The present study investigates whether the acquisition of a rhythmical bimanual coordination pattern is influenced by existing intrinsic coordination tendencies. Participants were required to learn 1 of 5 new coordination patterns, whose relative phase ϕ was either 36, 60, or 90° away from the 0° and 180° attractors, respectively. They performed 35 trials, each consisting of 2 conditions: In the augmented feedback condition, continuous visual guidance was provided, while in the normal feedback condition participants were required to rely on normal vision of their arms. We found that all to-be-learned patterns were performed with higher accuracy in the visually guided condition, whereas interference with pre-existing coordination tendencies was more pronounced in the normal vision condition. Comparing the learning progress of the 5 groups, we found for patterns close to anti-phase, a smaller improvement and significantly larger phase errors than for patterns close to in-phase. This indicates that the acquisition of a new phase relationship is influenced by existing attractors and that the 180º attractor interfered more strongly with the to-be-learned pattern than the 0º attractor.
Nicole Wenderoth, Otmar Bock, and Rainer Krohn
Nicole Wenderoth and Otmar Bock
Learning of a new bimanual coordination pattern was investigated by practicing rhythmical arm movements with a required relative phase of ϕ = 90°. To quantify the learning process, we determined the mean and the standard deviation of the relative phase, and the switching lime from a well-established coordination pattern to the to-be-leamed pattern. We then calculated for each parameter the time constant of improvement. We found that with practice, all three parameter improved but each following a significantly different time-course. We therefore conclude that the learning of a new bimanual coordination pattern is governed by three separate processes, which can be visualized in a potential landscape of the intrinsic dynamics as distinct topographical features—namely, the location, depth, and steepness of the attractor basin.
Satyajit Ambike, Daniela Mattos, Vladimir Zatsiorsky, and Mark Latash
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