria. The wp predictions were essentially identical for all of the sensory/motor noise models from the Stage 2 analysis that gave similar predictions of the remnant sway PSDs (cost function values of about 100). The particular wp prediction shown in Fig. 6 is from the S1M3 noise model. The wp predictions for the sensory only noise models were also nearly identical to the results shown in Fig. 6 (wg not shown since wg= 1 - wp).Fig. 6

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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