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Learning new perception–action solutions in virtual ball bouncing

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Abstract

How do humans discover stable solutions to perceptual-motor tasks as they interact with the physical environment? We investigate this question using the task of rhythmically bouncing a ball on a racket, for which a passively stable solution is defined. Previously, it was shown that participants exploit this passive stability but can also actively stabilize bouncing under perceptual control. Using a virtual ball-bouncing display, we created new behavioral solutions for rhythmic bouncing by introducing a temporal delay (45°–180°) between the motion of the physical racket and that of the virtual racket. We then studied how participants searched for and realized a new solution. In all delay conditions, participants learned to maintain bouncing just outside the passively stable region, indicating a role for active stabilization. They recovered the approximate initial phase of ball impact in the virtual racket cycle (half-way through the upswing) by adjusting the impact phase with the physical racket. With short delays (45°, 90°), the impact phase quickly shifted later in the physical racket upswing. With long delays (135°, 180°), bouncing was destabilized and phase was widely visited before a new preferred phase gradually emerged, during the physical downswing. Destabilization was likely due to the loss of spatial symmetry between the ball and physical racket motion at impact. The results suggest that new behavioral solutions may be discovered and stabilized through broad irregular sampling of variable space rather than through a systematic search.

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Notes

  1. The variability in the latency measurement is caused by an incompatibility between the Flock of Bird (120 Hz) and the video-projector (50 Hz) update rates, coupled with a lack of synchronization between the two components.

  2. Instead of correcting the overall 37.3 m s end-to-end latency of our VE, we chose to correct only 8.3 m s with a predictive polynomial regression because this 8.33 m s predictive filter leads to the minimal error in the approximation of the physical racket positions and velocities (mean position error = 0.0005 ± 0.0013 m; mean velocity error = 0.2925 ± 0.1278 m s−1).

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Acknowledgments

Supported by the French Fond National pour la Science (IUF 2002:2006), and by Enactive Interfaces, a network of excellence (IST contract #002114) of the Commission of the European Community, with additional support from the University of Paris Sud 11 (BQR-RV-2003).

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Authors and Affiliations

  1. UPRES EA 4042 Contrôle Moteur et Perception, Univ Paris Sud 11, 91405, Orsay Cedex, France

    Antoine H. P. Morice & Isabelle A. Siegler

  2. Institut Universitaire de France, 103 bd St Michel, 75005, Paris, France

    Benoît G. Bardy

  3. Motor Efficiency and Deficiency Laboratory, University Montpellier-1, 700 Avenue du Pic Saint Loup, 34090, Montpellier, France

    Benoît G. Bardy

  4. Department of Cognitive and Linguistic Sciences, Brown University, Box 1978, Providence, RI, USA

    William H. Warren

Authors
  1. Antoine H. P. Morice
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  2. Isabelle A. Siegler
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  3. Benoît G. Bardy
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  4. William H. Warren
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Correspondence to Isabelle A. Siegler.

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Morice, A.H.P., Siegler, I.A., Bardy, B.G. et al. Learning new perception–action solutions in virtual ball bouncing. Exp Brain Res 181, 249–265 (2007). https://doi.org/10.1007/s00221-007-0924-1

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  • DOI: https://doi.org/10.1007/s00221-007-0924-1

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