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Relative coordination reconsidered: a stochastic account

D R Collins¹, H Park, M T Turvey

¹Center for the Ecological Study of Perception and Action, University of Connecticut, Storrs, CT 06269, USA.

|June 30, 1998

Summary

This study proposes a stochastic interpretation of relative coordination, modeling it with variability around weak attractors. This approach better accounts for behavioral data uncertainty than deterministic models.

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Area of Science:

Motor control
Dynamical systems theory
Computational neuroscience

Background:

Intersegmental coordination was previously categorized into relative and absolute forms.
Relative coordination has been interpreted through deterministic chaos (intermittency).
Existing models struggle to account for noise and uncertainty in behavioral data.

Purpose of the Study:

To propose and detail a stochastic interpretation of relative coordination.
To contrast the stochastic model with deterministic intermittency models.
To present a method for estimating model parameters using stochastic variability.

Main Methods:

Modeling relative coordination using stochastic variability around weak attractors.
Approximating unknown underlying processes with Gaussian white noise.

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Utilizing stationary probability distributions and mean first passage time for parameter estimation.

Main Results:

The stochastic interpretation offers a more robust account of behavioral data compared to intermittency models.
The proposed method effectively estimates model parameters from experimental and simulated data.
Stochastic variability provides a better framework for understanding uncertainty in coordination.

Conclusions:

A stochastic model, based on noise approximation, is superior for explaining relative coordination.
The intermittency interpretation is less robust when noise is considered.
The study demonstrates a practical stochastic method for analyzing coordination dynamics.