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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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Movement Errors and Learning Performance as a Function of the Separation Distance(A) PE averaged across six subjects of Group 1 (d = 0.5 cm). Squares indicate catch trials. Error bars show SEM. The average SEM at the center is 1.3 mm in the baseline sets and 6.6 mm in the adaptation sets for Group 1. The average SEM is 2.1 mm in the baseline sets and 2.4 mm in the adaptation sets for Group 4.(B) Errors were averaged across six subjects of Group 4 (d = 12 cm).(C) Average learning index (Equation 1) across groups. Learning index is plotted against the separation distance between movements. Thin lines show the first adaptation set and thick lines show the last adaptation set. n = 6 for each distance. Error bars show SEM.(D) Generalization index (Equation 2) against spatial distance between movements.
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pbio.0000025-g002: Movement Errors and Learning Performance as a Function of the Separation Distance(A) PE averaged across six subjects of Group 1 (d = 0.5 cm). Squares indicate catch trials. Error bars show SEM. The average SEM at the center is 1.3 mm in the baseline sets and 6.6 mm in the adaptation sets for Group 1. The average SEM is 2.1 mm in the baseline sets and 2.4 mm in the adaptation sets for Group 4.(B) Errors were averaged across six subjects of Group 4 (d = 12 cm).(C) Average learning index (Equation 1) across groups. Learning index is plotted against the separation distance between movements. Thin lines show the first adaptation set and thick lines show the last adaptation set. n = 6 for each distance. Error bars show SEM.(D) Generalization index (Equation 2) against spatial distance between movements.

Mentions: As a measure of error, we used displacement perpendicular to target direction at 250 ms into the movement (perpendicular error [PE]). Figure 2 shows the error on each trial averaged across subjects and plotted in a time series for Group 1 (d = 0.5 cm; Figure 2A) and Group 4 (d = 12 cm; Figure 2B). A gradual decrease in error magnitude and an increase in aftereffects in catch trials were apparent in Group 4, but not in Group 1. A learning index combining performance on field and catch trials (see Materials and Methods) allows a comparison of performance across groups (Figure 2C). An ANOVA on the learning index showed a significant effect both for separation distance and set number (F = 41.78, d.f. = 3, p < 1.0 × 10−8 for distance factor; F = 3.02, d.f. = 4, p < 0.02 for set number factor), suggesting that subjects performed better in the groups where targets were spatially separated.


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)

Movement Errors and Learning Performance as a Function of the Separation Distance(A) PE averaged across six subjects of Group 1 (d = 0.5 cm). Squares indicate catch trials. Error bars show SEM. The average SEM at the center is 1.3 mm in the baseline sets and 6.6 mm in the adaptation sets for Group 1. The average SEM is 2.1 mm in the baseline sets and 2.4 mm in the adaptation sets for Group 4.(B) Errors were averaged across six subjects of Group 4 (d = 12 cm).(C) Average learning index (Equation 1) across groups. Learning index is plotted against the separation distance between movements. Thin lines show the first adaptation set and thick lines show the last adaptation set. n = 6 for each distance. Error bars show SEM.(D) Generalization index (Equation 2) against spatial distance between movements.
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC261873&req=5

pbio.0000025-g002: Movement Errors and Learning Performance as a Function of the Separation Distance(A) PE averaged across six subjects of Group 1 (d = 0.5 cm). Squares indicate catch trials. Error bars show SEM. The average SEM at the center is 1.3 mm in the baseline sets and 6.6 mm in the adaptation sets for Group 1. The average SEM is 2.1 mm in the baseline sets and 2.4 mm in the adaptation sets for Group 4.(B) Errors were averaged across six subjects of Group 4 (d = 12 cm).(C) Average learning index (Equation 1) across groups. Learning index is plotted against the separation distance between movements. Thin lines show the first adaptation set and thick lines show the last adaptation set. n = 6 for each distance. Error bars show SEM.(D) Generalization index (Equation 2) against spatial distance between movements.
Mentions: As a measure of error, we used displacement perpendicular to target direction at 250 ms into the movement (perpendicular error [PE]). Figure 2 shows the error on each trial averaged across subjects and plotted in a time series for Group 1 (d = 0.5 cm; Figure 2A) and Group 4 (d = 12 cm; Figure 2B). A gradual decrease in error magnitude and an increase in aftereffects in catch trials were apparent in Group 4, but not in Group 1. A learning index combining performance on field and catch trials (see Materials and Methods) allows a comparison of performance across groups (Figure 2C). An ANOVA on the learning index showed a significant effect both for separation distance and set number (F = 41.78, d.f. = 3, p < 1.0 × 10−8 for distance factor; F = 3.02, d.f. = 4, p < 0.02 for set number factor), suggesting that subjects performed better in the groups where targets were spatially separated.

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