The Sources of Variability in Saccadic Eye Movements

Beers, Robert J. van

doi:10.1523/jneurosci.2311-07.2007

Cited by 162 publications

(163 citation statements)

References 58 publications

(92 reference statements)

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“…Another study based on the covariances between saccade endpoints and kinematic properties of the movement found that a large portion of end-point variance in saccades is indeed due to target localization errors, not motor noise (van Beers, 2007). This last study found, however, a stronger contribution of motor noise compared with the result reported here.…”

Section: Discussioncontrasting

confidence: 68%

“…l (t) corresponds to the localization error of target T, as a result of sensory noise and/or an imperfect sensory-motor transform of the sensory input into a motor command. Errors in localization of the target largely contribute to the variability of saccadic eye movements and exceed the motor noise (van Beers, 2007). However, contrary to motor noise, these errors can be predicted from the efferent motor commands.…”

Section: E)mentioning

confidence: 99%

See 1 more Smart Citation

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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Section: Discussioncontrasting

confidence: 68%

Section: E)mentioning

confidence: 99%

Optimal Sensorimotor Control in Eye Movement Sequences

Munuera¹,

Morel²,

Duhamel³

et al. 2009

J. Neurosci.

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show abstract

“…There are many sources of variability in human goaldirected movements (van Beers 2007), and it seems safe to assume that people will try to move in a way that will minimize the detrimental effects of such variability (van Beers 2008;Brenner and Smeets 2007;Harris and Wolpert 1998;Müller and Sternad 2004;Trommershauser et al 2003). An obvious way to do so is by minimizing the variability itself, for instance by moving slowly.…”

Section: Introductionmentioning

confidence: 99%

Sources of variability in interceptive movements

Brenner

Smeets

2009

Exp Brain Res

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In order to successfully intercept a moving target one must be at the right place at the right time. But simply being there is seldom enough. One usually needs to make contact in a certain manner, for instance to hit the target in a certain direction. How this is best achieved depends on the exact task, but to get an idea of what factors may limit performance we asked people to hit a moving virtual disk through a virtual goal, and analysed the spatial and temporal variability in the way in which they did so. We estimated that for our task the standard deviations in timing and spatial accuracy are about 20 ms and 5 mm. Additional variability arises from individual movements being planned slightly differently and being adjusted during execution. We argue that the way that our subjects moved was precisely tailored to the task demands, and that the movement accuracy is not only limited by the muscles and their activation, but also-and probably even mainlyby the resolution of visual perception.

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“…A luminance change detection task ensured the proper accuracy of the fixations. The raw eye-tracker output was then least-squares fitted to horizontal and vertical eye orientations using two biquadratic equations (Bonnetblanc and Baraduc, 2007;van Beers, 2007).…”

Section: Methodsmentioning

confidence: 99%

“…Actually, the contribution of visual noise to saccade endpoint variance increases with target eccentricity (van Beers, 2007;Morel and Baraduc, 2010). We would hence expect a decrease of visual weight with T1 eccentricity.…”

Section: Optimal Integration Of Target and Background Information Optmentioning

confidence: 99%

Optimal and Suboptimal Use of Postsaccadic Vision in Sequences of Saccades

Morel

Baraduc

2011

Journal of Neuroscience

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Saccades are imprecise, due to sensory and motor noise. To avoid an accumulation of errors during sequences of saccades, a prediction derived from the efference copy can be combined with the reafferent visual feedback to adjust the following eye movement. By varying the information quantity of the visual feedback, we investigated how the reliability of the visual information affects the postsaccadic update in humans. Two elements of the visual scene were manipulated, the saccade target or the background, presented either together or in isolation. We determined the weight of the postsaccadic visual information by measuring the effect of intrasaccadic visual shifts on the following saccade. We confirmed that the weight of visual information evolves with information quantity as predicted for a statistically optimal system. In particular, we found that the visual background alone can guide the postsaccadic update, and that information from target and background are optimally combined. Moreover, these visual weights are adjusted dynamically and on a trial-to-trial basis to the level of visual noise determined by target eccentricity and reaction time. In contrast, we uncovered a dissociation between the visual signals used to update the next planned saccade (main saccade) and those used to generate an involuntary corrective saccade. The latter was exclusively based on visual information about the target, and discarded all information about the background: a suboptimal use of visual evidence.

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The Sources of Variability in Saccadic Eye Movements

Cited by 162 publications

References 58 publications

Optimal Sensorimotor Control in Eye Movement Sequences

Optimal Sensorimotor Control in Eye Movement Sequences

Sources of variability in interceptive movements

Optimal and Suboptimal Use of Postsaccadic Vision in Sequences of Saccades

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