A dynamical neural network for hitting an approaching object.

Joost C Dessing; Simone R Caljouw; Peper E Peper; Peter J Beek

doi:10.1007/s00422-004-0520-4

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A dynamical neural network for hitting an approaching object.

Joost C Dessing·Simone R Caljouw·Peper E Peper·Peter J Beek

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Abstract

Besides making contact with an approaching ball at the proper place and time, hitting requires control of the effector velocity at contact. A dynamical neural network for the planning of hitting movements was derived in order to account for both these requirements. The model in question implements continuous required velocity control by extending the Vector Integration To Endpoint model while providing explicit control of effector velocity at interception. It was shown that the planned movement trajectories generated by the model agreed qualitatively with the kinematics of hitting movements as observed in two recent experiments. Outstanding features of this comparison concerned the timing and amplitude of the empirical backswing movements, which were largely consistent with the predictions from the model. Several theoretical implications as well as the informational basis and possible neural underpinnings of the model were discussed.

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Biomechanical Phenomena Central Nervous System Humans Motion Perception Movement Neural Networks, Computer Orientation Psychomotor Performance Space Perception Journal Article

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BibTeX RIS

Joost C Dessing, Simone R Caljouw, Peper E Peper, Peter J Beek. 2004-11-19. A dynamical neural network for hitting an approaching object.. https://doi.org/10.1007/s00422-004-0520-4

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