From a nonlinear dynamics perspective, presence of movement variability before a change in preferred movement patterns is hypothesized to afford the necessary adaptability and flexibility for seeking novel functional behaviors. In this study, four novice participants practiced a discrete multiarticular movement for 12 sessions over 4 weeks. Cluster analysis procedures revealed how changes between preferred movement patterns were affected with and without the presence of variability in movement clusters before a defined change. Performance improved in all participants as a function of practice. Participants typically showed evidence of change between preferred movement clusters and higher variability in the use of movement clusters within a session. However, increasing variability in movement clusters was not always accompanied by transition from one preferred movement cluster to another. In summary, it was observed that intentional and informational constraints play an important role in influencing the specific pathway of change for individual learners as they search for new preferred movement patterns.
Jia Yi Chow, Keith Davids, Chris Button and Robert Rein
Robert Rein, Chris Button, Keith Davids and Jeffery Summers
The present paper proposes a technical analysis method for extracting information about movement patterning in studies of motor control, based on a cluster analysis of movement kinematics. In a tutorial fashion, data from three different experiments are presented to exemplify and validate the technical method. When applied to three different basketball-shooting techniques, the method clearly distinguished between the different patterns. When applied to a cyclical wrist supination-pronation task, the cluster analysis provided the same results as an analysis using the conventional discrete relative phase measure. Finally, when analyzing throwing performance constrained by distance to target, the method grouped movement patterns together according to throwing distance. In conclusion, the proposed technical method provides a valuable tool to improve understanding of coordination and control in different movement models, including multiarticular actions.