Visual Feedback Control of Hand Movements

Saunders, Jeffrey A.; Knill, David C.

doi:10.1523/jneurosci.4319-03.2004

Cited by 250 publications

(205 citation statements)

References 32 publications

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“…4a) (see supplemental material, available at www.jneurosci.org), which is approximately the final 90 -150 ms of the movement. The use of visual feedback has a latency of ϳ200 ms (Saunders and Knill, 2004), so the visual perturbation almost certainly occurred too late to affect the movement.…”

Section: No Compensation For Perturbation Of Visual Feedbackmentioning

confidence: 99%

“…For example, the motor system can counteract external physical (force) perturbations by increasing arm stiffness (Burdet et al, 2001;Franklin et al, 2003). In addition, visual and proprioceptive information about the position of the target and the hand as well as previously acquired information can be combined (Körding and Wolpert, 2004;Saunders and Knill, 2004;Sober and Sabes, 2005).…”

Section: Introductionmentioning

confidence: 99%

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Optimal Compensation for Changes in Task-Relevant Movement Variability

Trommershäuser¹,

Gepshtein²,

Maloney³

et al. 2005

J. Neurosci.

167

156

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Effective movement planning should take into account the consequences of possible errors in executing a planned movement. These errors can result from either sensory uncertainty or variability in movement planning and production. We examined the ability of humans to compensate for variability in sensory estimation and movement production under conditions in which variability is increased artificially by the experimenter. Subjects rapidly pointed at a target region that had an adjacent penalty region. Target and penalty hits yielded monetary rewards and losses. We manipulated the task-relevant variability by perturbing visual feedback of finger position during the movement. The feedback was shifted in a random direction with a random amplitude in each trial, causing an increase in the task-relevant variability. Subjects were unable to counteract this form of perturbation. Rewards and penalties were based on the perturbed, visually specified finger position. Subjects rapidly acquired an estimate of their new variability in Ͻ120 trials and adjusted their aim points accordingly. We compared subjects' performance to the performance of an optimal movement planner maximizing expected gain. Their performance was consistent with that expected from an optimal movement planner that perfectly compensated for externally imposed changes in task-relevant variability. When exposed to novel stimulus configurations, aim points shifted in the first trial without showing any detectable trend across trials. These results indicate that subjects are capable of changing their pointing strategy in the presence of externally imposed noise. Furthermore, they manage to update their estimate of task-relevant variability and to transfer this estimate to novel stimulus configurations.

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Section: No Compensation For Perturbation Of Visual Feedbackmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimal Compensation for Changes in Task-Relevant Movement Variability

Trommershäuser¹,

Gepshtein²,

Maloney³

et al. 2005

J. Neurosci.

167

156

View full text Add to dashboard Cite

show abstract

“…Some experiments suggest that humans use a Kalman filter when performing arm movements (Wolpert and Ghahramani, 2000;Saunders and Knill, 2004). However, it is still unclear whether the same principles apply to saccadic eye movements (Pierrot-Deseilligny et al, 2003).…”

Section: Introductionmentioning

confidence: 99%

Optimal Sensorimotor Control in Eye Movement Sequences

Munuera¹,

Morel²,

Duhamel³

et al. 2009

J. Neurosci.

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Fast and accurate motor behavior requires combining noisy and delayed sensory information with knowledge of self-generated body motion; much evidence indicates that humans do this in a near-optimal manner during arm movements. However, it is unclear whether this principle applies to eye movements. We measured the relative contributions of visual sensory feedback and the motor efference copy (and/or proprioceptive feedback) when humans perform two saccades in rapid succession, the first saccade to a visual target and the second to a memorized target. Unbeknownst to the subject, we introduced an artificial motor error by randomly "jumping" the visual target during the first saccade. The correction of the memory-guided saccade allowed us to measure the relative contributions of visual feedback and efferent copy (and/or proprioceptive feedback) to motor-plan updating. In a control experiment, we extinguished the target during the saccade rather than changing its location to measure the relative contribution of motor noise and target localization error to saccade variability without any visual feedback. The motor noise contribution increased with saccade amplitude, but remained Ͻ30% of the total variability. Subjects adjusted the gain of their visual feedback for different saccade amplitudes as a function of its reliability. Even during trials where subjects performed a corrective saccade to compensate for the target-jump, the correction by the visual feedback, while stronger, remained far below 100%. In all conditions, an optimal controller predicted the visual feedback gain well, suggesting that humans combine optimally their efferent copy and sensory feedback when performing eye movements.

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“…The movement of the hand itself needs to be anticipated. Saunders and Knill [53] study the visual feedback to control movements of the hand. Alternatively, Tucker and Ellis [59] are interested in the cortical area of visuomotor integration.…”

Section: Importance Of Vision In Motion Controlmentioning

confidence: 99%