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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 5 analysis. Shown are the stabilogram diffusion functions, SDFs, and their diffusion coefficients (short term, Ds, and long term, Dl) derived from linear fits (dotted lines) to the SDFs, and critical coordinates (critical time, Δtc, and displacement, ΔXc) determined by the intersection of both linear fits. The model-predicted SDF (a) was close to the experimentally estimated SDF derived from eyes-closed, quiet stance data of the same subjects (b)
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Fig8: Results of the Stage 5 analysis. Shown are the stabilogram diffusion functions, SDFs, and their diffusion coefficients (short term, Ds, and long term, Dl) derived from linear fits (dotted lines) to the SDFs, and critical coordinates (critical time, Δtc, and displacement, ΔXc) determined by the intersection of both linear fits. The model-predicted SDF (a) was close to the experimentally estimated SDF derived from eyes-closed, quiet stance data of the same subjects (b)

Mentions: The goal of the stage 5 analysis was to demonstrate that the stance control and noise models, which were characterized entirely by analysis of stimulus evoked sway, could also predict spontaneous sway behavior. Quiet standing center-of-pressure (CoP) displacement was simulated using the model parameters identified in the Stage 1 and 2 analyses. The results of the simulation were used to calculate a stabilogram diffusion function of the CoP movements in the anterior-posterior direction, and to measure the short and long term diffusion coefficients and the critical point coordinates that describe its characteristic shape (Fig. 8(a)). The model-derived stabilogram diffusion plot showed the typical shape described previously (Collins and De Luca 1993). That is, the function increased linearly with increasing time interval until the critical time point at Δtc, and then continued to increase linearly, but with a lower slope, at long time intervals. The results of the simulation were compared with the average across-subject stabilogram diffusion function (Fig. 8(b)) calculated from 363 s recordings of anterior-posterior CoP data obtained in eyes closed conditions. The diffusion coefficients and critical point coordinates of the model simulations and experiments resembled one another. The values of these parameters were in the range of those estimated from 30 s records of anterior-posterior CoP in eyes closed conditions (Collins and De Luca 1995), except for Ds and ΔXc, which were larger in our study for both the simulated and the experimental results. These differences may be due to the sensitivity of RMS measures of spontaneous sway to sample durations whereby the RMS sway increases with sample duration (Carpenter et al. 2001). Since our experimental data records were more than ten times longer than the duration of earlier studies, the sway magnitude, as reflected by the Ds and ΔXc parameters, was also larger.Fig. 8


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 5 analysis. Shown are the stabilogram diffusion functions, SDFs, and their diffusion coefficients (short term, Ds, and long term, Dl) derived from linear fits (dotted lines) to the SDFs, and critical coordinates (critical time, Δtc, and displacement, ΔXc) determined by the intersection of both linear fits. The model-predicted SDF (a) was close to the experimentally estimated SDF derived from eyes-closed, quiet stance data of the same subjects (b)
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC3108015&req=5

Fig8: Results of the Stage 5 analysis. Shown are the stabilogram diffusion functions, SDFs, and their diffusion coefficients (short term, Ds, and long term, Dl) derived from linear fits (dotted lines) to the SDFs, and critical coordinates (critical time, Δtc, and displacement, ΔXc) determined by the intersection of both linear fits. The model-predicted SDF (a) was close to the experimentally estimated SDF derived from eyes-closed, quiet stance data of the same subjects (b)
Mentions: The goal of the stage 5 analysis was to demonstrate that the stance control and noise models, which were characterized entirely by analysis of stimulus evoked sway, could also predict spontaneous sway behavior. Quiet standing center-of-pressure (CoP) displacement was simulated using the model parameters identified in the Stage 1 and 2 analyses. The results of the simulation were used to calculate a stabilogram diffusion function of the CoP movements in the anterior-posterior direction, and to measure the short and long term diffusion coefficients and the critical point coordinates that describe its characteristic shape (Fig. 8(a)). The model-derived stabilogram diffusion plot showed the typical shape described previously (Collins and De Luca 1993). That is, the function increased linearly with increasing time interval until the critical time point at Δtc, and then continued to increase linearly, but with a lower slope, at long time intervals. The results of the simulation were compared with the average across-subject stabilogram diffusion function (Fig. 8(b)) calculated from 363 s recordings of anterior-posterior CoP data obtained in eyes closed conditions. The diffusion coefficients and critical point coordinates of the model simulations and experiments resembled one another. The values of these parameters were in the range of those estimated from 30 s records of anterior-posterior CoP in eyes closed conditions (Collins and De Luca 1995), except for Ds and ΔXc, which were larger in our study for both the simulated and the experimental results. These differences may be due to the sensitivity of RMS measures of spontaneous sway to sample durations whereby the RMS sway increases with sample duration (Carpenter et al. 2001). Since our experimental data records were more than ten times longer than the duration of earlier studies, the sway magnitude, as reflected by the Ds and ΔXc parameters, was also larger.Fig. 8

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