Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2008 Sep;100(3):1455-64.
doi: 10.1152/jn.90334.2008. Epub 2008 Jul 2.

Reach adaptation: what determines whether we learn an internal model of the tool or adapt the model of our arm?

Affiliations

Reach adaptation: what determines whether we learn an internal model of the tool or adapt the model of our arm?

JoAnn Kluzik et al. J Neurophysiol. 2008 Sep.

Abstract

We make errors when learning to use a new tool. However, the cause of error may be ambiguous: is it because we misestimated properties of the tool or of our own arm? We considered a well-studied adaptation task in which people made goal-directed reaching movements while holding the handle of a robotic arm. The robot produced viscous forces that perturbed reach trajectories. As reaching improved with practice, did people recalibrate an internal model of their arm, or did they build an internal model of the novel tool (robot), or both? What factors influenced how the brain solved this credit assignment problem? To investigate these questions, we compared transfer of adaptation between three conditions: catch trials in which robot forces were turned off unannounced, robot-null trials in which subjects were told that forces were turned off, and free-space trials in which subjects still held the handle but watched as it was detached from the robot. Transfer to free space was 40% of that observed in unannounced catch trials. We next hypothesized that transfer to free space might increase if the training field changed gradually, rather than abruptly. Indeed, this method increased transfer to free space from 40 to 60%. Therefore although practice with a novel tool resulted in formation of an internal model of the tool, it also appeared to produce a transient change in the internal model of the subject's arm. Gradual changes in the tool's dynamics increased the extent to which the nervous system recalibrated the model of the subject's own arm.

PubMed Disclaimer

Figures

FIG. 1.
FIG. 1.
Experimental protocols. A: in the robot-force condition, subjects adapted their reaching movements to a clockwise, viscous curl field. Targets were presented at a 10-cm distance in one of 6 directions from the start position. After a period of making reaches in the robot-force condition, subjects were tested for transfer of adaptation to either: 1) the free-space condition, in which subjects continued to hold the handle, but the handle was completely detached from the robot arm and the robot arm was moved out of the work space; or 2) the robot-null condition, in which subjects continued to make reaching movements with the robot arm, but with force field turned off. B: order and number of trials in which subjects were exposed to the robot-null (blue), free-space (green), and robot-force (red) conditions.
FIG. 2.
FIG. 2.
Comparison of the magnitude of transfer of adaptation between the free-space and robot-null conditions. A: examples of a single subject's hand trajectories, plotted in the horizontal plane: before adaptation (dashed lines); late in the adaptation period in a trial in which the force field had been unexpectedly turned off (catch trial, red lines); and immediately after the adaptation period in the free condition (green line) or in the robot-null condition (blue line). The filled circles indicate the time of measurement of the perpendicular displacement (PD) error from a straight trajectory. B: mean (±SE) perpendicular displacement errors, binned in groups of 6 trials for subjects in Group 1 of experiment 1. The robot-null condition is shown in blue and the free-space condition is shown in green. During the adaptation period, trials in which the force field was turned on are shown in black; catch trials in which the force field was turned off are shown in red. Positive values indicate errors made in the counterclockwise direction. C: the test for transfer to free-space reaching took place after the first adaptation block for subjects in Group 1 and after the second adaptation block for Group 2. The transfer index (±SE) is the size of the aftereffect in the first 6 trials of generalization, divided by the size of the catch trials late in adaptation. The bar plots of the average (±SE) transfer index for the 2 groups show that the order of testing did not affect the relative magnitude of transfer of force adaptation to the free-space and robot-null conditions. D: the bar plots compare the amount of transfer of force adaptation between the free-space (gray) and robot-null (black) conditions for all subjects who participated in experiment 1. E: the bar plots compare the group average (±SE) learning index before and after subjects reached in either the free-space (gray) or robot-null (black) conditions. The learning index is the ratio of the magnitude of catch-trial errors to the magnitude of the difference between catch-trial errors and fielded-trial errors. Increasing values of the learning index indicate better compensation for and learning of the viscous force field. Washout of learning is indicated when the learning index is smaller after than before a block of reaching in the free-space or robot-null conditions.
FIG. 3.
FIG. 3.
A: in experiment 1, the handle height was fixed. In experiment 2, subjects controlled the handle height. Subjects were given audio feedback whenever the handle height moved outside of an approximately 1.5-cm window. B: the average (±SE) transfer of force adaptation to reaching in the free-space condition was a similar proportion of transfer to reaching in the robot-null condition, regardless of whether subjects were required to control the handle's height in experiment 2 (gray squares connected by dashed line) or whether the handle height was fixed in experiment 1 (black circles connected by solid line). C: the drop in the learning index (mean ± SE), indicating washout of learning, that occurred when subjects reached either in the free-space (hatched bars) or in the robot-null (solid bars) conditions was equivalent, irrespective of whether the handle's height and orientation had to be controlled. In both experiments, reaching in the robot-null condition produced greater washout of previous learning than reaching in the free condition.
FIG. 4.
FIG. 4.
Results of experiment 3. A: average error in the gradual group (top plot) in comparison to the abrupt group (bottom plot). B: comparison of transfer to the free condition between the gradual (black line) and abrupt (gray line) groups for the first 6 postadaptation reaches. The shaded area indicates reaching performance in free space prior to force adaptation (mean ± SD). C: average error immediately before and after the subjects performed a block of reaching in the free condition. The extent of washout is indicated by how much error increased from the before to the after periods. Reaching in free space washed out prior force adaptation to a greater extent for the gradual group (black) than for the abrupt group (gray).

Similar articles

Cited by

References

    1. Chen-Harris H, Joiner WM, Ethier V, Zee DS, Shadmehr R. Adaptive control of saccades via internal feedback. J Neurosci 28: 2804–2813, 2008. - PMC - PubMed
    1. Conditt MA, Gandolfo F, Mussa-Ivaldi FA. The motor system does not learn the dynamics of the arm by rote memorization of past experience. J Neurophysiol 78: 554–560, 1997. - PubMed
    1. Cothros N, Wong JD, Gribble PL. Are there distinct neural representations of objects and limb dynamics? Exp Brain Res 173: 689–697, 2006. - PubMed
    1. Criscimagna-Hemminger SE, Donchin O, Gazzaniga MS, Shadmehr R. Learned dynamics of reaching movements generalize from dominant to nondominant arm. J Neurophysiol 89: 168–176, 2003. - PubMed
    1. Diedrichsen J, Hashambhoy Y, Rane T, Shadmehr R. Neural correlates of reach errors. J Neurosci 25: 9919–9931, 2005. - PMC - PubMed

Publication types