The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Rapid adaptation to Coriolis force perturbations of arm trajectory
Abstract
1. Forward reaching movements made during body rotation generate tangential Coriolis forces that are proportional to the cross product of the angular velocity of rotation and the linear velocity of the arm. Coriolis forces are inertial forces that do not involve mechanical contact. Virtually no constant centrifugal forces will be present in the background when motion of the arm generates transient Coriolis forces if the radius of body rotation is small. 2. We measured the trajectories of arm movements made in darkness to a visual target that was extinguished as movement began. The reaching movements were made prerotation, during rotation at 10 rpm in a fully enclosed rotating room, and postrotation. During testing the subject was seated at the center of the room and pointed radially. Neither visual nor tactile feedback about movement accuracy was present. 3. In experiment 1, subjects reached at a fast or slow rate and their hands made contact with a horizontal surface at the end of the reach. Their initial perrotary movements were highly significantly deviated relative to prerotation in both trajectories and end-points in the direction of the transient Coriolis forces that had been generated during the reaches. Despite the absence of visual and tactile feedback about reaching accuracy, all subjects rapidly regained straight movement trajectories and accurate endpoints. Postrotation, transient errors of opposite sign were present for both trajectories and endpoints. 4. In a second experiment the conditions were identical except that subjects pointed just above the location of the extinguished target so that no surface contact was involved. All subjects showed significant initial perrotation deviations of trajectories and endpoints in the direction of the transient Coriolis forces. With repeated reaches the trajectories, as viewed from above, again became straight, but there was only partial restoration of endpoint accuracy, so that subjects reached in a straight line to the wrong place. Aftereffects of opposite sign were transiently present in the postrotary movements. 5. These observations fail to support current equilibrium point models, both alpha and lambda, of movement control. Such theories would not predict endpoint errors under our experimental conditions, in which the Coriolis force is absent at the beginning and end of a movement. Our results indicate that detailed aspects of movement trajectory are being continuously monitored on the basis of proprioceptive feedback in relation to motor commands. Adaptive compensations can be initiated after one perturbation despite the absence of either visual or tactile feedback about movement trajectory and endpoint error. Moreover, movement trajectory and end-point can be remapped independently.(ABSTRACT TRUNCATED AT 400 WORDS)
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Citations
Cites
Marr, D. (1969) · The Journal of Physiology · doi:10.1113/jphysiol.1969.sp008820 · Cited by 3,253
The effects of length and stimulus rate on tension in the isometric cat soleus muscle
Rack, P. M. H., & Westbury, D. R. (1969) · The Journal of Physiology · doi:10.1113/jphysiol.1969.sp008923 · Cited by 1,144
Kinematic networks: A distributed model for representing and regularizing motor redundancy
Ivaldi, F. A. M., Morasso, P., & Zaccaria, R. (1988) · Biological Cybernetics · doi:10.1007/bf00205967 · Cited by 251
Some Aspects of Sensory-Motor Control and Adaptation in Man
Lackner, J. R. (1981) · Intersensory Perception and Sensory Integration · doi:10.1007/978-1-4615-9197-9_5 · Cited by 31
Self-citation
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Signal-dependent noise determines motor planning
Harris, C. M., & Wolpert, D. M. (1998) · Nature · doi:10.1038/29528 · Cited by 2,498
Computational principles of movement neuroscience
Wolpert, D. M., & Ghahramani, Z. (2000) · Nature Neuroscience · doi:10.1038/81497 · Cited by 2,122
Error Correction, Sensory Prediction, and Adaptation in Motor Control
Shadmehr, R., Smith, M. A., & Krakauer, J. W. (2010) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-060909-153135 · Cited by 1,869
The central nervous system stabilizes unstable dynamics by learning optimal impedance
Burdet, E., Osu, R., Franklin, D. W., Milner, T. E., & Kawato, M. (2001) · Nature · doi:10.1038/35106566 · Cited by 1,097
Metadata
- Authors
- Lackner, J. R., & Dizio, P.
- Venue
- Journal of Neurophysiology
- Publisher
- American Physiological Society
- Published
- 1994-07-01
- Volume
- 72
- Issue
- 1
- Pages
- 299-313
- ISSN
- 0022-3077 (ISSN-L)
- 1522-1598
- OpenAlex
- W1706319856
- Cited by
- 776
- References
- 4
- FWCI
- 23.22
- Type
- journal article
- Language
- en
- Open access
- closed
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