Postural Sway and Gaze Can Track the Complex Motion of a Visual Target

Abstract

Variability is an inherent and important feature of human movement. This variability has form exhibiting a chaotic structure. Visual feedback training using regular predictive visual target motions does not take into account this essential characteristic of the human movement, and may result in task specific learning and loss of visuo-motor adaptability. In this study, we asked how well healthy young adults can track visual target cues of varying degree of complexity during whole-body swaying in the Anterior-Posterior (AP) and Medio-Lateral (ML) direction. Participants were asked to track three visual target motions: a complex (Lorenz attractor), a noise (brown) and a periodic (sine) moving target while receiving online visual feedback about their performance. Postural sway, gaze and target motion were synchronously recorded and the degree of force-target and gaze-target coupling was quantified using spectral coherence and Cross-Approximate entropy. Analysis revealed that both force-target and gaze-target coupling was sensitive to the complexity of the visual stimuli motions. Postural sway showed a higher degree of coherence with the Lorenz attractor than the brown noise or sinusoidal stimulus motion. Similarly, gaze was more synchronous with the Lorenz attractor than the brown noise and sinusoidal stimulus motion. These results were similar regardless of whether tracking was performed in the AP or ML direction. Based on the theoretical model of optimal movement variability tracking of a complex signal may provide a better stimulus to improve visuo-motor adaptation and learning in postural control.

Posture and gaze tracking of a vertically moving target reveals age-related constraints in visuo-motor coupling

“…Such age-related differences were not observed when tracking the horizontally moving target [13] which leads to the hypothesis that tracking of a target moving vertically may be more cognitively challenging for older adults. In addition, the lower cross-ApEn when tracking the chaotic target indicates greater gaze-target synchronicity confirming that the demand for attention to the target increases as a function of complexity for both age groups [13,18]. …”

“…Gain was calculated as an amplitude-coupling index while phase illustrated the temporal lag between signals, in degrees. The cross-Approximate Entropy (cross-ApEn) was calculated as an index of co-joint regularity between the target and performance signals in the non-linear space [18]. …”

“…In contrast, the unpredictable, complex nature of real life environmental stimuli renders the need for online, feedback based control of postural sway highly important [17]. For this reason, target motions that exhibit patterns of great complexity such as of mathematical chaos and/or random patterns have been employed lately in postural tracking research [13,18]. Tracking of complex targets is a more ecologically valid paradigm [13,18] because this mimics the spatiotemporal characteristics of the real world [17].…”

“…For this reason, target motions that exhibit patterns of great complexity such as of mathematical chaos and/or random patterns have been employed lately in postural tracking research [13,18]. Tracking of complex targets is a more ecologically valid paradigm [13,18] because this mimics the spatiotemporal characteristics of the real world [17]. Moreover, it was shown that older adults have greater difficulties, as compared to young participants, to couple their posture to complex (i.e.…”

“…Postural tracking was realized by shifting body weight in the AP direction. The matching directions of sway-target motions were experimentally set based on previous research [18] and pilot testing indicating the presence of an inherent spatial bias to match forward sway with an upward target motion and vice versa. We hypothesized that aging affects the coupling of gaze and AP postural sway to a visual target oscillating vertically while age-related limitations are a function of target’s motion complexity.…”