| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH |
|
|
|
|||||||
|
|||||||||
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
1 Biomedical Engineering, University of California - Irvine, Irvine, California, United States
2 Automatic Control, Universitat Politècnica de Catalunya, Barcelona, Spain
3 Irvine, California, United States; Mechanical and Aerospace Engineering, University of California - Irvine, Irvine, California, United States
4 Mechanical and Aerospace Engineering, University of California - Irvine, Irvine, California, United States
5 Dept. Mechanical & Aerospace Engineering, University of California, Irvine, Irvine, California, United States
* To whom correspondence should be addressed. E-mail: dreinken{at}uci.edu.
Motor adaptation to a novel dynamic environment is primarily thought of as a process in which the nervous system learns to anticipate the environmental forces in order to eliminate kinematic error. Here we show that motor adaptation can more generally be modeled as a process in which the motor system greedily minimizes a cost function that is the weighted sum of kinematic error and effort. The learning dynamics predicted by this minimization process are a linear, auto-regressive equation with only one state, which has been identified previously as providing a good fit to data from force field-type experiments. Thus, we provide a new theoretical result that shows how these previously identified learning dynamics can be viewed as arising from an optimization of error and effort. We also show that the coefficients of the learning dynamics must fall within a specific range for the optimization model to be valid, and verify with experimental data from walking in a force field that they indeed fall in this range. Finally, we attempted to falsify the model by performing experiments in two conditions (repeated exposure to a force field, exposure to force fields of different strengths) for which the single-state, auto-regressive equation might be expected to not fit the data well. We found however that the equation adequately captured the pattern of errors, and thus conclude that motor adaptation to a force field can be approximated as an optimization of effort and error for a range of experimental conditions.
This article has been cited by other articles:
|
|
 |
|
  Transformational Technologies in Single-Event Neurological Conditions: Applying Lessons Learned in Stroke to Cerebral Palsy (August 14-15, 2008) Neurorehabil Neural Repair, September 1, 2009; 23(7): 747 - 765. [PDF]
|
|
|
|
 |
|
 V. S. Huang and R. Shadmehr Persistence of Motor Memories Reflects Statistics of the Learning Event J Neurophysiol, August 1, 2009; 102(2): 931 - 940. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
J. Hidler, D. Nichols, M. Pelliccio, K. Brady, D. D. Campbell, J. H. Kahn, and T. G. Hornby Multicenter Randomized Clinical Trial Evaluating the Effectiveness of the Lokomat in Subacute Stroke Neurorehabil Neural Repair, January 1, 2009; 23(1): 5 - 13. [Abstract] [PDF]
|
|
|
|
 |
|
 V. Ethier, D. S. Zee, and R. Shadmehr Changes in Control of Saccades during Gain Adaptation J. Neurosci., December 17, 2008; 28(51): 13929 - 13937. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
D. W. Franklin, E. Burdet, K. Peng Tee, R. Osu, C.-M. Chew, T. E. Milner, and M. Kawato CNS Learns Stable, Accurate, and Efficient Movements Using a Simple Algorithm J. Neurosci., October 29, 2008; 28(44): 11165 - 11173. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
J. Izawa, T. Rane, O. Donchin, and R. Shadmehr Motor Adaptation as a Process of Reoptimization J. Neurosci., March 12, 2008; 28(11): 2883 - 2891. [Abstract] [Full Text] [PDF]
|
|
| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH |
| Visit Other APS Journals Online |
