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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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Results for all 5 subjects in Experiment 2. (a) Launch error improved by t-test (p = 0.008) to levels not significantly different from control (p = 0.339). (b) Adjustment error improved (p = 0.012) to control levels (p = 0.195). (c) Launch latency improved (p = 0.004) to control levels (p = 0.431). (d) Adjustment latency improved (p = 0.01), not quite to control levels (p = 0.036) but to within 80 ms of control.
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Figure 7: Results for all 5 subjects in Experiment 2. (a) Launch error improved by t-test (p = 0.008) to levels not significantly different from control (p = 0.339). (b) Adjustment error improved (p = 0.012) to control levels (p = 0.195). (c) Launch latency improved (p = 0.004) to control levels (p = 0.431). (d) Adjustment latency improved (p = 0.01), not quite to control levels (p = 0.036) but to within 80 ms of control.

Mentions: Here the relation between joystick and cursor was more complex: reflected vertically through the midline and rotated 30 degrees counterclockwise (Figure 1f). Five subjects took part -- one female, four males, all healthy, aged 21-48. None of them knew the joystick-cursor relation beforehand. All found it bewildering, and none was able to state it afterwards based on their experience. Four of the subjects were veterans of Experiment 1, and therefore had more joystick experience in this second part, but that fact is irrelevant here because our hypothesis and analysis involved no comparisons of the two experiments. The single-person data plot (Figure 6) is of the new subject, without joystick experience, but the key results were the same for all, as shown in Figure 7.


Learning course adjustments during arm movements with reversed sensitivity derivatives.

Abdelghani MN, Tweed DB - BMC Neurosci (2010)

Results for all 5 subjects in Experiment 2. (a) Launch error improved by t-test (p = 0.008) to levels not significantly different from control (p = 0.339). (b) Adjustment error improved (p = 0.012) to control levels (p = 0.195). (c) Launch latency improved (p = 0.004) to control levels (p = 0.431). (d) Adjustment latency improved (p = 0.01), not quite to control levels (p = 0.036) but to within 80 ms of control.
© Copyright Policy - open-access
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC3008695&req=5

Figure 7: Results for all 5 subjects in Experiment 2. (a) Launch error improved by t-test (p = 0.008) to levels not significantly different from control (p = 0.339). (b) Adjustment error improved (p = 0.012) to control levels (p = 0.195). (c) Launch latency improved (p = 0.004) to control levels (p = 0.431). (d) Adjustment latency improved (p = 0.01), not quite to control levels (p = 0.036) but to within 80 ms of control.
Mentions: Here the relation between joystick and cursor was more complex: reflected vertically through the midline and rotated 30 degrees counterclockwise (Figure 1f). Five subjects took part -- one female, four males, all healthy, aged 21-48. None of them knew the joystick-cursor relation beforehand. All found it bewildering, and none was able to state it afterwards based on their experience. Four of the subjects were veterans of Experiment 1, and therefore had more joystick experience in this second part, but that fact is irrelevant here because our hypothesis and analysis involved no comparisons of the two experiments. The single-person data plot (Figure 6) is of the new subject, without joystick experience, but the key results were the same for all, as shown in Figure 7.

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