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Strategy Switching in the Stabilization of Unstable Dynamics

Author

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  • Jacopo Zenzeri
  • Dalia De Santis
  • Pietro Morasso

Abstract

In order to understand mechanisms of strategy switching in the stabilization of unstable dynamics, this work investigates how human subjects learn to become skilled users of an underactuated bimanual tool in an unstable environment. The tool, which consists of a mass and two hand-held non-linear springs, is affected by a saddle-like force-field. The non-linearity of the springs allows the users to determine size and orientation of the tool stiffness ellipse, by using different patterns of bimanual coordination: minimal stiffness occurs when the two spring terminals are aligned and stiffness size grows by stretching them apart. Tool parameters were set such that minimal stiffness is insufficient to provide stable equilibrium whereas asymptotic stability can be achieved with sufficient stretching, although at the expense of greater effort. As a consequence, tool users have two possible strategies for stabilizing the mass in different regions of the workspace: 1) high stiffness feedforward strategy, aiming at asymptotic stability and 2) low stiffness positional feedback strategy aiming at bounded stability. The tool was simulated by a bimanual haptic robot with direct torque control of the motors. In a previous study we analyzed the behavior of naïve users and we found that they spontaneously clustered into two groups of approximately equal size. In this study we trained subjects to become expert users of both strategies in a discrete reaching task. Then we tested generalization capabilities and mechanism of strategy-switching by means of stabilization tasks which consist of tracking moving targets in the workspace. The uniqueness of the experimental setup is that it addresses the general problem of strategy-switching in an unstable environment, suggesting that complex behaviors cannot be explained in terms of a global optimization criterion but rather require the ability to switch between different sub-optimal mechanisms.

Suggested Citation

  • Jacopo Zenzeri & Dalia De Santis & Pietro Morasso, 2014. "Strategy Switching in the Stabilization of Unstable Dynamics," PLOS ONE, Public Library of Science, vol. 9(6), pages 1-24, June.
  • Handle: RePEc:plo:pone00:0099087
    DOI: 10.1371/journal.pone.0099087
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    References listed on IDEAS

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    1. Yoshiyuki Asai & Yuichi Tasaka & Kunihiko Nomura & Taishin Nomura & Maura Casadio & Pietro Morasso, 2009. "A Model of Postural Control in Quiet Standing: Robust Compensation of Delay-Induced Instability Using Intermittent Activation of Feedback Control," PLOS ONE, Public Library of Science, vol. 4(7), pages 1-14, July.
    2. Devjani J Saha & Pietro Morasso, 2012. "Stabilization Strategies for Unstable Dynamics," PLOS ONE, Public Library of Science, vol. 7(1), pages 1-13, January.
    3. Christopher M. Harris & Daniel M. Wolpert, 1998. "Signal-dependent noise determines motor planning," Nature, Nature, vol. 394(6695), pages 780-784, August.
    4. Etienne Burdet & Rieko Osu & David W. Franklin & Theodore E. Milner & Mitsuo Kawato, 2001. "The central nervous system stabilizes unstable dynamics by learning optimal impedance," Nature, Nature, vol. 414(6862), pages 446-449, November.
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