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A gain-field encoding of limb position and velocity in the internal model of arm dynamics.

Hwang EJ, Donchin O, Smith MA, Shadmehr R - PLoS Biol. (2003)

Bottom Line: The gain-field encoding makes the counterintuitive prediction of hypergeneralization: there should be growing extrapolation beyond the trained workspace.Furthermore, nonmonotonic force patterns should be more difficult to learn than monotonic ones.We confirmed these predictions experimentally.

View Article: PubMed Central - PubMed

Affiliation: Laboratory for Computational Motor Control, Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, Maryland, USA. ehwang@bme.jhu.edu

ABSTRACT
Adaptability of reaching movements depends on a computation in the brain that transforms sensory cues, such as those that indicate the position and velocity of the arm, into motor commands. Theoretical consideration shows that the encoding properties of neural elements implementing this transformation dictate how errors should generalize from one limb position and velocity to another. To estimate how sensory cues are encoded by these neural elements, we designed experiments that quantified spatial generalization in environments where forces depended on both position and velocity of the limb. The patterns of error generalization suggest that the neural elements that compute the transformation encode limb position and velocity in intrinsic coordinates via a gain-field; i.e., the elements have directionally dependent tuning that is modulated monotonically with limb position. The gain-field encoding makes the counterintuitive prediction of hypergeneralization: there should be growing extrapolation beyond the trained workspace. Furthermore, nonmonotonic force patterns should be more difficult to learn than monotonic ones. We confirmed these predictions experimentally.

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Adaptation to a Force Field That Depends on Both Position and Velocity of the Limb(A) The origin of the center movements is aligned with the subject's body midline, and the origins of the left and right movements are symmetrically positioned with a given separation distance (d) for each group. The target positions shown here are an example for a subject with typical arm lengths (20, 33, 34 cm shoulder, upper arm, and lower arm lengths, respectively). In order to help subjects distinguish among locations, different colors were provided for targets at different locations (yellow, green, and blue for the left, center, and right, respectively).(B) The average trajectories in three positions—left, center, and right (one subject per column)—for the first third of the movements from the first field set (trials 1–28). Dashed lines are movements during which force field is on and dotted lines are catch trials. Separation distances between neighboring movements (d) are not scaled in this figure.(C) The average trajectories for the first third of the fifth field set (trials 337–364). The task is much easier to learn when the three movements are spatially separated from each other.
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pbio.0000025-g001: Adaptation to a Force Field That Depends on Both Position and Velocity of the Limb(A) The origin of the center movements is aligned with the subject's body midline, and the origins of the left and right movements are symmetrically positioned with a given separation distance (d) for each group. The target positions shown here are an example for a subject with typical arm lengths (20, 33, 34 cm shoulder, upper arm, and lower arm lengths, respectively). In order to help subjects distinguish among locations, different colors were provided for targets at different locations (yellow, green, and blue for the left, center, and right, respectively).(B) The average trajectories in three positions—left, center, and right (one subject per column)—for the first third of the movements from the first field set (trials 1–28). Dashed lines are movements during which force field is on and dotted lines are catch trials. Separation distances between neighboring movements (d) are not scaled in this figure.(C) The average trajectories for the first third of the fifth field set (trials 337–364). The task is much easier to learn when the three movements are spatially separated from each other.

Mentions: Figure 1 describes an experiment in which subjects performed reaching movements in force fields that depended on both velocity and position of the limb. Subjects made movements in the horizontal plane while holding the handle of a robot. The task was to reach a target (displacement of approximately 10 cm; see Materials and Methods) within 500 ± 50 ms. Handle and target positions were continuously projected onto a screen placed directly above the subject's hand (Shadmehr and Moussavi 2000). Feedback on performance was provided immediately after target acquisition, but feedback related only to the subject's success in arriving at the target within the prescribed time window and not to the shape of the hand trajectories. After completion of each movement, the robot moved the hand to a new start position and another target was presented. The start positions were pseudorandomly chosen from three possible locations: left, center, and right. Twenty-four subjects were divided into four groups, and the start positions for the four groups were separated by 0.5 cm, 3 cm, 7 cm, or 12 cm, respectively (Figure 1A). Different colors were used for the left, center, and right start positions and targets so that we could be certain that subjects could distinguish the locations even when the separation distances were small. The targets were placed so that movements from all three starting locations required the same joint angle displacement. Thus, the movements were parallel in joint space and not in Cartesian space (Figure 1A). Therefore, the movements explored the same joint velocity space but at different joint positions.


A gain-field encoding of limb position and velocity in the internal model of arm dynamics.

Hwang EJ, Donchin O, Smith MA, Shadmehr R - PLoS Biol. (2003)

Adaptation to a Force Field That Depends on Both Position and Velocity of the Limb(A) The origin of the center movements is aligned with the subject's body midline, and the origins of the left and right movements are symmetrically positioned with a given separation distance (d) for each group. The target positions shown here are an example for a subject with typical arm lengths (20, 33, 34 cm shoulder, upper arm, and lower arm lengths, respectively). In order to help subjects distinguish among locations, different colors were provided for targets at different locations (yellow, green, and blue for the left, center, and right, respectively).(B) The average trajectories in three positions—left, center, and right (one subject per column)—for the first third of the movements from the first field set (trials 1–28). Dashed lines are movements during which force field is on and dotted lines are catch trials. Separation distances between neighboring movements (d) are not scaled in this figure.(C) The average trajectories for the first third of the fifth field set (trials 337–364). The task is much easier to learn when the three movements are spatially separated from each other.
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC261873&req=5

pbio.0000025-g001: Adaptation to a Force Field That Depends on Both Position and Velocity of the Limb(A) The origin of the center movements is aligned with the subject's body midline, and the origins of the left and right movements are symmetrically positioned with a given separation distance (d) for each group. The target positions shown here are an example for a subject with typical arm lengths (20, 33, 34 cm shoulder, upper arm, and lower arm lengths, respectively). In order to help subjects distinguish among locations, different colors were provided for targets at different locations (yellow, green, and blue for the left, center, and right, respectively).(B) The average trajectories in three positions—left, center, and right (one subject per column)—for the first third of the movements from the first field set (trials 1–28). Dashed lines are movements during which force field is on and dotted lines are catch trials. Separation distances between neighboring movements (d) are not scaled in this figure.(C) The average trajectories for the first third of the fifth field set (trials 337–364). The task is much easier to learn when the three movements are spatially separated from each other.
Mentions: Figure 1 describes an experiment in which subjects performed reaching movements in force fields that depended on both velocity and position of the limb. Subjects made movements in the horizontal plane while holding the handle of a robot. The task was to reach a target (displacement of approximately 10 cm; see Materials and Methods) within 500 ± 50 ms. Handle and target positions were continuously projected onto a screen placed directly above the subject's hand (Shadmehr and Moussavi 2000). Feedback on performance was provided immediately after target acquisition, but feedback related only to the subject's success in arriving at the target within the prescribed time window and not to the shape of the hand trajectories. After completion of each movement, the robot moved the hand to a new start position and another target was presented. The start positions were pseudorandomly chosen from three possible locations: left, center, and right. Twenty-four subjects were divided into four groups, and the start positions for the four groups were separated by 0.5 cm, 3 cm, 7 cm, or 12 cm, respectively (Figure 1A). Different colors were used for the left, center, and right start positions and targets so that we could be certain that subjects could distinguish the locations even when the separation distances were small. The targets were placed so that movements from all three starting locations required the same joint angle displacement. Thus, the movements were parallel in joint space and not in Cartesian space (Figure 1A). Therefore, the movements explored the same joint velocity space but at different joint positions.

Bottom Line: The gain-field encoding makes the counterintuitive prediction of hypergeneralization: there should be growing extrapolation beyond the trained workspace.Furthermore, nonmonotonic force patterns should be more difficult to learn than monotonic ones.We confirmed these predictions experimentally.

View Article: PubMed Central - PubMed

Affiliation: Laboratory for Computational Motor Control, Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, Maryland, USA. ehwang@bme.jhu.edu

ABSTRACT
Adaptability of reaching movements depends on a computation in the brain that transforms sensory cues, such as those that indicate the position and velocity of the arm, into motor commands. Theoretical consideration shows that the encoding properties of neural elements implementing this transformation dictate how errors should generalize from one limb position and velocity to another. To estimate how sensory cues are encoded by these neural elements, we designed experiments that quantified spatial generalization in environments where forces depended on both position and velocity of the limb. The patterns of error generalization suggest that the neural elements that compute the transformation encode limb position and velocity in intrinsic coordinates via a gain-field; i.e., the elements have directionally dependent tuning that is modulated monotonically with limb position. The gain-field encoding makes the counterintuitive prediction of hypergeneralization: there should be growing extrapolation beyond the trained workspace. Furthermore, nonmonotonic force patterns should be more difficult to learn than monotonic ones. We confirmed these predictions experimentally.

Show MeSH
Related in: MedlinePlus