The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Visual Feedback Control of Hand Movements
Open accessAbstract
We investigated what visual information contributes to on-line control of hand movements. It has been suggested that motion information predominates early in movements but that position information predominates for endpoint control. We used a perturbation method to determine the relative contributions of motion and position information to feedback control. Subjects reached to touch targets in a dynamic virtual environment in which subjects viewed a moving virtual fingertip in place of their own finger. On some trials, we perturbed the virtual fingertip while it moved behind an occluder. Subjects responded to perturbations that selectively altered either motion or position information, indicating that both contribute to feedback control. Responses to perturbations that changed both motion and position information were consistent with superimposed motion-based and position-based control. Results were well fit by a control model that optimally integrates noisy, delayed sensory feedback about both motion and position to estimate hand state.
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Citations
Cites
Humans integrate visual and haptic information in a statistically optimal fashion
Ernst, M. O., & Banks, M. S. (2002) · Nature · doi:10.1038/415429a · Cited by 4,844
Optimal feedback control as a theory of motor coordination
Todorov, E., & Jordan, M. I. (2002) · Nature Neuroscience · doi:10.1038/nn963 · Cited by 3,081
Is the cerebellum a Smith predictor?
Miall, R. C., Weir, D. J., Wolpert, D. M., & Stein, J. F. (1993) · Journal of Motor Behavior · doi:10.1080/00222895.1993.9942050 · Cited by 1,071 · cited 1×
“To account for sensorimotor delays, the filter runs in a delayed loop, and the delayed estimates of hand state are input into a forward model that uses estimates of the motor commands sent out during the delay period to “predict” the current state of the hand from the delayed estimate (Miall et al., 1993).”
Method
Cited by
The Bayesian brain: The role of uncertainty in neural coding and computation
Knill, D. C., & Pouget, A. (2004) · Trends in Neurosciences · doi:10.1016/j.tins.2004.10.007 · Cited by 2,588
Self-citationCoding and use of tactile signals from the fingertips in object manipulation tasks
Johansson, R. S., & Flanagan, J. R. (2009) · Nature Reviews Neuroscience · doi:10.1038/nrn2621 · Cited by 2,063
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
Optimality principles in sensorimotor control
Todorov, E. (2004) · Nature Neuroscience · doi:10.1038/nn1309 · Cited by 1,746
Bayesian decision theory in sensorimotor control
Körding, K. P., & Wolpert, D. M. (2006) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2006.05.003 · Cited by 867
Metadata
- Authors
- Saunders, J. A., & Knill, D. C.
- Venue
- The Journal of Neuroscience
- Publisher
- Society for Neuroscience
- Published
- 2004-03-31
- Volume
- 24
- Issue
- 13
- Pages
- 3223-3234
- ISSN
- 0270-6474 (ISSN-L)
- 1529-2401
- OpenAlex
- W2113473620
- Cited by
- 293
- References
- 3
- FWCI
- 4.64
- Type
- journal article
- Language
- en
- Open access
- hybrid
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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