A mechanism for sensory re-weighting in postural control

Abstract

A key finding of human balance experiments has been that the integration of sensory information utilized for postural control appears to be dynamically regulated to adapt to changing environmental conditions and the available sensory information, a process referred to as “sensory re-weighting.” We propose a postural control model that includes automatic sensory re-weighting. This model is an adaptation of a previously reported model of sensory feedback that included manual sensory re-weighting. The new model achieves sensory re-weighting that is physiologically plausible and readily implemented. Model simulations are compared to previously reported experimental results to demonstrate the automated sensory re-weighting strategy of the modified model. On the whole, the postural sway time series generated by the model with automatic sensory re-weighting show good agreement with experimental data, and are capable of producing patterns similar to those observed experimentally.

Appendix: Sensory difference threshold determination

To determine the threshold ε_SD (see Sect. 2.2), consider a Gaussian noise process $x {\sim}N(0,\sigma^{2})$. We would like to find the probability that 1−P(−ε_SD ≤ x ≤  ε_SD) < ζ, where ζ is a small number and ε_SD is the desired sensory difference threshold. Solving for ε_SD results in

$$ \epsilon_{\rm SD} \geq \sqrt{2} \cdot \sigma \cdot {\rm erf}^{-1}(1-\zeta) $$

(7)

where erf is the error function.

In our model simulations, we take the noise variance, i.e., σ², as the sum of the sensory channel noise variances, σ² = σ ²_p  + σ ²_g , with proprioceptive and graviceptive channel noise variances (post-filtering) set to 0.002 deg² and 0.0215 deg², respectively. With these values, and ζ = 1 × 10⁻³, Eq. 7 yields ε_SD ≥  0.50°. We therefore took ε_SD = 0.50°.