Research on interpersonal coordination has demonstrated that incongruent tasks lead to unintended movements in the orthogonal plane. These effects have been interpreted using both an embodied simulation and coordination dynamics approach. To distinguish between these two perspectives, two experiments examined whether this congruency effect is best defined spatially or anatomically. In the first experiment, participants coordinated congruent and incongruent rhythmic arm movements with an actor. To dissociate spatial and anatomical congruency, the actor was rotated 90° in the coronal plane for half of the trials. In the second experiment, participants coordinated movements of different limbs (leg and arm). Spatial and anatomical congruency was dissociated here by rotating the actor in the transverse plane. In both experiments, the unintended movements associated with the congruency effect emerged as a function of spatial congruency; there was no congruency effect associated with anatomical congruency. The data suggests that these unintended movements represent the recruitment of additional df necessary to stabilize an unstable form of coordination.

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Variability is commonly observed in complex behavior, such as the maintenance of upright posture. The current study examines the value added by using nonlinear measures of variability to identify dynamic stability instead of linear measures that reflect average fluctuations about a mean state. The largest Lyapunov exponent (λ1) and SD were calculated on mediolateral movement as participants performed a sit-to-stand task on a stable and unstable platform. Both measures identified changes in movement across postures, but results diverged when participants stood on the unstable platform. Large SD indicated an increase in movement variability, but small λ1 identified those movements as stable and controlled. The results suggest that a combination of linear and nonlinear analyses is useful in identifying the proportion of observed variability that may be attributed to structured, controlled sources. Nonlinear measures of variability, like λ1, can further be used to make predictions about transitions between stable postures and to identify a system’s resistance to disruption from external perturbations. Those features make nonlinear analyses highly applicable to both human movement research and clinical practice.