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Learning course adjustments during arm movements with reversed sensitivity derivatives.

Abdelghani MN, Tweed DB - BMC Neurosci (2010)

Bottom Line: Here we test a recent theory that the brain's estimates of sensitivity derivatives are revisable based on sensory feedback.The target jumped once during each movement.It is consistent with the idea of a single, all-purpose estimate of those derivatives; and it suggests that the estimate is a function of context, as one would expect given that the true sensitivity derivatives may vary with the state of the controlled system, the target, and the motor commands.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Physiology, University of Toronto, Toronto, Canada.

ABSTRACT

Background: To learn, a motor system needs to know its sensitivity derivatives, which quantify how its neural commands affect motor error. But are these derivatives themselves learned, or are they known solely innately? Here we test a recent theory that the brain's estimates of sensitivity derivatives are revisable based on sensory feedback. In its simplest form, the theory says that each control system has a single, adjustable estimate of its sensitivity derivatives which affects all aspects of its task, e.g. if you learn to reach to mirror-reversed targets then your revised estimate should reverse not only your initial aiming but also your online course adjustments when the target jumps in mid-movement.

Methods: Human subjects bent a joystick to move a cursor to a target on a computer screen, but the cursor's motion was reversed relative to the joystick's. The target jumped once during each movement. Subjects had up to 4000 trials to practice aiming and responding to target jumps.

Results: All subjects learned to reverse both initial aiming and course adjustments.

Conclusions: Our study confirms that sensitivity derivatives can be relearned. It is consistent with the idea of a single, all-purpose estimate of those derivatives; and it suggests that the estimate is a function of context, as one would expect given that the true sensitivity derivatives may vary with the state of the controlled system, the target, and the motor commands.

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Sample trajectories for one subject. (a) A typical trajectory under control conditions (without reversed sensitivity derivatives). The cursor is at position 1 when the target appears (dotted gray circle). The cursor moves (black line) towards the target. Gray arrows are cursor-velocity vectors. When the cursor is at 2 the target jumps to its new location (solid gray circle). Near 3 the cursor adjusts course appropriately. (The markers 1, 2 and 3 have these same meanings in panels d and g also). (b) The same pattern is seen when we plot components of cursor velocity. Before the target jump (vertical dashed line), we plot cursor velocity in the direction from initial cursor location to initial target location; appropriately, this velocity is positive. After the jump, we plot cursor velocity in the direction of the jump, again appropriately positive. (c) The same pattern is seen in averaged velocity traces. Here we see cursor velocity -- mean (black line) and standard deviation (light gray band) over 200 randomly chosen control trials. Velocity is mainly positive, as it should be, during both launch and adjustment. (d) In early reversed trials, launch and course adjustments go the wrong way. (e) These errors are revealed also in velocity traces. (f) The white line and dark gray band are the mean and SD of cursor velocity over the first 200 reversed trials. As in Panel c, the black line and light gray band are the control data, and the light gray is transparent so that the dark gray data show through, for comparison. In the reversed trials, launch and adjustment are impaired, i.e. they are not consistently positive, and those portions that are positive occur later than in the control trials. (g, h) In late reversed trials, after the subject has learned, launch and adjustment are both correct. (i) Velocities averaged over 200 randomly chosen late reversed trials resemble controls.
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Figure 2: Sample trajectories for one subject. (a) A typical trajectory under control conditions (without reversed sensitivity derivatives). The cursor is at position 1 when the target appears (dotted gray circle). The cursor moves (black line) towards the target. Gray arrows are cursor-velocity vectors. When the cursor is at 2 the target jumps to its new location (solid gray circle). Near 3 the cursor adjusts course appropriately. (The markers 1, 2 and 3 have these same meanings in panels d and g also). (b) The same pattern is seen when we plot components of cursor velocity. Before the target jump (vertical dashed line), we plot cursor velocity in the direction from initial cursor location to initial target location; appropriately, this velocity is positive. After the jump, we plot cursor velocity in the direction of the jump, again appropriately positive. (c) The same pattern is seen in averaged velocity traces. Here we see cursor velocity -- mean (black line) and standard deviation (light gray band) over 200 randomly chosen control trials. Velocity is mainly positive, as it should be, during both launch and adjustment. (d) In early reversed trials, launch and course adjustments go the wrong way. (e) These errors are revealed also in velocity traces. (f) The white line and dark gray band are the mean and SD of cursor velocity over the first 200 reversed trials. As in Panel c, the black line and light gray band are the control data, and the light gray is transparent so that the dark gray data show through, for comparison. In the reversed trials, launch and adjustment are impaired, i.e. they are not consistently positive, and those portions that are positive occur later than in the control trials. (g, h) In late reversed trials, after the subject has learned, launch and adjustment are both correct. (i) Velocities averaged over 200 randomly chosen late reversed trials resemble controls.

Mentions: In test blocks, both dimensions of cursor motion were reversed from control, flipping the signs of all components of ∂e/∂u (Figure 1e). Five subjects took part -- one female, four males, all healthy, aged 21-48. Three of them knew the experiment involved a reversed relation between joystick and cursor. One of these three had experience with joystick experiments, and one with joystick computer games. All our single-person data plots (Figure 2, 3, and 4) are of subjects who were unfamiliar both with joysticks and with the idea of motor adaptation to reversals, but the key findings were the same for all subjects, as shown in Figure 5.


Learning course adjustments during arm movements with reversed sensitivity derivatives.

Abdelghani MN, Tweed DB - BMC Neurosci (2010)

Sample trajectories for one subject. (a) A typical trajectory under control conditions (without reversed sensitivity derivatives). The cursor is at position 1 when the target appears (dotted gray circle). The cursor moves (black line) towards the target. Gray arrows are cursor-velocity vectors. When the cursor is at 2 the target jumps to its new location (solid gray circle). Near 3 the cursor adjusts course appropriately. (The markers 1, 2 and 3 have these same meanings in panels d and g also). (b) The same pattern is seen when we plot components of cursor velocity. Before the target jump (vertical dashed line), we plot cursor velocity in the direction from initial cursor location to initial target location; appropriately, this velocity is positive. After the jump, we plot cursor velocity in the direction of the jump, again appropriately positive. (c) The same pattern is seen in averaged velocity traces. Here we see cursor velocity -- mean (black line) and standard deviation (light gray band) over 200 randomly chosen control trials. Velocity is mainly positive, as it should be, during both launch and adjustment. (d) In early reversed trials, launch and course adjustments go the wrong way. (e) These errors are revealed also in velocity traces. (f) The white line and dark gray band are the mean and SD of cursor velocity over the first 200 reversed trials. As in Panel c, the black line and light gray band are the control data, and the light gray is transparent so that the dark gray data show through, for comparison. In the reversed trials, launch and adjustment are impaired, i.e. they are not consistently positive, and those portions that are positive occur later than in the control trials. (g, h) In late reversed trials, after the subject has learned, launch and adjustment are both correct. (i) Velocities averaged over 200 randomly chosen late reversed trials resemble controls.
© Copyright Policy - open-access
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC3008695&req=5

Figure 2: Sample trajectories for one subject. (a) A typical trajectory under control conditions (without reversed sensitivity derivatives). The cursor is at position 1 when the target appears (dotted gray circle). The cursor moves (black line) towards the target. Gray arrows are cursor-velocity vectors. When the cursor is at 2 the target jumps to its new location (solid gray circle). Near 3 the cursor adjusts course appropriately. (The markers 1, 2 and 3 have these same meanings in panels d and g also). (b) The same pattern is seen when we plot components of cursor velocity. Before the target jump (vertical dashed line), we plot cursor velocity in the direction from initial cursor location to initial target location; appropriately, this velocity is positive. After the jump, we plot cursor velocity in the direction of the jump, again appropriately positive. (c) The same pattern is seen in averaged velocity traces. Here we see cursor velocity -- mean (black line) and standard deviation (light gray band) over 200 randomly chosen control trials. Velocity is mainly positive, as it should be, during both launch and adjustment. (d) In early reversed trials, launch and course adjustments go the wrong way. (e) These errors are revealed also in velocity traces. (f) The white line and dark gray band are the mean and SD of cursor velocity over the first 200 reversed trials. As in Panel c, the black line and light gray band are the control data, and the light gray is transparent so that the dark gray data show through, for comparison. In the reversed trials, launch and adjustment are impaired, i.e. they are not consistently positive, and those portions that are positive occur later than in the control trials. (g, h) In late reversed trials, after the subject has learned, launch and adjustment are both correct. (i) Velocities averaged over 200 randomly chosen late reversed trials resemble controls.
Mentions: In test blocks, both dimensions of cursor motion were reversed from control, flipping the signs of all components of ∂e/∂u (Figure 1e). Five subjects took part -- one female, four males, all healthy, aged 21-48. Three of them knew the experiment involved a reversed relation between joystick and cursor. One of these three had experience with joystick experiments, and one with joystick computer games. All our single-person data plots (Figure 2, 3, and 4) are of subjects who were unfamiliar both with joysticks and with the idea of motor adaptation to reversals, but the key findings were the same for all subjects, as shown in Figure 5.

Bottom Line: Here we test a recent theory that the brain's estimates of sensitivity derivatives are revisable based on sensory feedback.The target jumped once during each movement.It is consistent with the idea of a single, all-purpose estimate of those derivatives; and it suggests that the estimate is a function of context, as one would expect given that the true sensitivity derivatives may vary with the state of the controlled system, the target, and the motor commands.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Physiology, University of Toronto, Toronto, Canada.

ABSTRACT

Background: To learn, a motor system needs to know its sensitivity derivatives, which quantify how its neural commands affect motor error. But are these derivatives themselves learned, or are they known solely innately? Here we test a recent theory that the brain's estimates of sensitivity derivatives are revisable based on sensory feedback. In its simplest form, the theory says that each control system has a single, adjustable estimate of its sensitivity derivatives which affects all aspects of its task, e.g. if you learn to reach to mirror-reversed targets then your revised estimate should reverse not only your initial aiming but also your online course adjustments when the target jumps in mid-movement.

Methods: Human subjects bent a joystick to move a cursor to a target on a computer screen, but the cursor's motion was reversed relative to the joystick's. The target jumped once during each movement. Subjects had up to 4000 trials to practice aiming and responding to target jumps.

Results: All subjects learned to reverse both initial aiming and course adjustments.

Conclusions: Our study confirms that sensitivity derivatives can be relearned. It is consistent with the idea of a single, all-purpose estimate of those derivatives; and it suggests that the estimate is a function of context, as one would expect given that the true sensitivity derivatives may vary with the state of the controlled system, the target, and the motor commands.

Show MeSH