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Humans use continuous visual feedback from the hand to control fast reaching movements

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Abstract

How visual feedback contributes to the on-line control of fast reaching movements is still a matter of considerable debate. Whether feedback is used continuously throughout movements or only in the "slow" end-phases of movements remains an open question. In order to resolve this question, we applied a perturbation technique to measure the influence of visual feedback from the hand at different times during reaching movements. Subjects reached to touch targets in a virtual 3D space, with visual feedback provided by a small virtual sphere that moved with a subject's fingertip. Small random perturbations were applied to the position of the virtual fingertip at two different points in the movement, either at 25% or 50% of the total movement extent. Despite the fact that subjects were unaware of the perturbations, their hand trajectories showed smooth and accurate corrections. Detectable responses were observed within an average of 160 ms after perturbations, and as early as 60% of the distance to the target. Response latencies were constant across different perturbation times and movement speed conditions, suggesting that a fixed sensori-motor delay is the limiting factor. The results provide direct evidence that the human brain uses visual feedback from the hand in a continuous fashion to guide fast reaching movements throughout their extent.

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Acknowledgements

We would like to thank Ittai Bushlin, Albin Yeung, and Kavita Kadiwar for their assistance collecting data. Parts of these results were reported at the first annual meeting of the Vision Sciences Society in 2001. This research was supported by NIH grant EY09383.

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Correspondence to Jeffrey A. Saunders.

Appendix

Appendix

The empirical goal of the paper was to measure the time at which subjects began to show corrective responses to perturbations in the visual position of the virtual fingertip. In our analysis, this shows up as a change in the weight given to the perturbation in a linear model that predicts the position of the subjects' fingertip at each point in time. The true value of this weight function is necessarily zero before subjects show a corrective response and goes negative after they begin to correct. The measurement problem is to find at what time the true weight function changes from zero based on noisy estimates of the weight function (see, for example, Fig. 6). This is a particularly difficult estimation problem when one does not have a model available for the form of the true weight function (a form of blind signal detection). In effect, we have assumed that the perturbation weight function is of the form

$$ {w{\left( t \right)} = \left\{ {\matrix{ {{0;\;t < \Delta }} \cr {{f{\left( {t - \Delta } \right)};\;t \ge \Delta }} \cr } } \right.} $$
(3)

where Δ is the response time of the subject and f() is some unknown function. The problem of estimating subject's reaction time is to estimate Δ from a noisy measurement of w(t).

In the paper, we estimated Δ by finding where the weight function w(t) crosses (and stays below) a negative threshold value (the function f() is constrained to be negative in our application, since it reflects a corrective response in the direction opposite to the perturbation). Because the noise levels in the estimated weight functions were high relative to the size of the effect we were attempting to detect, we smoothed the weight functions prior to detection. In order to be conservative in our estimate, we used a causal smoothing filter. The necessary effect of this is to create a positive bias in our estimates of Δ.

In order to explore the size of the bias, we modeled f() as a linear ramp with a slope that fits well to subjects' average data. Corrupting the model weight function by noise of the same magnitude as measured in subjects' data (using the first 40 estimates of the weight function, during which time we are confident that the true weight function is 0) and applying the estimation technique described in the paper does indeed give positive biases in estimates of Δ. We found that setting a threshold equal to 1 std. deviation of the variance in the smoothed weight function estimates (derived from a re-sampling procedure) gave a positive bias of approximately 25 ms on average across a large number of simulated data sets. When the threshold is set to 0, we found no measurable bias. Unfortunately, the decrease in bias is offset by a rapid increase in variance of the estimated values for Δ across the simulated dataset; therefore, we settled on 1 std. deviation as the threshold to use in the paper (values greater than 1 do not appreciably shrink the variance).

Not only do the simulations give a rough estimate of the bias in our estimates of Δ, they also provide a measure of the inherent uncertainty of the estimate, caused by noise in the estimated weight functions. We found that for noise equal to the average noise in the estimated weights, the method gave rise to a standard deviation of 13 ms in estimates of Δ across the simulated data set. This compares favorably with the 16 ms value we found for the variance in estimated response times across all subjects and conditions in the experiment. Thus, almost all of the variance (approximately 66%) of estimated response times across subjects is accounted for by the inherent uncertainty induced by the noisiness of individual subjects' data. The remaining 34% of the variance is primarily accounted for by individual differences between subjects, as reflected in the lack of significant condition effects found in the ANOVA.

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Saunders, J.A., Knill, D.C. Humans use continuous visual feedback from the hand to control fast reaching movements. Exp Brain Res 152, 341–352 (2003). https://doi.org/10.1007/s00221-003-1525-2

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